Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.8
2.0
Journals
Risks
Special Issues
Portfolio Selection and Asset Pricing
share announcement

Portfolio Selection and Asset Pricing

A special issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: 30 April 2025 | Viewed by 3346

Special Issue Editor

Prof. Dr. Hong Liu

E-Mail Website
Guest Editor
Olin Business School, Washington University in St. Louis, St. Louis, MO 63130, USA
Interests: optimal consumption and investment with frictions; asset pricing; market microstructure

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue titled “Portfolio Selection and Asset Pricing”. This Special Issue will explore the latest theoretical advancements, empirical findings, and practical applications in the fields of portfolio selection and asset pricing. We invite submissions that offer novel insights, innovative methodologies, and practical solutions to the challenges faced by investors and financial professionals in these domains.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Theoretical models of portfolio selection and optimization;
  • Innovations in asset pricing models and their empirical testing;
  • Risk management strategies and their impact on portfolio performance;
  • Behavioral finance perspectives on investment decisions and asset pricing;
  • The influence of market anomalies and inefficiencies on portfolio selection;
  • Applications of machine learning and artificial intelligence in asset pricing and portfolio management;
  • Impacts of macroeconomic variables and policy changes on asset prices and portfolio choices.

We welcome contributions from academics, researchers, and practitioners who are working at the forefront of these areas. Submissions should provide rigorous analysis, robust empirical evidence, and clear implications for theory and practice. Both full-length research papers and shorter, focused studies will be considered.

Please join us in advancing our shared understanding of and practices in portfolio selection and asset pricing through high-quality research and discourse.

Prof. Dr. Hong Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • portfolio selection
  • asset pricing
  • investment
  • optimal trading
  • market frictions

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 1960 KiB  
Article
Political Uncertainty-Managed Portfolios
by Thorsten Lehnert
Risks 2025, 13(3), 55; https://doi.org/10.3390/risks13030055 - 18 Mar 2025
Viewed by 298
Abstract
Forward-looking metrics of uncertainty based on options-implied information should be highly predictive of equity market returns in accordance with asset pricing theory. Empirically, however, the ability of the VIX, for example, to predict returns is statistically weak. In contrast to other studies that [...] Read more.
Forward-looking metrics of uncertainty based on options-implied information should be highly predictive of equity market returns in accordance with asset pricing theory. Empirically, however, the ability of the VIX, for example, to predict returns is statistically weak. In contrast to other studies that typically analyze a short time-series of option prices, I make use of a ‘VIX-type’ but a text-based measure of uncertainty starting in 1890, which is constructed using the titles and abstracts of front-page articles of the Wall Street Journal. I hypothesize that uncertainty timing might increase Sharpe ratios because changes in uncertainty are not necessarily correlated with changes in equity risk and, therefore, not offset by proportional changes in expected returns. Using a major US equity portfolio, I propose a dynamic trading strategy and show that lagged news-based uncertainty explains future excess returns on the market portfolio at the short horizon. While policy- and war-related concerns mainly drive these predictability results, stock market-related news has no predictive power. A managed equity portfolio that takes more risk when news-based uncertainty is high generates an annualized equity risk-adjusted alpha of 5.33% with an appraisal ratio of 0.46. Managing news-based uncertainty contrasts with conventional investment knowledge because the strategy takes relatively less risks in recessions, which rules out typical risk-based explanations. Interestingly, I find that the uncertainty around governmental policy is lower and, by taking less risk, it performs better during periods when the Republicans control the senate. I conclude that my text-based measure is a plausible proxy for investor policy uncertainty and performs better in terms of predictability compared to other options-based measures. Full article
(This article belongs to the Special Issue Portfolio Selection and Asset Pricing)
►▼ Show Figures

Figure 1

34 pages, 1327 KiB  
Article
Determinants of South African Asset Market Co-Movement: Evidence from Investor Sentiment and Changing Market Conditions
by Fabian Moodley, Sune Ferreira-Schenk and Kago Matlhaku
Risks 2025, 13(1), 14; https://doi.org/10.3390/risks13010014 - 16 Jan 2025
Viewed by 589
Abstract
The co-movement of multi-asset markets in emerging markets has become an important determinant for investors seeking diversified portfolios and enhanced portfolio returns. Despite this, studies have failed to examine the determinants of the co-movement of multi-asset markets such as investor sentiment and changing [...] Read more.
The co-movement of multi-asset markets in emerging markets has become an important determinant for investors seeking diversified portfolios and enhanced portfolio returns. Despite this, studies have failed to examine the determinants of the co-movement of multi-asset markets such as investor sentiment and changing market conditions. Accordingly, this study investigates the effect of investor sentiment on the co-movement of South African multi-asset markets by introducing alternating market conditions. The Markov regime-switching autoregressive (MS-AR) model and Markov regime-switching vector autoregressive (MS-VAR) model impulse response function are used from 2007 March to January 2024. The findings indicate that investor sentiment has a time-varying and regime-specific effect on the co-movement of South African multi-asset markets. In a bull market condition, investor sentiment positively affects the equity–bond and equity–gold co-movement. In the bear market condition, investor sentiment has a negative and significant effect on the equity–bond, equity–property, bond–gold, and bond–property co-movement. Similarly, in a bull regime, the co-movement of South African multi-asset markets positively responds to sentiment shocks, although this is only observed in the short term. However, in the bear market regime, the co-movement of South African multi-asset markets responds positively and negatively to sentiment shocks, despite this being observed in the long run. These observations provide interesting insights to policymakers, investors, and fund managers for portfolio diversification and risk management strategies. That being, the current policies are not robust enough to reduce asset market integration and reduce sentiment-induced markets. Consequently, policymakers must re-examine and amend current policies according to the findings of the study. In addition, portfolio rebalancing in line with the findings of this study is essential for portfolio diversification. Full article
(This article belongs to the Special Issue Portfolio Selection and Asset Pricing)
►▼ Show Figures

Figure 1

12 pages, 761 KiB  
Article
Automated Machine Learning and Asset Pricing
by Jerome V. Healy, Andros Gregoriou and Robert Hudson
Risks 2024, 12(9), 148; https://doi.org/10.3390/risks12090148 - 14 Sep 2024
Viewed by 2040
Abstract
We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory drawn from behavioural [...] Read more.
We evaluate whether machine learning methods can better model excess portfolio returns compared to the standard regression-based strategies generally used in the finance and econometric literature. We examine 17 benchmark factor model specifications based on Expected Utility Theory and theory drawn from behavioural finance. We assess whether machine learning can identify features of the data-generating process undetected by standard methods and rank the best-performing algorithms. Our tests use 95 years of CRSP data, from 1926 to 2021, encompassing the price history of the broad US stock market. Our findings suggest that machine learning methods provide more accurate models of stock returns based on risk factors than standard regression-based methods of estimation. They also indicate that certain risk factors and combinations of risk factors may be more attractive when more appropriate account is taken of the non-linear properties of the underlying assets. Full article
(This article belongs to the Special Issue Portfolio Selection and Asset Pricing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop