Implementation Of An Artificial Intelligence And Machine Learning Powered Financial Decision-Support System For Stock Market Speculators

Abstract

A chaotic, non-stationary, and intricate system, the stock market is always changing. A large number of people find value in this investing platform. The stock market is still a great place to make money, but investors are wary of it because of the inherent uncertainty in the banking sector and a number of other factors.Many several aspects that often pass unnoticed by innocent investors. If you want to succeed in the stock market, you need to be able to focus on the here and now while also giving some thought to how today's events may affect tomorrow. As a result, many investors encounter the possibility of failure every day. The current situation calls for a Decision Support System (DSS) that can determine the optimal time to buy stocks and which stocks to buy based on market trends, financial research, and strategy.To help individual investors make informed stock market purchases and sales, this study proposes the creation of an AI-based decision support system (DSS). To remove the behavioral biases inherent in human decision-making and to provide reasonably accurate long-term stock price predictions, the financial decision-support system must be based on mathematical modeling of the different financial characteristics. In this study, we primarily examined the use of two Machine Learning Models: Linear Regression and Artificial Neural Networks (ANNs). We discovered that ANNs outperform Linear Regression in terms of accuracy because of their capacity to produce non-linear outputs. Additionally, we discovered that ANNs are better suited for deep learning applications, as they can solve complicated financial regression problems using their multiple hidden layers. Neural networks powered by artificial intelligence (AI) have the potential to transform the way people make investing and financial decisions in almost every way.