Using Machine Learning to Forecast Future Earnings

TL;DR

This paper evaluates the effectiveness of machine learning models in forecasting corporate earnings, demonstrating improved accuracy and speed over traditional methods and highlighting their potential as valuable tools for analysts.

Contribution

It introduces a machine learning approach for earnings prediction, showing significant improvements over traditional statistical models and analysts' estimates.

Findings

Machine learning models outperform traditional statistical methods in accuracy.

The proposed model is faster and more reliable for earnings forecasts.

Potential for further improvements in predictive performance.

Abstract

In this essay, we have comprehensively evaluated the feasibility and suitability of adopting the Machine Learning Models on the forecast of corporation fundamentals (i.e. the earnings), where the prediction results of our method have been thoroughly compared with both analysts' consensus estimation and traditional statistical models. As a result, our model has already been proved to be capable of serving as a favorable auxiliary tool for analysts to conduct better predictions on company fundamentals. Compared with previous traditional statistical models being widely adopted in the industry like Logistic Regression, our method has already achieved satisfactory advancement on both the prediction accuracy and speed. Meanwhile, we are also confident enough that there are still vast potentialities for this model to evolve, where we do hope that in the near future, the machine learning model could generate even better performances compared with professional analysts.