Enhanced Stock Price Prediction Using Machine Learning and Deep Learning Techniques

The importance of stock price prediction is vital in most financial markets mainly due to its variable effect in risk management concerning investments. This paper aims to predict stock prices with respect to the S&P 500 index mainly using the deep-learning-based models Long Short-Term Memory and Gated Recurrent Unit in conjunction with the Random Forest algorithm. Support Vector Regression, K-Nearest Neighbors, and Hybrid have the features of LSTM + KNN, CNN + Random Forest, and Ensemble, or the machine learning method that combines many models to make more accurate predictions than one single model. The research uses the five-year numerical data of stock for the analysis by means of CFI differential analysis with respect to the controls: EMA, DMA, RSI, Bollinger Bands. Besides, correlation analysis is conducted for the relationship measure among 10 selected stocks indices using heatmap. The studies show that Machine learning algorithms actionable at higher levels would sharpen precision and add value to the decision-making exercise in finance.