我国商业银行业公司治理结构与经营绩效的实证研究

通过对8家股份制商业银行的公司治理结构与经营绩效的关系做回归分析,结果显示:经营绩效与董事会规模正相关;与第一大股东持股比例负相关;与前5大股东控制权分散度正相关。因此,国有商业银行应当适度增大董事会规模、明晰产权关系、强化监事会职能、加强管理层激励与信息透明度。     

基于VAR模型的山东省经济增长波动分析

本文通过建立向量自回归模型,运用脉冲响应函数和预测方差分解的方法对山东省经济增长的波动情况进行了实证分析。研究结果表明:各变量对山东经济增长的影响均不太明显。这说明山东经济一直保持稳定快速发展的势头。     

我国股份制商业银行薪酬激励有效性研究

我国股份制商业银行薪酬激励存在一些不足，原因在于薪酬激励的自身因素和薪酬环境因素两方面。自身因素主要包括忽视长期行为激励、缺乏同业竞争优势、轻视普通员工激励；环境因素主要有银行治理结构不完善、市场非充分有效、法规制度不健全等。本文通过对商业银行高管人员及普通员工的薪酬与业绩相关性进行实证检验，认为我国商业银行薪酬激励基本上是有效的，但是其有效性并不十分理想。据此，本文提出了提升我国商业银行薪酬激励有效性的具体建议：从微观层面看，优化薪酬激励机制：从中观层面看，强化商业银行治理结构；从宏观层面看，改善外部相关环境。     

The Effects of Attributes on the Repeat Sales Pattern of Residential Property in Hong Kong

There has been copious research work on the development of house price models and the construction of house price indices. However, results in some studies revealed that the accuracy of such indices could be subject to selection bias when using only information from a sample of sold properties to estimate value movements for the entire housing stock. In particular, estimated house price appreciation is usually systematically higher among properties that change hands more frequently. It therefore suggests that the determination of important factors affecting the transaction frequency or intensity of a housing unit should be a more fundamental research question. This paper examines the possible factors that determine the popularity of residential unit by means of a repeated sales pattern. The Poisson regression model and event history analysis techniques are employed to assess the effect of attributes on transaction frequency and intensity. The event history analyses technique can take account of transaction-specific as well as time-dependent covariates, and therefore is recommended for analyzing repeated sales data in a real estate market. All transaction records during the period 1993–2000 from the Land Registry of one of the most popular residential estates in Hong Kong were used to illustrate the method. Unlike a response to favorable transaction price, good quality units do not necessarily inherently display a high transaction frequency. Rather, units of average quality are more likely to be transactionally active.     

On the estimation of stock-market reaction to corporate layoff announcements

This study investigates the stock-market reaction to layoff announcements where more than 1000 workers are affected. We employ a dummy variable regression (DVR) version of the market model and compare the results obtained using ordinary least squares (OLS) versus exponential GARCH (EGARCH), and value-weighted (VW) versus equally weighted (EW) market index. We find that the stock market responds negatively to layoffs attributed to low demand. We also find that contrary to prior research, the market reacts positively to restructuring-related layoffs on the announcement date. This pattern of market reaction is observed regardless of the market index used or the parameter estimation methods employed, although the empirical results indicate that using EGARCH/VW market index tends to generate fewer statistically significant test results and smaller (in the absolute size of the cumulative) abnormal returns (ARs). Taken together, our study provides additional support for the claim that studies of stock-market reaction to corporate events must account for the time variation in return volatility. Ignoring these could result in erroneous inferences.     

Using Market Expectations to Test for Speculative Bubbles in the Crude Oil Market

The wide fluctuations of oil prices from 2003 to 2008 have attracted the interest of academics and policymakers. A popular view is that these fluctuations were caused by speculative bubbles due to the increased financialization of oil futures markets. This hypothesis, however, is difficult to examine since the fundamental price of oil is unobservable and, therefore, econometric evidence in favor of bubbles may actually be due to misspecified market fundamentals. In this paper, we extend two recently proposed methodologies for bubble detection that alleviate this problem by using market expectations of future prices. Both methodologies provide no evidence of speculative bubbles.     

Machine learning in individual claims reserving

Machine learning techniques make it feasible to calculate claims reserves on individual claims data. This paper illustrates how these techniques can be used by providing an explicit example in individual claims reserving.     

New development: Regression-to-the-mean explains the otherwise puzzling coefficients in NHS England’s formula for funding CCGs

This paper reviews NHS England’s recent report on the formula that guides the allocation of tens of billions of pounds to clinical commissioning groups (CCGs). It finds deficiencies and errors in the unprincipled construction of the formula and substitutes Sir Francis Galton’s logical concept of ‘regression to the mean’ for the report’s ad hoc conjectures about striking patterns of certain coefficients.     

A logistic regression point of view toward loss given default distribution estimation

We propose a new procedure to estimate the loss given default (LGD) distribution. Owing to the complicated shape of the LGD distribution, using a smooth density function as a driver to estimate it may result in a decline in model fit. To overcome this problem, we first apply the logistic regression to estimate the LGD cumulative distribution function. Then, we convert the result into the LGD distribution estimate. To implement the newly proposed estimation procedure, we collect a sample of 5269 defaulted debts from Moody’s Default and Recovery Database. A performance study is performed using 2000 pairs of in-sample and out-of-sample data-sets with different sizes that are randomly selected from the entire sample. Our results show that the newly proposed procedure has better and more robust performance than its alternatives, in the sense of yielding more accurate in-sample and out-of-sample LGD distribution estimates. Thus, it is useful for studying the LGD distribution.     

Effects of Label on Consumer Preferences: Focus on Hawaiian Avocado Industry

This study examines market challenges facing a local agriculture industry in a small island setting. Examination of the Hawai‘i local avocado industry indicates labeling can address the product information gap between consumers and producers, improve market share, and increase import substitution by local products. Currently, local avocados occupy only 33% of market share, compared with 67% captured by imports. Consumer preferences among local labeled, local unlabeled, and imported avocados were analyzed using multinomial logistic regression. Statistical results showed that labels influence consumers’ decisions to buy local or imported avocados, and their preferences were also influenced by sociodemographic variables, avocado characteristics, and purchasing behavior. Key market segments to target and policy implications are also discussed.