Advantageous Comparison and Rationalization of Earnings Management

This paper proposes that psychological factors can change managers' beliefs about earnings management when they choose to engage in it. I show that, under certain circumstances, engaging in a small amount of earnings management alters a manager's beliefs about the appropriateness of the act, which may increase the likelihood of further earnings management. Specifically, I predict and find in two experiments that participants who initially choose to manage earnings are motivated to rationalize their behavior. Participants who are exposed to an egregious example of earnings management (commonly the focus of enforcement actions and press reports) have the opportunity to rationalize their behavior through a mechanism called “advantageous comparison,” where participants compare their behavior against the egregious example and conclude that what they did was relatively innocuous and appropriate. My analysis also indicates that presenting participants with an example of earnings management that is similar to the initial decision they made mitigates advantageous comparison. These results have implications for academics interested in how earnings management, and perhaps fraud, can accrete over time and for regulators and practitioners who are interested in preventing it.     

What Are You Saying? Using topic to Detect Financial Misreporting

We use a machine learning technique to assess whether the thematic content of financial statement disclosures (labeled topic) is incrementally informative in predicting intentional misreporting. Using a Bayesian topic modeling algorithm, we determine and empirically quantify the topic content of a large collection of 10-K narratives spanning 1994 to 2012. We find that the algorithm produces a valid set of semantically meaningful topics that predict financial misreporting, based on samples of Securities and Exchange Commission (SEC) enforcement actions (Accounting and Auditing Enforcement Releases [AAERs]) and irregularities identified from financial restatements and 10-K filing amendments. Our out-of-sample tests indicate that topic significantly improves the detection of financial misreporting by as much as 59% when added to models based on commonly used financial and textual style variables. Furthermore, models that incorporate topic significantly outperform traditional models when detecting serious revenue recognition and core expense errors. Taken together, our results suggest that the topics discussed in annual report filings and the attention devoted to each topic are useful signals in detecting financial misreporting.     

A survey of commercial laundry detergents – how effective are they? Part I: powders

The effectiveness of 11 non-phosphate containing powdered detergents and 12 phosphate containing powdered detergents, of varying formulations, in cleaning a standard soiled fabric in soft water (5 ppm) were evaluated. It was found that in soft water there were no differences between washing with either phosphate or non-phosphate detergent. In general, washing with detergents which contained phosphates did give somewhat better results in warm water. Detergents that contained bleach as an additive did not result in a whiter fabric.     

The Marginal Revenue Product of a Women's College Basketball Player

This paper estimates the marginal revenue product of a premium women's college basketball player. Ordinary least squares estimates indicate that acquiring another one of these players generates nearly $250,000 annually for her team. Quantile regression estimates, however, show that these quantitative effects vary substantially across teams: Players at less successful programs generate little revenue for their team, while those at the elite programs generate considerable revenues. The results provide empirical evidence that schools may have the ability to capture economic rents from the best players at the top women's basketball programs.     

Forecasting Time Series Subject to Multiple Structural Breaks

This paper provides a new approach to forecasting time series that are subject to discrete structural breaks. We propose a Bayesian estimation and prediction procedure that allows for the possibility of new breaks occurring over the forecast horizon, taking account of the size and duration of past breaks (if any) by means of a hierarchical hidden Markov chain model. Predictions are formed by integrating over the parameters from the meta-distribution that characterizes the stochastic break-point process. In an application to U.S. Treasury bill rates, we find that the method leads to better out-of-sample forecasts than a range of alternative methods.     

Book Reviews

Books reviewed:
David McDermott Hughes, From Enslavement to Environmentalism: Politics on a Southern African Frontier
Edmund Abaka, Kola is God's Gift: Agricultural Production, Export Initiatives & the Kola Industry of Asante & the Gold Coast c.1820–1950
David Hall-Mathews, Peasants, Famine and the State in Colonial Western India
Linda Whiteford and Scott Whiteford (eds), Globalization, Water and Health
Penelope Francks, Rural Economic Development in Japan: From the Nineteenth Century to the Pacific War
Him Chung, China's Rural Market Development in the Reform Era