Article: Enhancing stock market predictions using hybrid machine learning approach with XGBOOST-LSTM and XGBOOST-GRU models Journal: International Journal of Applied Systemic Studies (IJASS) 2025 Vol.12 No.1 pp.73 - 87 Abstract: In the ever uncertain and volatile global world, it is a trend to extract valuable insights to inform decisions within investment sector. Predicting the performance of stock indices remain a challenge, necessitating continuous data analysis with integration models. Recent application of hybrid ML-models uncovers it is potentiality to construct and work on such forecasting model. Also, the stock market periodically faces some of the unpredictable events like global financial crisis, COVID-19, Russia-Ukraine war, etc., which have created need to explore the hidden variations in stock index movements. To address this need, we use a machine learning approach that achieves predictive performance comparable to other established methods. We propose and evaluate various ML models, specifically the hybrid XGBOOST-LSTM and XGBOOST-GRU models, on pre- and post-COVID Bank Nifty Index data to identify gaps in our understanding of market movements and provide a more precise stock market forecast. It utilises XGBOOST to select an optimised set of features, which used to train the LSTM and GRU models. We discover models provided insights into index data movements, demonstrating superior accuracy and predictive capabilities of the proposed hybrid models. Inderscience Publishers - linking academia, business and industry through research

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Title: Enhancing stock market predictions using hybrid machine learning approach with XGBOOST-LSTM and XGBOOST-GRU models

Authors: Manoranjan Dash; Christo Aditya Bikram Bepari; Bibhuti Bhusan Pradhan; Preeti Y. Shadangi

Addresses: Faculty of Management Sciences, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India ' Faculty of Management Sciences, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India ' Faculty of Management Sciences, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India ' Faculty of Management Sciences, Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India

Abstract: In the ever uncertain and volatile global world, it is a trend to extract valuable insights to inform decisions within investment sector. Predicting the performance of stock indices remain a challenge, necessitating continuous data analysis with integration models. Recent application of hybrid ML-models uncovers it is potentiality to construct and work on such forecasting model. Also, the stock market periodically faces some of the unpredictable events like global financial crisis, COVID-19, Russia-Ukraine war, etc., which have created need to explore the hidden variations in stock index movements. To address this need, we use a machine learning approach that achieves predictive performance comparable to other established methods. We propose and evaluate various ML models, specifically the hybrid XGBOOST-LSTM and XGBOOST-GRU models, on pre- and post-COVID Bank Nifty Index data to identify gaps in our understanding of market movements and provide a more precise stock market forecast. It utilises XGBOOST to select an optimised set of features, which used to train the LSTM and GRU models. We discover models provided insights into index data movements, demonstrating superior accuracy and predictive capabilities of the proposed hybrid models.

Keywords: deep learning; DL; COVID-19; machine learning; ML; hybrid model; stock market.

DOI: 10.1504/IJASS.2025.144921

International Journal of Applied Systemic Studies, 2025 Vol.12 No.1, pp.73 - 87

Received: 20 Nov 2023
Accepted: 17 May 2024
Published online: 10 Mar 2025 *

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Title: Enhancing stock market predictions using hybrid machine learning approach with XGBOOST-LSTM and XGBOOST-GRU models

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