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Diving Into the Performance of Supervised Learning Models for Forecasting the Indian Stock Market: A Case Study
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Author(s): Rudra Kalyan Nayak (VIT Bhopal University, India), Nilamadhab Mishra (VIT Bhopal University, India), Manan Sodha (VIT Bhopal University, India), Santosh Kumar Tripathy (National Institute of Technology, Patna, India), Ramamani Tripathy (Chitkara University, India), Dhawaleswar Rao CH (Centurion University of Technology and Management, India)and Mohammad Gouse Galety (Samarkand International University of Technology, Uzbekistan)
Copyright: 2025
Pages: 26
Source title:
Data Analytics and AI for Quantitative Risk Assessment and Financial Computation
Source Author(s)/Editor(s): Mohammad Gouse Galety (Samarkand International University of Technology, Uzbekistan), Jimbo Henri Claver (Samarkand Interntional University of Technology, Uzbekistan), A. V. Sriharsha (Mohan Babu University, India), Narasimha Rao Vajjhala (University of New York Tirana, Tirana, Albania)and Arul Kumar Natarajan (Samarkand International University of Technology, Uzbekistan)
DOI: 10.4018/979-8-3693-6215-0.ch009
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Abstract
Stock markets are lucrative for individuals and institutions to invest their money. Specific risks are involved when entering any stock market trade; to avoid this, different methods can be adopted, such as fundamental and technical analysis. This paper talks about the machine-learning approach to the stated problem. However, even this method does not guarantee only profits, and there are certain conditions where the predictions made by the algorithms can be highly inaccurate. This research discusses the efficacy of different models during different time frames and the effects of a highly volatile market can affect the prediction abilities of the models. For the study, twelve datasets were collected, transformed, and employed. This study includes comparisons of 6 regression models, compared based on errors and coefficient of determination. At the end of the study, linear regression was found to be a better-performing and adaptable algorithm than the other models.
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