Stock Price Prediction Incorporating Market Style Clustering

Abstract

Market style analysis is critical when designing a stock price prediction framework. Under different market styles, stocks may show quite different behaviors; thus, predictions will vary. Consequently, incorporating market styles into stock price predictions should help improve the prediction performance. In this paper, we investigate how to characterize market styles to improve stock prediction performance under varying market styles. First, stock time series data are divided into windows of different lengths. The windows are summarized and represented by technical indicators and news sentiment features. Second, hierarchical clustering is employed to cluster the windows and categorize their market styles; the window lengths and number of market styles are carefully tuned to achieve the best clustering results. Third, a distance measurement is proposed to distinguish among rotating patterns within the market styles to verify the usability of the market styles. Finally, a stock price prediction framework is constructed to predict future stock price trends based on data belonging to the same market styles. The experiments are conducted with five years of real Hong Kong Stock Exchange data that includes both stock prices and corresponding news. Two famous sentiment dictionaries (i.e., SenticNet 5 and the Loughran-McDonald financial sentiment dictionary 2018) are employed to analyze the news sentiments. Predictive models are compared both with and without incorporating market styles. The results demonstrate that the approach incorporating market styles outperforms the baseline, which does not incorporate market styles. There is a maximum 9 percent improvement in terms of accuracy and F1-score. Moreover, backtesting results show that incorporating market styles into trading signals earns trading strategies more profits on most stocks.