AQR’s Problem With Machine Learning: Cats Morph Into Dogs
Machine learning has done magic, such as beating human chess champions. But in finance, expectations for the technology may need to come down a notch or two, according to quantitative firm AQR. Machine learning changes the way problems are solved.
Traditional computer programmers define all of the rules or parameters of a game. Machine-learning applications, in contrast, are fed data so they can then determine the rules and relationships. Sorting pictures of dogs from cats is a well-known example of machine learning in action.
A traditional programmer couldn’t program in the infinite number of variations that exist between dogs and cats. But cats and dogs are not markets. In financial markets, the so-called signal-to-noise ratio is low, meaning outcomes aren’t particularly predictable.
If they were active, managers would consistently outperform their benchmarks. Machine learning doesn’t do well in that sort of environment. On top of that, markets are adaptive.
Financial markets are also different from other sectors in that asset managers need to explain their models to investors — not always easy in the machine learning world. Although big data can be useful, AQR also argued that finance is a time-series discipline. New data on returns, for example, are only generated with the passage of time.
Big data from social media or other platforms have a short history, which is of limited use in investments.