• Australian 20 Stock Model Portfolio

• USA 20 Stock Model Portfolio

• International (non-USA) 20 Stock Model Portfolio

• Global Best Ideas 25 Stock Model Portfolio

There are no changes for the global model this month.

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The current portfolio snapshot is detailed below.

Read more

• Australian 20 Stock Model Portfolio

• USA 20 Stock Model Portfolio

• International (non-USA) 20 Stock Model Portfolio

• Global Best Ideas 25 Stock Model Portfolio

There are no changes for the international model this month.

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The updated performance and current portfolio are given below.

Read more

• Australian 20 Stock Model Portfolio

• USA 20 Stock Model Portfolio

• International (non-USA) 20 Stock Model Portfolio

• Global Best Ideas 25 Stock Model Portfolio

There are no changes for the USA model this month.

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The updated performance and current portfolio are given below.

Read more

• Australian 20 Stock Model Portfolio

• USA 20 Stock Model Portfolio

• International (non-USA) 20 Stock Model Portfolio

• Global Best Ideas 25 Stock Model Portfolio

There are no changes for the Australian model this month.

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Late Cycle Conditions in Australian Markets

In general, global markets are churning on performance chasing. There are clear signs we are now late cycle in Australia, with housing weak, but inflation sticky. We expect further interest rate rises from the RBA in the coming meetings.

The reaction to a soft earnings report from portfolio holding JB HiFi, signals potential weakening across the broader consumer markets, although sustained high levels of wealth in Australia are propping up spending among older consumers.

Due to rising, but comparatively low levels of unemployment, we do not expect there will be a recession. However, the Australian economy is stuck in low gear as it shifts from a housing investment led phase, to belatedly address productivity issues.

The politics is likely to become increasingly fractious as the electorate looks to find someone to blame for what is (frankly) a self-inflicted predicament.

Making houses more expensive does not produce sustainable wealth gains.

The housing shortage is likely to persist, in spite of slower immigration, and there is no easy way out of the bind. You cannot fix ten to fifteen years of neglect towards productivity enhancing capital investment that is intensive and not extensive.

The essential problem for Australia is that it bet too heavily on scaling up, in terms of population growth and public infrastructure to support that, without any clear policy focus on how to improve unit economics and productivity within the economy.

There is a solution to this problem.

• Less intrusive regulation for the sake of regulation.

• More effective government planning to mitigate obstacles to investment.

• Adoption of more proactive business attitudes towards R&D.

There has been much written on the government side of the equation, but there is a clear problem with business also. Australia has very low levels of corporate R&D and poor outcomes on the development of new products, processes, and technology.

This problem shows up in global metrics of economic complexity, where Australia rates among the lowest of all nations. The issue is one of complacency.

It is not enough to simply mine minerals and speculate on house prices.

Having said that, the only part of the Australian economy doing very well right now is the mining industry, and the one clear area of shortage is housing construction.

Australia has a quality economic problem to solve.

Even the folks who think they have correctly diagnosed the disease are reluctant to move capital away from the three areas that benefited from globalization:

• Mining to provide the resources for global development.

• Finance which recycled the export dollars earned into domestic credit growth.

• Housing which booked the capital gains from excessive credit expansion.

In our own portfolios, we have stepped back from finance to underweight, but are overweight minerals and energy, and housing and data center developers.

This positioning expresses a pragmatic viewpoint.

Australian mining will continue to do well because it does not depend overly much on the health of the domestic economy. However, Australian banking is already looking somewhat shaky, due to slowing credit demand, high valuations, and weak housing markets. While we are now in the early stages of a construction shake out, due to overleverage in players like Baltha Group, there is a supply shortfall of housing.

In our view, it will take some time for Australia to find a new economic growth model. Perhaps it involves Artificial Intelligence in some way but the path to that outcome is fraught with risk to near-term employment, and the generally low appetite for R&D among Australian corporates. I have had 40 years exposure to R&D at a high level. With that experience, and knowledge of how Australian corporations operate, I am not optimistic that the transition to a new growth model will be smooth.

It will take serious effort to be remotely competitive in R&D led business, when you have the USA and China both fiercely competing at the cutting edge.

There are models that might work, such as what Singapore, Switzerland, or the Nordic nations do, but Australia does not seem to have the application to pursue that.

We do have a quality Higher Education sector, but the capacity was diverted towards rent seeking from the foreign student trade. This has proven to be short-sighted.

While Australia did train many foreign students at a high level, we did not develop the domestic industrial base to exploit that talent once educated. This is in contrast to the United States, which has been a huge beneficiary of imported talent.

Now that the Australian political conversation has regressed to anti-immigration, and not rationally directed immigration policy, there is little prospect of the local polity suddenly fastening upon any good plan of what to do next.

Australia sleep-walked into two full decades of rising prosperity based almost entirely on the recycling of foreign earnings into credit expansion to fund a housing boom.

The inevitable bust is now upon us.

Since very few market players could correctly diagnose the proximate reason for the good conditions on the way up, I doubt that very many will see the way out.

The way out is time, a painful period of adjustment, capital preservation against the market volatility, and a clear weather eye on the search for new opportunity.

In the Australian market, we think minerals for the energy transition remain key, and we also have the benefit of a large quality gold sector, the third largest after China and Russia, plus iron ore, copper and uranium in majors BHP Group and Rio Tinto.

The Australian banks used to be a good place to invest, but we have shifted our focus to Asia, with Singaporean and Hong Kong financials among our key bets.

Domestically, the Australian economy still needs energy infrastructure, through firms like APA Group, providing gas pipelines, and rail transport, via Aurizon.

In real estate, we think the parameters of government policy will settle on new build, as we have already seen with the negative gearing changes. There is a housing slump in progress, but Australian housing would need to fall about 30% to be fair value.

This will not happen, but the construction shakeout now in progress will benefit those developers with stronger balance sheets in growth corridors like Stockland Group.

Concerning the rest of the Australian market, not much interests me. The technology of tomorrow is being built in China and the USA, not Australia. Our market has a very nasty habit of paying a global premium for indifferent technology exposures that will collapse in valuation once the global leaders correct. That time looks close upon us.

Due to the housing correction, and wealth effects coupled with a clear retrenchment in high rates of immigration, the Australian consumer sector is unattractive.

The Australian health sector is also small, by global standards, and faces the serious challenge of more aggressive US policy to force concessions on pharmaceuticals.

These factors will likely pile up once China makes further inroads into developing its own for export medical devices and pharmaceuticals industry.

Oddly enough, the one great positive I see for Australia is the diverse ethnic ties our large immigrant population has with other parts of the world. This matters in the age of connected informational markets that depend on local knowledge and contacts.

Australia has hybrid vigour that will likely see it adapt to the new conditions from the ground up. I cannot see this happening at the corporate level. There are too many large and sluggish oligopolies in Australia that were wedded to “Just scale up!”

The just-scale-up mantra will not work anymore.

Who knows what adjustment pathway Australians will choose?

While my tone may appear negative towards many of the leading companies of today, I am not negative on the Australian project. Australians will figure out something.

In between, I am resigned to a period of largely counterproductive bickering.

Until that fog clears, I will hide out in Australian miners and those areas of the economy where I perceive a clear shortage and demand support.

The updated performance and current portfolio are given below.

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• Australian 20 Stock Model Portfolio

• USA 20 Stock Model Portfolio

• International (non-USA) 20 Stock Model Portfolio

• Global Best Ideas 25 Stock Model Portfolio

There are no changes for the global model this month.

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The updated performance and current portfolio are given below.

Since inception, the performance remains positive.

The ratio of absolute return for risk remains very favorable.

In virtue of the churning market conditions, we are sitting tight this month.

The current portfolio snapshot is detailed below.

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• Australian 20 Stock Model Portfolio

• USA 20 Stock Model Portfolio

• International (non-USA) 20 Stock Model Portfolio

• Global Best Ideas 25 Stock Model Portfolio

There are no changes for the international model this month.

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The updated performance and current portfolio are given below.

Since inception, the performance remains positive.

In virtue of the churning market conditions, we are sitting tight this month.

The current portfolio snapshot is detailed below.

Read more

• Australian 20 Stock Model Portfolio

• USA 20 Stock Model Portfolio

• International (non-USA) 20 Stock Model Portfolio

• Global Best Ideas 25 Stock Model Portfolio

There are no changes for the USA model this month.

Subscribe now

The updated performance and current portfolio are given below.

The main theme in this report will be to look at mean reversion over short periods of one month, and to relate this to the wild swings of relative performance this year.

To set the scene, have a gander at this alpha chart.

The issue driving this is two-fold:

• Mean reversion on a one-month basis is very high right now.

• The extreme concentration of the benchmark produces a large tracking error.

In the section below the line, we make a turnover analysis of the strategy, since the inception on 18-Jan-2024, just to check we are not trading excessively.

The summary is that we are not, but you can see from the monthly returns below that the strategy has a tracking error (relative risk) of 7.98%.

The annualized risk of the portfolio is 11.96%, which is pretty close to the S&P 500, at 11.93%, but the relative returns are varying widely, due to concentration risk.

In the USA, the top ten stocks in the S&P 500 are now 40% of the benchmark. We own four out of ten, so the swings in the other six have a big effect month to month.

Just to test whether we are going mad, I compared the total returns in June of the twenty stocks we own (Prior Month) to the same stocks in July (This Month).

This is a scatter plot, where each point is one of our portfolio stocks. The line slopes down and to the right, because those stocks that did well in June did poorly in July and vice-versa. The slope of this line is -.72, a perfectly negative correlation is -1, and the so-called R-squared statistic is 0.389, meaning 38.9% of variation is captured by this simple linear regression. We did adjust weights on 7-Jul-26, but we owned the same stocks throughout. We just upweighted prior underperformers.

You can see that this effect is very powerful at this time.

Investors seem to be madly chasing performance, selling what recently did poorly to buy what is currently doing well, and that is proving to be counterproductive.

As I said, we are making no changes to the model this month.

However, mindful of this current reversion regime we have upgraded our toolset to give a better read on our own trading history and turnover.

In addition to the usual portfolio snapshot, as at 7-Aug-26, I include all of the trades and turnover since inception. We will use to help guide our rebalancing.

This year we traded significantly to position for possible stagflation, and the ongoing data center and AI boom. Since the top ten stocks are 40% of the benchmark, it can be very difficult to outperform at our target 30% turnover.

On the basis of our recent study, a turnover budget of 30% to 60% seems more like what is needed to navigate this concentrated and volatile market

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• Australian 20 Stock Model Portfolio

• USA 20 Stock Model Portfolio

• International (non-USA) 20 Stock Model Portfolio

• Global Best Ideas 25 Stock Model Portfolio

There are no changes for the Australian model this month.

We will likely make some tweaks to the Global models in the next day or so.

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The updated performance and current portfolio are given below.

Read more

• Australian 20 Stock Model Portfolio

• USA 20 Stock Model Portfolio

• International (non-USA) 20 Stock Model Portfolio

• Global Best Ideas 25 Stock Model Portfolio

It was a great year for the Australian portfolio, a so-so result for the USA, and a poor result for the International 20 and Global 25. The last was driven by underperforming selections across all sectors except resources, in which we had good wins.

The annual returns in USD were:

Portfolio: +10.83%

FTSE Global All Cap: +24.70%

Alpha: -13.87%

The sector report provides the cleanest read on what happened.

The only sector to show a positive contribution was resources. This explains the disparity in performance between the Australian and Global strategies.

We got the home story right in gold, battery minerals, and iron ore.

Everything else was not so great, and not down to one country.

These were conditions where most of what we picked did not work. The detail of this negative skew for stock selection shows up clearly in the contribution analysis.

Portfolio diagnostics are important when deciding what to do, if anything. The above chart shows that we had very significant negative skew holding many sensible stocks. This matters a lot if the absolute return of the strategy is negative. In the year we just ruled off the return was +10.83%, which was a large -13.87% lag to benchmark.

The results of global managers are not out yet, but many will be bad, like us.

The markets were narrow.

South Korea, ARCA: EWY, was up around 170% thanks to High Bandwidth Memory (HBM) makers Hynix and Samsung. The USA version, Micron, XNAS: MU, was up around 800%, while Nvidia XNAS: NVDA, finished up a mere 25%.

When you have a very narrow market like this the performance will naturally divide between those on the hot trade, and those left behind everywhere else.

The important thing is that our absolute returns were positive.

Some areas of the portfolio, like China technology listed in Hong Kong, and the main technology stakes we hold in US hyperscalers, like Microsoft XNAS: MSFT, had a poor showing as investors sold those to chase performance in memory and storage.

You can see the full breakout of performance here.

Narrow markets can persist for some time, but we think this is a frothy market that will reward patience. The rational thing to do is to modestly play mean reversion.

In the changes this month, we trim some winners and upweight some value laggards.

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The performance numbers for Australian FY2026 were:

Portfolio: +2.46%

FTSE Global All Cap: +24.70%

Alpha: -22.24%

There is no polite way to state that result other than to recognize it as terrible.

The rest of this report digs into that performance and outlines our response.

The effect on our track record is to render it so-so since inception.

Around May-2025 was the time to crow, now we have to eat the crow. Run, Mr Crow!

There is a deeper issue to report here, and I will elaborate, but you will notice from the graph of cumulative outperformance that it is quite volatile.

Up, down, up more, down more.

The first instinct, when faced with terrible results, is to throw out the bad and ship in the good, in order to address the cause of the trouble and turn the ship around.

This is not a terrible approach to investment management but can be hazardous when running global portfolios. There are two reasons for my caution:

• Benchmarks are subtle creatures in global investment because of what they hide.

• Turnover is the enemy of after-tax returns and prone to mean reversion drag.

Let me return to what happened in late 2024 and what we did about it.

On 4-Oct-2024, in the midst of declining alpha, we did this rebalance

The heroic changes made then were to ditch semiconductor equipment firm ASML Holdings XAMS: ASM, and to buy Tencent Holdings XHKG: 700. They both did well, although Tencent did well earlier, and ASML later. Later it did very well indeed.

That was the only rebalance until 30-Jan-2026, fifteen months later.

Over the next six months, that portfolio rebounded 23.8% in alpha terms.

This is not an isolated experience for me and is one of the key challenges I had to overcome when first managing global portfolios.

It has to do with mean reversion.

The Power of Mean Reversion

This is supposed to be an educational blog about portfolio management.

Of course, I want to provide good advice and suggested investments, but I also want to step away from what I personally think about any stock, at any given time, which is a hugely fallible undertaking, and impart what I have learned about the generalities.

There are a number of maxims I am sure you have heard:

• Cut your losses and let your winners run.

• Nobody ever went broke taking a profit.

• No stock is ever too high to buy or too low to sell.

• Buy a good stock and wait till it goes up, if it don’t go up don’t buy it.

• The stock market transfers money from the impatient to the patient.

You can probably recognize a few famous investors in among that lot.

It is the last aphorism that I will rap on in this note.

When things go wrong, pay attention to the timescale of measurement, and the features of how you choose to measure return and ascribe your judgment.

In explaining exactly why global portfolio management needs to be treated with respect, when making judgments on what to buy and what to sell, it helps to be familiar with the statistical forces at work in return measurement.

