Neural network copula portfolio optimization for exchange traded funds

Zhao, Y., Stasinakis, C. , Sermpinis, G. and Shi, Y. (2018) Neural network copula portfolio optimization for exchange traded funds. Quantitative Finance, 18(5), pp. 761-775. (doi: 10.1080/14697688.2017.1414505)

Abstract

This paper attempts to investigate if adopting accurate forecasts from Neural Network (NN) models can lead to statistical and economically significant benefits in portfolio management decisions. In order to achieve that, three NNs, namely the Multi-Layer Perceptron, Recurrent Neural Network and the Psi Sigma Network (PSN), are applied to the task of forecasting the daily returns of three Exchange Traded Funds (ETFs). The statistical and trading performance of the NNs is benchmarked with the traditional Autoregressive Moving Average models. Next, a novel dynamic asymmetric copula model (NNC) is introduced in order to capture the dependence structure across ETF returns. Based on the above, weekly re-balanced portfolios are obtained and compared using the traditional mean–variance and the mean–CVaR portfolio optimization approach. In terms of the results, PSN outperforms all models in statistical and trading terms. Additionally, the asymmetric skewed t copula statistically outperforms symmetric copulas when it comes to modelling ETF returns dependence. The proposed NNC model leads to significant improvements in the portfolio optimization process, while forecasting covariance accounting for asymmetric dependence between the ETFs also improves the performance of obtained portfolios.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Shi, Dr Yukun and Stasinakis, Professor Charalampos and Sermpinis, Professor Georgios
• Authors: Zhao, Y., Stasinakis, C., Sermpinis, G., and Shi, Y.
• College/School: College of Social Sciences > Adam Smith Business School > Accounting and Finance; College of Social Sciences > Adam Smith Business School > Economics
• Journal Name: Quantitative Finance
• Publisher: Taylor & Francis
• ISSN: 1469-7688
• ISSN (Online): 1469-7696
• Published Online: 23 January 2018
• Copyright Holders: Copyright © 2018 Informa UK Limited
• First Published: First published in Quantitative Finance 18(5):761-775
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher