Comparison of Different Machine Learning Approaches for Forecasting Stock Prices

Abstract

Predicting stock prices is a crucial task that has significant implications for investment decisions, business strategies, and financial market stability. Accurate predictions can help investors make informed decisions, capture opportunities, and minimize risks. Understanding financial markets, economic data, company-specific elements, and a variety of statistical and analytical approaches are all necessary for making accurate stock price predictions. This paper employs three machine learning methods to forecast stock price data from a three-year time series dataset. Stock prediction plays a fundamental role in investment decisions, and accurate predictions are crucial. While various methods for predicting stock trends exist, different stock datasets may require different prediction models. Using three years of stock data, including gold and bitcoin, from the 2022 MCM Problem C, this study applies Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) to make predictions. Bayesian hyperparameter optimization is employed, and a comparative analysis is conducted. Ultimately, the results indicate that CNN outperforms the other methods, exhibiting relatively low error metrics and a high R-squared value, with no apparent signs of overfitting.