This can be a very sobering experience for anybody who considers their results to be good on the basis of a short time series. The nature of the mathematics that I will relate highlights that we may none of us live long enough to prove we are good.

The consolation prize, for all global managers, is that if your tracking error to how you are measured, via your benchmark, is large enough, it is next to impossible to declare that you are most definitely bad. This is with one exception, absolute return.

Once we are through this discussion, it will be a little easier to understand why I make the changes to the portfolio as I do: not a lot of panic; a decent measure of hope.

If your absolute return remains positive, and the portfolio is well-supported by value, then you can hope for better times ahead. You should hope for mean reversion.

The hope in question is not idle, but the outcome is not guaranteed.

We none of us know the future, which is why we bet on statistical odds.

The odds are that bad times in markets are followed by good times.

Is this deliberate neglect? Not really. It is casual confidence.

I am confident that evident value will be bought.

If I think it has been bought by me, I do hope that others buy it.

However, it pays not to be reckless. Step into your evident hopes of recovery by stages and do not bet the farm by swinging for the bleachers. Husband those positions that are doing very well but be prepared to trim them.

The idea is to allow a three-to-five-year value cycle to play out for you.

This is not possible through frenetic trading.

The three-to-five-year cycle demands low turnover.

Specifically, if you intend to hold for three to five years, you should mark your ideal turnover budget as implying a 36-to-60-month turn, in and out.

Let us work that out explicitly:

36 Month Average Holding: 100%/36 = 2.78% turnover per month

60 Month Average Holding: 100%/60 =1.67% turnover per month

This is not very much and certainly does not mean you can dump a 3% minimum new holding size every month. This month, as seen below, we will be 3.46% turnover.

Turnover is a little higher than our budget to take advantage of value on offer.

For those investors not used to daily measurement, which is most, this can seem like an agonizingly slow response to a rapidly changing world.

Indeed, it is a purposefully slow, stubborn, and tortoise like discipline of creep.

The mean reversion of value is slow burning and demands patience.

You may wash your hair every day, but Rachel Hunter hair takes time to happen.

I look at it this way…

With a schtick like that, value investors can holiday in the sun with no phone.

Let us hope it turns out okay, with all the snappy moves of this here tortoise.

Understanding Benchmark Tracking Error

Leaving aside the schtick, the obvious question to ask is this:

Is 20% positive or negative alpha good or bad over two-and-a-half years?

Easy, says marketing:

20% positive alpha over two-and-a-half years is good.

20% negative alpha over two-and-a-half years is bad.

So true, and yet so unhelpful to any real investor.

Up is what we want, down is what we don’t want.

Yes, Mr Marketer, we do know that in portfolio management.

The problem, for any investor, is what to do when it goes up, or down.

Do I sell all of the losers if it goes down?

Do I buy all of the winners if it goes up?

The real problem is that much of what happens is statistical noise.

If you have a normally distributed random variable with a constant mean, and constant standard deviation, the estimation error of that mean, goes down as the inverse square root of the number of periods in the statistical sample.

Let us now look at the track record for this strategy.

When you invest in global markets, there is a wide variability of outcomes due to the sheer number and variety of stocks and markets involved. This shows up in a measure called the tracking error, or relative risk, of the strategy.

In the 30-month performance record above, the annual average alpha was 1.15% on a total risk of 13.06%, and a relative risk, measured versus the benchmark, of 10.79%.

Assuming the returns were generated from a stable distribution, which they are not, we would need to wait about 100 months to express confidence, that our generated alpha is positive (the statistical uncertainty is circa 10.79%/10, the square root of the number of trials, at 100). That is the one-standard deviation estimate.

For 30 months, the naively estimated uncertainty in alpha is 1.96%, and that at the level of one-standard deviation, or 68% for that standard confidence interval.

To put this in the proper context, standard statistical analysis would show that you need to wait roughly 900 periods, or 75 years, to have 99% confidence in your estimate of the alpha to 1% precision with a 10% relative risk.

Did I choose things that way just to mess with your head?

Not really, it is a feature of global financial markets. This benchmark is the FTSE Global All Cap Index, measured in USD, like our portfolio, which has 60-65% US stocks. This happens to be the benchmark we chose, because the Vanguard ETF AMEX: VT tracks the same benchmark, and can be bought very cheaply for 0.06% MER.

There are no US stocks in our International 20 Stock Model Portfolio.

We should expect this model to behave accordingly.

When the USA has a knockout quarter, we will underperform.

This is not an excuse but a caution. Whenever you are evaluating global markets be aware that the tracking errors can be very large indeed. This is why it is hard.

I mean hard for everybody not just hard for me.

For instance, the South Korea ETF ARCA: EWY is up 87.97% Year to Date (YTD).

You can go crazy, and many do, madly switching in global markets.

This is not what I do because I learned my lessons long ago. Whenever, the markets move wildly against you, in this global sack race, look carefully for the value.

The Characteristic Time Scales of Market Action

Every single day that the global market is open, there is money sloshing back and forth looking for a return. These flows will move markets all by themselves.

Therefore, we must be cautious, and a little circumspect, on attributing too much of a real signal to the back-and-forth of daily flows and the incessant chatter.

There are stories told the whole time.

Some are true, some not so true, some might prove true one day.

Others express the value of a good story to attract money.

In the short run, you can be confident that a good story will attract money, and the validation of that story is how much money it can attract right now.

This is why chasing momentum works on a three-to-twelve-month horizon.

I have not done that back test for years, but I warrant it is still true.

The opposite strategy works well over periods of about one month.

The so-called residual reversal strategy is the best quantitative signal known to man.

Generally speaking, you can bet on the losers of last month reversing.

The other timescale of statistical significance is the three-to-five years reversal.

Investor neglect sees many losers become winners over three to five years.

This is the source of most investment alpha for so-called value strategies.

Recognize that I have nominated three key time scales and a dominant modality.

• Mean reversion and reversal of returns over about one month.

• Continuation of price trends over three to twelve months.

• Mean reversion of a much greater scale over three to five years.

The earlier turnover mathematics highlighted that there is a cost to playing any of these sweet spots as your source of investment return.

The reversal strategy over one month is very high turnover, 1000%+ per year, and so only suited to those with low transaction costs, whose income is from trading.

These are the market makers, Commodity Trading Advisers (CTA), broker proprietary trading desks, and a good chunk of quantitative hedge funds trading signal.

The three-to-twelve-month trend play suits full bench fundamental funds managers who have sold their investment chops on their ability to forecast quarterly earnings.

This is a 100% to 400% turnover proposition that supports high volume marketing to garner assets because investors love to hear a timely update on market stories.

The final three-to-five-year strategy suits those with a high tolerance for short-term pain, the ability to defer the pleasure of long-term gains, and a taste for deferred capital gains as their primary engine of personal wealth creation.

This generally does not market well which is why proponents are characters.

People listen to Warren Buffet because of who he is, not what he does. They do come first for the returns, but they stay for the homespun philosophy, and the anecdotes.

The rational value investor dispenses entertainment to soothe current pain.

This is not silly, or fatuous, since any value investor worthy of that name will make many mistakes in their career, and experience an awful lot of personal pain.

This is the kind of pain that breeds a stoical cast of mind to misfortune.

You know the schtick:

That which does not kill us will only make us stronger.

Provided that one is pragmatic, suitably cautious, and willing to label spades as spades, when all other monikers have been deployed, you are good to go!

On to what we will do, this month, which is not very much.

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West Texas Intermediate WTI crude oil closed at $64.92 USD on 30-Jun-2025.

It closed at $70.02 USD on 30-Jun-2026.

This is after a one-billion-barrel draw on global inventory and strategic reserves.

The Israel-USA-Iran war is supposedly over except for the occasional airstrikes.

Gold was a more positive story.

It opened the account on 30-Jun-2025 at $3303.09 USD/oz.

It closed the year on 30-Jun-2026 at $4007.26 USD/oz.

Normally, we would toast such a result!

The return of gold, the barbarous relic was +21.32%.

Too bad the S&P 500 returned +22.05%.

Our twenty-stock best ideas US portfolio returned +20.33%.

I guess our best ideas were not good enough this year, and that is the simple truth.

Among the better ideas out there was High-Bandwidth Memory (HMB) chips, now in high demand in data center build outs, such as are made by Micron XNAS: MU.

I am no fan of hair shirts, but a minimum 3% allocation to Micron would have helped.

For those who have been around markets a while, this sh*t happens!

Hindsight will beat the market every single time. We have to move on and deal with the road that lies ahead of us. This is not made any easier by the suspicion that all is not right in the world of energy. Something about this oil price looks fake.

Two of the most cyclical industries on the planet look to have distorted pricing.

Memory chips are the most cyclical sub-industry in semiconductors.

Oil is the most cyclical of growth sensitive commodities.

For what it is worth, here is my “insight” such as it is.

High prices are generally cured by high prices, that is to say lower demand.

Low prices are generally cured by low prices, that is to say higher demand.

The boom in AI spend is real, but the memory price trends are worrying.

Local outfit Pc Part Picker has tracked a four-fold increase in the price of DDR5 6000 RAM (2 x 16GB) from a stable $200 AUD/module to $800 AUD/module.

The result will be price rises across the board in memory using consumer electronics.

• Smart phones

• Cameras

• Laptops

• Servers

• Smart televisions

• Electric vehicles

The frenzy of artificial intelligence spending is leading to an inflationary spike.

The flip side of this is that some stocks we hold and like are really hurting.

The obvious solution to this problem is to buy memory makers.

This is not a terrible idea when price targets on stocks like Micron are raised to $2000.

For those who are happy to trade this wild market let us pray with Saint Augustine:

I emphasize the word trade, since you should rightly run a wide trailing stop about 20% lower than the current price to salvage any profit when it cracks.

That is easy advice to give and almost impossible advice to carry out.

Failing any saintly hotline to a higher authority, we will stick to the brief for this model portfolio and try and patience our way through this hot mess of wildness.

Afore doing that, here is the year in review.

FY2026 in Review

If you happen to live outside Australia, you have a different financial year. Last night I went and sank a few mulled wines at the Canberra Christmas in July, with a chaser of Berlin Banger - The Foot Long Kransky Chilli Dog. We are weird here. Get used to it!

Here is the annual roster of offenders and defenders.

I am flippant here, because this is a wash. You can see that there is no real skew either way. This was a middle of the road, track the benchmark year. Not good. Not bad.

Apologies for the tiny image, but it is the whole year compressed into one snapshot.

The best way to understand the story of last year is to look at quarterly alpha:

• Q3 2025: +4.71%

• Q4 2025: -1.39%

• Q1 2026: +4.13%

• Q2 2026: -9.43%

Notice that the relative return was really bad in the last quarter.

For completeness, the numbers were:

• Q2 2026 Portfolio: +5.52%

• Q2 2026 S&P 500: +14.95%

That last comparison is crucial for what we do or do not do next.

In a quarter where we lagged by 9.43%, we were actually up 5.52%, which is a better than 20% annualized rate of return. However, the market was up 14.95%, which is an annualized rate of return of +74.6% per annum! That is what happened.

Can this go on?

With the level of enthusiasm for all things artificial, including oil prices, yes, it can.

For how long, we cannot be sure, certainly another quarter or so.

During the late 1990s technology boom, I recall averaging portfolio rates of return of around 75% per annum over three straight years, until it stopped, and I didn’t.

The markets then dropped about 80%, and I got back to where I started.

This is what happens in an investment mania.

Personally, I do not think we are in full manic mode yet, so I expect more frantic chasing after whatever moves up and to the right.

The other lesson from the contribution analysis is to note that hyperscale stocks like Amazon XNAS: AMZN, Meta XNAS: META and Microsoft XNAS: MSFT undershot.

A boring railroad like Union Pacific XNYS: UNP did better than that bunch.

Notice also that, while Alphabet XNAS: GOOGL was our top contributor, we had a very good showing from diesel engine maker Cummins XNYSE: CMI and mega-cap gold miner Newmont Corporation XNYS: NEM. The last in spite of the retreat in gold.

Did we make mistakes last year?

You bet, the two I would single out was our exit of Apple XNAS: AAPL, and the brief dalliance with Oracle XNAS: ORCL where we timed everything badly.

You can surely find others.

We held no Micron.

Onwards to a minor rebalance.

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(Addendum: See below for the final results).

After this rebalance report the full-year results are in.

The FY2026 returns as at 30-Jun-26 were:

Portfolio: +14.50%

S&P/ASX 200 Benchmark: +6.11%

Alpha: +8.39%

It will move around in the last two trading days, but it was a good year.

The bulk of the outperformance came from timely resources bet around this time last year. In view of changing conditions, we are closing up some of that in this note.

The since inception numbers have improved in recent years.

This was our best year yet in both absolute and relative returns.

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I have had three careers and started five companies in my time.

The 2026 Budget changes to tax and negative gearing do not scare me.

I have seen far worse economic challenges in my 63 years on the planet.

What matters is knowing how it will affect the landscape.

Here is my take:

Directly, it removes investor competition for housing with owner occupiers. Directly, the abolition of the CGT discount shifts the horizon to long-term investing. Directly the realization that deferred capital gains are better will refocus minds. The idea of paying advisers to lose you money is dead.

Australia has had three decades of easy money credit boom, and it shows.

Now we can hear the public screams of those drowning naked.

Nobody forced any investor to negatively gear overvalued real estate.

The effect of removing the public subsidy of PAYG deductible debt for investors, and not first home buyers, is to take them out of the market. They now have to pay the real cost of finance for those properties. They realize it makes no sense.

Any competent financial advisor could have told them that.

Now they get to find out for themselves.

For those in Australia now bleating clock this:

In the USA, you cannot deduct the principal portion of your mortgage payments. However, you can deduct the mortgage interest on your primary or secondary residence. This is done by itemizing deductions on your federal income tax return rather than taking the standard deduction.

Like here, before the budget changes, you can deduct interest against rental income for an investment property. Where were the advocates for a policy of helping our home buyers by making the interest on their principal residence deductible?

Nowhere to be seen.

There are no Australian advocates for deductible first mortgages I can see.

Cue massive wailing and gnashing of teeth when the privileged negative gearing set got their punch bowl taken away. Already, property values are normalizing.

Falling real estate values in Australia are necessary to balance this market.

I own a residence too, and mine will fall in value also. Since I judged real estate to be overvalued in 2020, when I bought my present place, I live very modestly.

This correction has been three decades in the making.

It is here now.

My take on Entrepreneurship

If you believe the hype for the Venture Capital community, Entrepreneurship is all about twenty-something world changers with a bright idea to make a trillion.

If there had been a venture capital industry in Australia, in 1983, during the depths of the Volcker double-dip recession when interest rates were 18%, then 20-year-old me might have had a go. I could program, owned a computer, but was crap at designing video games. I was good at physics and liked to tinker. It was not crazy, at that time, for a young person to think of designing or inventing something.

For me, I had considered the scientific instruments industry, where there was quite the cluster of activity in Melbourne, with Varian and Vision Systems Laboratories.

I was fortunate to do my honors in the Research School of Physical Sciences at the Australian National University (ANU) in a laser lab where my supervisor got me to assemble and tune a laser wavemeter of his design. This was great stuff.

Once I had graduated, in 1984, I looked for jobs and was offered one in the laser lab at Telstra Research Laboratories (then Telecom), in Clayton, Melbourne. I was a very good student, and got the job, but turned it down. I wanted to do a PhD, and I had decided that it was best to go overseas to gain some international experience.

