The Predictive Power of Macroeconomic Variables on the Indian Stock Market Utilizing an Ann Model Approach: An Empirical Investigation Based on BSE Sensex
, , oraz | 09 gru 2023
Data publikacji: 09 gru 2023
Zakres stron: 116 - 131
Otrzymano: 05 mar 2023
Przyjęty: 25 wrz 2023
DOI: https://doi.org/10.2478/foli-2023-0022
Słowa kluczowe
© 2023 Himanshu Goel et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.
Research background
The paper focuses on the use of Artificial Neural Networks (ANNs) for forecasting time series data of the stock market since ANNs are dynamic and are more capable of handling complex data sets in comparison to conventional forecasting techniques such as regression, Logistic regression, and have massive potential for the prediction of stock market prices.
Purpose
Artificial neural networks are an effective method for forecasting time series. Therefore, this study aims to forecast the closing price of the BSE Sensex using artificial neural networks (ANNs).
Research methodology
The study uses nine input variables, including macroeconomic and global stock market factors, to estimate the BSE Sensex using scaled conjugate gradient algorithm artificial neural networks (SCGANNs) and Bayesian regularized artificial neural networks (BRANN).
Results
As per the empirical results of the study, the ANN model can forecast the closing values of the BSE Sensex with a Bayesian Regularization (BR) method with an accuracy of over 99 percent, thus leading to significant implications for domestic institutional investors (DIIs), foreign institutional investors (FIIs), investment houses, and so on. This study adds more value to the existing literature by proving that the BRANN models outperform SCGANN in stock market forecasting.
Novelty
This is the first study to employ macroeconomic variables as input variables for predicting the Indian stock market using ANN. The study highlights the ANN model’s forecasting potential, giving investors robust and accurate stock value prediction capabilities.