Previous research uses negative word counts to measure the tone of a text. We show that word lists developed for other disciplines misclassify common words in financial text. In a large sample of 10-Ks during 1994 to 2008, almost three-fourths of the words identified as negative by the widely used Harvard Dictionary are words typically not considered negative in financial contexts. We develop an alternative negative word list, along with five other word lists, that better reflect tone in financial text. We link the word lists to 10-K filing returns, trading volume, return volatility, fraud, material weakness, and unexpected earnings. * Loughran and McDonald are with University of Notre Dame. We are indebted to Paul Tetlock for comments on a previous draft. We also thank an anonymous referee, an anonymous associate editor, and seminar participants at the 2009 FMA meeting, University of Notre Dame, and York University for helpful comments. We thank Hang Li for research assistance.

Relative to quantitative methods traditionally used in accounting and finance, textual analysis is substantially less precise. Thus, understanding the art is of equal importance to understanding the science. In this survey, we describe the nuances of the method and, as users of textual analysis, some of the tripwires in implementation. We also review the contemporary textual analysis literature and highlight areas of future research.

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Defining and measuring readability in the context of financial disclosures becomes important with the increasing use of textual analysis and the Securities and Exchange Commission's plain English initiative. We propose defining readability as the effective communication of valuation‐relevant information. The Fog Index—the most commonly applied readability measure—is shown to be poorly specified in financial applications. Of Fog's two components, one is misspecified and the other is difficult to measure. We report that 10‐K document file size provides a simple readability proxy that outperforms the Fog Index, does not require document parsing, facilitates replication, and is correlated with alternative readability constructs.

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