![]() ![]() In this case, there would be no point in pursuing machine learning models because there isn’t enough signal left to “mine” to make the effort worthwhile. If we believe that the signal to noise ratio inherent in asset price data is very low, then we could come to the conclusion that traditional quant models already capture most of the signal already. I’ve simplified the math for illustrative purposes). (Mathematicians may note that this is not how the relationship between R-squared and alpha actually works, but it’s directionally correct. However, if the signal actually only accounts for 5% of the stock’s price variation, then the potential alpha we can generate goes down to 40% x 5%, that is 2%. In this case, our investment strategy could earn up to 40% x 10%, that is 4% of alpha from this stock. ![]() Let’s hypothetically say that signal accounts for 10% of a stock price’s variation, and that the stock’s price swings by roughly 40% per year. However, the extent to which this strategy can work is limited by the strength of the signal relative to noise. For example, if we build a model that predicts companies’ earnings with a high degree of accuracy, we could build a strategy that invests in companies who are about to make positive earnings reports. The noise component, on the other hand, consists of the unpredictable component of price movements.īecause the signal component is predictable, we can build investment strategies around them. For example, companies that reported good earnings have historically seen their stock prices go up, and the average amount by which companies’ stock prices have risen is considered the signal. Signal is the portion we can understand, model and predict. Quants generally divide asset price movements into two components-signal and noise. One of the main obstacles that the authors mentioned concerned the low signal to noise ratio of financial data. You can read some of my thoughts on the paper in this Twitter thread. AQR recently published a paper with the title ‘Can Machines 'Learn' Finance?’ In the paper, the authors talked about the feasibility of creating machine learning driven investment strategies, and voiced skepticism that it could be done due to several obstacles. ![]()
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