Machine Learning Based Portfolio Selection Under Systemic Risk

Weidong Lin and Abderrahim Taamouti

No 202311, Working Papers from University of Liverpool, Department of Economics

Abstract: This paper aims to enhance the classical mean-variance portfolio selection by using machine learning techniques and accounting for systemic risk. The optimal portfolio is solved through a three-step supervised learning model. Firstly, the Smooth Pinball Neural Network is employed to predict return distributions of individual assets and the market. Secondly, we use copula to model dependence between assets and the market, based on which we simulate return scenarios. Lastly, we maximize an ex-ante conditional Sharpe ratio conditioning on systemic events. We run a large-scale comparative study using nearly 600 US individual stocks over 37 years. Our set of predictors includes 94 firm characteristics, 14 macroeconomic variables, and 74 industry dummies. The backtesting results demonstrate the superiority of our proposed approach over popular benchmark strategies including a GARCH-based model. This outperformance is statistically significant and robust to the inclusion of transaction costs.

Keywords: portfolio optimization; systemic risk; neural network model; scenario analysis; forecasting (search for similar items in EconPapers)
JEL-codes: C45 C53 C58 G11 (search for similar items in EconPapers)
Pages: 54 pages
Date: 2023
New Economics Papers: this item is included in nep-big
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Forthcoming

Downloads: (external link)
https://www.liverpool.ac.uk/media/livacuk/schoolof ... m-near-certainty.pdf First version, 2023 (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:liv:livedp:202311

Access Statistics for this paper

More papers in Working Papers from University of Liverpool, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Rachel Slater ().