How Useful is Machine Learning in Finance?

Machine Learning (ML) is a type of Artificial Intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. It focuses on the development of computer programs that can access data and use it to learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow computers to learn automatically without human intervention or assistance and adjust actions accordingly.

Machine Learning has many applications in finance, such as predicting stock prices, detecting fraudulent activities, and automating investment decisions. For example, by using Machine Learning algorithms to analyze large amounts of data, traders can predict stock price movements. Similarly, Machine Learning models can be used to detect suspicious trading activities, such as insider trading, market manipulation, and fraud. Additionally, Machine Learning algorithms can be used to automate investment decisions such as asset allocation or stock selection.

But how accurate is Machine Learning prediction in finance? The truth is that its accuracy can vary widely depending on the type of data used and the model chosen. While Machine Learning has great potential for predicting financial markets, it is still in its early stages and has limitations.

Reference [1] discussed the problems that Machine Learning models are facing in finance,

• Nevertheless, there are also pitfalls in the use of ML. For example, ML models are particularly useful for applications with a large amount of data and a high signal-to-noise ratio. In financial market research, however, the data sets are comparatively small and the signal-to-noise ratio tends to be low.
• Importantly, financial markets are constantly evolving, and we might see detected anomalies being arbitraged away over time … in financial markets, all cats might morph into dogs once the algorithm has learned how to determine a cat in an image, and the algorithm must start learning all over again. This analogy cautions that the relevance of past data points is not constant, since the data-generating process may change over time.

The first bullet point refers to the well-known low signal/noise ratio of financial time series. The second one is the problem of non-stationarity.

The authors concluded the article by giving a more realistic picture of the usefulness of ML in finance,

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.

And finally, they highlighted a crucial point that is often ignored (sometimes intentionally) by ML practitioners; that is, in order to build a successful ML model, domain knowledge is required.

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.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Blitz, David and Hoogteijling, Tobias and Lohre, Harald and Messow, Philip, How Can Machine Learning Advance Quantitative Asset Management? (2023). https://ssrn.com/abstract=4321398

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