Discrete Wavelet Transform-Based Prediction of Stock Index: A Study on National Stock Exchange Fifty Index

TL;DR

This paper introduces a hybrid prediction method combining wavelet decomposition and machine learning to forecast stock index movements more accurately, demonstrating improved investment returns over traditional strategies.

Contribution

It presents a novel hybrid approach integrating MODWT with ANN and SVR for stock index prediction, outperforming standalone models in accuracy and investment return.

Findings

MODWT-SVR outperforms traditional models in prediction accuracy.

Predicted values lead to higher returns than buy-and-hold strategy.

Hybrid approach effectively captures non-linear, non-stationary stock data.

Abstract

Financial Times Series such as stock price and exchange rates are, often, non-linear and non-stationary. Use of decomposition models has been found to improve the accuracy of predictive models. The paper proposes a hybrid approach integrating the advantages of both decomposition model (namely, Maximal Overlap Discrete Wavelet Transform (MODWT)) and machine learning models (ANN and SVR) to predict the National Stock Exchange Fifty Index. In first phase, the data is decomposed into a smaller number of subseries using MODWT. In next phase, each subseries is predicted using machine learning models (i.e., ANN and SVR). The predicted subseries are aggregated to obtain the final forecasts. In final stage, the effectiveness of the proposed approach is evaluated using error measures and statistical test. The proposed methods (MODWT-ANN and MODWT-SVR) are compared with ANN and SVR models and, it was observed that the return on investment obtained based on trading rules using predicted values of MODWT-SVR model was higher than that of Buy-and-hold strategy.