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Robust monitoring machine: a machine learning solution for out-of-sample R $$^2$$ 2 -hacking in return predictability monitoring

Author

Listed:
  • James Yae

    (University of Houston)

  • Yang Luo

    (University of Houston)

Abstract

The out-of-sample $$R^2$$ 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 $$R^2$$ R 2 -hacking problem. Our approach, as a final touch, can further enhance the performance and stability of forecasts from any models and methods.

Suggested Citation

  • James Yae & Yang Luo, 2023. "Robust monitoring machine: a machine learning solution for out-of-sample R $$^2$$ 2 -hacking in return predictability monitoring," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
  • Handle: RePEc:spr:fininn:v:9:y:2023:i:1:d:10.1186_s40854-023-00497-z
    DOI: 10.1186/s40854-023-00497-z
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    1. Malvina Marchese & María Dolores Martínez-Miranda & Jens Perch Nielsen & Michael Scholz, 2024. "Robustifying and simplifying high-dimensional regression with applications to yearly stock return and telematics data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-16, December.

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    More about this item

    Keywords

    Machine learning; Out-of-sample R $$^2$$ 2 -hacking; Return predictability; Monitoring;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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