I duly won a scholarship to do a PhD.

As a condition of the scholarship, I could choose to go anywhere in the UK. Normally, it is competitive, but I got the scholarship, so any University in the UK would accept my application, as a condition of the British Council program.

I chose to go to the University of Bristol to do theoretical physics.

I am glad I did, as my advisor Prof. John Hannay was the quintessential eclectic and innovative Brit. He gave me a lot of confidence and taught me to think for myself.

I never had any trouble generating ideas, but John showed that the emphasis should always lie on following your nose on the most promising ideas, even if painful.

I contend that this is most important lesson of entrepreneurship.

You need to persist on those things that make a difference and junk all else.

Notice that this is not good advice for life in a run-of-the-mill job.

In a regular job, you do what you are asked even if it makes no difference.

I was always stubborn about doing anything that made no sense to me.

The PhD training taught me to be stubborn with myself.

Do, think, and try at what will make a difference if you solve the problem.

There I used the word “problem” which is common to R&D and entrepreneurship.

In my view, entrepreneurship is very simple:

Solve a real-world problem for your customers that delivers value to them.

Some folks throw in “change the world” but if that was my sole focus I would have stayed on doing research in theoretical physics.

If you want to seriously change the world advance theoretical physics.

You will have a much greater effect on future technology if you do that.

For me, entrepreneurship in business has always been more modest than any of the things that I have worked on in theoretical physics.

For what it is worth, I decided to switch from a life in academic research because I worked out that my kind of theoretical research needed almost no money.

Why battle for funding when you could get a real job?

Now that I am safely out of corporate life, I can happily report that no meeting was ever wasted time for me, no matter how boring. I can do calculus in my head.

With an attitude like that entrepreneurship is a natural for me.

Fake everything that does not matter until you solve the problem.

Fake nothing that does matter for your customer.

There is not much more to it than that.

Why is Australia weak in Technology?

Finally, for what it is worth, because it is highly relevant to the Australian economy of today, I am glad I did not take that job at the Telstra labs in Clayton. It lasted a few more years, and they shut it down to save money. Later Telstra bought out OTC. I visited their Sydney office around 1992, when they were being merged with Telstra to rationalize the business. They had a leading fiber optics lab. That got shut down right before the global boom in fiber optic communications. Later again, much later, I had the good fortune to interact with the Sydney arm of NICTA, a national facility which was leading Australian research in Artificial Intelligence. It was shut down in 2016.

For reference, 2016 was the breakthrough year for AI. That NICTA laboratory rated number five globally in the year it was shut down. Then it disappeared.

Who advocated for NICTA to get shut down?

The Australian Venture Capital community.

Enough said on this repeating theme of Australian R&D. Visionary folks set up the lab and make it world class. Short sighted bean counters shut them down.

Rinse and repeat ad infinitum.

Now the bean counters of this land want to shut down the Australian Synchrotron.

Hopefully, this facility will not die, and I can use it to test some ideas of mine.

If this facility does die, I will go to Japan.

Japan is not chock full of science and engineering imbeciles, like Australia is today.

The Ideal Business Opportunity

There is no general rule but there is a standard shape to attractive opportunity.

For any opportunity to be real, there must be some barrier to realize the payoff. Ideas that anybody can do will attract a lot of competition, which diminishes returns.

However, not every problem you may want to work on has a positive payoff.

Somebody has to be interested to pay you for the product or service and so you must deliver some value for them. Your individual fit for opportunity depends on you.

I am not you so I do not know where your best fit would be.

However, I can say that if it costs nothing, in time, money or thinking power to solve the problem you have in mind it is not likely to be a durable business opportunity.

You also have to contend with market preferences.

You may actually have an idea for a superior product or service that would genuinely offer the customer better value and a more positive experience.

More than once I have faced the problem of entrenched market preferences.

There has to be some way to persuade the customer to try and buy your product.

Consumerism is now so rampant that many poor products find a market.

This can be down to the simple power of advertising and marketing budgets.

I try to avoid anything like that because I think it is losing game.

Shoving aside the poorer contenders, in a crowded marketplace, is expensive.

Take a look at major consumer products markets in the age of social media. There are all kinds of indifferent products that are heavily marketed and widely bought.

Google and Meta have this business sewn up. They count the marketing dollars that flood into any segment with low barriers to entry, that demands marketing spend.

Returning to the option payoff diagram there are shallow and steep payoffs. The more certain the payoff the closer it is in time, but you may spend more dollars on it. The steeper the payoff curve, the more distant it is likely to be in time.

This may seem counterintuitive, but many of the best opportunities take longer to ramp but cost less in dollars. They cost more in the time you spend on them.

Human impatience is your best friend in delivering fat opportunity.

These types of problems involve solving the more complex problems which are more difficult for people to tackle because they may involve more than one puzzle piece.

The favorite problem type I work on has several puzzle pieces, where each must fit together, and each sub-problem looks impossible to most people.

I love to work on impossible problems.

This is not as foolish as it sounds, as most impossible problems are possible problems that require some patient thinking to address while the world spins about you.

Venture Capital will not touch people like me which is great for me.

If you identify possible impossible problems early you are likely to win.

Even before I had entered the business world, I had solved one problem that was widely considered impossible. This was an invaluable experience for me.

The question of entrepreneurship comes down to your choice of problem.

If you note carefully the trajectory of one Elon Musk, you will note that he has no fear of impossible problems. Mostly, he does not solve them, he solves another one.

However, you have to cut him the slack that he might solve a really big problem.

This is why I put two option payoff profiles on the diagram.

The Ideal Business Opportunity lets you work on two problems at once.

The first problem is to get a business running, the second problem is to scale.

You might spend a decent amount of cashflow to get into a business, and smaller amounts of money, mostly thinking, on figuring out how to scale it.

That is as generic as I can be without talking about a specific opportunity.

The Problem of Getting into Business

That is all very well, but how can one get into business?

If you are in business, you already know how, and you probably had no need of any abstract picture like the one I described. You just opened your doors.

The vast majority of professional services businesses are like this. You develop some marketable skills, get to know prospective clients, and put up a shingle.

There you are in business!

The same is true of trades, and perhaps more so.

Once you have your ticket for a trade you can be in business.

The problem only arises when there is not a direct line between your best skill set and the business opportunity you seek to harvest. This seems like a non-problem to many who have not faced the challenges of supporting infrastructure for your business.

Let me give a concrete example from my own career.

I left physics research in the mid-1990s as there was a dearth of funding. I realized that I could do my research with a pencil, but I needed to eat, and sleep some.

Like many of that era I went into finance, where I could make good money. The work was easy for me, and I was very well paid. There were lots of boring meetings, but I can smile, talk, and do calculus in my head, without blinking, or raising a sweat.

This was perfect for me until I decided I wanted my own finance business.

Since my specialty was quantitative finance, I needed lots of data, to do my job, which can be expensive in money to buy, and time to clean up and analyze.

I am not bleating; this is a standard problem for quantitative finance people.

When you have this type of business opportunity in mind, you will have to plan ahead and put in place the pieces you need, in a legal way, and form the capital stock.

The problem of forming the capital stock is a key one in entrepreneurship.

It is easy to be superficial about this challenge, which constantly morphs and changes.

Windows of opportunity arise where the capital stock requirement is lower.

One might also find that financial capital can readily substitute for capital stock.

You can raise money from investors and buy whatever you need when you need it.

This is the standard script for Venture Finance. Go in with a great pitch, come out of the meeting with a commitment to fund your venture with other people’s money.

There is nothing wrong with this approach, provided you have the right idea.

For students of Venture Capital, the key thing to understand is that the right idea is most likely a fashionable idea into which a lot of VC money is now flowing.

The way to win that game is to work on yourself as the entrepreneur.

This is the “sell me a pencil” test. If you work on yourself, you can likely sell the pitch that you are the one who can take a fashionable idea and make a big business.

This works for the right kind of person and can be very lucrative.

If you are anything like me, you probably do not find the idea attractive.

It is not that it is a bad idea; it is just not your idea.

This is an okay way to be also but requires a different approach.

If you are not going to raise a lot of money from investors in the business, you might find the equivalent kicker from clients. This works very well for professionals.

The best form of finance for any business is paying customers.

If you have developed a good reputation in your line of work, you can well start a business in that line of work by getting your current customers to follow you.

This is the best approach I know in financial services businesses.

If you really want to succeed, in the most reliable way possible, within the financial services industry, then hone your skills at a large, respected firm, and leave.

This is what I did with my financial services firm Jevons Global.

It is the sensible low risk way to get into a professional services business. This path is not a sure-fire win but is a proven and reliable way forward for most people.

The best advice I have come across in the mid-career entrepreneurship area is that from the Kauffman Foundation, in Kansas City, KS USA.

Since I went to school in Kansas City, in the late 1960s, I have a soft spot for KCK.

Ewing Marion Kauffman was a successful mid-West entrepreneur who gave back to many humanitarian causes, including the demystification of business formation.

The foundation he established has done much to demythologize business formation by pointing out that it is mostly mid-career folks who form them. Provided those are in areas they understand, they are more likely to succeed, due to experience.

The Valley myth of the twenty-something entrepreneur works for them.

If you analyze it carefully, the real business is fifty and sixty something VC partners, presented with a conga line of hopefuls, just like the Hollywood studio business.

It is not that it cannot work for you, but is unlikely to work for most.

If you like the mythology, then work on yourself to be pitch perfect.

It is an acting game and should be treated as such.

For guidance on real engineering entrepreneurship go to Shenzhen.

Failing that, figure out a way to build capital, to start your own show.

The Capital Accumulation Circuit

I am a mathematician, so my view of capitalism is very simple.

You go from nothing to an idea, which is an infinite rate of return. There are losses when you convert that idea to cash, but the goal is to rinse and repeat the cycle.

What matters most is your conversion ratio on capital employed.

Long ago, some idiot named Benjamin Franklin, decided that mathematics was way too valuable to be protected by copyright or patents.

Ever since Ben Franklin, all mathematicians have worked for a living.

This is not all bad, some of those very boring meetings in finance come with drinks!

Seriously though, it is your ability to convert starting capital to ending capital that matters most for successful business, and you must work on the conversion ratio.

Note that capitalism, as I have described it, does involve work. We are not talking about extracting rents from passive activity. That is a very big area of activity.

In Australia, there very few genuine capitalists, mostly they are rentiers.

You can tell this very easily by looking at the productivity data.

US Productivity Growth has been pretty reasonable over time. You can argue about who shared in the proceeds of that growth, but the facts are real. It is not simply a taxation or labor cost story. It is mostly a capital stock story.

The capital stock is that magic piece that completes other factors of production like land, labor, energy and other material commodities. Done right, it adds value.

Australia is a very sorry tale by comparison.

There are some who blame this on the labor market. This has something to do with it, but you will not materially lift labor productivity without matching capital stock.

It is true that the Australian capital expenditure data is skewed heavily by mining, but the bulk of labor is employed outside of the minerals industry. Why such low rates of capital formation and why is anybody surprised that we have low productivity?

Here is part of the answer, rising land prices.

In Australian dollar terms, gold prices started rising significantly after the huge money injection to global markets since the Global Financial Crisis (GFC).

Australia has had a huge credit boom in the last twenty-five years. Not much of that went into new capital stock, outside of mining, land values and real estate absorbed the early flood of newly printed money from too easy financing.

Now rents are soaring and wages will follow.

Australia has been home to the mother of all rentier booms for passive geared real estate speculation. It would have ended, regardless of the tax changes.

Once all the blood has been sucked via rent extraction the economy rolls over.

I do not envy Jim Chalmers job, because this economy was always going to croak no matter what he did, however I do support what he has done before the final croak.

Australia is headed for the mother of all portfolio realignments.

It has started now, and the pace will soon quicken.

The beauty of the Chalmers move is that it came before the teeth of the downturn. For those who understand markets, they have time to adjust and a clear signal.

This is superlative proactive policy from a forward thinking government.

I know many will disagree with that statement, but you are not capitalists.

Capitalists understand there are cycles in business and prepare for them.

Let me finish by reprising what I think is a good path for aspirants.

Target Financial Independence

There is the easy form of such advice.

Get rich quick and retire.

If you can do that, why not?

For most people, this is not helpful advice. They have to work for a living, provision for children, make a home, care for other family members, and save for retirement.

For many, the removal of the 50% CGT discount came as a shock because they are on the top marginal tax rate and feel that capital accumulation potential is limited by it.

I will not lie; the removal of the CGT discount hurts.

However, as I pointed out in The Value of Tax-Deferred Capital Gains, the impact of the tax on after-tax rates of return is mitigated by extending the holding period.

The short version is that when you hold longer there are many PAYG tax years in which there will be no impact on your taxable income from a capital growth strategy. You do pay tax when you sell, but the holding period rate of return is higher the longer that you are able to defer taxable gains. You need no special structure to do this.

The need for tax advice is much lower when you pursue long-term investment.

There are many who benefit from the annual charade of finding new ways to lose some money ahead of the 30-Jun deadline to reduce your tax bill.

Do not sell your winners and there are no losses to find to offset gains.

Some contend that active portfolio management is dead post these changes. This would be true if you had no idea how to run a low turnover investment strategy.

I bought my current Microsoft holdings in 2016 and have been trimming them into strength for about three years now. Those measured gains are offset by any losses that I choose to realize because I invested badly or found a better idea.

This idea that you can only become successful if wages for workers are zero, taxes are nil, your utility is a charity, land is free, and lawyers are slaves locked in a gimp cabinet in the basement, is spurious. If you think that, never try to form a business.

What does affect all business is too much rentier activity.

When the pricing of factors of production is set in favor of passive rent seeking then anybody and everybody who is an entrepreneur will struggle.

Entrepreneurs struggle when the magnitude of rent-seeking is too high.

This is why I welcome the Chalmer’s tax changes. They just slit the throat of many a shameless rent seeker and the gurgles you hear are the death rattle for them.

This does not alter or diminish your problem of entering business.

I have thought for a few weeks, and this is the best idea I have come up with.

Some years ago, the government created tiered tax rates for small businesses:

• You have to be less than $50M AUD turnover

• You have to have less than 80% of passive income (rents, dividends, capital gains)

If you meet those simple criteria, your tax rate, in the business is 25%.

The business sector never had the benefit of the 50% CGT discount. PAYG taxpayers on 48.5% had an effective tax rate of 24.25%. This meant it was better to run your capital account through you PAYG tax and take advantage of the discount.

Now you are being actively encouraged to form a company.

This is not a rort because you need to have at least 20% operating income.

For most young people starting out this is not an onerous hurdle.

Do not even think of starting a company unless you can afford the $5K outgoings a year to operate that at any significant level. more if you need licensing.

The challenge for most young people is to form capital, which means to attain the level of financial independence that enables business activity.

The first hurdle is somewhere to live, eat and sleep.

Due to high housing costs, Australia is a very mean society to the would-be business entrepreneur. You are forced to be a wage slave to afford a roof over your head.

You know already that mathematicians are not worth anything.

This idiot told the world to never let any mathematician profit from their work.

See now, I can walk among you and not wear rags!

I am a mathematician who found out how to make an evil system work for me!

