Portfolio Selection Under Non-Gaussianity And Systemic Risk: A Machine Learning Based Forecasting Approach

Weidong Lin and Abderrahim Taamouti

No 202310, Working Papers from University of Liverpool, Department of Economics

Abstract: The Sharpe-ratio-maximizing portfolio becomes questionable under non-Gaussian returns, and it rules out, by construction, systemic risk, which can negatively a§ect its out-of-sample performance. In the present work, we develop a new performance ratio that simultaneously addresses these two problems when building optimal portfolios. To robustify the portfolio optimization and better represent extreme market scenarios, we simulate a large number of returns via a Monte Carlo method. This is done by Örst obtaining probabilistic return forecasts through a distributional machine learning approach in a big data setting, and then combining them with a Ötted copula to generate return scenarios. Based on a large-scale comparative analysis conducted on the US market, the backtesting results demonstrate the superiority of our proposed portfolio selection approach against several popular benchmark strategies in terms of both proÖtability and minimizing systemic risk. This outperformance is robust to the inclusion of transaction costs.

Keywords: Portfolio optimization; probability forecasting; quantile regression neural network; extreme scenarios; big data. (search for similar items in EconPapers)
Pages: 55 pages
Date: 2023-08
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
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Forthcoming

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https://www.liverpool.ac.uk/media/livacuk/schoolof ... N,WP,202310,full.pdf First version, 2023 (application/pdf)

Related works:
Journal Article: Portfolio selection under non-gaussianity and systemic risk: A machine learning based forecasting approach (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:liv:livedp:202310

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