Big portfolio selection by graph-based conditional moments method

Zhoufan Zhu, Ningning Zhang and Ke Zhu

Journal of Empirical Finance, 2024, vol. 78, issue C

Abstract: This paper proposes a new graph-based conditional moments (GRACE) method to do portfolio selection based on thousands of stocks or even more. The GRACE method first learns the conditional quantiles and mean of stock returns via a factor-augmented temporal graph convolutional network, which is guided by the set of stock-to-stock relations as well as the set of factor-to-stock relations. Next, the GRACE method learns the conditional variance, skewness, and kurtosis of stock returns from the learned conditional quantiles via the quantiled conditional moment method. Finally, the GRACE method uses the learned conditional mean, variance, skewness, and kurtosis to construct several performance measures, which are criteria to sort the stocks to proceed the portfolio selection in the well-known 10-decile framework. An application to NASDAQ and NYSE stock markets shows that the GRACE method performs much better than its competitors, particularly when the performance measures are comprised of conditional variance, skewness, and kurtosis.

Keywords: Asset pricing knowledge; Big data; Big portfolio selection; Domain knowledge; High-dimensional time series; Machine learning; Quantiled conditional moments (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:empfin:v:78:y:2024:i:c:s0927539824000689

DOI: 10.1016/j.jempfin.2024.101533

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