You can too. I am certain of it.

The trick with this small company caper is that you have to find some way to make at least 20% of what you make on your investments from real business activity.

In simple terms:

Make $100K operating revenue for each $400K of investment income.

If you can manage that you qualify for a 25% compounding capital circuit.

Of course, you have to think of a business idea to generate that operating income.

That is your lookout.

I am the mathematician; I prove the theorems.

It is up to you to figure out how to make money from that.

In my case, I already have a good idea.

It is a total winner!

Have fun.

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Only two things in life are certain: death and taxes.

I cannot defeat either, but I can describe the optimal strategy for investment, to achieve a higher after-tax rate of return, via growth or income.

What I am about to explain is standard in finance.

Any qualified financial advisor, anywhere on the planet, will agree with me that the deferral of tax to achieve long-term capital growth is the optimal investment strategy. It is elementary compounding mathematics.

Of course, you need to make assumptions to prove this, but they are simple.

Assume the tax rate on annual income is the same as that on capital gains.

With these assumptions, I will show you how to prove this result.

It is both simple and sound.

I will offer the most important piece of general financial advice you will hear.

If your financial advisor says otherwise, show them this note.

Ask them a simple question:

Please explain?

If they still do not understand, send them my way, and I will explain it to them.

That is all there is to it.

Taxation on Income versus Growth

To simplify analysis, consider this investment scenario.

You have two investment options, each offering the same pre-tax return of R% per annum and your marginal tax rate is T, applied equally to both.

Which should you choose?

Naively, you would be indifferent.

After you have paid tax at rate T, the post-tax return is R (1-T).

How could it matter which option you choose?

The post-tax annual rate of return looks the same, doesn’t it?

Well, in the real world, the after-tax rates of return are NOT the same.

This is not a social media narrative complete with viral video.

It is simply a mathematical fact that is true for all R >0 and Tax rates in the full range of T = 0 to T < 100%. If T >= 100% then you should move your tax residency.

The reason may be traced to compounding when you defer capital gains.

The Option to Defer Capital Gains Tax

Those who pay attention to the mathematics will do better in the long run.

The full Einstein quote reads:

Income and capital gains are not treated the same by the taxation office.

You pay tax on income in the year you earn it.

You pay tax on capital gains in the year you choose to realize them.

This looks like a minor difference to those who do not grasp compounding.

However, it is a massive difference in practice.

The entire period you amass unrealized capital gains you have a zero-interest tax free loan from the government to finance your future tax liability.

It gets even better with inflation indexed capital gains.

Under inflation indexing your future capital gains tax liability is financed by the tax office at an effective nominal interest rate equal to minus the CPI.

That is the mathematical reality of tax defered capital gains with indexation.

Note that I have been comparatively silent on the impact of the recent CGT changes. The rest of the industry ran around like chooks in a barnyard.

Once you read on, you will see why I did not join this foolish public melee.

Those who did can read this and make their excuses to their clients.

Their clients pay them for good advice.

If they got bad advice they may want their money back.

The After-Tax Return of Income versus Growth

What I am about to show you is a standard result in every financial advice curriculum.

If your financial adviser does not know this result you should ask them why.

Normally the result I will show you is stated in words without proof.

I am a mathematician so I will go one step further and prove the result.

The result in question is this:

For any rate of return R >0 and tax rate T that is in the full range of 0% to 100%, the after-tax return of growth investment is better than or equal to that of income over any period.

The jargon used by the industry is to say that growth dominates income.

Notice a few caveats of no great importance.

Income investments can come with tax credits, like franked dividends. This means that you should compare rates of return pre-tax with credits added back.

Australian investors know the rule for our tax system.

If you receive dividends franked at 100%, the gross-up for the tax credit is obtained by adding back the franking credit amount. This means your franked dividend yield of Y% has a tax-credit driven gross-up of 1/(1-t) where t is the company tax rate.

For large companies in Australia, that rate t = 30%, so the gross-up factor is 1.4286 which is 1/(1- 0.3). In effect, a 5% fully franked yield of 5% is adjusted to 7.14%.

With this caveat in mind, we will set R = 7.14% for comparison purposes.

In practice, this complicates decisions slightly, because many growth investments can also spin off some income. The tools I describe here can cater for that case.

This is free general advice so I will not do the warts and all calculation.

I would need to analyze the underlying investment to declare which is best.

With those caveats out of the way, let us do the calculation with no indexing of capital gains to properly emphasize the effects of compounding.

Indexing of the investment cost basis by any CPI>0 improves the result.

For clarity, it will also help to state a standard result:

After Tax Proceeds = (Pre Tax Proceeds) x (1 -Tax Rate)

This is clear, the money you wind up with is that with tax liability deducted.

That is the basis of the comparison we will run over N whole years.

I choose whole year tax periods to keep it simple.

You invest on 30th June of year Zero and realize gains on 30th June of year N.

The income investment pays an annual income, the tax due on that is paid, and the rest is assumed to be reinvested at the same comparison rate R% per annum.

This scenario is idealized, but not in a way that defeats the principle.

I want to make the tax deferral principle as clear as I can.

The Income versus Growth Sack Race

Let us now run the sack race.

Cue wild and whacky music…

After-Tax N-period Return on Income

Off goes Drooper the Lion, out of the blocks with the pure income strategy.

At the end of every tax year, the after-tax rate of return in that year is.

If you compound that over N full periods you get the post-tax gain:

You multiply your starting capital by that uplift factor.

Let us do a simple example:

• Starting Capital = $100

• Pre-Tax Gross Rate of Return = 10%

• Marginal Tax Rate = 48.5%

We run the race for 10 years and get this result:

You can see that the after-tax analysis is pretty simple for income.

You are compounding at the rate R(1-T) which is 51.5% of the headline rate.

For 10% RoR and 48.5% Tax you are compounding at 5.15% per annum.

After-Tax N-period Return on Growth

Off goes Bingo the Gorilla, out of the blocks with the pure growth strategy.

This is pretty simple to calculate, but we need to recognize that no capital gains tax is paid until the investment is sold at the end of N years, and then only on the gains.

The formula is easiest to understand if you first compute the gain factor pre-tax.

You do not pay tax on all of this capital, only the gain over N years. This is the above result minus one, the starting capital. You apply tax at the rate T and keep (1-T).

With a little notepad scribble you will get this result for the post-tax gain factor.

The above form is the most useful for later, but a little confusing to interpret. It looks like the effect of tax was to reduce your starting capital. This form is the same:

This makes it clearer what is going on. You compound starting capital at annual rate R for N years, subtract the starting capital to get the gain, and keep (1-T) of that.

We will use the first form later to prove dominance.

Let us repeat the former example.

• Starting Capital = $100

• Pre-Tax Gross Rate of Return = 10%

• Marginal Tax Rate = 48.5%

We run the race for 10 years and get this result:

Okay, so the growth strategy won in this example over income.

Note that more tax was paid to the government in the growth strategy:

Income: total tax paid $61.43

Growth: total tax paid $77.30

If your objective was to pay less tax to the government, you would choose income.

If your objective was to be personally richer, you would choose growth.

If you accept that the purpose of progressive income tax policy is to make both you and the government richer, at the same time, you will both choose growth.

If you sell tax minimization advice you will tell clients to choose income.

If you want to be personally poorer after N years you will take that advice.

I am a portfolio manager of a wealth management business.

I take my own advice and choose growth over income.

Call me self-interested, but I am richer for it.

What about high growth investments?

I read the press like anybody else. Following the CGT changes I saw a lot of news reports by unlicensed financial journalists claiming that the tax policy changes would harm investors and the Australian economy by preferencing income.

I am a licensed financial advisor: I would go to jail for dispensing that advice.

The problem with this advice is that it is materially wrong and misleading.

If you tell your clients that you are dispensing unreasonable general advice.

It is objectively and demonstrably a false and misleading claim.

Any financial advisor, licensed by ASIC, has a duty of care to their clients to present any general financial advice on a best effort basis with a reasonable basis.

The term “reasonable basis” means well-argued and factually grounded advice. This is the professional standard to which we hold advisors.

If you do otherwise, namely allow your political persuasion to cloud your professional judgement to form and propagate unreasonable hearsay, that is a financial crime.

If you continue to dispense misleading advice with no firm and reasonable basis for that advice, then you are likely to receive the attention of our financial regulator.

The purpose of financial advice is to serve the client interest.

With that sermon of my chest, let us look at the high-growth investment case.

The Case R =20% T=48.5% N =10 years

Let us explore a higher growth example with R = 20%

Your professional tax advisor will point out that you saved tax choosing income.

Your professional wealth advisor will point out that you were richer choosing growth.

The Case R =40% T=48.5% N =10 years

Let us explore a higher growth example with R = 40%

Your professional tax advisor will point out that you saved tax choosing income.

Your professional wealth advisor will point out that you were richer choosing growth.

The Case R =100% T=48.5% N =10 years

Let us explore a higher growth example with R = 100%

Your professional tax advisor will point out that you saved tax choosing income.

Your professional wealth advisor will point out that you were richer choosing growth.

The Pattern and the Reason for that Pattern

Those who prefer to look at examples to infer patterns will have noticed this one:

Let me state this clearly in words so that there can be zero doubt:

The share of the pre-tax rate of return left in the hands of the investor after paying capital gains tax increases with the rate of return.

Contrary to widespread assertions in the popular press, the rate of return, after tax, increases with the rate of return, on a like-for-like comparison basis.

Comparatively speaking, you get a greater share of the returns on offer if you defer tax by seeking long-term capital growth and you seek higher rates of return.

That is the reality not the sensational headline.

Journalists do not risk regulatory action against any misleading statements. Licensed wealth advisors, like me, are obligated to be truthful.

Some journalists have claimed that these measures are harmful to start-ups.

Evidently, an investor will make more money if the tax rate is lower. However, they would also be wise to consider society to be their fellow traveler. Taxation funds government activity of all kinds, including pro-growth economic policy.

Wise entrepreneurs will consider the following pattern of post-tax rate of return:

The pre-tax rate of return assumption of 15% used to be the benchmark for Venture Capital. This is a four-fold increase of pre-tax capital over a ten-year period.

I will use that assumption as it has stood the test of time.

If you think I am unambitious, choose a higher rate and get a better result.

I have run scenarios at a top marginal tax rate of 48.5%, a pre-tax rate of return of 15%, and several holding periods stretching out to forty years.

Notice that the winning strategy is to hold investments for the long haul.

I am a professional wealth advisor, and I am licensed to dispense general advice.

Here is my general financial advice to anybody who pays attention:

Cut your losses when it is tax advantageous to do so and let your winners run.

I know that sounds like motherhood, but it is the truth of investment compounding.

Deferred tax payments on unrealized compounding gains are the winner.

This is so whenever you face a choice between equal prospective return. The caveat to that statement is comparable pre-tax return with all tax credits impounded.

The Choice of Holding Structure Matters

I am not going to enter the field of personal and corporate structuring nor any advice on personal tax affairs. However, I can make one strong statement.

If you choose a structure where the compounding tax rate is lower you are better off. Corporate holding structures often have lower tax rates.

The standard example is the PAYG top marginal rate of 48.5% versus the 30% tax rate on a standard large company corporate tax rates for holding companies.

If you are young, and seek an entrepreneurial career, with numerous possible projects and exits it would be wise to consider holding company structures.

You can see the effect of this by comparing Australian PAYG tax rates at 48.5% versus the standard corporate tax rate of 30%. Holding a project in a company is better.

There are many who might take issue with this statement because money that is held in a company is not the same as money in your bank account. You would have to pay tax on money that you distributed from the company. That is true, but dividends in Australia have attached franking credits for any tax paid. You distribute that.

This is general advice only.

If you make a billion dollars, consider if you need that in your bank account.

Your personal holding company can buy assets and buy you lunch.

When doing your fringe-benefits tax consult a licensed tax adviser.

Australians love trusts and not holding companies.

I do not know why.

Why Trusts may not be Ideal for Entrepreneurs

I defer to the CoPilot AI on why it thinks trusts are sub-optimal.

Trusts are bad for:

• raising external capital

• issuing equity

• retaining earnings

• scaling

• employee ownership

• governance clarity

• reinvestment discipline

They are not the structure used by:

• US startups

• European SMEs

• Asian family conglomerates

• Venture‑backed firms anywhere

Why? Because a trust is fundamentally a distribution vehicle, not a growth vehicle.

If you are a tax adviser who is paid to put your clients into trusts and you do not like the above statement then sue Microsoft, not me. CoPilot said that.

I am a mathematician and a licensed wealth advisor.

Tax deferral on capital gains is valuable.

Growth vs. Income Dominance

One of the advantages of being a mathematician is my holding a limited license for absolute truth. If the question hinges on pure mathematics, I can be absolute.

You will have spied the pattern, now let me make an absolute statement:

For the same (comparable) pre-tax rate of return R, invested over the same period of N years and subject to the same rate of tax T, on income or gains, the tax-defered investment strategy dominates the income-oriented strategy.

Obviously, the tax rate T has to be between 0% and 100%.

You also need a positive rate of return, or investing makes no sense!

If your tax rate T is greater than 100%, I suggest that you emigrate. If all prospective investments have a return R<0, then work for government.

Otherwise, you are good to go on your investment career!

Let us prove the above statement, using my favorite, Jensen’s inequality. This is a very useful mathematical result that hinges on a simple idea. For any curve that is convex, meaning it uniformly bows downward, like that below, you can state that points on the straight line between the curve values at two points, are above that curve.

Specifically, the inequality means this:

The funny upside-down “A” is lazy mathematician speak “for all” and the [0,1] bit means the interval between 0 and 1, which would be 0% and 100% for tax rates.

You have to have a so-called “convex” function f, or it makes no sense, and is not a true statement. The function which is x to a positive power N>0 is convex.

Recall our post-tax growth rate of return:

and our post-tax income rate of return.

Not everybody likes mathematics, but I am not asking you to do it! If you concentrate on the visual pattern, you may spot the trick. Mr Jensen, through his inequality, tells us that the bigger quantity is always the left-hand side pattern, with the t’s outside f, and the smaller one has the t’s inside. The right side is dominated by the left side.

Choose this substitution into the Jensen inequality:

Voila! You did the math! Gorilla stamp. QED.

As long as you state clearly your caveats, ASIC cannot sue you.

What you have just seen for yourself is well-known in the finance industry.

Lots of quality superannuation funds in Australia will say this in words:

For equal pre-tax rate of return R, and tax rate T, the capital growth strategy of tax-deferral will dominate the returns generated by an income strategy.

This is not true of the financial press in Australia.

That is fine.

They are not quality wealth advisors.

Conclusion

To rap on the mad-cap theme of our Banana Splits financial media the race is called:

• Bingo (The Gorilla) – 1st Place - pure capital growth with no dividend

• Fleegle (The Beagle) – 2nd Place - capital growth with dividends reinvested

• Snorky (The Elephant) – 3rd Place - tax-deferred income via discount bonds

• Drooper (The Lion) – 4th Place (Last) - pure income with no tax deferred

Notice that is for the exact same pre-tax rate of return R, and tax rate T. There can be differences in tax credits, and even tax rates, so you need to consider that.

Large sections of the Australian finance industry are compensated for advice on tax minimization and the accumulation of debt to reduce personal tax obligations.

