Stock Price Forecasting Based on Wavelet Filtering and Ensembled Machine Learning Model

Abstract

Financial data are not only characterized by time-domain correlations but also heavily influenced by numerous market factors. In stock price analysis, the prediction of short-term movements is of much interest to investors and traders. In this paper, we consider forecasting price movements based on ensembled machine learning models, which is generally viewed as a challenging task due to noise components inherent in the data and uncertainties in various forms of financial information related to stock prices. To enhance the accuracy of trend predictions, we propose to use wavelet packet decomposition (WPD) and kernel-based smoothing techniques to remove high-frequency noise from the data, based on which we further perform feature engineering to obtain a comprehensive list of multidimensional technical features. Subsequently, we employ the light gradient boosting machine (lightGBM) algorithm to classify the change in the direction of the price trend that occurs in ten trading days. Numerical results on the Shanghai composite index show that the proposed approach has noticeable advantages over traditional statistical and machine learning methods when predicting near term price trends. Index terms - ensembled machine learning, feature correlation, financial data, LGBM, and wavelet denoising.