中文目錄
1.CEO過(guò)往公司經(jīng)歷與股利政策
2.披露情緒：機(jī)器學(xué)習(xí) VS 字典方法
3.公司數(shù)據(jù)都去哪了
4.企業(yè)ESG表現(xiàn)與銀行關(guān)系
5.公司的聯(lián)盟網(wǎng)絡(luò)有多重要
6.長(zhǎng)期獨(dú)立董事和公司業(yè)績(jī)

1.Hot-Stove Effects: The Impact of CEO Past Corporate Experiences on Dividend Policy (JAE2022)

The personal traits of chief executive officers (CEOs) have been found to influence corporate policy decisions. We examine the impact of CEO past corporate distress experiences on payout policy. CEOs who have experienced a distress event in their career, while working in a non-CEO position at a different firm, subsequently alter corporate payout policy once in the CEO position. They are less likely to pay dividends and repurchase shares, pay out lower levels of dividends, and are less likely to increase dividends. They further exhibit preference toward repurchases. Overall, we report that experience-driven conservatism affects payout policy, a novel finding in the literature.

2.Disclosure Sentiment: Machine Learning vs. Dictionary Methods (MS2022)

We compare the ability of dictionary-based and machine-learning methods to capture disclosure sentiment at 10-K filing and conference-call dates. Like Loughran and McDonald [Loughran T, McDonald B (2011) When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks.?J. Finance?66(1):35–65.], we use returns to assess sentiment. We find that measures based on machine learning offer a significant improvement in explanatory power over dictionary-based measures. Specifically, machine-learning measures explain returns at 10-K filing dates, whereas measures based on the Loughran and McDonald dictionary only explain returns at 10-K filing dates during the time period of their study. Moreover, at conference-call dates, machine-learning methods offer an improvement over the Loughran and McDonald dictionary method of a greater magnitude than the improvement of the Loughran and McDonald dictionary over the Harvard Psychosociological Dictionary. We further find that the random-forest-regression-tree method better captures disclosure sentiment than alternative algorithms, simplifying the application of the machine-learning approach. Overall, our results suggest that machine-learning methods offer an easily implementable, more powerful, and reliable measure of disclosure sentiment than dictionary-based methods.

3.Where Has All the Data Gone (RFS2022)

Since the finance industry is transforming into a data industry, measuring the quantity of data investors have about various assets is important. Informed by a structural model, we develop such a cross-sectional measure. We show how our measure differs from price informativeness and use it to document a new fact: data about large high-growth firms is becoming increasingly abundant, relative to data about other firms. Our structural model offers an explanation for this data divergence: large high-growth firms’ data became more valuable, as big firms got bigger and growth magnified the effect of these changes in size.

4.Corporate ESG Profiles and Banking Relationships (RFS2022)

We show that banking relationships promote corporate environmental, social, and governance (ESG) policies. Specifically, banks are more likely to grant loans to borrowers with ESG profiles similar to their own and positively influence the borrower’s subsequent ESG performance. Their influence is more pronounced when (1) banks have significantly better ESG ratings than borrowers and (2) borrowers are bank dependent. We exploit M&A among lenders as a source of quasi-exogenous variation in the lender’s ESG standard to alleviate endogeneity concerns. Overall, our study presents the first evidence on the interplay between responsible bank lending and borrowers’ ESG behavior.

5.How much does the firm's alliance network matter? (SMJ2022)

Extant empirical work partitioning the variance in firm (business segment) profitability has identified industry, corporate parent, business segment, and time as key sources. However, this variance decomposition research stream has treated firms as atomistic, autonomous entities. We employ a fast-unfolding community-detection algorithm to detect firms' network memberships and use the Shapley Value method to isolate the effect of the firm's alliance network, in addition to industry, corporate parent, business segment, and year effects, on the variance in business unit performance. Our findings demonstrate that the effect of the firm's alliance network explains 11% of the variance in firm ROA among 16,381 business segments from 1979 through 1996. We also extend the time period through 2018 and find that our results broadly hold.

6.Long-tenured independent directors and firm performance (SMJ2022)

Agency perspectives suggest long-tenured independent directors (LTIDs) may be cronies of the CEO, making their boards less effective, but we theorize that LTIDs may have particular expertise and motivation to improve board effectiveness and ultimately firm performance. We find strong support for this prediction using 15?years of data on the S&P 1,500 firms and an instrument based on director age at time of hire. We also find that an LTID adds more value to firms that are complex or mature, had more CEOs during the LTID's tenure, and have less entrenched management. Post hoc analyses of director deaths and shareholder class action lawsuits and activist motions provide additional evidence that LTIDs add value to the firms on whose boards they serve.