Measurement Error in Google Ticker Search

We quantify and illustrate the effects of measurement error in the Google ticker search volume index (“SVI”)—a commonly used proxy for investor attention. Based on a dataset of roughly 2.7 billion website visits following S&P 500 ticker searches, we estimate that 69% of searches are not by investors searching for information, and find that this measurement error is highly correlated with firm characteristics. We then show that measurement error in SVI can cause erroneous inferences in three common types of tests. First, in tests of investor attention around information events, measurement error biases coefficients towards zero and can generate false-negative results (type 2 errors). Second, because SVI measurement error is correlated with firm characteristics, it can easily generate false-positive results in cross-sectional tests (type 1 errors). Third, tests that compare SVI to other attention proxies can produce erroneous inferences due to difference

The Financial Crisis and Credibility of Corporate Credit Ratings

Thesis (Ph.D.)--University of Washington, 2013Credit ratings on certain structured finance products significantly underestimated default risk prior to the recent financial crisis. Rating agency executives acknowledge that these failures damaged the agencies' credibility with respect to credit ratings on structured finance products. I investigate whether the agencies' credibility with respect to corporate credit ratings also suffers as a result of the financial crisis, as well as how credibility damage affects the use of corporate ratings and accounting information in debt pricing. I find evidence consistent with credibility concerns motivating debt market participants to simultaneously decrease their reliance on corporate credit ratings and increase their reliance on accounting data in the post-crisis period. Additional tests are consistent with corporate ratings being viewed as optimistically biased as opposed to simply inaccurate. Most directly, my study provides insight as to the credibility effects of the financial crisis on the credit rating agencies. More broadly, my study provides new empirical evidence on the relation between credit rating credibility and usage, and also informs the literature about the substitutability between corporate credit ratings and accounting information in debt markets

Measurement Error in Google Ticker Search

We quantify and illustrate the effects of measurement error in the Google ticker search volume index (“SVI”)—a commonly used proxy for investor attention. Based on a dataset of roughly 2.7 billion website visits following S&P 500 ticker searches, we estimate that 69% of searches are not by investors searching for information, and find that this measurement error is highly correlated with firm characteristics. We then show that measurement error in SVI can cause erroneous inferences in three common types of tests. First, in tests of investor attention around information events, measurement error biases coefficients towards zero and can generate false-negative results (type 2 errors). Second, because SVI measurement error is correlated with firm characteristics, it can easily generate false-positive results in cross-sectional tests (type 1 errors). Third, tests that compare SVI to other attention proxies can produce erroneous inferences due to difference

Measurement Error in Dependent Variables in Accounting: Illustrations Using Google Ticker Search and Simulations

This paper illustrates how measurement error (“ME”) in dependent variables not only reduces power but, under common conditions in accounting and finance studies, can lead to statistical biases and erroneous inferences. These confounds exist because ME in accounting-based proxies is typically nonadditive, which violates the simple assumptions discussed in most econometrics texts. We demonstrate the effects of nonadditive ME in papers using Google ticker search volume index (“SVI”) as a measure of investor attention. We show that ME in SVI generates both type I and II errors in published studies, and we introduce a new measure of investor-specific ticker search to reduce biases in future research. We also use simulations to show that small amounts of ME in accounting asset values can confound inferences in commonly-used accounting-based proxies such as ROA and Tobin’s Q. Our findings contribute to the literature by improving researchers’ understanding of the effects of ME in common analyses

Reputation Repair After a Serious Restatement

How do firms repair their reputations after a serious accounting restatement? To answer this question, we review firms\u27 press releases and identify 1,765 reputation-building actions taken by: (1) 94 restating firms in the periods before and after their restatement; and (2) a set of matched control firms during contemporaneous periods. We posit that firms have incentives to target multiple stakeholders in a reputation repair strategy-including capital providers, customers, employees, and geographic communities-and that actions targeting each group generate positive market returns as reputation capital is repaired. Consistent with our predictions, the frequency of, and stock returns to, reputation-building actions are greater for restating firms in the period after their restatement than for the control groups. In addition, firm characteristics predict the types of stakeholders targeted by firms. Finally, actions targeted at both capital providers and other stakeholders are associated with improvements in the restating firm\u27s financial reporting credibility

Reputation Repair After a Restatement

How can reputations be repaired after a financial reporting scandal such as an accounting restatement? We read 23,078 press releases and identify 1,219 reputation-building actions taken by 94 restating firms to answer this question. We posit that firms have incentives to target multiple stakeholders in a reputation repair strategy – including capital providers, customers, employees, and geographic communities – and that actions which target each of these groups will generate positive market returns. Consistent with our predictions we observe significant increases in the frequency of, and abnormal stock returns to, reputation-building actions targeted at these stakeholders in the year after the restatement relative to a control period. Further, we demonstrate that reputation-building actions directed towards customers, employees, and communities complement actions directed towards capital providers, and that firm characteristics predict the specific types of stakeholders that managers choose to target. Finally, we show that actions targeted at both capital providers and other stakeholders are associated with improvements in the restating firm’s financial reporting credibility

Measurement Error in Dependent Variables in Accounting:Illustrations Using Google Ticker Search and Simulations

This paper illustrates how measurement error (“ME”) in dependent variables not only reduces power but, under common conditions in accounting and finance studies, can lead to statistical biases and erroneous inferences. These confounds exist because ME in accounting-based proxies is typically nonadditive, which violates the simple assumptions discussed in most econometrics texts. We demonstrate the effects of nonadditive ME in papers using Google ticker search volume index (“SVI”) as a measure of investor attention. We show that ME in SVI generates both type I and II errors in published studies, and we introduce a new measure of investor-specific ticker search to reduce biases in future research. We also use simulations to show that small amounts of ME in accounting asset values can confound inferences in commonly-used accounting-based proxies such as ROA and Tobin’s Q. Our findings contribute to the literature by improving researchers’ understanding of the effects of ME in common analyses