AI-Driven Financial Planning Using Bayesian Optimization AND Deep Learning

Abstract

Market volatility, nonlinear financial behaviors, and the uncertainty in economic environments have made financial planning and investment forecasting much more difficult. Conventional statistical forecasting models are not usually effective in delivering dynamic and intelligent financial decision support in ever-changing market situations. The present study hypothesizes a financial planning system based on AI and combining Bayesian optimization and deep learning methods to make intelligent investment predictions, optimize portfolios, and make risk-sensitive financial decisions. The suggested system uses the Yahoo Finance Historical Market Dataset stock prices, trading volume, and market indicators data to analyze financial prediction. Normalization and feature engineering of data are used to preprocess the data before Long Short-Term Memory (LSTM) and Deep Neural Network (DNN) models are trained. The Bayesian optimization is utilized to optimize the hyperparameters automatically, e.g., learning rate, batch size, hidden neurons, and dropout rate, to enhance the prediction efficiency and the convergence performance. The experiments have shown that the proposed Bayesian-optimized LSTM model has better forecasting performance with an RMSE of 2.97, MAE of 2.41, prediction accuracy of 96.83%, and Sharpe ratio of 1.38 than traditional forecasting models. The suggested framework is effective in minimizing the forecasting error, increasing the ability to provide investment recommendations, and minimizing the risk of portfolios. The study adds to the smart and versatile financial planning framework applicable to the real-time fintech and wealth management systems.