Can we profit from BigTechs' time series models in predicting earnings per share? Evidence from Poland

Abstract

Forecasting earnings for publicly traded companies is of paramount significance for investments, which is the background of this research. This holds particularly true in emerging markets where the coverage of these companies by financial analysts' predictions is limited. This research investigation delves into the prediction inaccuracies of cutting-edge time series forecasting algorithms created by major technology companies such as Facebook, LinkedIn, Amazon, and Google. These techniques are employed to analyze earnings per share data for publicly traded Polish companies during the period spanning from the financial crisis to the pandemic shock. My objective was to compare prediction errors of analyzed models, using scientifically defined error measures and a series of statistical tests. The seasonal random walk model demonstrated the lowest error of prediction, which might be attributable to the overfitting of complex models.

Keywords

• earnings per share
• financial forecasting
• machine learning
• deep learning
• prophet by facebook
• silverkite by linkedin
• deepar by amazon
• tft by google
• warsaw stock exchange