Integrating a hybrid Machine Learning approach for stock price prediction and realistic modeling

Abstract

The stock market’s increasing volatility makes predicting accurate trends more challenging. Since the stock market influences the economy, precise predictions help investors maximize profits or minimize losses. This paper proposed a hybrid approach to enhance stock market predictions with high accuracy by integrating multiple models, stock market indicators, stock options, realistic modeling, and news sentiments. It was hypothesized that this approach would yield low error margins and realistically model the stock market while minimizing computational intensity. The model achieved a mean absolute percentage error of 2.93%, demonstrating high prediction accuracy compared to actual prices. Data were sourced from Yahoo Finance, including stock prices, options, indicators, news, and other financial data. Monte Carlo simulations trained, tested, and validated machine learning models. Mathematical modeling techniques were also employed to ensure accurate predictions and disciplined modeling. A paired linear regression test was conducted to analyze prediction accuracy across training and testing datasets. Under a 95% confidence level, the p-value of 0.6047 was greater than the ��-value of 0.05, indicating the hybrid model architecture is a dependable, precise, and efficient alternative to conventional prediction models