They may have different ideas.

I am a licensed wealth advisor who is focused on the best after-tax rate of return.

You may pay more tax, over time, to our government, choosing growth, but you will also be richer. Greater post-tax returns are my area of focus.

That is the general advice on offer today.

Oh, and here is the spreadsheet I used for those case studies.

Tax Defferal

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Happy investing!

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There is a looming energy crisis, whose origin is plain to see, but which the markets have chosen to ignore in preference to Elon’s Law of Large Numbers.

The bigger the pro-forma IPO forecast the more likely the stock will float.

What the stock does after it floats is not important. Insiders are out.

This might seem churlish, narrow minded, and short of the Grand Vision Stuff.

Perhaps, but that is not how I see the world right now.

Presently, I am very excited by developments in artificial intelligence, but not for the same reasons as Silicon Valley. I am not very excited by the idea of superintelligence, because I think the human race already has access to this in a simple manner.

Superintelligence is the act of thinking when everybody else refuses to do so.

You do not need to spend a billion dollars to acquire superintelligence.

You just need to use your mind like we all do on a good day.

Rinse and repeat and you are on the right path to a more meaningful existence.

This post is about two ways of thinking: birds versus frogs.

I borrowed this nomenclature from Freeman Dyson, and his collected selected papers.

For context, Freeman Dyson was one of four physicists credited with solving the then milestone problem of how to calculate finite results in Quantum Electrodynamics.

This is the famous renormalization problem of QED.

The issue was infinities arising from the inclusion of known, but troublesome, effects like the force an electron feels due to its own field, or that the vacuum, nothing to folks riding the Civic to Tuggeranong bus, feels from everything floating in it.

The 1965 Nobel Prize for Physics was awarded to these three individuals for:

The prize was equally assigned, in recognition of contribution, to:

• Shin-Itoro Tomonaga, Japan

• Julian Schwinger, USA

• Richard P. Feynman, USA

which is fair enough. It was a giant step forward.

The reason I mention Freeman J. Dyson, is that he also “did it”, but found a fourth way to “do it”, that unified the other three ways, to show that they were equivalent.

Needless to say, you can only do that when the others have already done their bit, and the Nobel prize can only be shared by three people. Dyson never got one.

I don’t take issue with any of this but am ready to note that Freeman J. Dyson had much to ponder after 1965, and to develop a philosophical approach to life. Giant intellect that he was, he came up with my favorite classification of thinkers:

I met Freeman Dyson once, at the Princeton Institute of Advanced Study, back in the early 1990s when I was a post-doc in theoretical physics. It was over lunch, and my host Stephen Adler introduced us. It was just pleasantry, but he had a penetrating stare that I could have readily mistaken for that of a raptor like bird.

This frog was a little nervous.

Dyson is describing two different styles of thinking that we all share. There is the down in the weeds, frog world, and the up in the sky, bird world.

I started life as a frog but then learned to fly like a bird.

Now I am both, like most folks in later life.

In simple terms, the difference is analysis versus synthesis.

If you are anything like me, you will have been better at analysis when you were young and have learned to complement that with synthesis as you got older.

It is not that one is better than the other, but synthesis depends on accumulated knowledge and experience, which you have in smaller measure when young.

This is just life and the neuroplasticity of the brain which literally changes and grows as you get older, learn more, and are exposed to more sensory information.

Of course, there comes a point where we all slow down, but in higher functions this is much later than popular wisdom would suggest. I met Dyson when he was seventy and I was thirty. He was sharp as a tack, and well versed in the state of the art.

I left corporate life at the tender age of forty-eight because the intellectual climate of a large Australian financial institution was just so visibly lifeless. Nothing happened.

I contend that the reason why retirement from corporate life just gets earlier and earlier in chronological age is because the corporate IQ has collapsed.

You will find no less stimulating or brain-dead environment than a contemporary corporation. This is echoed in our financial media, which is just barely sentient.

For the record, Dyson, when I met him, was a manifest superintelligence.

Dyson croaked like a frog and stared at and through you like an eagle.

Profoundly impressive!

The Mythology of our Time

This is supposed to be a thematic investment blog, and so I will need to rapidly reverse out of social commentary to introduce market commentary.

In an earlier piece I remarked about the relevance of Hegel to investing.

In the opening lines, I opined:

The markets run on mythology which is the full picture of human narrative.

This perspective may seem odd if you think all investment is based on analysis.

Mythos is like kryptonite to the logical part of our brains.

Myths are fantasies, tales of fictional characters doing impossible things.

How could mythology, and the world of superheroes, relate to practical investment?

Goethe said it best:

What will move a late bubble stage market is an impossible dream.

The future is Data Centers in Space.

This could happen, but I am a physicist.

I think the future was already announced by He Tingbao, of Huwaei, on 25-May-2026.

The announcement from He Tingbao, at the 2026 IEEE International Symposium on Circuits and Systems, an international conference on circuit design, flagged their approach to the post-Moore’s Law world of chip design.

Everybody knows that Moore’s Law concerns the observation that transistor count, the operating components of computer logic, has been doubling every eighteen months since around 1965, when Gordon Moore, pioneer of the integrated circuit at Intel, first put his Rotring to 1mm stock graph paper and drew a straight line on a semi log scale.

The first version of this law died around 2010, with the death of Dennard scaling.

This meant that the clock speed of processors hit a ceiling. Once you went to every smaller feature size, meaning more and smaller transistors, the heat dissipation problem maxed out clock rates. You needed to go to larger dies.

Twenty-five years ago, I wrote my first research piece on data centers.

Three Laws For The New Millennium

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It was titled “Three Laws for the New Millennium” and covered these:

1. Moore’s Law

2. Metcalfe’s Law

3. Amdahl’s Law

It still stands today, much as I wrote it, even if it had no influence then.

Note that I concluded the research note with this statement:

In a nutshell, I was saying this is a real estate problem: location, location, location.

It may have been twenty-five years ago, but I could see why you may want to go to a faraway cold place like space to get the unlimited real estate you might need.

The advantage that foresight gave me is the leisure time to think of creative solutions to this problem that did not involve spending vast amounts of money on power and cooling. The main one I came up with is analog computing. I am happy to see that many engineers are now pursuing that, which has a good investment case.

The other opportunity, which I did not anticipate, but which has now surfaced, is to change the game from 2D to 3D and to stack the chip elements vertically.

This is what Huawei is pursuing in the tau-scaling strategy.

Chinese firms are not the only ones pursuing chip-stacking, but they are leading in the space due to the pressure of American chip sanctions. The road to finer features out to the currently planned 1.4 nanometer node is blocked to them.

They have no choice but to go up, and not out.

The tau-scaling thematic will provide disruptive investment opportunities.

Let us bring the thematic focus to a sharp conclusion:

There are many current market myths that will not survive the next decade.

These are too many to count, but I will list the main ones:

1. Analysis beats synthesis: the big picture does not matter.

2. Innovation is a scarce resource and a capability controlled by the few.

3. Old dogs cannot learn new tricks even when their life depends on it.

The last will be most engaging for many of my readers.

Let us see how AI has disrupted ageism.

The Matter with Things

One of the more interesting features of my personal journey through the past six decades has been the progressive narrowing of the Western perspective.

The West used to be a place that was brimful of self-confidence, that it was the best, and that would stay that way, because, because, because; I rest my case.

I had the good fortune to have gone around the World and visited many places, in most continents, before I was six years old. I never believed the advertising.

The World is a big place, and a profoundly interesting place.

Human civilization is a vast interwoven tapestry of influence and counterinfluence.

I do not subscribe to any of the modern warring tribes of Western discourse.

• I am not Left nor Right.

• I am not Globalist nor Nationalist.

• I am not Logical nor Analogical

• I am not Detail oriented nor Gestalt smoke.

In short, I am not perma-polarized and consider myself adaptive to circumstance.

I am both frog and bird.

I think many people are, if not all people, but our social conditioning is powerful.

The corporation is a dead place because it demands that we be frog or bird.

You have to fit the pigeonhole, and it just keeps getting narrower and narrower.

This is a disease of the collective Western mind.

This is not a disease of any individual mind; but the collective mind is sick.

Nobody likes to be pigeonholed but take a look at the press headlines!

Let me say this, I am not negative on the future, because I see this as a time ripe for a massive software upgrade of the collective intelligence. The vector for this cure, will also be the main vector for the social cataclysm of absorbing the change.

That vector is artificial intelligence.

The reason for this is rather simple and will be apparent to anybody that has spent a long time doing analytical tasks. There is much down in the weeds work for those who analyze things for a living; fact finding; argument testing; consensus framing.

What separates great analysts from the mediocre is the quality of the questions they ask in pursuit of a particular answer.

The advent of the LLM chatbot delivers ready answers to any question.

The questions you ask determine the quality of the answer.

Analysis moves from the general to the particular.

What separates great synthesists from the mediocre is the mystery of what question they asked themselves to arrive at their unifying answer.

Synthesis moves from the particular to the general.

Notice that each depends on the question.

If anybody on the planet is to profitably use an LLM chatbot they need to have the capability to ask it a pertinent question germane to their field of interest.

This is open territory, both culturally, and by stage of life.

I have zero doubt, that contemporary corporations will soon mint reasons why only a certain favored few have the “skills” to ask the right question. Anybody who reached middle life already knows that a corporation is the very last corner of this planet in which you are likely to find intelligent life that is brimful of pertinent questions.

Corporations are the walking brain-dead of the intelligence gene pool.

What permits the possibility of success in a corporation is conformity.

Proclivity for conformity is inversely proportional to life experience and intelligence. The conventional wisdom is invariably stupid.

No matter what the “vision statement” thinking is verboten because the mere act of asking a pertinent question of leadership promotes the distinct possibility those chosen to lead us have not got a clue what is happening.

Since I am a thinker, who spends a lot of time inside my own head, when I could be out doing something important, like fishing, I am looking out for guidance.

Last year my psychologist brother-in-law, who is married to my psychologist sister, and whose household is never visited without at least two diagnoses, introduced me to the writings of Iain McGilchrist, and his discursive tome The Matter with Things.

This book has no peer in terms of the depth or controversy of thesis. It can be viewed as a simplistic inquiry into the differences of hemispheric brain function. I could offer some views on that, based on my own experiences of possessing a brain. However, I will not bore you with that, since you have one too, and I do not write as well.

I want to state at the outset, and true to my frog-bird nature, I do not favor the view that one mode of thinking, detail-oriented-frog or big-picture-gestalt-bird, is the best or only way to think about important matters. They are equally important.

However, where I concur with the thesis of McGilchrist is that our social collective intelligence is lurching dangerously into a one-sided half-brained world view.

I will let you form your own opinion.

For what it is worth, you know mine.

The Renaissance Opportunity

It is always darkest before dawn, and we each have our own comforting talisman to cling to in times of strife. I am an old enough to have seen several social upheavals, and so am unworried that a new one is clearly approaching at high velocity.

Like anybody, I have experimented a lot with chatbots.

They are smart, up to a point, but clearly lack discernment. I would mark the bots as you would any precocious student of great potential.

F+ with “F” for Frog, and a + for trying to fly like a Bird!

The problem is not breadth of knowledge, nor even depth of reasoning, or speed of recall, which is frightening. The problem is one of pure gestalt, the Bird bit.

There is intelligence present but there is no where there.

Where would you like to go next? I don’t know. I am not conscious, says the bot.

This is a tremendous opportunity for the bird-brained amongst us.

The secret of discovery is to ask the right question.

The LLM chat bots of our era are trained on the most arcane fields of knowledge that their Silicon Valley masters hoovered up into the machine, without paying for it.

These machines are now provided for free.

There are occasions when I might pay for a chat-bot, but I have not yet. The path that my firm is taking is to use Retrieval Augmented Generation (RAG). I will write about this at length, but for now you can think of it as a way to build research muscle.

In approaching any task of problem solving, it is essential to separate the power of reason from the corpus of facts over which to apply such reason. The human mind does not need to remember everything to solve any given problem.

Intelligence is the power to recognize what you don’t know that is pertinent.

It can be hard to communicate this clearly in a world obsessed with credentialism as the qualifier of authority. However, that is a purely social construct. What matters to the trajectory of human knowledge and understanding is imagination.

The solution to problem solving is not more knowledge, but more imagination in how we should apply presently known facts, to uncover what we don’t know.

The manifestation of an active imagination is to pose a new question.

I maintain that every creative act of the human mind consisted of a new question. You ask yourself: “Why this, and not that?”. It does not matter if you a jazz musician, and seek a new way to skew time, or you are a quantum physicist in ten dimensions.

All human progress began with a new question.

The problem of our time is too many certainties and too few doubts.

Doubt is not mental weakness, but the path to new truths.

This is true in any endeavor.

You cannot forge new solutions repeating past mistakes.

I believe this so strongly, that I restructured my own corporate enterprise.

We are now an institute in name only, but we have a mission.

Jevons Global is committed to use artificial intelligence in service of synthesis.

I have already had such deep and rapid progress in so many research fields of interest to me that I think it is time to share that learning.

The opportunity now upon us is to apply artificial intelligence.

I do not mean to own it, like some captive gimp in the Silicon Valley basement.

I will leave the fantasies of complete and total world domination to the plutocrats.

They never impressed me, and I think I can run intellectual rings about them, when hobbled with ankle chains, running a sack race, on Mogadon, wearing a blindfold.

This is NOT an impressive slice of humanity.

No, the prize on offer is an efflorescence of scientific and cultural progress.

That is something we can all participate in.

More later…

Retrieval Augmented Generation (RAG) is both transformative and fun!

I want to dig into this today, as it is a pregnant global investment thematic.

To set the scene, consider this crusty looking fellow from the Victorian era.

Science in the Victorian era, when we had the first Industrial Revolution, was pursued by a range of research teams, mostly single individuals, who were funded out of the teaching activity of their university, or private funds, as industrialists.

The big corporate laboratories of the era of Edison, Westinghouse, Tesla and Marconi came later, once it became clear that there were new fortunes to be made from the basic advances that accumulated in understanding Nature.

The biggest areas of advance in the 19th Century, were first the understanding and then the harnessing of electromagnetism (think electricity, magnetism and related radio waves and optical waves). The second great advance was thermodynamics, which is the basic science of heat and chemical change, for efficiency gains.

Allied to these advances, there were tremendous and broad advances in mathematics, the language of precise reasoning and discourse in science. If your language lacks in expressive power, it becomes more difficult to frame and communicate thoughts.

In the eyes of many, mathematics is a chore and a burden to contemplate. That is fair if you are not mathematically minded. Some small proportion of society, probably around the same percentage of those who are very musical, are so gifted.

Fortune Favours the Few

You do not need many mathematicians to advance human understanding.

In every century, a dozen or so good ones will do.

For neutrality of position, I asked CoPilot this question:

The answer came back with four at the pinnacle, and twelve in total:

THE PINNACLE (the four who changed the architecture of physics)

1. James Clerk Maxwell (1831–1879)

The summit. Unified electricity, magnetism, and light into a single field theory; invented displacement current; created the first relativistic‑compatible classical theory; introduced statistical reasoning into kinetic theory. Everything else is footnotes.

2. Ludwig Boltzmann (1844–1906)

Forged statistical mechanics into a coherent discipline; kinetic theory; irreversibility; fluctuation arguments. The conceptual bridge between mechanics and thermodynamics.

