Using Neural Network for Financial Forecasting

The domain of financial forecasting has witnessed a paradigm shift with the advent of neural network models. To understand the source of wealth or their whereabouts at a particular time, financial study and its prediction has become a major method that cannot be ignored in national economic development. The paper presents a comprehensive analysis of the applications of neural network approaches in financial forecasting. It scrutinizes the effectiveness of the models in capturing the complex patterns and dependencies which is inherited in financial data, which enables accurate prediction of market trends, stock prices, exchange rates, and other critical financial parameters. Furthermore, the paper studies various methodologies and techniques employed for data preprocessing, feature engineering, and model optimization specifically tailored for financial forecasting tasks It investigates the challenges and limitations encountered in utilizing neural networks for financial predictions, addressing issues related to model interpretability, overfitting, and data quality. We have used LSTM, RNN, which is a suitable model for time series data and CNN which can be used to extract features from time series data and have obtained the results.