We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Robust monitoring machine: a machine learning solution for out-of-sample R2-hacking in return predictability monitoring.
- Authors
Yae, James; Luo, Yang
- Abstract
The out-of-sample R 2 is designed to measure forecasting performance without look-ahead bias. However, researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample. Using ensemble machine learning techniques, we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations. We apply this approach to robust monitoring, exploiting a dynamic shrinkage effect by switching between a proposed forecast and a benchmark. Considering stock return forecasting as an example, we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15% (in terms of mean-squared-error) and reduces the variance of its relative performance by 46% while avoiding the out-of-sample R 2 -hacking problem. Our approach, as a final touch, can further enhance the performance and stability of forecasts from any models and methods.
- Subjects
MACHINE learning; RATE of return on stocks; VIRTUAL reality; PREDICTION models; INTUITION
- Publication
Financial Innovation, 2023, Vol 9, Issue 1, p1
- ISSN
2199-4730
- Publication type
Article
- DOI
10.1186/s40854-023-00497-z