多层时序网络视角下的最优投资组合策略研究

doi:10.16381/j.cnki.issn1003-207x.2023.0851

Abstract

Abstract:

Stocks of varying quality have put forward new requirements for investors’ investment research capabilities， especially for Chinese investors who are mainly natural persons （retail investors）. Since retail investors have weak risk tolerance and high degree of loss aversion， facing the increasing asset scale， how to obtain higher returns with the lowest possible risk is undoubtedly the issue that investors are most concerned about. The stock market is studied from the linear and nonlinear correlation and dynamic evolution characteristics between stock assets. First， it describes the stock market with multilayer temporal network， and designs network risk measurement indicators based on the eigenvectors centrality measure. Then Combining it with optimization theory， the Global Minimum Network-Risk portfolio model is proposed to simulate dynamic investment process based on the data of HS300 constituent stocks from 2010 to 2022， and then the portfolio model with a variety of evaluation indicators is evaluated.Experimental results are as follows （i）The multilayer temporal network can comprehensively ascertain the correlation structure and evolution characteristics of stock assets， accurately describe the structure of complex financial systems， and identify high-quality investment assets；（ii）The peripheral portfolio model can obtain better investment performance and cumulative return rate， and the return is not offset by systematic risk factor exposure；（iii）The peripheral Global Minimum Network-Risk portfolio model has the best investment performance during the out-of-sample period， and it still maintains strong robustness in stock market fluctuations， which is suitable for investors with low risk tolerance. Theoretical and practical references are provided for investors with different risk preferences especially for retail investors with low risk tolerance， the traditional mean-variance portfolio theory is expanded and improved based on multilayer temporal network， and further research in this field is enriched.

Key words: multilayer temporal network, stock market, portfolio optimization

CLC Number:

F832.5

Chao Liu, Lantao Xu. Study on Optimal Portfolio Strategy from the Perspective of Multilayer Temporal Network[J]. Chinese Journal of Management Science, 2025, 33(9): 46-56.

Figures/Tables 11

$η$	投资者态度
$η = 0.25$	较厌恶CVaR
$η = 0.5$	同等厌恶CVaR和网络风险
$η = 0.75$	较厌恶网络风险
$η = 1$	只厌恶网络风险

股票数	投资组合		SR	OR	Beta	Alpha
n=5	HS300		0.179	1.534	1.000
	(1/n)_C		-0.765	0.720	1.438	-0.186
	(1/n)_P		0.437	3.239	3.024	0.354
	GMC_C		0.027	1.215	0.874	-0.025
	GMC_P		-0.036	0.843	2.063	-0.166
	GMCN_C	$η = 0.25$	-0.145	0.644	1.304	-0.235
		$η = 0.5$	-0.083	0.807	1.394	-0.148
		$η = 0.75$	-0.048	0.888	1.422	-0.115
	GMCN_P	$η = 0.25$	0.274	2.235	3.195	0.173
		$η = 0.5$	0.404	2.957	3.079	0.319
		$η = 0.75$	0.430	3.350	2.998	0.385
	GMN_C	$η = 1$	-0.026	0.939	1.438	-0.097
	GMN_P	$η = 1$	0.444	3.600	2.952	0.424
n=10	(1/n)_C		-0.100	0.710	1.510	-0.175
	(1/n)_P		0.268	1.982	3.018	0.180
	GMC_C		-0.198	0.250	0.483	-0.286
	GMC_P		0.246	2.245	1.875	0.226
	GMCN_C	$η = 0.25$	0.195	1.931	2.209	0.112
		$η = 0.5$	0.163	1.555	2.217	0.033
		$η = 0.75$	0.125	1.364	2.164	-0.012
	GMCN_P	$η = 0.25$	0.274	2.235	3.175	0.173
		$η = 0.5$	0.337	2.702	4.030	0.267
		$η = 0.75$	0.242	1.862	3.728	0.100
	GMN_C	$η = 1$	0.094	1.270	2.127	-0.035
	GMN_P	$η = 1$	0.157	1.518	3.553	0.008
n=15	(1/n)_C		-0.028	0.993	1.653	-0.096
	(1/n)_P		0.310	2.238	3.217	0.175
	GMC_C		0.136	1.498	1.235	0.050
	GMC_P		0.448	3.524	1.764	0.433
	GMCN_C	$η = 0.25$	0.080	1.220	1.810	0.013
		$η = 0.5$	0.165	1.502	1.804	0.061
		$η = 0.75$	0.193	1.619	1.912	0.059
	GMCN_P	$η = 0.25$	0.491	4.555	2.525	0.445
		$η = 0.5$	0.360	2.664	3.104	0.232
		$η = 0.75$	0.274	2.053	3.544	0.131
	GMN_C	$η = 1$	0.164	1.543	2.017	0.037
	GMN_P	$η = 1$	0.226	1.797	3.800	0.081

模型		SR	OR	Beta	Alpha
HS300		-0.004	0.990	1.000
(1/n)_P		0.044	1.128	1.193	0.064
GMC_P		-0.004	0.979	1.175	-0.008
GMCN_P	$η = 0.25$	0.018	1.056	1.213	0.031
	$η = 0.5$	0.035	1.102	1.194	0.052
	$η = 0.75$	0.043	1.125	1.178	0.062
GMN_P	$η = 1$	0.046	1.141	1.167	0.069

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Study on Optimal Portfolio Strategy from the Perspective of Multilayer Temporal Network

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