Deal size,acquisition premia and shareholder gains

This study examines the contradictory predictions regarding the association between the premium paid in acquisitions and deal size. We document a robust negative relation between offer premia and target size, indicating that acquirers tend to pay less for large firms, not more. We also find that the overpayment potential is lower in acquisitions of large targets. Yet, they still destroy more value for acquirers around deal announcements, implying that target size may proxy, among others, for the unobserved complexity inherent in large deals. We provide evidence in favor of this interpretation.     

Forecasting the fragility of the banking and insurance sectors

Linkages between banks and insurance companies are important when forecasting the fragility of the banking and insurance sectors. We propose a novel empirical framework that allows us to estimate unobserved linkages in panel data sets that contain observed regressors. We find that taking unobserved common factors into account reduces the root mean square forecasts error of firm specific forecasts by up to 9%, of system forecasts by up to 14%, and by up to 39% for systemic forecasts of more distressed firms relative to a model based on observed variables only. Estimates of the factor loadings suggest that the correlation of financial institutions has been relatively stable over the forecast period.     

Earnings Conservatism and Value Relevance Across the Business Cycle

Abstract:  Prior research has demonstrated higher value relevance of current earnings during economic expansions relative to contractions. We largely attribute such a result to expected growth prospects being captured in the current earnings coefficient when a direct proxy for expected future earnings is omitted from the returns-earnings model. We demonstrate that the conservatism and value relevance of current earnings is actually higher during economic contractions when including a proxy for future earnings expectations. We further demonstrate that the value-relevance of expected future earnings is higher during expansions, when the association between historical accounting information and future growth opportunities likely weakens.     

The Relation between Stock Market Movements and NYSE Seat Prices

Exchange seat prices are widely reported and followed as measures of market sentiment. This paper analyzes the information content of NYSE seat prices using: (1) annual seat prices from 1869 to 1998, and (2) the complete record of trades, bids and offers for the seat market from 1973 to 1994. Seat market volumes have predictive power regarding future stock market returns, consistent with a model where seat market activity is a proxy for unobserved factors affecting expected returns. We find abnormally large price movements in seats prior to October 1987, consistent with the hypothesis that seat prices capture market sentiment.     

审计师变更的经济后果研究——来自中国证券市场的经验证据

本文运用2002—2008年我国上市公司数据研究了审计师变更的经济后果。以公司市场价值与其财务盈利的相关系数(简称为盈利可信度)作为审计师变更的经济后果的替代变量,通过研究我们发现:审计师变更总体上降低了上市公司的盈利可信度;变更前一年度财务报告被出具非标准审计意见或者处于财务困境的公司,发生审计师变更使其盈利可信度下降的程度更大;出于对审计服务级差需求的审计师变更,无论是升级变更还是降级变更公司的盈利可信度均显著高于同级变更的公司,而升级变更为声誉较好的国际四大会计师事务所带来的盈利可信度的提高程度最大;审计委员会的设立与否以及上市公司的盈余管理程度对于审计师变更的经济后果没有显著影响,这可能是由于我国上市公司审计委员会的独立性不强以及投资者对上市公司的盈余管理行为识别不足造成的。研究结论同时还说明审计意见分歧、上市公司财务困境以及对审计服务的级差需求是我国上市公司变更审计师的主要原因。本文的研究发现有助于包括上市公司、会计师事务所、投资者以及证券市场监管部门在内的信息使用者更好地理解和评价我国证券市场中的审计师变更现象,并为相关领域的研究提供了新的经验证据。     

Measuring consumption smoothing in CEX data

A new method of measuring the degree of consumption smoothing is proposed and implemented using data from the Consumer Expenditure Survey. The structure of this Survey is such that estimators previously used in the literature are inconsistent, simply because income is measured annually and consumption is measured quarterly. An AR(1) structure is imposed on the income process to obtain a proxy for quarterly income through a projection on annual income. By construction, this proxy gives rise to a measurement error which is orthogonal to the proxy itself—as opposed to the unobserved regressor—leading to a consistent estimator. Our estimates are contrasted with the output of two estimators used in the literature. This comparison shows that while the first (OLS) estimator tends to overstate the degree of risk sharing, the second (IV) estimator grossly understates it.     

Returns to scale at large banks in the US: A random coefficient stochastic frontier approach

This paper investigates the returns to scale of large banks in the US over the period 1997–2010. This investigation is performed by estimating a random coefficient stochastic distance frontier model in the spirit of Tsionas (2002) and Greene (2005, 2008). The primary advantage of this model is that its coefficients can vary across banks, thereby allowing for unobserved technology heterogeneity among large banks in the US We find that failure to consider unobserved technology heterogeneity results in a misleading ranking of banks and mismeasured returns to scale. Our results show that the majority of large banks in the US exhibit constant returns to scale. In addition, our results suggest that banks of the same size can have different levels of returns to scale and there is no clear pattern among large banks in the US concerning the relationship between asset size and returns to scale, due to the presence of technology heterogeneity.     

Standard Error Biases When Using Generated Regressors in Accounting Research

We analyze the standard error bias associated with the use of generated regressors—independent variables generated from first-step regressions—in accounting research settings. Under general conditions, generated regressors do not affect the consistency of coefficient estimates. However, commonly used generated regressors can cause standard errors to be understated. Problematic generated regressors include predicted values, coefficient estimates, and measures derived from these estimates. Widely used generated regressors in accounting include measures of earnings persistence, normal accruals, litigation risk, and conditional conservatism. Using simple regression models and simulation, we demonstrate how generated regressors can produce understated standard errors in accounting research settings. We also demonstrate how the magnitude of the standard error bias is inversely related to the precision of the generated regressor. Finally, we discuss bootstrapping as a correction for the bias and demonstrate the pairs cluster bootstrap as a tool to improve inferences in common accounting settings involving generated regressors.     

The Duration of Foreclosures in the Subprime Mortgage Market: A Competing Risks Model with Mixing

This paper examines what happens to mortgages in the subprime mortgage market once foreclosure proceeding are initiated. A multinomial logit model that allows for the interdependence of the possible outcomes or risks (cure, partial cure, paid off, and real estate owned) through the correlation of associated unobserved heterogeneities is estimated. The results show that the duration of foreclosures is impacted by many factors including contemporaneous housing market conditions, the prior performance of the loan (prior delinquency), and the state-level legal environment.     

A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output

We compare a number of widely used trend‐cycle decompositions of output in a formal Bayesian model comparison exercise. This is motivated by the often markedly different results from these decompositions—different decompositions have broad implications for the relative importance of real versus nominal shocks in explaining variations in output. Using U.S. quarterly real GDP, we find that the overall best model is an unobserved components model with two features: (i) a nonzero correlation between trend and cycle innovations and (ii) a break in trend output growth in 2007. The annualized trend output growth decreases from about 3.4% to 1.2%–1.5% after the break. The results also indicate that real shocks are more important than nominal shocks. The slowdown in trend output growth is robust when we expand the set of models to include bivariate unobserved components models.