Abstract: I examine the role of information processing costs on post earnings announcement drift. I distinguish between hard information -quantitative information that is more easily processed -and soft information which has higher processing costs. I find that qualitative earnings information has additional predictability for asset prices beyond the predictability in quantitative information. I also find that qualitative information has greater predictability for returns at longer horizons, suggesting that frictions in information processing generate price drift. Using a tool from natural language processing called typed dependency parsing, I demonstrate that qualitative information relating to positive fundamentals and future performance is the most difficult information to process. 2 One of the most puzzling-and robust-challenges to the market efficiency hypothesis is the evidence that security prices underreact to public news. Researchers have found evidence of underreaction to earnings announcements (Ball and Brown (1968) , Bernard and Thomas (1989, 1990)), share repurchases (Ikenberry et al. (1995) ), dividend initiations and omissions (Michaely et al. (1995) ), seasoned equity offerings (Loughran and Ritter (1995) ), and stock splits (Ikenberry et al. (1996) ). These results are puzzling because an efficient market should incorporate all publicly available information immediately; thus, future returns should not be predictable based on current information. As the evidence has mounted for underreaction to information, many have begun to argue that the assumption that financial agents have unlimited capacity to process information is too strong. If information processing is costly, some information may not be immediately incorporated into prices. Empirical papers that link costly information processing to underreaction have almost exclusively focused on the idea that limited attention makes information hard to process at certain times. According to this literature, limited attention may explain why underreaction to earnings announcements is greater on Fridays (Della Vigna and Pollet, (2006) In this paper, rather than focusing on time series variation in attention, I focus on cross sectional differences in the objective costs of information processing that are intrinsically embedded in each type of information. The motivation behind this analysis is simple: information is not homogeneous in type. While some news is easy to decipher and is incorporated quickly into market prices, other news requires more costly processing and-depending on the size of the information processing costs-will be incorporated into market prices only over time. For example, Plumlee (2003) argues that the complexity of information is one reason why analyst forecasts may not incorporate public information. She shows that the rate of incorporation of tax information into forecasts is decreasing in complexity. To measure differential information processing costs, I classify news into hard/quantitative information that is more easily processed and soft/qualitative information that is more costly to process. To illustrate this point, consider the difference between evaluating a firm's income statement and the transcript of its conference call. An income statement is made up largely of numbers organized in a standardized fashion so that individuals can process it quickly and efficiently. A summary of its content (like earnings per share) can be easily created, stored, compared to other firms, and transmitted. On the other hand, the text of a conference call may not be so easy to process. Understanding its content may take a sophisticated understanding of language, tone or nuance. A summary of its content may be more difficult to create, more subjective, less comparable across firms, and more difficult to transmit. Similar arguments about the processing costs of hard and soft information are given by Petersen (2004) . I study the differential effect of information processing cost in the context of earnings announcements and the post-earnings announcement drift (PEAD). I choose this particular event as it contains both hard and soft information, is repeated over time, and is the "granddaddy of underreaction events" (Fama (1998)). To measure the qualitative content of the earnings surprise, I apply a method introduced by Tetlock (2007) that counts the number of negative words as defined by the Harvard Psychological Dictionary in the text of Dow Jones News Service (DJNS) stories about firms' earnings announcements. To measure the quantitative content of the surprise, I use the Standardized Unexpected Earnings (SUE), which is defined as the difference between firm earnings and the analyst median forecast scaled by a normalization factor. I find that qualitative earnings information embedded in the DJNS has additional predictability for asset prices beyond the predictability in SUE. The qualitative information predicts larger price changes at longer horizons than SUE, consistent with the hypothesis that information with high processing costs diffuses slowly into asset prices. I then perform a series of robustness checks that exploit heterogeneity in the cross section of investors and firms. With respect to investor heterogeneity, I find that stocks held by superior processors of information-those with high institutional ownership-experience less predictability from costly information. With respect to firm heterogeneity, I find that stocks in complex information environmentshigh-tech firms and those with large R&D expense -experience more predictability from costly information. Finally, I explore the kind of soft information that is most difficult to process. Using a tool from natural language processing called typed dependency parsing, I pair negative words with words that belong to several different categories in order to identify the kind of qualitative information embedded in the news reports that is most difficult to process. I find that qualitative information about positive fundamentals and future performance is most important for the prediction of future returns; however, this is not the case for analysts' forecasts. To my knowledge, this is one of the few times natural language processing -a way of processing language through models of sentence structure-has been used in accounting or finance. In the past, methods of machine learning like Naïve Bayes have been used Frank (2003, 2004)). These methods search for the appearance of words in text in order to classify the text in predetermined categories. They differ from Natural Language Processing because they do not attempt to understand the meaning of the text, which requires, among other things, identifying parts of speech, sentence structure and grammar. Das and Chen (2007) consider several methods to classify sentiment in text including one method which identifies part of speech. "may appear to be more ad hoc" relative to models built upon psychological biases, my results could be described using their approach of modeling costly information processing as I illustrate in the Appendix.
REF categorizes news as either quantitative or qualitative information and finds that earnings announcements with more qualitative information have stronger post-announcement drifts compared to those with more quantitative information.
Costly Information Processing: Evidence from Earnings Announcements
SSRN Electronic Journal
SSRN Electronic Journal
Economics