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Novel Applications of Data Analytics in Financial Markets: A Review and Exploration of Predictive Power
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Author(s): Adithya Shivashankar (Christ University, India), Chirag Lokesh (Christ University, India), Chethan Adhikary (Tata Consultancy Services, India)and Vaishnavi Balaji (Christ University, India)
Copyright: 2025
Pages: 18
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.ch010
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Abstract
The world of stocks is ever-changing and implementing predictive analytics is a cornerstone for making astute investment decisions. This paper delves into the effectiveness of advanced machine learning algorithms, notably Long Short-Term Memory, Artificial Neural Networks, and Linear Regression, in analyzing historical data to possibly project future trends while documenting existing papers and providing a breakdown of the most popular and sought-after machine learning algorithms. The objective is to find intricate, underlying patterns that offer invaluable insights for analysis and gauging risk for analysts and potential investors. By looking into such topics, this paper contributes novel ideas to the ongoing research surrounding utilizing machine learning algorithms in the dynamic world of financial markets. The paper consists of two segments: one provides a detailed review of recent methodologies used for market prediction on a region-wise basis, while the other focuses on using Long Short-Term Memory, Artificial Neural Networks, and Linear Regression.
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