Does the Equity Market Reward “Superior” Management Earnings Forecast? Evidence from the U.S. Quarterly Earnings Guidance

This paper examines whether equity markets reward the controversial practice of issuing short-term management earnings forecasts. Using a large sample of quarterly earnings forecasts, this research found that firms may temporarily reduce stock price volatility by issuing quarterly earnings forecasts. Furthermore, the analysis showed that not all guidance issuers are equally rewarded by equity capital markets. The benefits of reduced stock price volatility and favorable market valuation primarily accrue to firms with a track record of supplying accurate and timely short-term earnings forecasts. Findings suggest that superior short-term earnings guidance, which fosters transparent financial information environments and reduces investor information uncertainty, is indeed rewarded by equity capital markets. As limited research examines the association between forecast attributes and the capital market consequences of quarterly earnings guidance, this study aimed to provide empirical evidence on equity capital market rewards by issuing high-quality quarterly earnings guidance. A practical implication is that firms need to invest in accounting information systems and accounting talent in order to achieve capital market benefits of supplying high-quality short-term earnings forecasts. Keywords: quarterly earnings guidance, forecast attributes, accounting information system, equity market rewards, United States

Estimating value-added returns to labor training programs with causal machine learning

Technology may displace tens of millions of workers in the coming decades. Part of the explanation for the projected displacement is an expanding mismatch in skills that employers seek and the skills that workers possess. Effects of labor force displacement disproportionately affect low-income workers and workers within industries where technological change replaces labor. As a result, a great deal of emphasis is placed on training and reskilling workers to ease transitions into new careers. However, utilization of training programs may be below optimal levels if workers are uncertain about the returns to their investment in training. While the U.S. spends billions of dollars annually on reskilling programs and unemployment insurance, there are few measures of program effectiveness that workers and government can use to guide training investment decisions and ensure delivery of valuable reskilling and improved outcomes. In a nationwide conjoint survey experiment, we find job seekers prefer information on the value-added returns to earnings following enrollment in training and reskilling programs. We identify a clear demand for value-added measures. For every 10% increase in expected earnings, workers are 17.4% more likely to express interest in a training program. To meet this demand for information, governments can provide return on investment measures. Fortunately, the data to estimate these returns are available in state administrative data. We demonstrate a causal machine learning method that provides these missing causal estimates of value-added that workers prefer and that can provide correct incentives in the market for labor training. Focusing on a set of workforce training programs in Rhode Island, our causal machine learning estimates suggest that training increases enrollees’ future quarterly earnings by \$605. We estimate that return on investment ranges between -\$1,570 in quarterly earnings for the lowest value-added program to \$3,470 in quarterly earnings for the highest value-added program.

The Effects of Career and Technical Education: Evidence from the Connecticut Technical High School System

We examine the effect of attending stand-alone technical high schools in Connecticut using regression discontinuity. Male students are 10 percentage points more likely to graduate from high school and have half a semester less time enrolled in college. Male students have 32% higher average quarterly earnings. Earnings effects may in part reflect general skills: male students have higher attendance rates and test scores, industry fixed effects explain less than 1/3rd of earnings gains and large earnings gains persist past traditional college going years. Attending a technical high school does not affect the outcomes of female students.

Asymmetric Monitoring of Quarterly Financial Statements by Big 4 Auditors and the SEC's Timely Review Requirement

We investigate if Big 4 firms are asymmetrically more effective than non-Big 4 firms in monitoring income-increasing vs. income-decreasing quarterly earnings management. We also study the Securities and Exchange Commission's (SEC) 2000 requirement that audit firm reviews of quarterly financial statements be completed prior to their filing with the SEC ("timely reviews"). We find Big 4 firms are more effective than non-Big 4 firms in curbing income-increasing earnings management around seasoned equity offerings (SEOs), but not income-decreasing earnings management around open market repurchases (OMRs). In the post-2000 period, after the SEC's mandate for timely reviews began, we find income-increasing earnings management around SEOs declined significantly, and this decline is primarily driven by the clients of Big 4 firms. We provide evidence that timely quarterly reviews improve earnings quality, especially when companies have incentives to engage in income-increasing accruals and are reviewed by Big 4 firms.

Country Risk

We construct new measures of country risk and sentiment as perceived by global investors and executives using textual analysis of the quarterly earnings calls of publicly listed firms around the world. Our quarterly measures cover 45 countries from 2002-2020. We use our measures to provide a novel characterization of country risk and to provide a harmonized definition of crises. We demonstrate that elevated perceptions of a country's riskiness are associated with significant falls in local asset prices and capital outflows, even after global financial conditions are controlled for. Increases in country risk are associated with reductions in firm-level investment and employment. We also show direct evidence of a novel type of contagion, where foreign risk is transmitted across borders through firm-level exposures. Exposed firms suffer falling market valuations and significantly retrench their hiring and investment in response to crises abroad. Finally, we provide direct evidence that heterogeneous currency loadings on global risk help explain the cross-country pattern of interest rates and currency risk premia.

Transfer Learning and Textual Analysis of Accounting Disclosures: Applying Big Data Methods to Small(er) Data Sets

We introduce and apply machine transfer learning methods to analyze accounting disclosures. We use the examples of the new BERT language model and sentiment analysis of quarterly earnings disclosures to demonstrate the key transfer learning concepts of: (i) pre-training on generic "Big Data", (ii) fine-tuning on small accounting datasets, and (iii) using a language model that captures context rather than stand-alone words. Overall, we show that this new approach is easy to implement, uses widely-available and low-cost computing resources, and has superior performance relative to existing textual analysis tools in accounting. We conclude with suggestions for opportunities to apply transfer learning to address important accounting research questions.