Quantifying StockTwits semantic terms' trading behavior in financial markets: An effective application of decision tree algorithms

Growing evidence is suggesting that postings on online stock forums affect stock prices, and alter investment decisions in capital markets, either because the postings contain new information or they might have predictive power to manipulate stock prices. In this paper, we propose a new intelligent trading support system based on sentiment prediction by combining text-mining techniques, feature selection and decision tree algorithms in an effort to analyze and extract semantic terms expressing a particular sentiment (sell, buy or hold) from stock-related micro-blogging messages called “StockTwits”. An attempt has been made to investigate whether the power of the collective sentiments of StockTwits might be predicted and how the changes in these predicted sentiments inform decisions on whether to sell, buy or hold the Dow Jones Industrial Average (DJIA) Index. In this paper, a filter approach of feature selection is first employed to identify the most relevant terms in tweet postings. The decision tree (DT) model is then built to determine the trading decisions of those terms or, more importantly, combinations of terms based on how they interact. Then a trading strategy based on a predetermined investment hypothesis is constructed to evaluate the profitability of the term trading decisions extracted from the DTmodel. The experiment results based on 122-tweet term trading (TTT) strategies achieve a promising performance and the (TTT) strategies dramatically outperform random investment strategies. Our findings also confirm that StockTwits postings contain valuable information and lead trading activities in capital markets. © 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).