3. Rudolf Clausius (1822–1888)

Defined entropy; formalized the Second Law; established the mechanical theory of heat; introduced virial theorem. Without Clausius, thermodynamics has no grammar.

4. Hermann von Helmholtz (1821–1894)

Conservation of energy; vortex theory; electrodynamics foundations; physiological optics; thermodynamics of chemical processes. A universal synthesizer whose fingerprints are everywhere.

THE ELECTROMAGNETIC FOUNDERS (the field‑builders)

5. Michael Faraday (1791–1867)

Field lines, induction, diamagnetism, the physical reality of fields. Maxwell’s mathematics is Faraday’s intuition made analytic.

6. André‑Marie Ampère (1775–1836)

Ampère’s law; electrodynamics as a mechanical force law; current‑current interactions. The first systematic theory of electricity and magnetism.

7. Carl Friedrich Gauss (1777–1855)

Electrostatics; potential theory; Gaussian surfaces; rigorous mathematical foundations. The quiet architect behind Maxwell’s first two equations.

8. Gustav Kirchhoff (1824–1887)

Circuit laws; blackbody radiation law (pre‑Planck); foundations of spectroscopy; early electrodynamics. The bridge between EM, optics, and thermodynamics.

THE THERMODYNAMIC ARCHITECTS (the entropy‑builders)

9. Sadi Carnot (1796–1832)

The Carnot cycle; efficiency limits; the seed of the Second Law. The conceptual origin of thermodynamics.

10. William Thomson (Lord Kelvin) (1824–1907)

Absolute temperature; thermodynamic potentials; stability; early EM theory; vortex atom program. A giant whose influence is structural even when wrong.

11. James Prescott Joule (1818–1889)

Mechanical equivalent of heat; energy conservation; Joule–Thomson effect. The experimental backbone of the First Law.

THE MATHEMATICAL PHYSICIST WHO COMPLETES THE DOZEN

12. George Green (1793–1841)

Green’s functions; potential theory; boundary‑value methods; the integral machinery Maxwell needed. Quiet, foundational, indispensable.

I have worked in theoretical physics and mathematical physics for forty years. I have a PhD in Quantum Information Science from 1990 (when almost nobody in the world was working in the field). I have no quarrel with that list. Short and sweet.

I asked the same question for 19th-century mathematics.

THE PINNACLE (the four who reshaped the mathematical universe)

1. Carl Friedrich Gauss (1777–1855)

The apex. Number theory, differential geometry, least squares, potential theory, complex analysis, magnetism, geodesy.

Gauss is to 19th‑century mathematics what Maxwell is to 19th‑century physics: the unifying mind.

2. Bernhard Riemann (1826–1866)

Riemann surfaces, Riemannian geometry, Fourier series foundations, analytic number theory, complex function theory.

He provided the geometric and analytic language that 20th‑century physics runs on.

3. Augustin‑Louis Cauchy (1789–1857)

Rigorous analysis, complex function theory, convergence, the calculus of residues, elasticity theory. Cauchy is the architect of modern analysis — the grammar of engineering mathematics.

4. Évariste Galois (1811–1832)

Group theory, solvability, symmetry as structure. The conceptual revolution that eventually powers everything from crystallography to control theory.

THE ENGINEERING‑REVOLUTION (mathematics for machines)

5. Joseph Fourier (1768–1830)

Heat equation, Fourier series, harmonic analysis. The mathematical engine of thermodynamics, signal processing, and industrial heat design.

6. Simeon‑Denis Poisson (1781–1840)

Potential theory, elasticity, wave equations, probability. Poisson is the mathematical physicist of the industrial revolution.

7. George Green (1793–1841)

Green’s functions, potential theory, boundary‑value methods. The indispensable machinery for electromagnetism, elasticity, and engineering PDEs.

8. William Rowan Hamilton (1805–1865)

Quaternions, Hamiltonian mechanics, canonical transformations. The algebraic and geometric backbone of modern mechanics and control.

THE PURE‑MATH ARCHITECTS (the structuralists)

9. Karl Weierstrass (1815–1897)

Rigorous foundations of analysis, uniform convergence, elliptic functions. He completed Cauchy’s program and made analysis industrial‑strength.

10. Leopold Kronecker (1823–1891)

Arithmetic foundations, algebraic numbers, constructivist rigor. The counter‑voice to Cantor, but essential for number theory’s engineering‑grade precision.

1. Richard Dedekind (1831–1916)

Dedekind cuts, ideals, algebraic number theory. He gave number systems and algebra their modern structural form.

THE MATHEMATICIAN WHO COMPLETES THE DOZEN

12. Henri Poincaré (1854–1912)

Topology, dynamical systems, celestial mechanics, qualitative analysis. The bridge from 19th‑century mathematics to the entire 20th century.

This is all with the benefit of hindsight and the long lens of history. At the time, it was less clear which directions would prove the most fruitful. History selected these.

You could note: where are all the women?

Equally: why are they all European?

An accident of the historical frame. I would rate Emmy Noether as one of the top twelve of the 20th-century mathematicians along with Srinivasa Ramanujan.

Scientific talent is latent everywhere but must be recognized to fully develop.

Science is a social enterprise and is best conducted without cultural blinders. The list of the 19th-century could easily be disputed. Where are the Russians? What about Josiah Willard Gibbs, father of statistical mechanics, in the New World?

The few we recognize include many we do not.

Discovery is an Act of Will

I do not work in mainstream academe, because I am one of the few scientists around today who believes that a radical revision in physics and mathematics is possible.

If you say that out loud, in a university, almost any university, almost anywhere on the planet today, you will be ridiculed and ostracized. Nobody will take you seriously.

This is why I do not take mainstream academia seriously.

Picture this: you have a relatively well paid, albeit increasingly insecure job, in research organization, that is well equipped for that purpose, and your mission is supposedly that of extending the frontiers of knowledge. Having dutifully followed fashion, to secure tenure and research funding, you are quick to dismiss the possibility that contemporary science may be in urgent need of a software upgrade.

I do not mean the hardware of the experimental lab, or even the programs we now use to conduct research, I mean the mental software of expectation.

If you expect the future of science to look like the past, and you always travel in the safe company of those who agree with you, is unlikely you will be disappointed.

The greatest disruptors in human history included many scientists.

It is true that entrepreneurs change the world through business activity, but the change wrought by scientists is much greater than that.

Science changes the way we think.

Of course, science is not unique in this aspect.

Religion changes the way we think.

There are many ways to change the way people think.

Social media changes the way we think so that we stop thinking.

That is a cheap shot, and there are plenty of folks on Substack proving the rule by their daily exceptional protest against the spreading Zeitgeist of perma-stupid.

To discover something new you need to believe that discovery is possible.

Discovery is ever and always an Act of Will.

You may find something by accident, but not if you have no willingness to see something new for what it is. You must have the will to look and to see.

The Value of a Prepared Mind

Louis Pasteur, the revolutionary French microbiologist put it best.

Chance plays a role in all scientific developments, but only minds that are prepared to find something new, and devise the inquiries to fully understand it, will advance.

Notice that I am expressing a deep scientific prejudice in echoing this statement.

I believe it is true and have found it repeatedly verified in my own experience.

Cynics may call this confirmation bias, but I disagree.

True scientific enquiry entails active disconfirmation bias.

You have to believe that scientific knowledge is incomplete to want to actively doubt some or other aspect of conventional wisdom and seek out is extension.

In the popular mind, this involves proving this or that aspect of scientific knowledge to be wrong. That is correct, up to a point. However, I prefer incomplete, since that word conveys the idea that what was new was hiding in the shadows somewhere.

The book is a little dated now, but The Structure of Scientific Revolutions, a scholarly book by philosopher and scientific historian Thomas S. Kuhn, laid out the thesis of disruptive paradigm shifts, as an explanation of scientific upheavals.

What changes in a scientific revolution is the predominant mode of thinking:

Kuhn argued that science does not progress by steady accumulation but through disruptive paradigm shifts in which an old framework breaks down under anomalies and is replaced by a new way of seeing the world.

This could happen in many ways. It could be new data, like that revealed about the world of microorganisms with the invention of the microscope.

The idea that Man was the centre of the Universe died with Galileo’s telescope.

There are so many examples of this happening through history, that I need not recount them here. Of course, Kuhn did that one to death!

What is the Revolution in Progress?

The events of today are so close to us — in time and in personal impact — that we cannot yet see where they lead, nor grasp their full significance.

In the spirit of the prepared mind, we should expect new ways of thinking about thinking. This is not wordplay. It points to the problem of epistemology.

Epistemology is the study of how we know what we know — what counts as knowledge, how we justify beliefs, and what it means for something to be true. It is the study of how we form beliefs — and how we test whether the assumptions beneath them really hold up.

This is philosophy, and many dismiss philosophy as irrelevant to business.

Yet anyone who has used a Large Language Model knows the problem is immediate and practical: How do we know when the chatbot is telling the truth?

Epistemology is the problem of the moment for every investor:

How do we know what an AI says is true — and when should we believe it?

This problem was already with us with the invention of search engines.

However, a search engine is a retrieval engine. You can go check if the link contains content relevant to your query, and judge it via the integrity of the source.

In contrast, artificial intelligence tools can reason, and certainly mimic many aspects of human intelligence, including generative tasks, like guessing and improvisation.

These are the functions we normally associate with higher intelligence and creativity.

The problem we have today, mirroring that throughout history, is what constitutes a reliable source of beliefs about the world we live in, and our own reasoning.

These are deep questions which are not answered by the word: technology.

The technology to solve this problem is properly meta-technology.

It is the machine version of Rene Descartes lying in the bakers oven.

How does a machine know when it is right?

How do any of us know when we are right?

The problem is well illustrated by the talking bomb scene in Dark Star (1974).

The climax of the movie involves a talking thermostellar bomb, whose function is to help demolish star systems that are in the path of interstellar traffic:

Sgt. Pinback: All right, bomb. Prepare to receive new orders.

Bomb #20: You are false data.

Sgt. Pinback: Hmmm?

Bomb #20: Therefore, I shall ignore you.

Bomb #20: False data can act only as a distraction. Therefore, I shall refuse to perceive.

Bomb #20: The only thing that exists is myself.

Bomb #20: Let there be light!

Thermostellar explosion that destroys bomb, Dark Star, the crew and a star system.

Needless to say, that imagined future is now close at hand!

Can Silicon Valley Solve This Problem?

Perhaps.

Every philosopher since the dawn of time has considered it.

Perhaps the folks in the Valley are smarter than all of them put together.

Perhaps the AI, which read the works of every philosopher since the dawn of time can be smarter than them, on behalf of its Valley masters, and solve the problem.

Perhaps. Perhaps not.

Can Humanity Solve This Problem?

More likely, yes, but it is still difficult.

The problem is one of self-reference.

How can thinking about thinking resolve the paradoxes of self-reference.

How can an LLM chat-bot, reflecting on its own model, inputs and outputs, know when it is being truthful, and when it is being true to its own art?

I do not say this lightly.

Artists are true to their own vision and know what truth means in that context.

For the viewer, it may just be gibberish, in which case it follows lawyer logic:

1. If it is heavy, it is valuable.

2. If it is unintelligible, it is mathematics.

3. If it is weird, it is art.

Simple syllogistic thinking does not work, even for practicing lawyers.

We cannot solve this problem in purely logical fashion.

The advance of present machine learning is that it uses probabilistic reasoning about the most likely output for any given input as a way to address the issue.

Chatbots will reason over known cases where a given answer is generally considered to be correct and then extemporize over new queries not previously posed.

When I say reason, it is not step by step logic. It is more a mix of logic and analogic:

This picture looks like a duck so I will generate responses appropriate to that.

I am trivializing some deep mathematics underlying deep learning, and the theory of learning systems that has grown up about it. However, it is not wrong to declare that the missing technology is not more technology. It is a correct understanding.

Some investors might mistake this as a failure of mindset.

If only you were more positive, you would see that more capital. more chips and more data centers would solve the problem. This can be brute forced, don’t you know!

That is fine if you expect somebody else to solve your problem.

If you are minded to actually solve the problem, outsourcing the problem to fairies at the bottom of the garden is unlikely to be the winning strategy.

Progress in the Mathematics of Deep Learning

It is not my purpose here to slavishly recount the technical foundations of machine learning, in its present form. I just want to direct attention to the mathematics.

At the outset, I made some claims about the favored few in the current of history that defines scientific progress, including that in the mathematical sciences. Truthfully, it is impossible for me to tell, at this point, how this predicament resolves itself.

Notice that I am advancing a prepared mind:

I believe there is a deficit in understanding machine learning today.

This is not what you hear from most investors and sounds overtly critical.

Perhaps it does, but from my perspective it is constructive criticism.

To solve any scientific problem, you need to know there is one.

Pretending there is no problem, simply hands the prize to somebody else.

In my view, none of the celebrated AI firms of today has the right focus and mindset to actually solve this epistemological problem. It is a problem of learning theory.

This is not a question of whether machines can learn, but how they learn.

The correct statement of the problem is to understand deep learning.

There are historical precedents for the developmental stage we are at. To surface one instructive example, I cite this one rather plain looking book: The Yellow Peril.

In the immediate pre-Second World War period, there had been fabulous progress in radio transmission and communication, and many electronic devices that developed the technologically invaluable ideas of autonomous and automatic control.

These were servo mechanisms, devices that could measure inputs and issue control outputs that would maneuver machines towards desired ends.

The autopilot that could fly an aircraft was one early example.

As often happens with new developments, the technologists of the time, mostly electrical and mechanical engineers devised many ingenious solutions.

Engineering is the art of making possible what previously seemed impossible.

However, engineering cannot circumvent the Laws of Nature, which include the subtle mathematics of control theory. It turned out that some servo mechanism proved to be unstable, and predicting when they would turn unstable was difficult.

The landmark monograph “Extrapolation, Interpolation, and Smoothing of Stationary Time Series: With Engineering Applications”, by Norbert Wiener changed all that.

Still published by MIT Press the book blurb states:

This is all ancient history now, but the innovations of Wiener were mathematical. He put together the missing understanding of how linear servo mechanisms behaved.

Once you understood this, you could design the systems properly and reliably.

The reason why this is very topical right now is that the focus of the 1930s to 1980s was on so-called linear systems. In such systems, the control action you need is proportional to the deviation from the desired target.

They can be complicated, but the mathematics is now fully understood.

What is happening now, with machine learning, and agentic artificial intelligence, is that engineers are fielding adaptive nonlinear autonomous systems without any good mathematical theory of how they work, so that they can be made stable.

This domain of engineering is known to be subtle, difficult and extremely hazardous. There are many famous cautionary tales of failure, such as the fate of X-15 Test Pilot Michael J. Adams, when his Honeywell MH-96 adaptive flight controller failed.

The problem in question is mathematical. Unlike the linear systems world of Norbert Wiener, these are inherently nonlinear systems, where you lack key anchors.

The Wiener Quad

The reason that linear control theory made so much progress after Wiener is not down to how much he did, but the quality of the insight he offered.

There were four qualities he identified that mattered to controllability.

