Using Machine Learning to Forecast Future Earnings
This work addresses the need for better forecasting tools for financial analysts, but it appears incremental as it builds on existing machine learning approaches without introducing a new paradigm.
The paper tackled the problem of forecasting corporate earnings by evaluating machine learning models, finding that their method achieved satisfactory advancements in prediction accuracy and speed compared to traditional statistical models like Logistic Regression.
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.