Expertise Makes Perfect: How the Variance of a Reviewer's Historical Ratings Influences the Persuasiveness of Online Reviews

Online reviews have become an important source of information for consumers’ purchase decisions. Drawing upon the consumer expertise and persuasion literature, this study proposes that consumers are more willing to accept a reviewer's recommendation when his/her historical ratings in a certain product domain display greater variance. Five experiments provide consistent support for this hypothesis and the underlying process. Study 1 tests the proposed effect of a reviewer's rating variance on consumers’ willingness to accept the reviewer's recommendation. Studies 2 and 3 show that this effect can be attributed to perceptions regarding the reviewer's expertise. Moreover, this “variance-expert inference” effect is attenuated when the consumption experience of the reviewer is limited (Study 4) and when the consumers are familiar with the products (Study 5). The theoretical implications for the online review and persuasion literature and practical implications for online retailers are discussed.     

Are all five points equal? Scaling heterogeneity in hotel online ratings

Online hotel ratings play a critical role in hotel online reputation building and greatly influence travelers’ decision making. However, self-reported online ratings are vulnerable to scaling heterogeneity due to reviewers’ unique response styles, leading to some incomparability issues. A research framework was developed based on latent state-trait theory and empirically tested using hotel online review data from TripAdvisor. A hierarchical ordered probit (HOPIT) model, which captured threshold differences in hotel online ratings, confirmed the presence of scaling heterogeneity and response styles in the online rating context. Results indicated that younger travelers, women, and travelers with less review expertise used lower thresholds when rating hotels online. Business travelers had the highest rating threshold compared to other types of travelers. Guests staying in high-class hotels tended to have more extreme response styles than those staying in low-class hotels. These findings offer valuable insights for hotel managers and online rating/review sites.     

私有信息、评级偏差和中国评级机构的市场声誉

考虑到评级机构拥有市场声誉的本质在于其可以通过扭曲评级从而对市场产生影响,本文分两步研究中国发行人付费评级机构的市场声誉:第一步,将信用评级对各种基本面因素进行回归,并以实际评级与回归预测值的差值作为评级偏差的量度。与既有文献相比,本文的重要改进是在基本面因素中引入了发债企业与各评级机构(分支机构)最短距离的均值和方差,这能较好地控制因发债企业私有信息可能造成的选择偏误。第二步,考察评级偏差和机构特征如何影响企业的发债成本。研究表明,中国评级机构作为一个整体具有显著的市场声誉,但各评级机构之间存在很大的差异性。最后,考虑到评级机构与发债企业在选址上可能会有集聚效应,我们基于高铁开通事件进行双重差分检验,研究表明本文结论是稳健的。     

Socially responsible investment,ethical rating,and separating contracts: A theoretical exploration in hospitality industry

This study theoretically examines the role of the ethical rating agencies. First, in an infinitely repeated game framework, it shows that hotels with low environmental, social, and governance risk would prefer to signal their worthiness to isolate them from risky ones. Next, by employing the Bayesian approach, this study shows that a socially responsible investment (SRI) would advise a hotel to obtain an ethical rating when the expected value of sample information turns positive. This analysis could guide future empirical investigation and policy frameworks about the ethical screening.     

信任被定价了吗？ ——来自债券市场的证据

仅依赖法律等正式制度无法确保金融或经济的长期发展,信任等非正式制度在其中的作用不可或缺。鉴于此,本文研究信任这一非正式制度在债券市场中的价值。研究发现,信任不仅有助于提高债券信用评级,还有助于降低债券信用利差。同时,利用最高法“失信被执行人”数据还发现,失信及失信程度的增加会降低债券信用评级,提高债券信用利差。此外,本文还利用路径分析法发现,信任不仅会直接影响债券评级和定价,还会通过提升公司财务报告质量间接地发挥作用,即不仅投资者主观上更愿相信高信任度地区的企业,客观上这些企业也的确更为可信。进一步地,信任对债券评级和定价的积极作用在国有企业、金融业更发达地区的企业以及发债次数更多的企业中更为显著,即投资者与发行人之间的重复博弈机会越多,信任的价值也越大。本文研究结论提醒政府在推动企业融资成本下降时,应着重加强对信任这种地区乃至国家公共品的培育和建设。     

Prediction of bank financial strength ratings: The case of Turkey

Bank financial strength ratings have gained widespread popularity especially after the recent financial turmoil. Rating agencies were criticized because of their ratings and failure to predict the bankruptcy of the banks. Based on this observation, we investigate whether the forecast of the rating of bank's financial strength using publicly available data is consistent with those of the credit rating agency. We use the data of Turkish banks for this investigation. We take a country-specific approach because previous studies found that proxies used for environmental factors (political, economic, and financial risk of the country) did not have any explanatory power and it is hard to find international data for other important factors such as franchise value, concentration, and efficiency. We use two popular multivariate statistical techniques (multiple discriminant analysis and ordered logistic regression) to estimate a suitable model and we compare their performances with those of two mostly used data mining techniques (Support Vector Machine and Artificial Neural Network). Our results suggest that our predictions are consistent with those of Moody's financial strength rating in general.. The important factors in rating are found to be profitability (measured by return on equity), efficient use of resources, and funding the businesses and the households instead of the government that shows efficient placement of the funds.     

Upfront versus rating contingent fees: Implications for rating quality

This paper theoretically investigates whether compensating a credit rating agency (CRA) with an upfront fee, rather than a rating contingent fee, can improve rating quality. I show that an upfront fee delivers the same rating quality as the rating contingent fee if the CRA sets its rating policy before the issuer solicits a rating, whereas it can potentially improve quality if the rating policy is set only after a rating is solicited. These results suggest that the “Franken Amendment” that has been removed from the Dodd-Frank Act might be crucial for the proposed upfront fee regime to improve rating quality.     

我国企业债券信用评级的因素分析——基于Altman的Z计分模型的实证研究

企业债券的信用评级是企业债券市场快速发展的基石.本文运用Altman的Z模型对我国发债企业的主体信用进行了评级,将其结果与现有企业债券评级结果相对照后,发现存在众多信用评级相背离现象,并对我国企业债券的信用评级提出了政策建议.     

Blurred stars: Mutual fund ratings in the shadow of conflicts of interest

Using unique data on trading commission payments to mutual fund rating companies (MFRCs) by mutual funds in China, this paper investigates whether the conflicts of interest arising from trading commission payments bias MFRCs’ mutual fund star ratings and hence affect their informativeness. We find the rating of a mutual fund is more optimistic when the MFRC either (i) receives trading commission fees from the mutual fund or (ii) can potentially receive fees in the future. The paper further shows that the usefulness of ratings in terms of predicting a fund’s future performance is negatively impacted by conflicts of interest. There is also evidence that investors can see through the problem, responding less enthusiastically (in terms of fund flows) to the ratings of conflicted MFRCs. We further find that the introduction of a rating qualification system that aims to improve mutual fund rating quality exacerbates the rating bias.     

对产业集群客户授信风险管理的思考

贷款客户集中度是衡量贷款风险的一项重要指标。特别是2008年世界金融危机发生后,各国都在防范金融风险上加强控制,随着巴塞尔协议3逐步广泛得到世界各国的认同和实施,贷款集中度问题越来越应该引起重视。但是,由于事实上的微观动作,贷款集中有趋重的趋势,其中产业集群客户授信是重要手段。本文结合授信风险管理存在的问题提出建议。