From bullish to cautious: Why 2026 may bring lower returns and higher volatility
Let’s state the obvious right at the outset: No one knows for certain if the equity market will crash.
The reality of investing is that we can rarely identify a bubble until after it has burst, which means we can never be sure when we are inside one. However, with that necessary disclaimer in place, I am reasonably confident that investors should prepare for a shift in 2026.
That’s because, while the last few years have been kind to bulls, I currently expect greater volatility and lower returns in 2026.
There’s a number of reasons for a shift from the unbridled bullish disposition we have held since 2022.
Back in 2022, I “hitched my wagon to the bullish camp” on the back of two basic premises. The first was that disinflation, economic growth and liquidity would support a bull market, just as those factors have every time they have convereged since the 1970s. The second reason was simple arithmetic; with P/E ratios at near historic lows in 2022, and investor could earn a return equal to the earnings-per-share (EPS) growth rate of the companies they purchased, even if the stock market never gained in popularity again. All an investor had to do, was hold on.
Today, an investor could also earn, over the next five years, the EPS growth rate of the companies they pick, but the stock market would also need to remain very, very popular, as it is today.
The price-to-earnings (P/E) ratio is a measure of the stock market’s popularity. If you buy when P/E ratios are broadly low, as they were in 2022, you can assume the stock market will gain popularity again sometime in the future, and as the P/E ratios expand, you’ll earn a return that is even better the earnings-per-share (EPS) growth rate of the company you have invested in.
But if you buy when the P/E ratio is extremely high – when the stock market is extremely – popular – you run the risk of it becoming less so in the future. That will eat into your returns.
Remembering the aphorism, ‘the higher the price you pay, the lower your return’, it’s reasonable to expect lower returns for the broad index from here.
Since 2022, our thesis has played out well. But now the supportive pillars of that narrative are beginning to crack.
First, the requirement for disinflation. Inflation is sticky. It isn’t declining much, if at all.
Second, economic growth is slowing. And under the surface of the U.S. economy, a majority of growth can be attributed specifically to the construction of data centres.
Third, liquidity is drying up. Global liquidity growth is slowing and even after a bump last week, it remains below the all times highs. That might explain why the Federal Reserve (the Fed) has commenced Quantitative Easing (QE) again.
Finally, precisely because we’ve had three years of solid returns, valuations are now elevated. As Robert Shiller suggests, from these valuation levels, future returns for the S&P 500 are expected to be in the low single digits.
For index investors, are low single-digit returns enough to justify the risk of being ‘all in’?
A little bit of mania
Unless you’ve had your head in the sand, you can’t have missed the latest investment hype surrounding the artificial intelloigence (AI) theme. Thematic booms come and go, and while their birth is always attributable to ample liquidity – when central banks provide excess money, it needs to find a home/theme – they can create a life of their own when the theme is perceived to be ‘structural’. When that coincides with a new general-purpose technology (GPT), capital converges on the companies that sell that tech.
Today’s AI boom is undoubtedly perceived as structural. The problem with such perceptions is that they eventually run up against the realities of a cyclical customer and a business cycle. Developing datacentres will be met with opposition about their location, the impact on energy prices and their use of precious water resources. And customers may not have the dollars to be able to buy enough AI tools to generate a decent return on the AI capital expenditure (capex) being committed to by the AI hyperscalers.
We see this clearly in Nvidia, where its market capitalisation reached US$5 trillion, despite forecasts for 2026 revenue just US$65 billion – that’s a market valuation 77 times revenue.
And the hype is spreading. Take prediction market companies – those e-businesses that allow people to bet on absolutely anything. For the last six years, a betting market company called Kalshi grew slowly, raising just US$100 million. But since June, the U.S. company’s valuation has increased 450 per cent, as it raised US$1 billion in early December, valuing the company at US$11 billion.
