Quantifying Market Moves: A Comparative Analysis of LSTM and Random Forest in Stock Trend Prediction

Making well-informed decisions in the financial markets requires the capacity to accurately foresee trends. In the context of stock market trend forecasting, this paper explores the comparative analysis of two well-known predictive models: Random Forest and Long Short-Term Memory (LSTM).This paper investigates the predictive performance of two distinct computer programmes, Random Forest and Long Short-Term Memory (LSTM), in identifying patterns in the stock market. We utilize historical data to assess the precision and dependability of these programs. We also look at how well they work in different market situations and their ability to find hidden patterns in the data. The results of our research provide useful information for people who analyze finances, showing how effective these programs are in predicting stock market trends. As financial markets change, our study adds to the ongoing discussion about how artificial intelligence and stock market analysis come together.