A Novel Machine Learning Approach for Predicting the NIFTY50 Index in India

Pavan Kumar Nagula and Christos Alexakis
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Pavan Kumar Nagula: ESC Rennes School of Business - ESC [Rennes] - ESC Rennes School of Business
Christos Alexakis: ESC Rennes School of Business - ESC [Rennes] - ESC Rennes School of Business

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Abstract: Over the past decade, extensive research on stock market prediction using machine learning models has been conducted. In this framework, different approaches for data standardisation methods have been used for financial time series analysis and to assess the impact of data standardisation on the final prediction outcome. The paper uses the feature-level optimal rolling-window batch data standardisation method to improve the machine learning model's predictive power significantly. Along with the standardisation method, the paper explores the performance of the automated feature interactions learner (Deep Cross Networks) effect on a plethora of technical indicators aiming at predicting the movements of the NIFTY50 index in India, as these predicted changes are reflected in options contracts.

Date: 2022-11
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Published in International Advances in Economic Research, 2022, 28 (3-4), pp.155-170. ⟨10.1007/s11294-022-09861-8⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04020165

DOI: 10.1007/s11294-022-09861-8

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