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JRFM
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Asset Allocation
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Asset Allocation

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Economics and Finance".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 18022

Special Issue Editor

Dr. Anil Mishra

E-Mail Website
Guest Editor
School of Business, Western Sydney University, Sydney, NSW, Australia
Interests: home bias; asset allocation; empirical asset pricing; behavioral finance

Special Issue Information

Dear Colleagues,

This Special Issue of JFRM focuses on “Asset Allocation”. This Special Issue will accept papers that will enrich the literature on various aspects of asset allocation. The particular themes within this topic will accept submissions related to the latest optimization techniques used for asset allocation. This issue welcomes papers that examine the role of asset pricing on asset allocation and also those which study the behavioral role on asset allocation.

Dr. Anil Mishra
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. Journal of Risk and Financial Management 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 1400 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

  • Asset allocation
  • Optimization techniques
  • Asset pricing
  • Behavioral finance

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Published Papers (3 papers)

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Research

10 pages, 979 KiB  
Article
Bankruptcy Prediction Using Machine Learning Techniques
by Shekar Shetty, Mohamed Musa and Xavier Brédart
J. Risk Financial Manag. 2022, 15(1), 35; https://doi.org/10.3390/jrfm15010035 - 13 Jan 2022
Cited by 41 | Viewed by 12372
Abstract
In this study, we apply several advanced machine learning techniques including extreme gradient boosting (XGBoost), support vector machine (SVM), and a deep neural network to predict bankruptcy using easily obtainable financial data of 3728 Belgian Small and Medium Enterprises (SME’s) during the period [...] Read more.
In this study, we apply several advanced machine learning techniques including extreme gradient boosting (XGBoost), support vector machine (SVM), and a deep neural network to predict bankruptcy using easily obtainable financial data of 3728 Belgian Small and Medium Enterprises (SME’s) during the period 2002–2012. Using the above-mentioned machine learning techniques, we predict bankruptcies with a global accuracy of 82–83% using only three easily obtainable financial ratios: the return on assets, the current ratio, and the solvency ratio. While the prediction accuracy is similar to several previous models in the literature, our model is very simple to implement and represents an accurate and user-friendly tool to discriminate between bankrupt and non-bankrupt firms. Full article
(This article belongs to the Special Issue Asset Allocation)
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15 pages, 2295 KiB  
Article
Equity Premium with Habits, Wealth Inequality and Background Risk
by Christos I. Giannikos and Georgios Koimisis
J. Risk Financial Manag. 2021, 14(7), 321; https://doi.org/10.3390/jrfm14070321 - 12 Jul 2021
Viewed by 1918
Abstract
In an exchange economy with endowment inequality, we investigate how preferences with external habits affect the equity risk premium. We show that the dynamics of external additive habits with wealth inequality are complex when a background risk is present. It is ambiguous whether [...] Read more.
In an exchange economy with endowment inequality, we investigate how preferences with external habits affect the equity risk premium. We show that the dynamics of external additive habits with wealth inequality are complex when a background risk is present. It is ambiguous whether wealth inequality will increase or decrease the equity premium even when the income uncertainty is low. This result extends literature by suggesting that wealth inequality has a small role in explaining asset pricing puzzles. Full article
(This article belongs to the Special Issue Asset Allocation)
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17 pages, 417 KiB  
Article
Price Discovery and Learning during the German 5G Auction
by Thomas Dimpfl and Alexander Reining
J. Risk Financial Manag. 2021, 14(6), 274; https://doi.org/10.3390/jrfm14060274 - 18 Jun 2021
Cited by 1 | Viewed by 2971
Abstract
The auctioning of frequency has to comply with a multitude of requirements in order to guarantee a transparent and efficient process. The German Federal Network Agency (Bundesnetzagentur) has opted for a design that provides participants with information on the highest bid after each [...] Read more.
The auctioning of frequency has to comply with a multitude of requirements in order to guarantee a transparent and efficient process. The German Federal Network Agency (Bundesnetzagentur) has opted for a design that provides participants with information on the highest bid after each round for every band along with information on the bidder. We evaluate the price formation efficiency in this setup to see how fast prices become informative about the final auction value. We find that prices are partially informative right from the beginning which allows us to conclude that participants were able to learn fast from their competitors’ bidding behavior and validates the choice of the agency to implement the auction in the present format. Full article
(This article belongs to the Special Issue Asset Allocation)
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