Evaluation of Machine and Deep Learning Models for Utility Mining-Based Stock Market Price Predictions

Abstract

This paper evaluates the performance of numerous machine and deep learning models in utility mining-based stock marketplace rate predictions. With the stock market being an important component of global economic infrastructure, accurate and efficient prediction of stock expenses has tremendous value for investors aiming to maximise profit whilst minimizing risk. Despite the considerable array of methodologies, inclusive of essential, technical, and quantitative analysis, predicting stock marketplace prices remains difficult due to the risky nature of financial markets. This study delves into the efficacy of various artificial intelligence strategies, that specialize in machine gaining knowledge of (ML) and deep learning (DL) models, to forecast stock charges. Models inclusive of Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and long short-time period memory (LSTM) networks have been analyzed in diverse situations, including the turbulent periods of the COVID-19 pandemic. By comparing these models on the basis of accuracy, error rates, and their capability to handle large records sets and nonlinear relationships, this research aims to identify the most effective techniques for stock market prediction. The paper additionally explores the mixing of technical, essential, and sentiment evaluation to enhance prediction accuracy. The outcomes of this study have significant implications for traders, financial analysts, and policymakers in developing more sophisticated and reliable investment strategies.

Keywords—Stock Market, Predictions, ML, Artificial Intelligence, financial analyst, efficiency.