Valuation of Stocks by Integrating Discounted Cash Flow With Imitation Learning and Guided Policy

Abstract

The valuation of discounted cash flow (DCF) is one of the most appropriate methods to measure a company’s value, and it is also a fundamental method according to Warren Buffett to value a stock for investment. DCF analysis requires the estimations of the future free cash flow (FCF) and the weighted average cost of capital. However, these factors are often subject to an appraiser’s bias. To obtain a more objective DCF analysis, in this study, we develop a machine learning mechanism: imitation learning, which encompasses a teacher to demonstrate state-action pairs. To facilitate imitation learning, we adopt a guided policy search with the chronological order of a Levy distribution. This distribution can cover the stages of growth, decay, and long fat tail that correspond to a business life cycle. In this way, learning the trajectory of FCF can optimize the fitness between the FCF and the chronological order of a Levy distribution. The fitness achieved allows us to monitor the data by fitting the chronological order for the correct stage. The results show that regarding the Sharpe ratio and precision of DCF valuation, our approach outperforms the popular classification models -random forest and long short-term memory network. Code is available at https://github.com/YuanLongPeng/DCFValuation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Appraisers own knowledge for valuation of stocks to project FCFs and WACC. The knowledge has a broad spectrum, such as prospect of economic growth and competition in the industry. Threat of substitute products. This study proposed a method to automate the process of valuation by learning FCFs and WACC from data. The result of valuation is reproducible and free of the bias of appraisers.