Machine learning-based stock picking using value investing and quality features

Abstract

Value Investing stands as one of the most time-honored strategies for long-term equity investment in financial markets, specifically in the domain of stocks. The essence of this approach lies in the estimation of a company's "intrinsic value," which serves as an investor's most refined gage of the company's true worth. Once the investor arrives at an estimation of the intrinsic value for a given company, she proceeds to contemplate purchasing the company's stocks solely if the prevailing market price of the stocks significantly deviates below the estimated intrinsic value, thus presenting an enticing buying opportunity. This deviation, referred to as the "margin of safety," represents the disparity between the intrinsic value and the current market capitalization of the company. Within the scope of this endeavor, our objective is to automate the stock selection process for value investing across a vast spectrum of US companies. To accomplish this, we harness a combination of value-investing principles and quality features derived from historical financial reports and market capitalization data, thereby enabling the identification of favorable value-driven opportunities. Our methodology entails the utilization of an ensemble of classifiers, where the class is determined as a function of the margin of safety. Consequently, the model is trained to discern stocks that exhibit value characteristics warranting investment. Remarkably, our model attains a success rate surpassing 80%, effectively identifying stocks capable of yielding an annualized return of 15% within a three-year timeframe from the recommended stock purchase date provided by the model.