Optimizing Business Decision-Making with AI-Powered Predictive Analytics in Financial Markets

Tanishq Rathore

Optimizing Business Decision-Making with AI-Powered Predictive Analytics in Financial Markets

Author(s)	Tanishq Rathore
Country	India
Abstract	Financial markets are inherently complex systems, shaped by dynamic interactions between macroeconomic indicators, geopolitical developments, and investor sentiment. These factors introduce significant volatility, rendering traditional statistical forecasting models such as ARIMA and GARCH increasingly inadequate. Their reliance on linear assumptions limits their adaptability in capturing the non-linear and abrupt shifts that frequently occur in modern financial environments. In contrast, this study investigates the potential of AI-driven predictive models— namely Long Short-Term Memory (LSTM) networks, Random Forest, and XGBoost—to enhance forecasting accuracy and support strategic decision-making in the financial domain. Utilizing a diverse, real-world dataset spanning from 2010 to 2024, which includes stock indices (e.g., S&P 500, NASDAQ), commodity prices (e.g., oil and gold), and macroeconomic variables (e.g., GDP, inflation, unemployment), the models were assessed using key performance metrics such as Mean Squared Error (MSE), directional accuracy, and the Sharpe Ratio for risk-adjusted return. Among the models, XGBoost demonstrated the highest predictive accuracy (MSE: 0.0095; Accuracy: 75.9%), followed closely by LSTM, which proved effective in volatile market conditions. The primary contribution of this research lies in its application of advanced machine learning models to a robust, multi-source dataset that spans both stable and crisis periods—including the COVID-19 pandemic and recent inflation shocks—thereby offering empirically grounded insights for improving financial forecasting, investment strategy, and risk management. This study further emphasizes the practical value of AI in navigating uncertainty, enabling data-driven decisions with increased resilience in real-world financial contexts.
Keywords	Artificial Intelligence, Predictive Analytics, Financial Forecasting, LSTM, Random Forest, XGBoost, Risk Management, Stock Market Prediction, Time Series Analysis, Business Decision-Making
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 5, Issue 1, January-February 2024
Published On	2024-01-18

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Optimizing Business Decision-Making with AI-Powered Predictive Analytics in Financial Markets

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