Time and date: 14 February 2024 at 2:00 pm | Location: Abacws 1.04 | Speaker: James Lewis-Cheetham
Successfully forecasting stock returns has a clear benefit for investors. However, there is significant debate about the effectiveness of signals commonly used for forecasting, and conventional economic theory suggests that opportunities to profit from forecasting are rare. Despite these considerations, publications have shown evidence that machine learning models perform well at this task. My current research aims to address this discrepancy. I am assessing a recently published novel machine-learning forecasting model to determine if the results hold up under scrutiny. This talk introduces the financial background of my research before diving into the methodology of assessing the predictive power and profitability of the forecasting model under investigation.
