Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms
This is an incremental study that addresses the challenge of stock market prediction for finance practitioners by applying existing methods to new data.
This paper tackled short-term stock price forecasting by comparing four machine learning models (XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression) on three NYSE stocks from March 2020 to May 2022, finding that XGBoost achieved the highest accuracy based on metrics like RMSE and MAPE, though it took up to 10 seconds to run.
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.