As systematic investors, we are less concerned with discretionary forecasts. We conceive of our forward-looking outlook using metrics we can quantify and compare with history. With that in mind, here are a few measures that we’re either watching now or will be examining closely over the next year.
1. Absolute value
With equity markets grinding ever higher, many are concerned with absolute valuations in the market. We tend to focus more on relative valuations within the market as it is immensely difficult to time the market (at least on a valuation basis). That said, a lot of the market cap in equities has a potentially exorbitant valuation. The enterprise value-to-sales ratio (EV/sales) contrasts a company's total enterprise value with its annual sales revenue. It is used to assess if a company's stock is undervalued or overvalued by comparing its total market value to its sales, with a lower ratio often suggesting a potentially undervalued name. Currently, over 25% of the weight in MSCI World has an EV/sales ratio of more than 10x, exceeding the prior highs from the Dot-Com bubble and the post-COVID market. We think this will likely start to normalise – the question is if revenues can grow fast enough to allow these specific securities to keep rising (most of which are US technology stocks).
Figure 1: The total MSCI World weights of sectors with over 10x (top) and 20x (bottom) EV/sales ratios
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Source: Man Numeric.
2. Forward earnings-to-price (E/P) ratios
Putting aside potential absolute valuation issues, the global equity landscape does not appear especially abnormal when we hone in on other valuation metrics. Figure 2 shows our favoured view of ‘valuation stretch’ (that is, when we think prices have exceeded an asset’s fundamental value). This metric, which zeroes in on the industry-relative convergence of forward earnings-to-price ratios across various markets, helps us determine a company’s value based on its future potential by comparing a company's current share price to its projected earnings per share (EPS) over the next 12 months in the context of its sector.
The y-axis shows the historical z-score of the metric by region. While the opportunity for cross-sectional value appeared very wide in the early 2000s, 2009, and 2020 to 2022, it is now in fairly average territory, with the two exceptions being US large caps and Japan. In US large caps, value stretch is nearly +1 sigma (or one standard deviation to the right of the mean value on a normal distribution curve) – indicating a larger than usual opportunity set, while in Japan it is about -0.8 sigma (or nearly one standard deviation to the left), indicating the opposite. On this basis, we might expect a fairly normal year for value performance in 2026, all else equal, with possibly more muted performance in Japan and more potential in US large caps.
Figure 2: Forward E/P convergence methodology
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Source: Man Numeric.
3. Macro environments: past vs. present
Another area we focus on is identifying what macro environment we’re in and what has occurred from a style or industry perspective during similar points of the past. Figure 3 shows the historical similarity to today1 starting from 1994 to one year ago. Today’s environment looks a lot like a year ago, and like 2020 (except for the pandemic meltdown!), parts of 2010 to 2013, and 2003. Some of what is driving the similarity across those periods are fairly healthy equity markets, steepening interest rate curves, and strong precious metal performance. Because of that the model is favourably disposed towards the Momentum factor today, and to a lesser extent Quality, Profitability, and Growth. Interestingly, Energy and some Basic Material industries are also forecast to do well, but gold and precious metals are forecast to do poorly! Clearly, that is very different than what we have recently seen, so it will be interesting to see how the gold trade behaves throughout 2026.
Figure 3: What macro environment are we in?
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Source: Man Numeric.
4. AI-related capex
Lastly, an important question for equity investors in 2026 is how sustainable the current pace of artificial intelligence-related capital expenditure (AI capex) will be. Over the last few earnings seasons we have been focused on capex guidance from the hyperscalers. Any change in the spending trajectory would likely have a significant impact on the related companies, as well as the entire equity market and global economy.
Nvidia, Microsoft, Amazon, Meta, Alphabet, and Oracle combined make up 26.5% and 18.5% respectively of the S&P 500 and MSCI World.2 The capital expenditures of the last five of those companies (the four legacy hyperscalers and the newest entrant) has risen dramatically over the last two years. We expect it to continue rising, such that free cash flow may be lower in 2025 and 2026 than it was in 2024.
Figure 4: Microsoft, Amazon, Meta, Google and Oracle cash flows and capex (in US$ billion)
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Source: Bloomberg.
Prepare, don’t predict
As quants, we don’t forecast. But, based on our models, we’d say that even if absolute valuations today look rich, the opportunity set for US large cap performance appears reasonably steady for 2026. That is even with increased AI capex and question marks about what the return on investment will be over the short-to-medium term – enhanced growth, cost efficiencies or nothing at all.
Investors should therefore be sceptical of the 'AI Everywhere' narrative. Rather than waiting for aggregate productivity statistics, focus on currently high R&D-intensity firms (Software, Healthcare) and track firm-level indicators: gross profit growth outpacing R&D spending growth, research output metrics (scientific papers, patents, drug approvals), and any task-specific productivity measurements. Consequently, the earliest confirmation of AI’s economic promise will not be found in aggregate GDP statistics, but in micro-level indicators, especially at R&D intensive firms.
1. As of 9 October 2025
2. As of 9 October 2025
Important Information
This material is for professional investors only and is not intended for retail distribution. The information contained herein is based on our systematic models and historical analysis. These are model-based expectations and actual results may vary significantly. All investments involve risk, including potential loss of principal. Past performance is not indicative of future results. The views expressed are subject to change without notice and should not be construed as investment advice or recommendations to buy or sell any securities.
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