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This study aims to evaluate the effectiveness of machine learning in enhancing financial forecasting accuracy.

May 15, 20260 citationsOpen Access

Machine Learning for Financial Forecasting: A Multidimensional Study

Key Points

This study aims to evaluate the effectiveness of machine learning in enhancing financial forecasting accuracy.
Application of machine learning techniques to forecast financial trends, such as stock price movements.
Investigation of advanced models, including recurrent neural networks, to analyze historical data.
Exploration of challenges related to data quality and interpretability in financial forecasting.
Machine learning models provide more precise and timely financial predictions, improving decision-making.
Recurrent neural networks outperform traditional methods in capturing complex market patterns.
Identification of challenges, including ethical implications and data quality issues, that affect machine learning applications.

Abstract

Machine Learning for Financial Forecasting: A Multidimensional Study Dr. Shrikant Chandrakant Dudhal Asst Prof. and Research Guide, Dept of Accountancy, D. S. Garad Mahavidyalaya, Mohol, Solapur Abstract Financial forecasting plays a critical role in modern finance and investment decision-making. Traditional approaches, such as time-series analysis and regression models, often struggle to capture the complexity and volatility of contemporary financial systems driven by dynamic market conditions and large-scale digital data. In this context, machine learning has emerged as a powerful tool for enhancing the accuracy and efficiency of financial predictions. This study explores the application of artificial intelligence, particularly machine learning techniques, in forecasting financial trends such as stock price movements, economic indicators, and cross-border transactions. Advanced models, including recurrent neural networks, demonstrate superior capability in learning from historical data and identifying complex patterns over time. These models enable more precise and timely predictions, supporting informed decision-making for businesses, investors, and policymakers. However, despite its advantages, machine learning also presents challenges, including data quality issues, interpretability concerns, and ethical implications. The study highlights both the potential and limitations of machine learning in financial forecasting while emphasizing its growing significance in the evolving financial landscape. Keywords: Machine Learning, Financial Forecasting, Artificial Intelligence, Stock Price Prediction, Time-Series Analysis, Recurrent Neural Networks, Predictive Analytics

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