Nonlinear modeling for equity valuation by machine learning

Abstract

The purpose of this study is to separate the intrinsic equity value and the mispricing caused by irrational investor sentiments from the actual stock price. We applied machine learning approach to improve traditional valuation models like the DDM, the RIM, and the OVM and to detect nonlinear and nonstationary dynamics hidden in financial growth of corporate companies. However, because the intrinsic equity value is completely unobservable, we confirmed the validity of our approach by stock portfolio simulations based on the value and growth strategy from the viewpoint of market efficiency.