Kynigakis, Iason and Panopoulou, Ekaterini (2022) Does Model Complexity add Value to Asset Allocation? Evidence from Machine Learning Forecasting Models. Journal of Applied Econometrics, 37 (3). pp. 603-639. DOI https://doi.org/10.1002/jae.2885
Kynigakis, Iason and Panopoulou, Ekaterini (2022) Does Model Complexity add Value to Asset Allocation? Evidence from Machine Learning Forecasting Models. Journal of Applied Econometrics, 37 (3). pp. 603-639. DOI https://doi.org/10.1002/jae.2885
Kynigakis, Iason and Panopoulou, Ekaterini (2022) Does Model Complexity add Value to Asset Allocation? Evidence from Machine Learning Forecasting Models. Journal of Applied Econometrics, 37 (3). pp. 603-639. DOI https://doi.org/10.1002/jae.2885
Abstract
This study evaluates the benefits of integrating return forecasts from a variety of machine learning and forecast combination methods into an out-of-sample asset allocation framework. The economic evaluation of the forecasts shows that model complexity translates to improved results in the majority of cases considered, with shrinkage methods and shallow neural networks generating the highest individual performance. Overall, an investor would consistently realize superior out-of-sample gains by incorporating forecast combinations of machine learning models in the portfolio formation process.
Item Type: | Article |
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Uncontrolled Keywords: | Forecast Combination, Machine Learning, Portfolio Optimization, Return Predictability |
Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Essex Business School |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 14 Sep 2021 10:06 |
Last Modified: | 16 May 2024 20:53 |
URI: | http://repository.essex.ac.uk/id/eprint/31085 |
Available files
Filename: ml_paper_essex_depository.pdf