Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms

TL;DR

This study compares four machine learning models to forecast short-term stock prices of NYSE stocks using exogenous variables, highlighting XGBoost's superior accuracy despite longer training times.

Contribution

It evaluates and compares the performance of four ML models for stock prediction using exogenous data, providing insights into their relative accuracies and computational times.

Findings

XGBoost achieved the highest accuracy among the models.

Model performance varied with different evaluation metrics.

Further tuning and additional variables could improve results.

Abstract

Creating accurate predictions in the stock market has always been a significant challenge in finance. With the rise of machine learning as the next level in the forecasting area, this research paper compares four machine learning models and their accuracy in forecasting three well-known stocks traded in the NYSE in the short term from March 2020 to May 2022. We deploy, develop, and tune XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression models. We report the models that produce the highest accuracies from our evaluation metrics: RMSE, MAPE, MTT, and MPE. Using a training data set of 240 trading days, we find that XGBoost gives the highest accuracy despite running longer (up to 10 seconds). Results from this study may improve by further tuning the individual parameters or introducing more exogenous variables.