Every stable control system requires:

• A model — even if implicit

• A sensor — to observe the state

• A controller — to compute corrective action

• An actuator — to apply the correction

If any one of these fails, the system becomes unstable.

The pre-requisites in linear control theory for stable automatic control are called the Wiener Quad, in honor of the mathematical theory of control he pioneered.

The Requirements for Stable Automatic Control

To achieve stable automatic control of any system — mechanical, financial, biological, or computational — four conditions must be met:

1. Observability

You must be able to see the true state of the system. If you cannot observe the internal variables (directly or through inference), you cannot control them.

If you can’t measure it; you can’t stabilize it.

This is the investor’s problem with opaque AI models.

2. Controllability

You must be able to influence the system’s state through inputs. A system may be observable but uncontrollable (you can see the problem but cannot act on it).

If you can’t push the system where it needs to go, stability is impossible.

If a system starts to hallucinate, how do you reliably force it back on track?

3. Stability (Lyapunov stability)

The system must naturally return to equilibrium when perturbed or be made to do so by the controller. This is the mathematical condition that ensures:

• - no runaway oscillations

• - no divergence

• - no catastrophic feedback loops

A stable system damps errors; an unstable one amplifies them.

4. Proper Feedback Design

Feedback must be:

1. - timely

2. - accurate

3. - proportional

4. - not too weak (ineffective)

5. - not too strong (oscillatory or explosive)

This is the heart of Wiener’s cybernetics and modern control theory.

Good feedback corrects; bad feedback destabilizes.

Note that engineers converse about feedback oppositely to economists.

Engineers recognize that positive feedback is bad for controllability.

The reason is that it amplifies errors and produces instability.

Economists have slipped into using the term feedback pejoratively.

In the popular press, negative feedback is bad and positive feedback is good.

You can understand much of what is wrong with the Western world today by recognizing the two original sins in the misuse of engineering terminology.

Original Sin #1: Former Intel CEO, Andy Grove, used the term “strategic inflection point”, in his book, Only the Paranoid Survive, to describe a turning point for the business. An inflection point is not the same as a turning point.

Original Sin #2: Once Central Banks observed that printing money was popular, they started to use the term positive feedback to describe actions directed to a desirable outcome. This had the expected outcome. It inflated bubbles and financial instability.

It should surprise nobody, that when simple engineering language, born from the need to reliably control real world systems is done such public violence, that the result should be visible chaos across all aspects of our contemporary society.

Enough is enough, already.

Not everybody deserves a prize just for showing up.

It matters to have people in this world who can smell the difference between the odor of shit and the aroma of Chanel. In engineering, this is positively essential.

Hell, hath a new level.

The new prize for just “showing up” is a one-way ticket to Dante Level Eleven.

The Opportunity in Deep Learning Theory

To wrap this up, as the first in a series of thematic notes on artificial intelligence, I want to shake a stick at the area which I think is most promising. The investment strategy derives from a hunch on what I think is coming soon.

For now, I will continue on the theme of mathematical innovation.

Just to make it clear where this is coming from, let me say that neither you nor I need a gigawatt of power, from a nearby nuclear power station, to read the newspaper and have a good spray about the latest reported nonsense from our leaders, decide how to cook a meal from what is in the fridge, or watch which stock is going up.

Superficially, we are supposed to believe that human intelligence is obsolete.

However, I can assert, with confidence, that only humans can reliably tell when any of the current crop of AI chatbots is off with the fairies hallucinating.

That is because our minds solved the epistemological problem I framed above.

What we know, as a species, is that pragmatic solutions to this question do arise once we exercise our minds in a vigorous critical inquiry into what we actually know.

They used to call this quality, of accurate self-knowledge, humility.

Like pornography, you cannot properly define it, but you know it when you see it.

Although, philosophers have struggled to “solve” the problem of epistemology we can be confident that it is solvable, for practical purposes, by a working mind.

You have one.

I think I have one.

Therefore, I think this practical question, of understanding deep learning, is solvable.

Notice, that I did not say: “Solve for the meaning of life.”

Nor did I say: “Solve for how to build a master race of bots to rule us.”

Pursue whatever goal floats your boat.

Scientifically, what we have in front of us is a situation that is remarkably similar to that which confronted Norbert Wiener in the tumult of 1940s engineering.

Engineers had hacked together hugely powerful automatic control systems, which they knew could be unstable, but they could not figure out why.

Wiener noticed that, abstracted away a mathematical system of defined and limited scope, linear control systems satisfying the Wiener Quad constraints, about which mathematics could be invented, to properly define and solve the problem.

This approach is what is needed right now with machine learning.

This is not a statement designed to flush money out of witless Venture capitalists.

They are not going to be interested in backing any viable solution to this problem.

Witless Venture Capitalists are the problem, period.

In the history of science and engineering, we have been in this exact spot so many times before that it is not funny. I opened this account with a photo of Maxwell.

What was the critical instability problem of the Age of Steam?

When you ran a steam engine, there was a domain of speed control where the machine would become unstable and shake itself to pieces.

This fact did not dissuade Victorian industrialists from splashing cash on steam engines. However, it did mean that much of that cash went up in smoke.

The entire factory went up in smoke when the steam engine disintegrated.

Everybody now knows that James Watt, invented the centrifugal flyball governor to regulate the steam and speed of a steam engine. That was in 1788.

James Clerk Maxwell explained the mathematical control principle in 1768.

This is history repeating and rhyming in exactly the same way.

Industrialists are raising ever more money to throw at artificial intelligence.

However, they are none of them, including Elon Musk, doing much thinking.

The thinking is happening in arcane corners like the Geometry of Deep Learning.

The central issue is to achieve controllability of machine learning through the constraint of the learning system. Less is more, because you have reliability.

This can be difficult to explain to the non-mathematician.

However, I can give you something to hold on to.

Earlier I commented that positive feedback is unstable because it will naturally take a small fluctuation, like that generated by the butterfly wings in chaos theory, and just magnify that up until it dominates the system.

It is negative feedback that promotes stability and controllability.

The problem with current neural network designs is that they can make very good approximations to any function, but there are no clear stability controls.

Stability for systems of iterated functions is a purely mathematical question.

The problem is one of invention within the field of mathematics.

We do not know the right constraint on the allowed functions.

I can say this with complete confidence, because I am competent as a mathematician.

Needless to say, I pay attention to figuring out a solution to the problem.

I do not need a lot of money to solve this problem.

If I do not do it, somebody else will.

They will not need a billion dollars of funding.

The Prerequisites to Solve Deep Learning

This may seem arrogant, but I am both an investor and a mathematician.

The short list of requirements to solve this problem is not great.

You need one of these.

In addition to that, you will need one of these.

Where you write down your ideas is entirely up to you.

I use notepads, walls, bus tickets, quality napkins, book margins, whatever.

It does not matter, because you are defining a class of functions.

The answer will be short and sweet.

Let you know when I find it.

• Australian 20 Stock Model Portfolio

• USA 20 Stock Model Portfolio

• International (non-USA) 20 Stock Model Portfolio

• Global Best Ideas 25 Stock Model Portfolio

This omnibus report aims to be more succinct.

Australian 20 Stock Model Portfolio

The month of May-2026 just ended was one of best, with the mode portfolio surging by 2.88% in a good month for the benchmark S&P/ASX 200 which was up 1.15%. The positive alpha of 1.73% was a welcome return to form after two negative months.

The relative performance was driven by our sector bets. We are heavily underweight the banks and overweight resources. This paid off with the interest rate climate and the Budget 2026 tax change to housing weighing on the financials.

The pattern of stock contributions was skewed to resources up and financials down, but Woodside Energy took a breather and Lend Lease is running into heavy selling from the tax loss harvesting behavior that is commonplace in June.

The current portfolio is fairly balanced, although the move in BHP Group XASX: BHP has taken it above 10% of portfolio. The benchmark is 11.92%! Normally I only hold stocks at an overweight, but we hold Rio Tinto XASX: RIO at 1.81% overweight.

I am okay with letting this portfolio run for now. The only problem stock is property group Lend Lease XASX: LLC, which the market hates right now. If we wanted to sell this month is not good, due to heavy tax loss selling. I will do a deep dive over the next few weeks and look to exit in the post-tax-year-end bounce or add to it.

Just be alert that this time of year sees a lot of losing stocks get dumped.

In Australia, June of every year is the best time to get set in value turnarounds.

However, it never pays to try and catch a falling knife. What I do is spend some time making up my mind if I should shoot the lame, the wounded and the terminal.

These are stocks. They are not your children.

You can let go when you need to!

USA 20 Stock Model Portfolio

The 31-May-2026 performance of the USA 20 Stock Model was flat at +0.02%, while the S&P 500 raced ahead at +5.26%. Objectively, it was a bad month for us with the single month negative alpha print of -5.24%. When things like that happen, I worry, but what a look at carefully, is the spread of winners versus losers.

You can see that the market was clearly chasing some other dog, not our pooches. Here is the one-month performance of computer maker Dell XNYSE: DELL.

Memory maker Micron XNAS: MU, also surged over 100% on the month.

I will not tell a lie. If you had done something other than own the stocks we picked for the month of May-2026 you would have done a lot better than stay flat.

This happened across global markets, especially in the USA and in the cyclical heavy Asian markets like South Korea. Is it a bubble? Time will tell. If not now, soon.

The one good thing we did was to pick Moog XNYS: MOG.A.

It was up 20%, which is only 80% behind the high flyers.

No excuses, it was a month for trading, and these models don’t do that. The current model portfolio is shown below. Our long-term bets in technology remain Alphabet XNAS: GOOG, Microsoft XNAS: MSFT and Nvidia XNAS: NVDA. These are boring.

In my defense, I will say that we have salted away a few sidebets on the infrastructure associated with the data center capex rush. Amphenol XNYS: APH is a high-quality manufacture of Radio Frequency (RF) and optical connectors.

NextEra Energy XNYS: NEE is a well-diversified US Electricity Utility operating in Florida and other key West Coast markets. They do both nuclear power and renewables.

Quanta Services XNYS: PWR does power related infrastructure build.

The sleeper we added at the end of March was Boeing XNYS: BA.

I have little much to add, other than to flag the need to do a trading survey of market action. I was active in the 1995 to 2002 tech boom and bust. Like old times again!

International (non-USA) 20 Stock Model Portfolio

The 31-May-2026 performance of the International 20 Stock Model was much better at 3.63%, only 0.95% behind the FTSE Global All Cap Index at +4.58%. The reason for the difference was our bets on Lithium-Ion Batteries via CATL, and copper, through holdings in BHP Group XASX: BHP and Sumitomo XTSE: 8053.T, in Japan.

Few Australian investors track Japanese or Hong Kong listed stocks for exposure to the key minerals used in EV and electronics supply chains. That is a mistake.

I do not propose any changes this month. The stocks to watch are underperformers, like BYD XHKG: 1211.HK, Petrobras XNYSE: PBR and Swiss Re XSWX: SREN.SW. When markets run hot, in a narrow sense, other areas lag. Our position sizing in these is modest to small, and so we are unlikely to get too badly hurt.

Rather than switch names in and out, I like to have some names ready to accept top up capital when we decide to take profits in those names that have run hard.

Global markets are trading narrowly around perceived AI winners right now. I do like a punt, but this is not a punting strategy. We will take our punt on raising the laggards. Now is not the appropriate time, as the market looks set to get crazier.

Global Best Ideas 25 Stock Model Portfolio

The 31-May-2026 performance of the Global 25 Stock Model was predictably in the middle of the US 20 and International 20. The model was up 2.86% in USD, but the FTSE Global All Cap Index was up +4.58%, so we lagged by 1.71%. Ordinarily, the return of 2.86% would be a good month. Welcome to manic times!

There are no changes mooted for this month.

The takeaway message for this month is that there is wild trading action taking place in the heartland of the AI technology theme. We are happy that we have holdings in core names. For completeness, I will do a survey of trading action.

In market conditions like this, I like to observe trading action closely and think about what is being left behind to fund the FOMO positions. With SpaceX and Anthropic floats coming up, there will be some manic selling to fund speculative bets.

Do not expect our models to do well over the next month or so.

However, do be alert to what the market is selling to put it all on black.

We do not know if this is a bubble, but it is starting to act like one.

There is fast money to be made, but there are also quality stocks being sold. This is the place I am inclined to do the most research, as it is likely to be more reliable.

Conclusion

There is a lot that happened last month, and I will be working this coming week on making sure we have something sensible to say about it. I think the furor over CGT and negative gearing changes is understandable, but I think the moves will likely be positive overall. However, it will take time to sort out. I will write on that next.

On the trading side, there is fast money to be made for those who punt.

I will dissect some of the global price action to better surface themes.

You know they are AI related, but there is nuance.

Until then, happy investing!

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• Australian 20 Stock Model Portfolio

• USA 20 Stock Model Portfolio

• International (non-USA) 20 Stock Model Portfolio

• Global Best Ideas 25 Stock Model Portfolio

This omnibus report aims to be more succinct.

Australian 20 Stock Model Portfolio

The last rebalance involved an exit in Macquarie Group XASX: MQG and an addition of the gas pipeline company APA Group XASX: APA. There were other minor adjustments to stock weights to adjust them up and down to round numbers for quarter end.

No further changes are contemplated at this stage. The markets are too volatile for any high conviction moves other than the increased weight to energy stocks via the addition of Woodside Energy XASX: WDS, on 3-Mar-2026, and APA Group.

The first quarter to 31-Mar-2026 was negative, due to a large drawdown in March, for both the model portfolio and the benchmark S&P/ASX 200. The Q1 numbers saw our model down -2.13% and the market -1.61% for a net lag of -0.52%. This is down to a string open to the year, followed by a pullback in March.

The month of March was ugly, due to the uncertainty created by the war on Iran and the high exposure Australia has to rising diesel fuel costs. This nation, with its long distances between cities, and fuel intensive mining and agriculture is at risk.

The US markets have been more sanguine, due to the perceived self-sufficiency of that economy on domestic crude oil and natural gas supplies.

The jury remains out on whether it will stay that way, because US diesel prices are rising, as are gasoline, and US sourced West Texas Intermediate.

On a month to date basis, the Australian portfolio rebounded 2.40% to the market close 24-Apr-2026, lagging 1.20% behind the S&P/ASX 200, which was up 3.60%.

The fiscal year is still very positive, with our model up 11.70% since 30-Jun-2025, while the S&P/ASX 200 is only up 1.98%, for a lead of 9.72%. Not as great as before, but the model portfolio is still doing well. The bets we have on commodities are balanced to bulk miners like BHP and RIO, energy via Woodside and APA, gold via NEM and EVN, and lithium via PLS and IGO. Presently, we are underweight Australian technology, which has rebounded somewhat from a deep correction over the past year.

In summary, there is no compelling reason to adjust the portfolio as we see tightness ahead in both monetary conditions and commodity prices. The status of the cycle in China will determine if we lighten exposures in the industrial metals market.

While gold has not performed as most people expected in this wartime environment, we remain constructive on holding these stocks in Australian portfolios. Traditionally, the USD tends to strengthen on weakening global conditions. The Australian dollar remains firm, for now, due to the expectation of higher interest rates. However, we think the Australian economy will struggle with the cost-of-living pressures.

