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

Prévision court terme de valeurs boursières par apprentissage automatique et variables exogènes

Albert Wong, Steven Whang, Emilio Sagre, Niha Sachin, Gustavo Dutra, Yew-Wei Lim, Gaétan Hains (gaetan.hains@proton.me), Youry Khmelevsky and Frank Zhang
Additional contact information
Albert Wong: Langara College
Steven Whang: Langara College
Emilio Sagre: Langara College
Niha Sachin: Langara College
Gustavo Dutra: Langara College
Yew-Wei Lim: Langara College
Gaétan Hains: UPEC FST - Université Paris-Est Créteil Val-de-Marne - Faculté des sciences et technologie - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12
Youry Khmelevsky: Okanagan College - University of Brithish Columbia
Frank Zhang: UFV - University of the Fraser Valley

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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.

Keywords: Stock Price Predictions Exogenous variables Support Vector Regression Multilayer Perceptron Random Forest XGBoost Machine Learning Algorithmic Trading; Stock Price Predictions; Exogenous variables; Support Vector Regression; Multilayer Perceptron; Random Forest; XGBoost; Machine Learning; Algorithmic Trading (search for similar items in EconPapers)
Date: 2023-09-08
Note: View the original document on HAL open archive server: https://hal.u-pec.fr/hal-04201060v1
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