Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms. (arXiv:2309.00618v1 [q-fin.TR])

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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