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

• URL: http://arxiv.org/abs/2309.00618v1
• Date: Wed, 17 May 2023 07:04:32 GMT
• Title: Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms
• Authors: Albert Wong, Steven Whang, Emilio Sagre, Niha Sachin, Gustavo Dutra, Yew-Wei Lim, Gaetan Hains, Youry Khmelevsky, Frank Zhang
• Abstract summary: This research paper compares four machine learning models and their accuracy in forecasting three well-known stocks traded in the NYSE from March 2020 to May 2022. We deploy, develop, and tune XGBoost, Random Forest, Multi-layer Perceptron, and Support Vector Regression models. Using a training data set of 240 trading days, we find that XGBoost gives the highest accuracy despite running longer.
• Score: 3.2732602885346576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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