Testing loss aversion and diminishing sensitivity in review sentiment

This article analyzes the relationship between ratings and review sentiment by introducing, for the first time, the tenets of prospect theory. Specifically, we test loss aversion and diminishing sensitivity on a sample of 132,486 reviews and find that: first, negative deviations in ratings (receiving a service with worse performance than expected) bring about a higher impact on review sentiment than positive deviations of equal magnitude (receiving a service with better performance than expected), thus, confirming loss aversion; and second, regardless of whether the service received is better or worse than expected, variations in ratings closer to the reference point result in higher marginal impacts on sentiment than equivalent variations further away from the reference point, thus, proving diminishing sensitivity. These results have relevant theoretical implications related to the use of relative vs absolute measures and the cognitive bias involved, and managerial implications linked to meeting expectations and service recovery.     

A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo,Egypt

Abstract

The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequently lead to biased conclusions and poorly designed countermeasures. This is particularly true for imbalanced data where the number of samples in one class far outnumber the other. To improve the classification performance of the injury severity, the paper presents a robust noise filtering technique to deal with the mislabels in the imbalanced crash dataset using the advanced machine learning algorithms. We examine the state-of-the-art filtering algorithms, including Iterative Noise Filtering based on the Fusion of Classifiers (INFFC), Iterative Partitioning Filter (IPF), and Saturation Filter (SatF). In the case study of Cairo (Egypt), the empirical results show that: (1) the mislabels in crash data significantly influence the injury severity predictions, and (2) the proposed M-IPF filter outperforms its counterparts in terms of the effectiveness and efficiency in eliminating the mislabels in crash data. The test results demonstrate the efficacy of the M-IPF in handling the data noise and mitigating the impacts thereof.     

Dialectical mind can be sweet toward crisis‐associated products/brands

Consumers’ thinking style (e.g., dialectical thinking), which is fundamental to cognition, has been paid inadequate attention in the consumer literature. This research explores the impact of dialectical thinking on Chinese consumer responses toward crisis‐associated products/brands. Findings in three experimental studies indicate that, in comparison to those primed with nondialectical thinking, consumers primed with dialectical thinking report higher levels of purchase intention and trust for a crisis‐associated product/brand. This is the case not just for products with Chinese cultural background, but also for products with western cultural background. Consumers’ perceived severity of crises moderates the impact of dialectical thinking, with a stronger impact in a highly severe crisis situation than in a less severe one. A survey study indicates a similar pattern. These findings are important in suggesting ways of encouraging Chinese consumers to be insistent on quality standard in order to better protect their consumer rights, and moreover, for multinational corporations on how best to respond and formulate strategies of handling product‐harm crises in the Chinese market.     

Modeling severity risk under PD–LGD correlation

In this article, a generic severity risk framework in which loss given default (LGD) is dependent upon probability of default (PD) in an intuitive manner is developed. By modeling the conditional mean of LGD as a function of PD, which also varies with systemic risk factors, this model allows an arbitrary functional relationship between PD and LGD. Based on this framework, several specifications of stochastic LGD are proposed with detailed calibration methods. By combining these models with an extension of CreditRisk+, a versatile mixed Poisson credit risk model that is capable of handling both risk factor correlation and PD–LGD dependency is developed. An efficient simulation algorithm based on importance sampling is also introduced for risk calculation. Empirical studies suggest that ignoring or incorrectly specifying severity risk can significantly underestimate credit risk and a properly defined severity risk model is critical for credit risk measurement as well as downturn LGD estimation.     

操作风险损失的广义帕累托分布参数估计及其应用

极值理论表明大于某一阀值的样本服从广义帕累托分布,该结论在金融风险计量和保险精算中有着广泛的应用。然而,由于其参数没有可接受的估计方法,致使其应用受到限制。论文在推导出广义帕累托分布的条件矩的基础上,研究了基于操作风险损失的广义帕累托分布的参数估计问题。并且基于我国商业银行1994~2008年的操作风险损失数据对经济资本配置进行了算例分析。     

上市商业银行贷款拨备计提发展方向研究

银行作为经营风险的企业,前瞻性地计提贷款准备金具有必然性。银行风险管理的前瞻性和预见性越来越成为衡量一家商业银行市场竞争能力、抵御金融风险能力的重要指标。巴塞尔新资本协议(BaselⅡ)从资本监管的角度对商业银行信贷资产信用风险损失的评估方法进行了规范。本文从国内外上市商业银行自身风险管理的角度出发,通过目前会计标准和监管标准的简要对比,对其贷款拨备计提的发展方向提出了建议,并力求通过二者关键因素的分析,从而建议商业银行有可能在实施BaselⅡ和会计准则过程中找到一个兼容并蓄的应用方案。     

Estimating the capital charge for external fraud risk： A clinical study

The main objective of this paper is to estimate the economic capital for covering the external fraud risk within a financial institution. This is a kind of operational risk which is due to acts of a type intended to defraud, misappropriate property or circumvent the law, by a third party. From the methodological point of view, we apply the Loss Distribution Approach （LDA）, based on the Internal Operational Loss Database （IOLD） provided by a Spanish Saving Bank. More specifically, we asses the potential impact of the severity distribution on the Capital at Risk （CAR）. In absence of normality, we try to adjust the Lognormal, Weibull and Exponential functions when modelling the severity of losses. As a result, we find a high divergence in terms of capital charge depending on the statistical model selected. In consequence, in order to obtain a realistic model, we highlight the relevance of the goodness of fit between the empirical and the theoretical distribution.     

中国企业税负高低之谜:寻找合理的企业税负衡量标准

作者梳理了企业税负衡量标准的相关文献,运用微观经济学中的"死角损失"原理,观测中国近年来的广义宏观税费负担,发现其依然处于偏高的水平上。尤其对于贡献绝大部分税费的企业来说,整体税负过重的倾向并无根本改观,其中非公经济的税负更是连年超过国有企业。在对企业税负的衡量中,作者采用了世界银行的总税率指标进行国际比较,同时在对企业实际税费负担的计算中以上市及非上市公司为代表,测算了企业销售利润率与税利率之间的变动关系,进而阐述中国税制所存在的问题,据此提出针对性较强的政策建议。     

中国和东盟人民币贸易结算的经济学分析

本文构建了一个三经济体的贸易模型,从贸易损失的角度分析中国和东盟地区采用人民币进行贸易结算对中国和东盟,以及对世界其他主要经济体的福利影响。分析结果表明:中国和东盟地区结成货币联盟对世界主要经济体都将产生正面效应;由于东盟地区整体技术稍低,中国和东盟结成货币联盟将使东盟贸易损失下降更多和产出增加更大,从而提升东盟地区的整体福利;中国和东盟进行人民币结算对各经济体而言,属于帕累托改进,符合激励相容原则,具备理论上的可行性;中国和东盟进行货币联盟的优势在于两大经济体之间的地缘优势和生产技术类似。