The role of industry classification in estimating discretionary accruals

This study compares the properties of the Global Industry Classification Standard (GICS) with three alternatives: Standard Industrial Classification, North American Industry Classification System, and Fama–French classification. First, we demonstrate that GICS results in more reliable industry groupings for financial analysis and research; in particular, we find that estimations of performance-adjusted discretionary accruals (PADA) based on GICS significantly outperform estimates derived using each of the three alternative classifications systems in capturing discretionary accruals. Second, we show that the difference between GICS and the other systems can provide significantly different results, and hence different inferences, in empirical studies that rely on industry classification. Specifically, we revisit findings by Teoh et al. (J Financ 53[6]:1935–1970, 1998a) and assess the conclusion that initial public offering (IPO) issuers with high abnormal accruals during the IPO year experience subsequent poorer long-term stock performance than issuers with low discretionary accruals do. We find that this result disappears when PADA estimates are based on GICS. Our results call for serious consideration of using GICS classifications in research, either in the primary analysis or as a necessary corroboration.     

What's My Line? A Comparison of Industry Classification Schemes for Capital Market Research

This study compares four broadly available industry classification schemes in a variety of applications common to capital market research. Standard Industrial Classification (SIC) codes have been available since 1939 but are being replaced by North American Industry Classification System (NAICS) codes. The Global Industry Classifications Standard (GICS)^SM system, jointly developed by Standard & Poor's and Morgan Stanley Capital International (MSCI), is popular among financial practitioners, whereas the Fama and French [1997] algorithm is used primarily by academics. Our results show that GICS classifications are significantly better at explaining stock return comovements, as well as cross‐sectional variations in valuation multiples, forecasted and realized growth rates, research and development expenditures, and various key financial ratios. The GICS advantage is consistent from year to year and is most pronounced among large firms. The other three methods differ little from each other in most applications.     

市场结构、股权结构与证券公司成本效率——基于PR模型的静态、动态考察

本文基于静态、动态PR模型,对我国证券业、创新类及规范类证券公司的市场结构进行测度,运用SFA法测度证券公司的成本效率,进而实证分析市场结构、股权结构与证券公司成本效率之间的关系。结果显示:2006-2010期间我国证券业的市场结构为垄断竞争,且创新类证券公司之间的市场竞争比规范类证券公司之间的竞争要激烈得多。市场结构指标——市场竞争度与证券公司效率之间是一种倒U型关系。国有性质证券公司的效率低于非国有性质证券公司的效率,股权性质对规范类证券公司效率的影响更明显;股权集中度与证券公司效率之间是一种U型关系,并且,股权集中度对创新类证券公司影响较大。     

Delta-hedging correlation risk?

While the Gaussian copula model is commonly used as a static quotation device for CDO tranches, its use for hedging is questionable. In particular, the spread delta computed from the Gaussian copula model assumes constant base correlations, whereas we show that the correlations are dynamic and correlated to the index spread. It might therefore be expected that a dynamic model of credit risk, which is able to capture the dependence between the base correlations and the index spread, will have better hedging performances. In this paper, we compare delta hedging of spread risk based on the Gaussian copula model, to the implementation of jump-to-default ratio computed from the dynamic local intensity model. Theoretical and empirical analysis are illustrated by using the market data in both before and after the subprime crisis. We observe that delta hedging of spread risk outperforms the implementation of jump-to-default ratio in the pre-crisis period associated with CDX.NA.IG series 5, and the two strategies have comparable performance for crisis period associated with CDX.NA.IG series 9 and 10. This shows that, although the local intensity model is a dynamic model, it is not sufficient to explain the joint dynamic of the index spread and the base correlations, and a richer dynamic model is required to obtain better hedging results. Moreover, although different specifications of the local intensity can be fitted to the market data equally well, their hedging results can be significant different. This reveals substantial model risk when hedging CDO tranches.     

