Improving Financial Forecasting Models Through Advanced Machine Learning Approaches
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Improving Financial Forecasting Models Through Advanced Machine Learning Approaches
AUTHOR 1 : Mrs. P. SWAPNA M. Sc (CS), MBA.
Assistant Professor, And, Head, Department Of Computer Science (P.G)
Keshav Memorial Institute Of Commerce And Sciences
Hyderabad,Telangana,India.
AUTHOR 2 : Dr. MADIREDDI SSV SRIKUMAR
Associate Professor Koneru Lakshmaiah Education Foundation (Klef) Deemed To Be University, Hyderabad,Telangana, India.
Abstract
Financial forecasting plays a crucial role in strategic planning, investment decisions, and risk management. Traditional statistical models such as ARIMA and linear regression have been widely used; however, they often struggle to capture nonlinear patterns and complex dependencies present in financial markets. This study explores the effectiveness of advanced machine learning approaches, including Random Forest, Support Vector Machines, and Artificial Neural Networks, in improving forecasting accuracy. A sample size of 54 financial observations was analyzed to compare traditional and machine learning models using performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared values. The findings indicate that machine learning models significantly outperform traditional models in predictive accuracy and robustness. The study integrates mathematical evaluation techniques to validate performance improvements and highlights the adaptability of machine learning algorithms in volatile market conditions. The results suggest that advanced machine learning techniques can enhance decision-making processes in finance by providing more reliable forecasts. This research contributes to the growing body of knowledge in financial analytics and demonstrates practical implications for financial institutions seeking data-driven forecasting solutions.
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