Measuring Qualitative Information in Capital Markets Research

A growing stream of research in accounting and finance tests the extent to which the tone of financial disclosure narrative, also referred to as its qualitative information, affects security prices, over and above the disclosed financial performance. These studies typically measure tone by counting the relative frequency of positive versus negative words in a given disclosure such as earnings press releases. Critical to word-frequency based analysis is the list of words deemed to be positive or negative. Because general wordlists (GI or Diction) likely omit words that would be considered positive or negative in the context of financial disclosure and include words that would not, we expect that these general wordlists be less powerful for hypothesis testing compared to wordlists specifically for the domain of financial disclosure (FD). Using a sample of 29,712 earnings press releases, we find that the context-specific FD wordlist produces a more powerful predictor of market reaction than the general wordlists. Additionally, in smaller samples – demonstrated here with 250 regressions using randomly-selected subsamples ranging in size from 50 to 2,000 – the domain-specific FD wordlist retains predictive ability, with rejection rates exceeding 97 percent for samples of 2,000 while the rejection rates for the general wordlists are less than 30 percent. The FD wordlist also performs better than an alternative, domain-specific wordlist. Overall, our findings indicate that the domain-specific FD wordlist provides an alternative, more powerful measure of tone for capital markets researchers. Finally, we show that equal weighting of word occurrences is more intuitive, easier to implement, and more amenable to replication than alternative sample-dependent weighting methodologies advocated by certain concurrent research. We thank workshop participants at the University of Colorado, Bill Mayew, DJ Nanda, Sundareash Ramath, Steve Rock, and Peter Wysocki for helpful comments and suggestions. We also thank the University of Miami School of Business for financial support.