Analyzing the performance of machine learning in predicting stock market trends

Abstract

Stock prediction has been a significant area of interest for investors, traders, and researchers. Exact stock price foretelling can lead to informed investment decisions, risk management, and potential financial gains. Machine learning techniques have gained eminence in current years as powerful tools for stock prediction due to their ability to process massive quantities of data and recognize composite patterns. This research paper presents a comprehensive study on prediction of stock price by Long Short-Term Memory (LSTM) neural networks applied to a decade's worth of historical data (2013-2023) of various companies Tesla, Netflix, Meta, Amazon, and Apple. The purpose of study is to analyze the performance of machine learning LSTM model in contrast to CNN (Convolutional Neural Networks), RNN (Recurrent Neural Network) and Random Forest models in capturing complex temporal patterns within stock price data, facilitating more accurate predictions for investment decisions and risk management. The findings of this study hold practical significance for investors, traders, and researchers, offering a basis for making well-informed investment decisions and improving risk management strategies within the dynamic landscape of stock markets.