We are now at a pivotal moment in history. For the first time, primates (in particular, humans) have developed machines for making informed decisions. It is therefore no surprise that machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations.
When I ask about potential uses of ML in finance, the most popular response I get is predicting prices. Of course, pricing is a complex problem where ML can achieve greater accuracy than standard quantitative techniques. However, there are plenty of alternative uses of ML in finance, from portfolio construction, to regime switch detection, bet sizing, strategy selection, etc. One popular misconception is that price forecasting is the only way to generate returns. That can be easily counter-argued. For example, one investor could have an edge at predicting prices, however poor bet sizing will easily ruin him.
The great promise of ML is that it will allow researchers to identify patterns never unveiled before by traditional methods. Like any groundbreaking technology, it offers unique opportunities to early adopters. Quantopians are well positioned to take advantage from large firms' reluctance to embrace change. At the same time, ML is fraught with perils. A ML algorithm will always identify a pattern, even if there is none! It is critical for researchers to understand this statement:
FINANCIAL ML ≠ ML ALGORITHMS + FINANCIAL DATA
Anyone who tells you that you can take a standard ML algorithm, plug-in some financial series and succeed is setting you for failure. Financial ML is a subject in its own right, with its own tools, methods and approaches. If you are interested in learning more about these tools and methods, please visit my free website (www.QuantResearch.org) and read my new book: https://www.amazon.com/dp/1119482089.
I would be very interested in hearing from the community about examples of how they have applied ML. What are your favorite ML applications in finance?