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Does Model Complexity add Value to Asset Allocation? Evidence from Machine Learning Forecasting Models

Kynigakis, Iason and Panopoulou, Ekaterini (2021) 'Does Model Complexity add Value to Asset Allocation? Evidence from Machine Learning Forecasting Models.' Journal of Applied Econometrics. ISSN 0883-7252 (In Press)

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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
Uncontrolled Keywords: Forecast Combination, Machine Learning, Portfolio Optimization, Return Predictability
Divisions: Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Elements
Date Deposited: 14 Sep 2021 10:06
Last Modified: 14 Sep 2021 10:06
URI: http://repository.essex.ac.uk/id/eprint/31085

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