Iterative Deep Learning Approach to Active Portfolio Management with Sentiment Factors

Abstract

We suggest using deep learning networks to create expert opinions as part of an iterative active portfolio management process. These opinions would be based on posts from the X platform and the fundamentals of stocks listed in the S&P 500 index. Expert views are integral to active portfolio management, as proposed by Black–Litterman. The method we propose addresses the original subjectivity of the opinions by incorporating innovation and accuracy to generate views using analytical techniques. We utilize daily data from 2010 to 2022 for stocks from the S&P 500 and daily posts from Twitter API v2, collected under a research account license spanning the same period. We found that incorporating sentiment factors with machine learning techniques into the view generation process of the Black–Litterman model improves optimal portfolio allocation. Empirically, our results notably outperform the S&P 500 market when considering the annualized alpha.