Multi-Model Approach for Stock Price Prediction and Trading Recommendations

The financial market has been at the forefront of machine learning applications since the 1980s, yet accurate stock price prediction remains a significant challenge due to market complexity and inherent volatility. This paper presents a comprehensive approach to stock market prediction through the integration of Linear Regression (LR), Long Short-Term Memory (LSTM), and Autoregressive Integrated Moving Average (ARIMA) methods. We evaluate these approaches using historical data from five major stocks across different market sectors, demonstrating that traditional time series analysis methods can achieve comparable or superior performance to complex deep learning approaches when properly optimized. To validate our findings, we implement an integrated prediction and trading support system that provides automated data processing and real-time updates, enabling effective decision-making in dynamic market conditions. Our results suggest that the combination of multiple prediction approaches, coupled with automated trading support, can significantly enhance investment decision-making capabilities.