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.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Notes on contributors

Satya Verma

Satya Verma did B.E. in Computer Science and Engineering from Pt. Ravishankar Shukla University, Raipur, India in 2003. She received M. Tech. degree in Computer Technology from National Institute of Technology Raipur, India in 2011. She is Ph.D. Scholar in the Department of Information Technology, National Institute of Technology Raipur, India. She worked as faculty in the various Engineering College and University of Chhattisgarh, India. Her research interest includes Data Mining, Machine Learning, Optimization Techniques, Image Processing, and Time Series Analysis.

Satya Prakash Sahu

Satya Prakash Sahu is an Associate Professor in the Department of Information Technology at National Institute of Technology Raipur, India. He received B.E. and M. Tech. degrees in Computer Science and Engineering from Rajiv Gandhi Technological University, Bhopal, India, and the Ph.D. degree from National Institute of Technology Raipur, India. He has more than 18 years of teaching and research experience. His primary research areas are Artificial Intelligence, Computer Vision, Digital Image Processing, Soft Computing and Medical Imaging.

Tirath Prasad Sahu

Tirath Prasad Sahu is an Assistant Professor in the Department of Information Technology at National Institute of Technology Raipur, India. He received M. Tech. degree in Computer Science and Engineering from Samrat Ashok Technological Institute, Vidisha, in 2012 and Ph.D. in Computer Science and Engineering from National Institute of Technology Raipur, India in 2018. His research interests include Data Mining, Text Analytics, Bio-Informatics, Optimization Techniques and Image Processing.