Enhancing Predictive Accuracy and Efficiency in Financial Forecasting Using Artificial Neural Networks

Enhancing Predictive Accuracy and Efficiency in Financial Forecasting Using Artificial Neural Networks

Ankpal Mahipal Dalal
Department of Computer Science, Pratibha College of Commerce & Computer Studies, Pune

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Abstract -

This paper presents a theoretical examination of Artificial Neural Networks (ANNs) in the context of financial forecasting, particularly emphasizing their comparative advantages over traditional models like ARIMA. It explores how ANNs address the challenges posed by non-linear and volatile financial data through their ability to learn from complex patterns. The research investigates multiple ANN architectures including Feedforward Neural Networks, Convolutional Neural Networks, and hybrid CNN-LSTM models. Based on secondary data and academic literature, the study concludes that hybrid models, especially CNN-LSTM, demonstrate superior theoretical accuracy and efficiency in financial forecasting tasks. The paper also discusses the implications of ANN-based approaches for real-time forecasting and highlights potential research directions involving hybrid methodologies, model interpretability, and data preprocessing.

Key Words: Financial forecasting, Artificial Neural Networks, ARIMA, CNN, LSTM, Hybrid models.