Equity Research Report-Driven Investment Strategy in Korea Using Binary Classification on Stock Price Direction

Abstract

This research examines and proposes an investment strategy by combining the natural language processing on the equity research reports published in the Korean financial market and machine learning algorithms for binary classification. At first, we deduce the part-of-speech from the report using the KoNLPy and Mecab. Then, we define 33 features as the input variables and perform the binary classification on the price direction of the stocks recommended in the report using various machine learning algorithms. Note that we investigate the model performance in detail by dividing the entire period into three sub-periods, including pre-COVID-19 for the sideways market, COVID-19 for the crashing market, and post-COVID-19 for the extreme bullish market. We confirm that the random forest is the best classifier for all periods, so we utilize its results on positively predicted stocks in the test set as the investment universe for the monthly re-balancing and buy-and-hold investment. The proposed strategy shows a significantly higher return on investment than benchmarks during the pre-COVID-19 and COVID-19 periods, whereas the comparable return during the post-COVID-19.