Causal language intensity in performance commentary and financial analyst behaviour

Shuyu Zhang, Walter Aerts, Huifeng Pan

    Research output: Contribution to journalArticleScientificpeer-review


    We use automated techniques to measure causal reasoning on earnings‐related financial outcomes of a large sample of MD&A sections of US firms and examine the intensity of causal language in that context against extent of analyst following and against properties of analysts’ earnings forecasts. We find a positive and significant association between a firm's causal reasoning intensity and analyst following and analyst earnings forecast accuracy respectively. Correspondingly, analysts’ earnings forecast dispersion is negatively and significantly associated with causal reasoning intensity. These results suggest that causal reasoning intensity provides incremental information about the relationship between financial performance outcomes and its causes, thereby reducing financial analysts’ information processing and interpreting costs and lowering overall analyst information uncertainty. Additionally, we find that decreases in analyst following are followed by more causal reasoning on performance disclosure. We also find that firms with a considerable increase of causal disclosure especially attract new analysts who already cover many firms. Overall, our evidence of the relationship between causal reasoning intensity and properties of analyst behaviour is consistent with the proposition that causal reasoning is a generic narrative disclosure quality characteristic, able to provide incremental information to analysts and guide analysts' behaviour.
    Original languageEnglish
    Pages (from-to)3-31
    JournalJournal of Business Finance & Accounting
    Issue number1-2
    Publication statusPublished - Jan 2019


    • accuracy
    • analyst following
    • causal reasoning
    • dispersion
    • narrative disclosure


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