金融衍生产品对上市公司风险管理影响实证研究

论文选取有色金属矿产行业以及能源交通运输行业229家上市公司作为样本，以上市公司是否使用衍生产品为解释变量，同时引用一系列控制变量，通过实证分析上市公司使用衍生产品是否会有效降低公司风险。实证过程涉及参数检验、非参数检验、相关性分析和回归分析，实证结果显示我国上市公司使用衍生产品会降低公司风险，这与西方主流的财务管理理论相一致而与我国学者以前的研究相反，由此推测我国上市公司运用衍生产品的能力逐渐娴熟。     

FORECASTING VOLATILITY FOR PORTFOLIO SELECTION

The volatility of an asset is a primary input to the portfolio selection problem. Information about volatility is available from two sources, namely the share market and the option market. This paper examines the forecasting performance, over a three month investment horizon, of time series forecasts (from the share market) and option based implied volatilities. Three time series models, including GARCH, are used and twenty four implied volatility estimation models are employed. Using a data set of twelve UK companies, it is demonstrated that implied volatilities produce better individual forecasts than time series. However, more remarkably, forecasts combining implied volatilies and time series estimates significantly outperform both component forecasts.     

信托业发展与经济增长——基于我国省际面板数据的实证分析

基于金融发展理论分析信托业发展的经济效应传导机制,在数据可收集的前提下,采用2004-2018年我国各省市信托公司的面板数据,运用静态面板数据模型和动态面板数据的系统GM M估计方法对信托业发展和经济增长的关系进行实证分析,结果显著证明了信托业发展能促进经济增长,反映信托业发展的重要指标——新增集合信托规模、数量以及实收信托规模的增加能显著提高人均GDP增长率和GDP增长率.因此需大力发展我国信托业,回归"受人之托,代人理财"的信托本源,提高信托业发展质量,推动经济增长.     

Relative performance evaluation in board cash compensation: UK empirical evidence

This paper presents an empirical examination of whether evidence of the implicit use of relative performance evaluation (RPE) can be found in the cash compensation of boards of directors for 169 UK non-financial listed companies that existed for all of the period from 1971 to 1998. We perform two types of analyses. Initially, we estimate individual firm time series regressions of the change in board cash compensation against measures of firm and peer group performance. The measures of firm performance we use are annual cash stock market returns and pre-tax accounting earnings. Peer group measures of performance are industry value-weighted average cash stock market returns and industry value-weighted average pre-tax accounting earnings. Subsequently, we analyse the data as a balanced panel.We provide evidence that board cash compensation is positively related to accounting earnings and negatively associated with peer group pre-tax accounting earnings. Some evidence suggests that board cash compensation is related to firm stock market returns but none suggests it is related to peer group market returns. This result implies the presence of RPE based on accounting earnings in the design of UK board compensation, with the cash compensation of boards of directors implicitly (partially) protected from industry uncertainties.     

An efficient graph-based peer selection method for financial statements

Comparing companies can be useful for various purposes. Despite the widespread use of industry classification systems as a peer selection standard, these have been criticized for various reasons. Financial statements, however, offer a promising alternative to such classification systems. They are standardized, widely available, and offer deep insights into the nature of the company. In this paper, we present a graph distance metric for financial statements using the earth mover's distance. When using the distance metric on real-world tasks such as peer identification and industry classification, it shows promising results in terms of accuracy and computational efficiency.     

The relation between environmental performance and environmental disclosure: a research note

Previous studies of the relation between environmental performance and environmental disclosure have consistently documented a lack of significance. This study examines the relation between 1990 annual report environmental disclosures for a sample of 131 US companies and their environmental performance as based on toxics release data from 1988 (made available in 1990). In contrast to the previous examinations, results indicate that, controlling for firm size and industry classification (two factors previously shown to be related to the extent of environmental disclosure), there is a significant negative relation between performance and disclosure for the sample firms. However, the disclosure level of firms from non-environmentally sensitive industries is more affected by toxic release levels than is the disclosure of firms from environmentally sensitive industries.     

Forecasting financial time-series using data mining models: A simulation study

In this paper, we examine the static and dynamic predictive ability of artificial neural networks and random forests for financial time series within a simulation context. Our simulation design, in which we generate data from an AR(1)-GARCH(1,1) model, allows for several degrees of persistence in the mean equation to mimic the behavior of short and long-horizon asset returns. While the true data generating process beats the data mining techniques in terms of static forecasting, the novelty in this paper is to demonstrate that the data mining techniques outperform the true model under a dynamic forecasting scheme for moderate to highly persistent time series. We provide an empirical application using one-day and long-horizon returns on two exchange rates. Our empirical findings corroborate our simulation results in that the data mining models exhibit superior predictive ability for highly persistent time series. We discuss the importance of our findings for asset allocation and portfolio management.     

