Numerical Simulation and Design of Stock Market Forecasting Using Neural Networks and Machine Learning Method

Abstract

The modern stock market is a complex and dynamic system, influenced by a multitude of factors such as economic indicators, market sentiment, and geopolitical events. Predicting its behavior is notoriously difficult but immensely valuable for various stakeholders, including individual investors, financial analysts, and policymakers. This study aims to develop a robust and accurate stock market forecasting model by leveraging the capabilities of neural networks and machine learning algorithms. The paper explores various machine learning techniques, including feedforward neural networks, recurrent neural networks (RNNs), and long short-term memory networks (LSTMs), and compares their performance against traditional timeseries models like ARIMA. Real-world historical data is used to train and validate the models. The study also involves feature engineering to incorporate external variables like trading volume, interest rates, and social media sentiment. Numerical simulations are conducted to evaluate the efficacy of each model in terms of predictive accuracy, robustness, and computational efficiency. The results indicate that machine learning-based models, particularly LSTMs, outperform traditional models in forecasting stock market indices with a higher degree of accuracy. Sensitivity analyses further reveal that the performance of the machine learning models is significantly improved by tuning hyperparameters and including external feature variables The study opens up new avenues for employing advanced machine learning techniques in financial market analytics and offers practical guidelines for developing more reliable and efficient stock market prediction systems