FollowAKOInvestor: Stock recommendation by hearing voices from all kinds of investors with machine learning

Abstract

As an increasing number of investors share their opinions on social networks, a critical challenge is to provide advice and assistance in making well-informed investment decisions by considering enormous online sentiments. A typical way is to aggregate investors’ sentiments into an investing signal. Most previous studies of sentiment aggregation rely on human-defined rules summarized from experience, which are widely accepted for simplicity and interpretability. However, the relations between investor sentiments and wise investment decisions generated by integrating sentiments could be complex and counter-intuitive, which may not be fully described by human-defined rules and require further investigation. This paper proposes a novel machine learning-based method called FollowAKOInvestor to integrate investors’ sentiments more effectively for highly profitable stock recommendations. It divides investors into groups by clustering algorithm or their expertise levels, then extracts sentiment features from each group to train a machine learning model. The outputs are utilized to score stocks and provide investment recommendations. Based on an extensive dataset spanning from January 2017 to December 2019, sourced from StockTwits, we simulate a one-year recommendation procedure to compare and evaluate the performance of the proposed method and baselines. The experimental results support the superiority of machine learning over human-defined rules in making the most of sentiments from various investors for stock recommendations.