Machine Learning in the Stock Market

Abstract

As artificial intelligence becomes more prominent in the state of technology, one substantial aspect of its use is in finance - specifically the stock market. Considering four distinct models: linear regression, neural networks, decision trees, and random forests, this research paper has the primary objective of investigating the efficacies of each model and using that knowledge to accurately predict stock market prices for the next day. This is done by conducting a comparative analysis of the percent errors of each model to determine their accuracy; by establishing a simulation framework leveraging past stock market data and splitting it into subsets for testing and training to recursively evaluate each model, the weights of the models will change based on trends and data to predict the price of the next day. Using this model, a feasible stock market predictor is generated - highlighting the potential of artificial intelligence to guide investment strategies and shape the future of finance. This potential is best displayed through the results of our simulation - making over $15,105 by the end. Accentuating the advantages of various stock market approaches while underscoring the limitations, this research provides valuable insights into the advancement of predictive tools in financial markets - offering a broad overview of how machine learning in stock markets will be implemented and allowing investors to navigate the complexities of AI-stock market dynamics in a high-level, abstracted manner.