Agency,Delayed Compensation,and the Structure of Executive Remuneration

In this paper we examine the factors affecting the structure of executives' compensation packages. We focus particularly on the role of various types of delayed compensation as means of “bonding” executives to their firms. The basic problem is to design a compensation package that rewards actions that are in the long-run interest of the stockholders. Firms must take into account their ability to discern unfortunate circumstances from mismanagement, the extent to which a compensation package forces the executive to face risks beyond his control, and the willingness of a given executive to bear this risk. We use our theory to interpret some executive compensation data from the early 1970s.     

The Value Relevance of Transparency and Corporate Governance in Malaysia Before and After the Asian Financial Crisis

This paper investigates whether the value relevance of financial reporting transparency and corporate governance in Malaysia increased after the Asian financial crisis of 1997. Our sample comprised 94 companies listed on the Kuala Lumpur Stock Exchange in both 1996 and 2001. The disclosure indexes were significantly value relevant for 2001 but not for 1996. Our corporate governance measure was also significantly value relevant for 2001 but not for 1996. Accordingly, it would appear that after the Asian financial crisis, investors have become more sensitized to transparency and corporate governance issues.     

Further Evidence On Investor Overreaction and Stock Market Seasonality

In a previous paper, we found systematic price reversals for stocks that experience extreme long-term gains or losses: Past losers significantly outperform past winners. We interpreted this finding as consistent with the behavioral hypothesis of investor overreaction. In this follow-up paper, additional evidence is reported that supports the overreaction hypothesis and that is inconsistent with two alternative hypotheses based on firm size and differences in risk, as measured by CAPM-betas. The seasonal pattern of returns is also examined. Excess returns in January are related to both short-term and long-term past performance, as well as to the previous year market return.     

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.