Machine Learning in the financial markets.

What is Machine Learning?

Machine Learning is generally understood to be the idea that computer programs can improve themselves over time through learning effects. In terms of stock market trading, this means the next step in the evolution of statistical and quantitative methods. Although the influence of humans in the quantum business was already diminishing, decision makers continued to play an important role - for example, by first determining the model to be used at the beginning of quantitative analyses.

In machine learning, this human influence is again much less or, ideally, no longer exists. The algorithms can view many models in parallel and then decide for themselves which framework to use for further investigations.

For many established asset managers this step seems revolutionary. Accordingly, opinions diverge widely, ranging from skepticism, confusion and lack of understanding to the modern thinking that this enables progressive, better decision making.

A new word for old ideas?

In the paper "Can Machines 'Learn' Finance?", Ronen Israel, Bryan Kelly and Tobias Moskowitz write that the high range of opinions is related to the fact that machine learning is a rapidly developing but very technical field in which few market participants have a full understanding. That's why the term is sometimes used in practice in the way that suits it, for example for marketing purposes.

But how does machine learning differ from previous quantitative concepts? The paper names three criteria that the algorithms fulfill: [1]

Criteria of machine learning:

Application of "large" models with many parameters (features) and/or complex non-linear relationships between inputs and outputs with the aim of achieving maximum forecast quality on the basis of an unknown pricing model of the market

Select a preferred model from any number of different models, taking into account regulatory techniques to limit granularity and cross-validation methods with simulated out-of-sample tests to avoid over-optimization

Innovative approaches to efficient model optimization that reduce the computational effort in big-data environments, such as Stochastic Gradient Descent, which considers only random parts of the data without a large loss of accuracy

The authors write that ideally "Goldilocks models" are used as input for machine learning. These are large enough to reliably identify the real, potentially complex relationships with predictive character in the data. At the same time, they are not flexible enough to generate over-optimization on historical data, which would lead to disappointing performance out of sample.

When does Machine Learning work?

According to the paper, the success stories of machine learning in areas such as speech recognition, strategic games and robotics to date come from environments that combine two critical factors: [1]

Big data environment: Machine learning algorithms benefit from a massive flood of training data. For example, AlexNet, a neural network for image recognition, has around 61 million parameters. This makes it possible - assuming sufficient computing power is available - to map highly complex relationships between different patterns.

Signal-to-noise ratio: This ratio describes the degree of predictability within a system. The higher the value, the better. For example, high ratios are often achievable in the field of image recognition, which leads to stable, reliable results of the corresponding algorithms.

Machine Learning - Possible applications

The paper points out that the black box problem can be avoided by structural modeling. Machine learning is implemented within a superordinate, theoretically sound (economic) model accepted by human experts, which allows a certain interpretation of the output. Within the defined framework, the algorithms can then act freely and identify correlations that represent potential added value.

Another paper, "Empirical Asset Pricing via Machine Learning", points out that machine learning offers added value, especially when using complex models such as neural networks and regression trees. 2] The improvements that can be achieved with machine learning are mainly due to the non-linear relationships that are not recognized by simpler models. At the same time, however, there is a suspicion that complex models in particular discover a prediction quality in the data that is associated with high transaction costs - which at the same time could explain why this potential source of returns has not yet been exploited by other market participants.

Another way to use machine learning is to focus on better risk assessment - especially covariances between stocks or factors - instead of returns. In this way, it would also be possible to achieve better risk-adjusted returns out of sample compared to a classic benchmark. Perhaps this is a more realistic application for machine learning, although these improvements are somewhat overshadowed by the "Holy Grail" of a true alpha strategy.

According to the authors, another possible application is transaction cost management. Usually there is sufficient data available in this area to allow the use of well calibrated models.

Conclusion

Machine Learning has the potential for significant progress in the field of quantitative investments. According to the authors of the study under consideration, however, this is not a real revolution, but rather the consistent further development and automation of already applied statistical and quantitative methods - in the sense of potentially faster, better and more flexible modeling. At the same time, the authors use the small-data environment as well as the signal-to-noise ratio to point out that the expectations for what the method can actually achieve should not be too high.

Structural modeling seems to be a suitable concept for implementing machine learning. Here, the method is implemented as described within a given framework - such as a simple, general factor model - and, if necessary, with the involvement of human expertise. This already indicates that humans will not be completely absent from the quant business in the foreseeable future.