Elsewhere, Anthropic, the company behind the Claude Large Language Model (LLM), completed a funding round that valued it at US$183 billion. That doesn’t seem unusual, except that the valuation, struck in September, is three times higher than its valuation of US$61.5 billion six months earlier.
And OpenAI, the company that birthed LLMs now has a half a trillion U.S. dollar valuation even though it will incur a loss of US$9 billion this year, and the losses are projected to grow to an annual loss of US$74 billion by 2028.
Did someone say, ‘other people’s money’?
The little bit of mania that has crept into the markets, has created a disconnect that is the primary driver for the volatility I foresee in 2026.
The unit economics problem: AI is not SaaS
Investors need to recognise a fundamental difference between the tech darlings of the last decade and the artificial intelligence (AI) giants of today.
Since 2010, investors have loved Software as a Service (SaaS) businesses. SaaS businesses enjoy massive margins (up to 90 per cent) because code is written once and sold infinitely. AI is not Saas. Delivering AI-generated outputs introduces a tangible, physical cost to every single interaction. While apps like ChatGPT, Grok, or Gemini feel like elegant software, under the hood, they require multi-billion-dollar models, thousands of Graphic Processing Units (GPUs) (built on $US250 million EUV scanners), and massive data centres requiring city – or even country – sized power supplies, and significant water consumption (locally estimated at 20 per cent of Sydney’s water supply).
And then there’s the marginal cost of supplying each answer to a prompt. The financial difference that results is staggering. A standard Google search reportedly costs Alphabet roughly $US0.00003. A generative AI response, however, costs upwards of $US0.01 to $0.02. That’s a 300 to 400-fold increase in marginal operating costs.
The real world
Beyond the financials, there are the physical constraints.
OpenAI’s Sam Altman has stated his “audacious long-term goal” is to build 250 gigawatts of capacity by 2033. To put that into perspective, that’s reportedly more than the entire peak power demand of India in 2025 – the world’s fourth-largest economy with nearly 300 million households.
To achieve this, news site Truthdig estimates OpenAI would need to purchase 30 million GPUs a year, run them 24/7, 365 days a year, and utilise the world’s 10 most advanced fabricators around the clock, forever, to make them.
Of course, running hardware this hard will burn out the GPUs, requiring more frequent replacement. This level of energy and manufacturing demand would squeeze the industry, drive up prices, and strain clean water supplies.
It’s just hard to imagine it working out without speed bumps along the way. Those speed bumps are the source of my predicted volatility.
Stock markets tend to “cast their shadow before them”, meaning investors will exit long before the problems arise.
For all these reasons, I suspect the easy ride is over. 2026 will look very different from the last three years. I am not saying there will definitely be a crash. But I am saying it’s reasonable to expect lower returns from here with the possibility of a little more turbulence thrown in.
So what?
The worst thing to do is to sell up and run for the hills. That’s an immature approach to investing. It is wiser to think about your allocations. What percentage of your wealth do you want in the stock market? What percentage in property? In cash? In income-producing assets such as Private Credit (with no property development exposure of course!). What about alternatives?
Each year you should rebalance back to those proportions that reflect your risk profile and financial needs.
Three years of solid stock market gains could mean your allocation to equities is higher than the original settings. Going back means rebalancing some profits to other asset classes.
And to properly diversify, rebalance towards asset classes or funds with very low correlations to the stock market from which you’ve just harvested some profits.
For more information about rebalancing into a Private Credit fund, visit our Private Credit webpage here or call David Buckland or Rhodri Taylor on (02) 8046 5000.
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Roger Montgomery is the Founder and Chairman of Montgomery Investment Management. Roger has over three decades of experience in funds management and related activities, including equities analysis, equity and derivatives strategy, trading and stockbroking. Prior to establishing Montgomery, Roger held positions at Ord Minnett Jardine Fleming, BT (Australia) Limited and Merrill Lynch.
He is also author of best-selling investment guide-book for the stock market, Value.able – how to value the best stocks and buy them for less than they are worth.
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