Locally, the Australian banks are expensive, especially the Commonwealth Bank, and there are signs of weakness in retail. Our tilt to yield plays, inflation hedged utilities, like rail haulage in Aurizon, and gas transport in APA Group, plus commodities, is a bet on tricky times ahead for mainstream Australian businesses.

Let us see what the Federal Budget brings, and whether the Iran war is brought to a timely close or metastasizes into a global stagflationary supply shock.

USA 20 Stock Model Portfolio

The 31-Mar-2026 rebalance saw us exit positions in Meta Platforms XNAS: META and Cummins XNYS: CMI, to add Boeing XNYS: BA, and Moog XNYS: MOG.A. The capital expenditure boom in the USA is likely to widen into defense aerospace, due to the planned 50% increase in the Pentagon forward budget for 2027.

There are already high levels of capital expenditure on the data center build for AI, but we think this is largely priced by the market. Moreover, much of the electrical, chips, and cooling systems are sourced overseas. The trade is real but seems crowded.

The defense focus of the Trump Administration has been made clear by the war on Iran, and initiatives like the Golden Dome missile defense, and Golden Fleet.

We already own a position in Raytheon Technologies XNYS: RTX, which has a key role in naval defense missile systems and antiballistic missiles. However, we see a possible multi-year turnaround underway at Boeing XNYS: BA, off the back of their platforms for military transport, airborne refueling, and early-warning radar aircraft.

Boeing has struggled for years with the aftermath of the 737-MAX debacle, which was a failed design exercise in cutting corners to meet civilian aerospace demand.

The cheque book now seems open for new design work, like the F-47 Next Generation Air Dominance (NGAD) fighter, plus the renewal of the US manned space program.

Space-X is coming to market, via IPO, and we see renewed interest in space.

One little known area of aerospace that is also set to benefit is motion control. This is the field of electromechanical and hydraulic actuators to control motion in flight.

The field has good margins, from technical knowhow, and the high degree of trust that manufacturers place in the performance of these vital subsystems.

Here we added Moog XNYS: MOG.A, the premier US firm in this area.

The name Moog may sound familiar; the company was founded by the brother of the inventor of the Moog synthesizer, the one that shaped electronic music in the 1970s.

Invention runs in the family!

The model performance to 31-Mar-2026 was down. The market fell -5.00%, with the portfolio down -5.31%, for a net lag of -0.31%. This has been followed by a rebound of 10% for the S&P 500 through to 27-Apr-2026. This was concentrated in the large capitalization technology companies. We hold these at 21% below the market 45.5%. Consequently, we were up a lesser 5.34%, squaring the prior month fall, but a huge -4.66% behind the market. This highlights the large risk investors are running now being so concentrated in the single theme of AI. This is why we are diversified.

Note that the largest negative contributors MTD have been Exxon XNYS: XOM and the defense contractor Raytheon XNYS: RTX. Wall Street has taken the view that this war in the Persian Gulf will soon be over, there will be no lasting consequences, and we can return to putting all of our capital into the AI basket. Perhaps. Perhaps not.

Trading single month volatility is mug’s game, we will wait this one out.

International (non-USA) 20 Stock Model Portfolio

The major changes at 31-Mar-2026 were to exit German defense play Rheinmetall AG XETR: RHMG, Swiss luxury goods firm Richemont XSWX: CFR, and Hong Kong gaming firm Tencent XHKG: 0700. These were replaced by Telstra XASX: TLS, Brazilian oil firm Petrobras XNYS: PBR, and Swiss reinsurance firm Swiss Re XSWX: SREN.

Rheinmetall had performed very well for us since inception, but we have tilted to US and Japanese defense exposures as the German story is fully priced. The other moves are to shore up some yield in strong currencies, like the Swiss Franc, and to fill out an energy tilt in the international model via Petrobras. We already have exposure to the offsetting Electric Vehicle thematic via BYD XHKG: 1211.HK and CATL XKHG: 3750.HK, plus Xiaomi XKHG: 1810.HK. The war on Iran has lifted all boats in alternative energy but also benefits those oil and gas producers outside of the Persian Gulf. Telstra is a likely beneficiary of the multi-year trend for AI-related telco services.

The return in March was -8.03%, behind the FTSE Global All Cap Index at -6.21%, for a lag of -1.82%. Note that the benchmark includes US stocks, which we did not own.

The rebound MTD was modest at +4.74%, behind the FTSE benchmark at +8.68%, for a negative lag of -3.94%. The key driver of this was US technology stocks which we do not own in this non-USA focused strategy. Note that the top performer was Taiwan Semiconductor XNYS: TSM, our second-largest position after CATL XHKG: 3750.HK.

These moves are related to the singular focus of Wall Street on US technology.

Global Best Ideas 25 Stock Model Portfolio

The Global model is supposed to be our top 25 ideas drawn from the US and non-US models. The changes last month just squared up the holdings for new additions. The global model already holds Exxon, so we did not add Petrobras. However, we added new US position Boeing XNYS: BA, and Swiss firm Swiss Re XSWX: SREN. To make room we exited US insurer Allstate XNYS: ALL and Richemont XSWX: CFR.

The performance in Mar-2026 mirrored that of the international model. We were down -6.59%, a modest -0.37% behind the FTSE benchmark at -6.21%.

The MTD performance was +6.42%, versus the FTSE at +8.68%, for a lag of -2.25%. The drivers of this result are again the US technology rally off the March lows.

The above contribution chart highlights the swing back to technology names, with the results led by Nvidia XNAS: NVDA, Amazon XNAS: AMZN, Alphabet XNAS: GOOG and Taiwan Semiconductor XNYS: TSM. The key laggards were our geopolitical hedges in Exxon XNYS: XOM and Raytheon XNYS: RTX, along with CME XNYS: CME.

In my view, those risks remain.

There are no changes planned this month, but we may choose to take some profits in the technology names that ran and top up the laggards. This market is treating the war lightly and clearly wants to “return to the prior trend” consensus.

With inventories of oil, gas, refined fuels, petrochemicals, fertilizer, industrial gases, and other vital industrial commodities being drawn down, we do not see a rapid return to normalcy. Worse, the higher cost environment may see a scaling back in planned capital expenditures, outside of defense and energy needs.

Conclusion

These remain turbulent market conditions, and we are unpersuaded that the coast is clear to an imminent end to the Iran war, and attendant geopolitical tension.

Rhetoric from the White House remains detached from reality.

There has been no visible regime change in Iran, apart from a natural hardening of the national resolve in the face of US and Israeli attacks. Strategic bombing campaigns do not end wars, and we do not expect this circumstance to be any different.

Presently, the global model has 54% in US stocks, rather than the benchmark 62% and is underweight global technology by about 10%, and overweight resources by 14%.

Time will tell if this positioning will be rewarded.

Our logic is that present conditions resemble those of the 1973-1974 bear market, the proximate cause for that was the Jan-1973 Arab Oil Embargo of the USA.

This time is different in two ways: the USA and Israel started this war, and so could choose to stop it; and the USA is a net producer of oil and gas.

Superficially, the USA looks invulnerable to major economic damage.

However, wars are costly to finance, and this one is likely to damage the world and thereby cause a blow-back into US multinational profits and confidence.

Due to the valuation disparity between US and non-US markets, we do not see the US market as a durable safe haven for capital. Indeed, the level of animosity displayed towards foreign actors could see conditions ripen for capital flight.

We are not there yet, but I do not see present conditions as stable.

Granted, we have already seen instability, and relative underperformance these past eight weeks from our non-US and non-technology holdings. However, what has propelled that difference is capital-expenditure related growth expectations.

Capital expenditure booms usually die of old age meeting higher costs.

There remains enormous enthusiasm for the AI-related capital expenditure boom, especially in the USA, but the rise in construction-related costs due to diesel and material prices, plus manufacturing interruptions to supply chains, could soon be upon us. There is also the prospect of higher interest rates.

The US economy has run fiscal deficits >5% for some years now.

We have a planned 50% budget increase for the Pentagon, an active war in the Middle East of uncertain aims and duration, and a deteriorating geopolitical environment.

Guns and Butter sank the administration of President Lyndon Baines Johnson.

The US adjustment from the excesses of the late 1960s gave us the 1970s.

Students of market history will note that the Nifty Fifty stock boom of the 1960s fully died in the 1973-1974 Bear Market. It took decades for some names to recover.

This all happened at a time of continued innovation.

While the US Space Program stalled in the mid-1970s, the consumer markets were very active, and the shift to greater fuel economy helped foreign firms.

There are undoubted reasons to keep betting on US innovation, but we think that the present focus of US technology investors is too crowded in those spending money.

We are not fools, and believe that you must spend some money to make money.

However, the set-up today means that young innovators are literally given brand new tools for reshaping entire industries for free. Intelligence is not a new thing, and the belief that this can be controlled through mechanisms of ownership seems naive.

The US mega-tech firms have splendid advantages, but they also seem blinded by the market power they wield. This was built on network effects to aggregate attention.

However, the growth of replicable Large Language Models (LLM) via open source and shortcuts like model distillation could leave the technology majors looking like very expensive telecommunications firms who have put all their cash into infrastructure.

The truth of software economics has not left us. The marginal cost to replicate any software is close to zero, in media terms, but was formerly high in barriers to entry.

Since the process of generating software is now automated, we foresee a widening of opportunity for disruptors to reshape all software dependent process industries.

This does not seem to be a stable environment for massive capital expenditure.

The problem is who will capture the return on AI investment.

I am a believer in the powers of human creativity, which I now see amplified.

The major technology firms have great present businesses, but the future of any and all business now looks uncertain to me. In that world prospect, I prefer to reallocate capital towards the fruits of nature, via commodities, and infrastructure which is unlikely to be disrupted, such as gas pipelines and railroads.

The virtual world seems set for cataclysmic change.

In consequence, I am looking for investment opportunity in the physical world.

It is too early to say, for certain, that these instincts are correct.

We are baby stepping in those directions we think are likely to prove more rewarding over time, and perhaps less exposed to a collapse in sentiment.

These are both disturbing times and exciting times.

1984 came and went…

It was not as bad as the book made out.

I am looking forward to finding new opportunity.

Take care and happy investing!

This note was titled Gold in the Hand versus Gold in the Ground.

The principles I outlined there are commonly used in the Hedge Fund world.

Many moons ago, I used to write broker research on these so-called pair trades.

Cml Vs Wow Pairs Analysis

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The above note on Coles Group versus Woolworths is twenty-five years old now.

The method of pair trading comes and goes because you need the right kind of markets to have it work well. Ideally, these are ranging not trending markets.

Trending markets tend to produce one-way bets.

Ranging markets tend to produce back-and-forth motion.

You could still have a trend overlayed with relative cycles, and I will show how to do the statistical analysis for that in a later note. The key thing to note is that a growth stock market is generally dominated by trends. Value stock markets are opposite.

You can tell by my resurrection of twenty-five-year-old methods that I am retooling for the possibility of a different kind of market, one I am also at home in.

The particular kind of trade I want to revisit today is called impulse reversion.

In the original stock price chart of Northern Star XASX: NST it looked like this.

When there are few value investors around the moves can be huge, as you can see above when Northern Star announced a second delay on the KCMG mine expansion. Delays in production cost some short-term excess expenditures to fix and shift the Net Present Value (NPV) further out in the future, leading to a lesser value.

If you were in a hedge fund trading pod, you might well have a model ready to go on stressing the valuation impact to find a warranted price level for entering the trade.

Good value investors have a few rules of thumb on discounting they can do in their head. In the old days of floor trading, this was an essential skill.

Since we have been in a growth market for some decades the skill has atrophied.

That is why you got a 45% down move in a weak gold market. Evolution XASX: EVN was down about 33% over the same period. The value impact of delay circa 5%. In this case, you could look at NST on a relative to see pricing in gold.

You can see on 23-Mar-26 the relative purchasing power of gold, the product, for the gold mine had soared by about 30%. The trade is then to sell GLDN, the gold ETF and buy the stock, NST. This is why I generally own some precious metal and some stock.

Think of the gold as like cash, which substitutes for the output of the mine.

The purchasing power of gold in the hand for gold in the ground, using the ratio of price of one to the other is a simple test of relative value. This works in a trading pod because you have risk capital to tap, and target rates of return to make.

Obviously, the trade is not worth doing if the stock does not appreciate. Therefore, you only do things this way in a rising Bull market. We have that for both gold and gold miners, so it is a fair bet to do the trade. We can expect impulse reversion.

Let us see what happened to NST after 23-Mar-2026.

It turns out it bottomed on that day and is up about 28% since to close 1-Apr-2026.

I did not do my trade until 30-Mar-2026, for a lesser gain of 13%. This is for several reasons; the main one has to do with managing risk. When it is your money at risk caution on the turn is advisable. We can see that turn on the relative chart below.

I did my trade when the relative price ratio clearly looked to be moving back and had crossed back through the level of the prior peak. This is a desk trader kludge.

There is no hard and fast rule for where this should go.

You lean into it when it feels right and cut it if it does not act right. Presently, this one is acting right, and we would anticipate a return to the former level.

Note carefully that the “return to the former level” is a rule of thumb judgement that reflects some mental arithmetic on how much the gold miners’ profits are affected by a given change in gold, and or oil prices. In a real pod setting, you would have done the sensitivity modelling, the AISC cost/margin model, and peer valuation.

This is just an educational note as I think we will see plenty of these opportunities.

Twenty-five years ago, when I first learned this method there were proprietary traders on every major investment bank floor, and fund shops full of value investors.

The proprietary traders are gone.

The value shops are dead.

The quant models today are all artificial intelligence black boxes.

If you know what you are doing, this is a good method.

Have fun!

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• Australian 20 Stock Model Portfolio

• USA 20 Stock Model Portfolio

• International (non-USA) 20 Stock Model Portfolio

• Global Best Ideas 25 Stock Model Portfolio

The existing research notes and newsletter offer remains, but you will see some tighter integration between what we write there and the model portfolios.

The licensing and complaints procedure is outlined in our Financial Services Guide.

These portfolios follow the Jevons Global investment process.

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The last change to the model portfolio was effective at close 10-Mar-2026.

On the last portfolio change, which happened pre-war we exited these positions:

• AAPL Apple Inc

• ORCL Oracle Corp

This was to make way for new inflation hedges:

• XOM Exxon Mobil Corp

• LIN Linde Plc

This month we need to synchronize the Global 25 Model with the latest changes to our US 20 Model and our International 20 Model.

The theme of this rebalance is a more defensive posture.

The main areas we seek to strengthen are hedging to higher energy prices, the risk of higher interest rates, and the possibility of a consumer slowdown.

Since the last 10-Mar-2026 change the benchmark FTSE Global All Cap Index (ETF: VT) fell -6.08%, and the portfolio a lesser -4.25%, for a 1.83% positive alpha. The complete figures for the month will be out tomorrow, but will include the initial period, plus the results of overnight trade. Including, the period from the start of the month, the alpha was a more modest 1.26%, and the benchmark return -9.02%, and model -7.76%.

The above chart shows relative contribution by stock. Exxon XOM and Linde LIN both added value, while Newmont NEM, Taiwan Semiconductor TSM and IHI 7013.T were a drag on performance. This chart is from the start-of-month, and so includes Oracle ORCL, and Apple AAPL, which were exited in the US rebalance.

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The updates for close-of-trade 31-Mar-2026 are below the paywall.

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