<p>In this paper, we study champions of corporate social responsibility (CSR) performance among the U.S. publicly traded firms and their common characteristics by utilizing machine learning algorithms to identify predictors of firms’ CSR activity. We contribute to the CSR and leadership determinants literature by introducing the first comprehensive framework for analyzing the factors associated with corporate engagement with socially responsible behaviors by grouping all relevant predictors into four broad categories: corporate governance, managerial incentives, leadership, and firm characteristics. We find that strong corporate governance characteristics, as manifested in board member heterogeneity and managerial incentives, are the top predictors of CSR performance. Our results suggest policy implications for providing incentives and fostering characteristics conducive to firms “doing good.”</p>

<p>We investigate the relationships among search symbols, trading performance, and investor participation. We use two specific datasets from Google Trends’ search volume index. The search volume by number ticker significantly predicts high returns and high investor participation when applied by active retail investors investing in large firms. This does not hold true for less active retail investors who use Chinese company name tickers as their search terms. Our results indicate that the heuristic usage of number tickers to search for company information helps active retail investors to obtain better trading performance compared with less active retail investors who use Chinese company name tickers.</p>

Considering the inherent stickiness of abnormal audit fees, our study contributes to the literature by decomposing abnormal audit fees into a jump component and long-run sticky component. We investigate whether and how changes in credit ratings asymmetrically affect the jump component of abnormal audit fees. We document a positive association between rating downgrades and the jump component. We find that heightened bankruptcy risk and misstatement risk are the mechanisms that drive this relationship. Further analysis shows that firms experiencing rating downgrades are more likely to receive a going concern opinion and experience longer audit report lags. Taken together, our findings provide direct evidence that credit ratings are significantly associated with abnormal audit fees, particularly with the jump component. Given the serial correlation of abnormal audit fees, our study sheds light on the importance of disaggregation of the abnormal audit fee residuals into the jump and long-run sticky components.

Using a supervised machine learning framework on a large training set of questions and answers, we identify 1,364 trigrams that signal nonanswers in earnings call questions and answers (Q&amp;A). We show that this glossary has economic relevance by applying it to contemporaneous stock market reactions after earnings calls. Our findings suggest that obstructing the flow of information leads to significantly lower cumulative abnormal stock returns and higher implied volatility. As both our method and glossary are free of financial context, we believe that the measure is applicable to other fields with a Q&amp;A setup outside the contextual domain of financial earnings conference calls.

This study explores the evolution of accounting research by utilizing an unsupervised machine learning approach. We aim to identify the latent topics of accounting from the 1980s up to 2018, the dynamics and emerging topics of accounting research, and the economic reasons behind those changes. First, based on 23,220 articles from 46 accounting journals, we identify 55 topics using the latent Dirichlet allocation model. To illustrate the connection between topics, we use HistCite to generate a citation map along a timeline. The citation clusters demonstrate the “tribalism” phenomenon in accounting research. We then implement the dynamic topic model to reveal the dynamics of topics to show changes in accounting research. The emerging research trends are identified from the topic analytics. We further explore the economic reasons and in-depth insights into the topic evolution, indicating the economic development embeddedness nature of accounting research.

<p>We study how and why local biodiversity affects residential property values. Leveraging remotely sensed greenness indicators and a novel dataset of granular property listings, we examine how changes in vegetation load on real estate prices. Hikes in greenness are associated with higher listing prices, fewer properties listed, and reduced liquidity in housing markets. These results suggest that price hikes in housing markets are driven by supply-side constraints instead of a “greenium” that buyers might be willing to pay due to innate preferences. Exogenous zoning shocks to foster biodiversity corroborate the presence of supply side constraints as price drivers in residential housing markets. Our findings emphasize the need to calibrate biodiversity and (social) housing policy objectives more explicitly.</p>

<p>This paper examines the impact of complex yet precise language, particularly financial jargon, on information dissemination and ultimately market efficiency. As a natural laboratory, we analyze the information exchanged during earnings conference calls, where we instrument jargon with the Plain Writing Act of 2010. Our findings suggest that the Act‘s promotion of plain language usage results in a reduction in complex financial jargon for US firms. However, in contrast to the presumed benefits of accessible language, this reduction in jargon is associated with a decrease in market efficiency, implying that the Act may inadvertently hinder information flow. This finding is particularly important at the juncture where human-generated information is received by machines, which are known to be vunerable to ambiguous inputs.</p>

<p>We study real estate lending responses to tighter macroprudential policy (MPP) in the form of lower required loan-to-value (LTV) ratios. Contract details of 2.4 million mortgage loans originated between 2008 and 2020 reveal significantly fewer new loan issuances in response to contractionary MPP, commensurate with an average reduction in aggregate lending of 21 percent. Loan-level analyses reveal, however, that banks comply with lower LTVs by systematically more benevolent valuations of residential real estate pledged as collateral instead of reducing loan size. Exploiting earthquakes as plausible exogenous shocks to property values corroborates these risk-shifting patterns by banks in the form of inflated property valuations after LTV shocks.</p>

We process the natural language of verbal firm disclosures in order to study the use of context specific language or jargon and its impact on financial performance. We observe that, within the Q&amp;A of earnings conference calls, managers use less jargon in responses to tougher questions, and after a quarter of bad economic success. Moreover, markets interpret the lack of precise information as a bad signal: we find lower cumulative abnormal returns and a higher implied volatility following earnings calls where managers use less jargon. These results support the argument that context specific language or jargon helps to efficiently and precisely transfer information.