财产保险公司资产负债管理多维量化评估体系研究

良好的资产负债管理是保险业可持续发展的基石,也是支持保险业在日益复杂的风险环境中保持稳健发展、防范系统性风险的重要保障。近年来,随着我国金融市场发展,业务产品创新加快,保险业在资产端与负债端的业务结构和风险特征出现了新情况、新变化。特别是部分保险公司缺乏有效的治理结构,采取激进经营、激进投资的策略,导致业务快进快出、风险敞口过大以及流动性问题,对保险公司资产负债匹配管理、风险控制提出了挑战。本文介绍了财产保险公司资产负债多维度量化评估规则设计原理、主要评估模型和评估方法,针对财产保险公司的负债特性提出的沉淀资金匹配,在成本收益匹配中有机地将资产投资收益与承保业务综合成本进行匹配,在现金流匹配模式中打破了僵化的匹配模式,解决了长期困扰财产保险公司的资产负债期限不匹配的问题,对财产保险公司资产负债管理具有重要意义。     

Measuring What Matters: Industry Specificity Helps Companies and Investors Gain Traction on Sustainability

Financial analysts interpret the performance of companies and their securities through an industry lens. Just as an industry approach is critical in financial analysis, it's also critical in helping investors evaluate sustainability performance, since sustainability issues differ from one industry to the next—in large because of differences in how companies use natural and other social resources when bringing their goods and services to market, and how they impact society and the environment in the process. The Sustainability Accounting Standards Board (SASB) was created in 2012 to deliver a full set of sustainability accounting standards that can be used to guide industry‐specific corporate sustainability disclosure to the capital markets. SASB has now issued provisional standards for 79 industries, thereby enabling companies and investors for the first time to identify patterns of sustainability risks and opportunities both across and within industries. Although high‐level issues such as climate change, product safety, and resource intensity and scarcity have material impacts across a variety of sectors, those impacts often vary greatly from one industry to the next. Thus, although the risk may be ubiquitous, it is also differentiated to the point that each industry has its own distinct sustainability profile. Understanding these unique profiles can help companies better manage the issues that are most likely to present material risks to their industries.     

Spillover of stock price crash risk: Do environmental,social and governance (ESG) matter?

This study measures the static and dynamic crash risk connections across ESG networks from 2015 to 2020, using the generalized vector autoregressive framework. In particular, it highlights the mixed results of the crash risk connections across ESG three pillars and the different spillover performance when firms with different ownership structures and qualification of margin-trading and short-selling. Our results reveal that stocks with higher ESG ratings display more negative net spillover effects, which is consistent with the ideas that stock groups with good ESG performance experience lower crash risk, and thus transmitting smaller crash risk to other ESG levels. Among the three ESG pillars, good social performance (S) significantly lowers the total crash risk connections. In contrast, firms with well environment performance (E) do not transmit lower crash risk. Moreover, SOEs and firms with qualification of margin-trading and short-selling have lower total crash risk connections among ESG ratings. Using propensity score matching to match companies with high ESG and low ESG quarterly, we find the results are still robust. When dividing the sample according to the outbreak of COVID-19, we find the crash risk connections across ESG networks are stronger during crisis.     

The effect of domestic to foreign ownership change on firm performance in Europe

This paper examines the relationship between ownership change from domestic to foreign and firm performance. Using European private company data for the period of 2008–2014 and the propensity score matching method, we pair 850 companies that experience ownership change with similar companies that do not. Consistent with the managerial discipline hypothesis, the results show that foreign investors acquire larger and less profitable firms and come from bigger, wealthier, and better-governed countries. After matching firms on propensity scores for country, industry, size, return on assets and leverage, we find that, in the short term, ownership change is associated with higher sales growth but lower return on assets (ROA) and profit margin. In the long term, however, ownership change is positively related to operational efficiency (sales per employee and asset turnover). Our results also show that the origin of the acquirer matters for firm performance; the targets acquired by foreign owners from better-governed countries experience better performance improvement compared to targets acquired by foreign owners from countries with weaker governance.     

