Journal article
Dynamic sparse portfolio rebalancing model: A perspective of investors’ behavior-related decisions
Knowledge-based systems, Vol.251, p.109224
09/05/2022
DOI: 10.1016/j.knosys.2022.109224
Abstract
By using the elaboration likelihood model (ELM) and prospect theory (PT) to model investors’ behavior-related decisions in portfolio optimization, we propose a novel dynamic behavior-based sparse portfolio selection model (BPSM) operating over multiple periods. With the BPSM model, we complement recent research that involves only investors’ sentiments by also considering market sentiments to model investors’ portfolio rebalancing behavior. Market sentiments are obtained by analyzing the online information through deep learning text analysis algorithms based on the Bi-directional Long Short-Term Memory (Bi-LSTM) model. The stochastic neural networks-based algorithm is designed to solve the BPSM. We demonstrate the effectiveness of the BPSM model on the Shanghai 50 and Hushen 300 data sets. The frame of experiments includes a dynamic portfolio rebalancing model, in which both the investors’ sentiments and the market sentiments are modeled to analyze investors’ dynamic portfolio rebalancing behavior. The experiment results show that, first, by updating the expected return rate of each period according to investors’ sentiments and market sentiments, in all cases, the BPSM model achieves a higher investment return per unit risk (Rpr) than the conventional Mean–variance (MV) model to minimize investment risk. Second, compared with the two baseline models that include only investors’ sentiments, the BPSM realizes a portfolio policy that improves investment return per unit risk (Rpr) in 70% of situations. These results reveal that incorporating investors’ behavior-related signals into the portfolio selection model is beneficial to investors’ investment results, which offers implications for financial stakeholders.
Details
- Title: Subtitle
- Dynamic sparse portfolio rebalancing model: A perspective of investors’ behavior-related decisions
- Creators
- Ju Wei - Shanghai University of Finance and EconomicsXipeng Liu - Shanghai University of Finance and EconomicsWeiguo Fan - Department of Business Analytics, Tippie College of Business, University of Iowa, IA, 52242, United States
- Resource Type
- Journal article
- Publication Details
- Knowledge-based systems, Vol.251, p.109224
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.knosys.2022.109224
- ISSN
- 0950-7051
- eISSN
- 1872-7409
- Grant note
- 18ZDA088 / Major Program of National Fund of Philosophy and Social Science of China (http://dx.doi.org/10.13039/501100013071) CXJJ-2020-425 / Graduate Innovation Fund of Shanghai University of Finance and Economics (http://dx.doi.org/10.13039/501100007931)
- Language
- English
- Date published
- 09/05/2022
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380400902771
Metrics
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