A COMPREHENSIVE STUDY MICRO-FINANCIAL ANALYSIS WITH AN EMPHASIS ON ARTIFICIAL INTELLIGENCE (AI), MACHINE LEARNING(ML), AND BIG DATA ANALYTICS IN FINANCIAL MARKETS
Drishti Arora
Goel, Gupta, Maheshwari Associates, Gurugram, Haryana, India
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The prediction of financial time series stands as one of the most crucial aspects in guiding financial decisions. Within this context, the Tehran Stock Exchange holds significant importance for both domestic and international financial arenas. Drawing from past economic events and data, it offers a valuable framework for future profitability. This study delves into the efficacy of various machine learning methods in forecasting time series within financial markets. A prevailing challenge in this domain is the ongoing demand from economic practitioners and the scientific community for more precise forecasting algorithms. Meeting this demand holds the potential to elevate forecasting accuracy, thereby enhancing profitability and efficiency. Through this paper, we not only introduce the most effective features but also demonstrate the potential of leveraging financial time series technical variables present in the Tehran stock market to attain valuable results. As such, this paper offers an analysis and schematic overview of AI, ML, and BDA applications within financial markets.
Keywords:
Artificial Intelligence; Machine Learning; Finance
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