Journal of Risk and Financial Management

225 KiB

Open AccessArticle

The Effect of Monitoring Committees on the Relationship between Board Structure and Firm Performance

by Aymen Ammari, Sarra Amdouni, Ahmed Zemzem and Abderrazak Ellouze

J. Risk Financial Manag. 2016, 9(4), 14; https://doi.org/10.3390/jrfm9040014 - 05 Dec 2016

Cited by 3 | Viewed by 4779

The purpose of this study is to investigate the impact of board structure on the performance of French firms in the presence of several monitoring committees. We studied 80 publicly listed French firms spanning from 2001 to 2013. We concluded that large board [...] Read more.

The purpose of this study is to investigate the impact of board structure on the performance of French firms in the presence of several monitoring committees. We studied 80 publicly listed French firms spanning from 2001 to 2013. We concluded that large board size has a negative effect on market performance. While large board size in combination with the existence of at least three committees enhances accounting performance and does not have any impact on market performance, the existence of a board dominated by independent directors with the presence of at least three committees seems to have only a negative impact on accounting performance. Our findings indicate that monitoring committees are beneficial for shareholders only for corporations with a large board size. Full article

(This article belongs to the Section Economics and Finance)

870 KiB

Open AccessArticle

Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function

by Jian Shi and Benlian Xu

J. Risk Financial Manag. 2016, 9(4), 13; https://doi.org/10.3390/jrfm9040013 - 07 Nov 2016

Cited by 7 | Viewed by 4726

Abstract

Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied [...] Read more.

Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied in the fields of statistics and machine learning. In this paper a novel fuzzy support vector machine (SVM) credit scoring model is proposed for credit risk analysis, in which fuzzy membership is adopted to indicate different contribution of each input point to the learning of SVM classification hyperplane. Considering the methodological consistency, support vector data description (SVDD) is introduced to construct the fuzzy membership function and to reduce the effect of outliers and noises. The SVDD-based fuzzy SVM model is tested against the traditional fuzzy SVM on two real-world datasets and the research results confirm the effectiveness of the presented method. Full article

(This article belongs to the Special Issue Credit Risk)

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296 KiB

Open AccessArticle

The Design and Risk Management of Structured Finance Vehicles

by Sanjiv Das and Seoyoung Kim

J. Risk Financial Manag. 2016, 9(4), 12; https://doi.org/10.3390/jrfm9040012 - 28 Oct 2016

Viewed by 4484

Abstract

Special investment vehicles (SIVs), extremely popular financial structures for the creation of highly-rated tranched securities, experienced spectacular demise in the 2007-2008 financial crisis. These financial vehicles epitomize the shadow banking sector, characterized by high leverage, undiversified asset pools, and long-dated assets supported by [...] Read more.

Special investment vehicles (SIVs), extremely popular financial structures for the creation of highly-rated tranched securities, experienced spectacular demise in the 2007-2008 financial crisis. These financial vehicles epitomize the shadow banking sector, characterized by high leverage, undiversified asset pools, and long-dated assets supported by short-term debt, thus bearing material rollover risk on their liabilities which led to defeasance. This paper models these vehicles, and shows that imposing leverage risk control triggers can be optimal for all capital providers, though they may not always be appropriate. The efficacy of these risk controls varies depending on anticipated asset volatility and fire-sale discounts on defeasance. Despite risk management controls, we show that a high failure rate is inherent in the design of these vehicles, and may be mitigated to some extent by including contingent capital provisions in the ex-ante covenants. Post the recent subprime financial crisis, we inform the creation of safer SIVs in structured finance, and propose avenues of mitigating risks faced by senior debt through deleveraging policies in the form of leverage risk controls and contingent capital. Full article

(This article belongs to the Special Issue Credit Risk)

3965 KiB

Open AccessArticle

Portfolios Dominating Indices: Optimization with Second-Order Stochastic Dominance Constraints vs. Minimum and Mean Variance Portfolios

by Neslihan Fidan Keçeci, Viktor Kuzmenko and Stan Uryasev

J. Risk Financial Manag. 2016, 9(4), 11; https://doi.org/10.3390/jrfm9040011 - 04 Oct 2016

Cited by 6 | Viewed by 6035

Abstract

The paper compares portfolio optimization with the Second-Order Stochastic Dominance (SSD) constraints with mean-variance and minimum variance portfolio optimization. As a distribution-free decision rule, stochastic dominance takes into account the entire distribution of return rather than some specific characteristic, such as variance. The [...] Read more.

The paper compares portfolio optimization with the Second-Order Stochastic Dominance (SSD) constraints with mean-variance and minimum variance portfolio optimization. As a distribution-free decision rule, stochastic dominance takes into account the entire distribution of return rather than some specific characteristic, such as variance. The paper is focused on practical applications of the portfolio optimization and uses the Portfolio Safeguard (PSG) package, which has precoded modules for optimization with SSD constraints, mean-variance and minimum variance portfolio optimization. We have done in-sample and out-of-sample simulations for portfolios of stocks from the Dow Jones, S&P 100 and DAX indices. The considered portfolios’ SSD dominate the Dow Jones, S&P 100 and DAX indices. Simulation demonstrated a superior performance of portfolios with SD constraints, versus mean-variance and minimum variance portfolios. Full article

(This article belongs to the Special Issue Advances in Modeling Value at Risk and Expected Shortfall)

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J. Risk Financial Manag., Volume 9, Issue 4 (December 2016) – 4 articles

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