International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Artificial Intelligence in Algorithmic Trading: Enhancing Efficiency, Profitability, and Market Quality

Author(s) Mr. Chetan C Benagi, Ms. Usha J. C
Country India
Abstract This article discusses the use of Artificial Intelligence (AI) in algorithmic trading and how it can be applied to improve efficiency, profitability, and quality of the market in the Indian equity market. Based on a sample of 47 firms and ten industries, the paper compares the predictive ability of the Long Short-Term Memory (LSTM), Regression and Moving Average models. The statistical validation is performed with the help of the p-values aggregation method by Fisher and volatility analysis with GARCH. Findings reveal that LSTM is always better than the conventional models in terms of predictive accuracy and profitability, whereas GARCH can give important information about the volatility clustering and stability of the base. The analysis of the sector wise shows that calendar anomalies are still present in IT, Banking, Telecom as well as Automotive sectors and they do not subscribe to Efficient Market Hypothesis (EMH). The results indicate that AI-based trading is not only more efficient in the results of individual traders but also leads to the overall resilience of a market. The paper ends with recommendations to traders, regulators and policymakers, and provides the future research directions.
Keywords AI, Algorithmic Trading, LSTM, GARCH, Time-Series Forecasting, NSE, Stock Market, Risk-Adjusted Returns, Volatility, Market Efficiency, India
Field Business Administration
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-31
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.57864
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E-ISSN 2582-2160
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