Public companies' cybersecurity risk disclosures

Though cybersecurity risks are significant and could materially affect business operations and the integrity of financial reporting, there is limited empirical research on the cybersecurity risk disclosure trends and practices of public companies. In this study, we conduct a longitudinal study of the content and linguistic characteristics of public companies' cybersecurity risk disclosure practices as well as factors that may drive disclosure trends. The results show that the two most commonly disclosed cybersecurity risks are risks of service/operation disruption and risks of data breach. Item 1A of the 10-K Report is the most commonly used disclosure location, but some companies also use Items 1 and 7 to disclose regulation risks and cyber incidents, respectively. The length of cybersecurity risk disclosures increases linearly during the period of our study. This increase is associated with the issuance of SEC guidance (2011 and 2018), industry, overall cybersecurity risks in the general environment, company size, and prior cybersecurity breach incidents. Disclosures have also become more difficult to read in general. They are more difficult to read as firm size increases and are easier to read as the proportion of intangible assets increases or after an executive change. Firms have increased their usage of litigious words in their disclosures. Bigger firms, on average, tend to use less litigious language, but companies in industries with high business information technology intensity (e.g., consumer services, software and services, and banking) tend to use more litigious language than other companies.     

Is Director Industry Experience Valuable?

We investigate whether investor reactions to the announcement of a new outside director appointment significantly depend upon the director's experience in the appointing firm's industry. Our sample includes 688 outside director appointments to boards of S&P 500 companies from 2005 to 2010. We find significantly higher announcement returns upon appointments of experienced versus inexperienced directors. To alleviate endogeneity concerns, we use the deaths of 200 directors holding 280 outside directorships as an identification strategy and find significantly more negative announcement returns associated with the deaths of experienced versus inexperienced directors. However, while our results are robust to accounting for time‐fixed unobservable director and firm characteristics, we still cannot completely rule out endogenous firm‐director matching driving our results.     

The Performance Consequences of Operational Restructurings

This study provides new evidence on the long-term performance effects of operational restructurings. While managers claim that restructurings increase the efficiency and profitability of companies, prior studies using accounting data have reached mixed conclusions regarding the post-restructuring operational effectiveness of these events. Using metrics that provide control for firm and industry performance in the absence of restructuring, and examining a time horizon extending five years subsequent to the restructuring, our results are not consistent with an improvement in operating performance resulting from restructuring after controlling for ex ante expectations of firm and industry performance. JEL Classification: G340, M410     

中国文化传媒上市公司投融资行为影响因素研究

选取中国文化传媒上市公司2006~2011年的非平衡动态面板数据,运用系统 GMM 方法构建了融资-投资模型,从内源融资、债务融资和股权融资三个方面对融资因素对投资行为的影响进行实证研究。结果表明：内源融资无法满足中国文化上市公司投资行为的资金需求,存在一定的负效应;债务融资和股权融资对投资行为起着较强的正向促进作用,且后者的影响更大。此外,中国中小型文化传媒公司存在较明显的股权融资偏好。     

Eine goldene Regel für die Modellierung von Daten zur Erstellung von Data-Mining-Klassifikationsmodellen unter besonderer Berücksichtigung von Problemstellungen in der Versicherungswirtschaft

A golden rule for data modelling for data mining classification models with special considerations of problems in insurances. To create classification models to avoid contract cancellations and for cross selling purposes to be used in marketing and sales of insurance companies the necessary data modelling will be discussed. Starting from a binary classification variable — cancelled contracts and active contracts, customers of a branch and non-customers of a branch — we in particular focus on the importance of historical data: To be able to detect decision patterns for cancellations respectively for new contracts in the data with the help of data mining tools, it is necessary for such contracts respectively customers not to use actual data, but data as they were at the time of decision. This obvious, but rarely used principle, is presented in detail as a golden rule for correct data modelling in such situations. As a case study a project and results for nine branches in each case of the Gothaer Versicherungen is presented.