Enhancing Stock Price Forecasting: Integrating Supply Chain Factors into LSTM Models and Comparative Performance Analysis

Stock price forecasting has always been a challenging task due to its high volatility and complexity. Recently, various machine learning models have been employed to improve the accuracy of stock price predictions. This research aims to enhance stock price forecasting by integrating supply chain factors into LSTM models and conducting a comparative performance analysis with other models such as ANN, RNN, and GRU. A comprehensive literature review was conducted to understand the history of machine learning in finance, the role of different models in stock market prediction, the impact of varied factors in stock market prediction, and the challenges and limitations of artificial intelligence in stock forecasting. The methodology involved problem analysis, defining the research purpose, and project design which includes data collection, model building and training, and performance evaluation and validation. The models were trained and assessed on a dataset that incorporated supply chain factors. The experimental results showed that the GRU model outperformed the other models in terms of R2 score, MAE, and MSE. The study contributes to the existing body of knowledge by providing empirical evidence on the importance of incorporating supply chain factors into predictive models and by comparing the performance of different models. The findings have practical implications for investors, analysts, and policymakers who rely on accurate stock price predictions for decision-making.