Wavelet decomposition-based multi-stage feature engineering and optimized ensemble classifier for stock market prediction

Abstract

Expanded feature engineering to handle complex and noisy stock data is not much explored in financial forecasting. Along with feature engineering, hyperparameter tuning is also important. This paper provides a solution to handle these issues by elaborating on Discrete Wavelet Transform (DWT)-based feature engineering and hyperparameter-tuned ensemble model. The Multi-Stage Feature Engineering (MSFE) is proposed in which DWT-based decomposition is utilized to handle the noise. DWT decomposition expands the features; therefore, two-stage feature reduction is proposed in which first the filter method is used, and then the probabilistic method is utilized. Next, hyperparameter tuning of the ensemble model is offered through Particle Swarm Optimization (PSO). The proposed model is named as Wavelet-Particle Swarm Optimization (WPSO). WPSO is tested and evaluated on the three stock indices (NIFTY, NASDAQ, and NYSE), and provided 92.51%, 94.18%, and 87.62% accuracy for NIFTY, NASDAQ, and NYSE, respectively. The WPSO is validated by comparing it with state-of-the-art methods. The performance of the WPSO is statistically analyzed through the Bonferroni–Dunn post hoc test where WPSO positioned at rank 1 for all the evaluation metric and datasets. The WPSO empirically verifies that improving feature quality through MSFE and hyperparameter tuning of ensemble model significantly improves the predictive outcomes.