中文目錄
1.運用大數(shù)據(jù)的實踐問題
2.公司的綠色人力招聘
3.企業(yè)緣何直接從非銀機構借款
4.金融公司與金融科技在小貸業(yè)務中的崛起
5.員工評論文本衡量企業(yè)不當行為風險
6.品牌資本和股價崩盤風險

1.Practical issues to consider when working with big data(RAS2022)

Increasing access to alternative or “big data” sources has given rise to an explosion in the use of these data in economics-based research. However, in our enthusiasm to use the newest and greatest data, we as researchers may jump to use big data sources before thoroughly considering the costs and benefits of a particular dataset. This article highlights four practical issues that researchers should consider before working with a given source of big data. First, big data may not be conceptually different from traditional data. Second, big data may only be available for a limited sample of individuals, especially when aggregated to the unit of interest. Third, the sheer volume of data coupled with high levels of noise can make big data costly to process while still producing measures with low construct validity. Last, papers using big data may focus on the novelty of the data at the expense of the research question. I urge researchers, in particular PhD students, to carefully consider these issues before investing time and resources into acquiring and using big data.

2.Green new hiring(RAS2022)

The mere marketing of firms as environmentally friendly does not mean that the firms are genuinely green. In this paper, we propose a new measure,?Green Score, to capture firms’ investment in green human capital based on the concentration of green skills required in firms’ job postings. First, we find that firms that increase their?Green Score?have higher future profitability. Second, firms that increase their?Green Score?generate more green patents, and those green patents are of higher quality and receive more citations. Third, traditional ratings widely used to evaluate firms’ environmental efforts do not consider firms’?Green Score. Overall, our new action-based measure is simpler and less subjective and it offers a larger time-series variation than traditional disclosure-based environmental ratings.

3.Why Do Firms Borrow Directly from Nonbanks(RFS2022)

Analyzing hand-collected credit agreements for a sample of middle-market firms over 2010–2015, we find that one-third of all loans are directly extended by nonbank financial intermediaries. Two-thirds of such nonbank lending can be attributed to bank regulations that constrain banks’ ability to lend to unprofitable and highly levered borrowers. Firms with negative EBITDA and debt/EBITDA greater than six are 32%%?and 15%%?more likely to borrow from nonbanks. These firms pay significantly higher interest rates, especially following the 2013 leveraged loan guidance revisions. Nonbank borrowers also receive different nonprice terms compared to firms borrowing from banks.

4.The Rise of Finance Companies and FinTech Lenders in Small Business Lending(RFS2022)

We document that finance companies and FinTech lenders increased lending to small businesses after the 2008 financial crisis. We show that most of the increase substituted for a reduction in bank lending. In counties in which banks had a larger market share before the crisis, finance companies and FinTech lenders increased their lending more. We find no effect of reduced bank lending on employment, wages, and new business creation by 2016. Our results suggest that finance companies and FinTech lenders are major suppliers of credit to small businesses and played an important role in the recovery from the 2008 financial crisis.

5.Tone at the Bottom: Measuring Corporate Misconduct Risk from the Text of Employee Reviews(MS2022)

This paper examines whether information extracted via text-based statistical methods applied to employee reviews left on the website Glassdoor.com can be used to develop indicators of corporate misconduct risk. We argue that inside information on the incidence of misconduct as well as the control environments and broader organizational cultures that contribute to its occurrence are likely to be widespread among employees and to be reflected in the text of these reviews. Our results show that information extracted from such text can be used to develop measures with useful properties for measuring misconduct risk. Specifically, the measures we develop clearly discriminate between high- and low-misconduct-risk firms and improve out-of-sample predictions of realized misconduct risk above and beyond other readily observable characteristics, such as Glassdoor firm ratings, firm size, performance, industry risk, violation history, and press coverage. We provide further evidence on the efficacy of our text-based measures of misconduct risk by showing that they are associated with future employee whistleblower complaints even after controlling for these same observable characteristics.

6.Brand Capital and Stock Price Crash Risk(MS2022)

We examine the relationship between brand capital and stock price crash risk. Crash risk, defined as the negative skewness in the distribution of returns for individual stocks, captures asymmetry in risk, and has important implications for investment choices and risk management. Using a sample of 39,685 publicly listed U.S. firm-year observations covering 1975 to 2018, we show that brand capital is significantly and negatively related to crash risk. We also use an advanced machine learning approach and confirm that brand capital is a strong predictor of future stock price crashes. Our cross-sectional analyses show that this negative relationship is more evident for subsamples with transitory poor earnings performance or persistent good earnings performance, greater corporate tax avoidance, and weak corporate governance structures. The results survive numerous robustness tests, including the use of alternative measures of brand capital, crash risk, and several endogeneity tests. In sum, our findings are consistent with agency theory, suggesting that high levels of brand capital expose firms to investor and customer scrutiny, which reduces managerial opportunistic behavior that may include the accumulation and concealment of negative information.