How can machine learning advance quantitative asset management?

The extant evidence suggests that machine learning can boost quantitative investing by uncovering exploitable nonlinear patterns and interaction effects in the data. Being mindful of a positive publication bias, we caution that ML is not a panacea, as users need to make important methodological choices, the models can overfit the data, and they are based on the premise that past relations will continue to hold in the future.Many studies that report strong results for ML models focus on predicting next 1-month returns based on a large number of traditional factor characteristics as input features. Although the models load on traditional short-term return predictors they are able to exploit additional nonlinear alpha opportunities. The challenge with these models is to turn the resulting fast alpha signals into a profitable investment strategy after costs and other real-life implementation frictions. The corresponding literature is scarce, and the few works naturally suggest that the opportunity set for ML models to outperform traditional ones is often reduced given the reliance of ML models on high-turnover signals. For this reason, there has recently been an effort to integrate economic structure into loss functions to have the ML model focus on better tradable stocks. Such efforts are expected to increase the likelihood of monetizing the predictive power of ML models. Apart from forecasting returns, there are other promising use cases for ML, such as enhancing traditional factors, creating new variables from unstructured data, and predicting metrics other than return, such as risk or sustainability.

So far, ML methods in asset management have therefore been more of an evolution than a revolution. Presumably, asset managers who will disregard advances in ML will see their performance wane relative to those who embrace ML. Naturally, mostly big institutional players can enjoy economies of scale in this competition given the high costs of running such operations in practice.

The ability to automate tasks of traditional analysts, such as reading, seeing or hearing ultimately promises large gains in productivity—provided the asset manager possesses the necessary infrastructure and can investigate different big data sets and signals at scale. Yet, discarding economic theory altogether and turning to a fully data-driven approach can vice versa also set one up for failure.

Investors can identify and evaluate the ability of an asset manager to succeed in advancing his investment process accordingly by scrutinizing what research protocol is in place. The latter is key to the success of ML in practice and to navigate the many pitfalls. Altogether, researchers have just been scratching the surface of the endless possibilities offered by machine learning, and many exciting new discoveries can be expected in the years ahead. However, human domain knowledge is likely to remain important, because the signal-to-noise ratio in financial data is low, and the risk of overfitting is high.

A full version of the research is available to download.

Disclaimer

This does not constitute an offer, solicitation or recommendation to sell or an offer to buy any securities, investment products or investment advisory services. This information generated by the charts, tables, and graphs presented herein is for general informational and general comparative purposes only.
This document may contain forward-looking statements that are based on their current beliefs and assumptions and on information currently available that they believe to be reasonable, however, such statements necessarily involve risks, uncertainties and assumptions, and investors may not put undue reliance on any of these statements.
References to indices or benchmarks herein are for informational and general comparative purposes only. Indexes are unmanaged and have no fees or expenses. An investment cannot be made directly in an index.
The information in this presentation is not intended to provide, and should not be relied upon for, accounting, legal, or tax advice or investment recommendations. Each recipient should consult its own tax, legal, accounting, financial, or other advisors about the issues discussed herein.