Investors wondering whether artificial intelligence has the potential to improve returns should look no further than quantitative equity investing. The technology's unprecedented processing power enables investment models to trace ever more complex relationships among ever greater numbers of data series.
Traditional quantitative approaches tended to be restricted to analysing a relatively small number of market effects that result in temporary mispricings. This then provides exposure to broader market drivers, known as factors, such as value or momentum.
AI takes this approach but widens its reach, offering portfolio managers the possibility of analysing hundreds of potential signals/features at higher frequency. These are generated from data that includes company accounts, share prices, analyst notes, press reports, investor responses to new information over the short and longer term. And that’s just scratching the surface.
AI can generate greater insights into what’s driving stock prices
AI can capture much more complex associations within the data pool, which allows it to have much greater insights into what’s driving stock prices. Being able to identify more complex, non-linear relationships boosts its ability many-fold to find associations between data series.
For example, in a traditional quantitative model, an analyst upgrade of a company would suggest its stock would outperform. However, there are many reasons why such a relationship might not hold on a given occasion, or not be captured in time to generate alpha.
A non-linear machine learning model that is trained with historical data can identify the relationships that tell us when the analyst upgrade would most effectively forecast future outperformance. This could be because there’s a broad range of analyst forecasts, or the particular forecast is an outlier or because of timing – say if the company is due to report results soon. If the stock is widely shorted by hedge funds, the model could identify that it is likely to be subject to a short squeeze resulting in a far more dramatic jump in share prices than the analyst upgrade might otherwise warrant. There are potentially tens of thousands of these “conditioning” non-linear relationships across traditional financial data sets that can generate additional alpha.
| Traditional quantitative | Quant with AI | |
|---|---|---|
| Factor exposure | Classic factors | Factor neutral |
| Number of signals / features | Curated number of signals | Hundreds of features |
| Investment horizon | Medium | Short to medium |
| Model type | Linear | Non-linear |
| Construction | PM defined | Trained with data |
Source: Pictet Asset Management
AI helps to deliver factor-neutral returns
This much more intricate framework opens the way for the portfolio managers to isolate the stock-specific effects that influence the share price. To do so, they strip out a multitude of common factors (market, sector, region, industry, country, styles, economic exposures) from each stock’s performance. In doing so, they can identify and extract pure company-related alpha.
Over time, the algorithms evolve, understanding changing economic and market dynamics and incorporating new data series.
Yet be consistently effective, AI-driven models need humans to set investment parameters.
The power of incremental alpha over time
Because these parameters limit risk, they will also limit the amount of alpha the strategy generates. However, the compounding effect even of incremental alpha is powerful over time. And given that expected returns on equities are forecast to fall to mid-single digits amid high valuations following several banner years, even one to two percentage points of alpha will make a difference over medium to longer term time horizons. For example, if we assume a hypothetical global equities market return over the next 10 years of c. 7% per annum and an assumed outperformance of 1.5 percentage points per year net of feesAfter strong equity returns in recent years, the Pictet AM Strategy Unit forecasts only c. 7% per annum from global equities over the next 10 years. Source: Pictet AssetManagement Secular Outlook of July 2026: https://am.pictet.com/ch/en/intermediaries/investment-research/secular-outlook-2026, the wonder of compounding could generate a cumulative difference of about 30%. $100k at 7% would give you $196k over 10 years. Compounding at 8.5% would give you $226k.
How can Pictet AM’s AI-powered enhanced index strategies benefit portfolios?
Our range of AI-enhanced quantiative equity strategies harness the scale and efficiency of AI for stock selection with the aim of generating above-index returns with a low tracking error.
But unlike actively managed strategies – which also aim to outperform the market – the fact that our AI approach requires relatively fewer humans means that it has lower management costs. Typically our approach to AI costs little more than a passive strategy notwithstanding the expertise needed to build and maintain the model.
What makes our approach to AI different?
Our proprietary AI models were developed over many years by our experts, who are typically PhDs in physics and mathematics. The models have been trained with some 400 characteristics from multiple data series spanning a period of around 15 years and then repeatedly tested under different economic backdrops. The rigorous process is designed not only to maximise forecasting power but also to ensure that the models aren’t overfitted – that’s to say that they don’t only produce accurate results under a narrow set of circumstances but that they work under changing economic environments.
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The models have been trained with some 400 characteristics from multiple data series
