Forecasting Stock Prices Using Multi-Macroeconomic Regressors Based on the Facebook Prophet Model

Qixuan Huang

doi:10.54691/bcpbm.v25i.1762

Authors

Qixuan Huang

DOI:

https://doi.org/10.54691/bcpbm.v25i.1762

Keywords:

Facebook Prophet model, macroeconomics, predicting stock prices

Abstract

This paper utilizes four Machine Learning (ML) models to forecast the stock prices of Meta Platforms, including Facebook Prophet with five regressors, Facebook Prophet with no regressor, NeuralProphet, and the ARIMA model. Facebook Prophet serves as the primary model for forecasting in this study. Five macroeconomic regressors are applied to the Facebook Prophet to increase the precision of Meta closing price prediction. The experiment also incorporates the NeuralProphet model and the ARIMA model to predict future Meta stock closing prices. NeuralProphet uses Neural Networks to model time-series data. A comparative study of the four models is made as part of the result analysis. No regressors are added to NeuralProphet or ARIMA for the purpose of comparative analysis. The experimental results show that the Facebook Prophet model with five regressors is the superior model for predicting stock prices compared to the NeuralProphet model and the ARIMA model. The forecasting done by Facebook Prophet with multi-regressors scores a mean absolute error of 14.08259, the lowest of the four models. It also scores the lowest for three other measures of errors. The results indicate that using five regressors related to the macroeconomy can achieve high forecasting accuracy.

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