09 Financial Accounting 2: Disclosure

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    The Informativeness of Text, the Deep Learning Approach
    ( 2020-08-16) Huang, Allen ; Wang, Hui ; Yang, Yi
    This paper uses a deep learning natural language processing approach (Google's Bidirectional Encoder Representations from Transformers, hereafter BERT) to comprehensively summarize financial texts and examine their informativeness. First, we compare BERT's effectiveness in sentiment classification in financial texts with that of a finance specific dictionary, the naïve Bayes, and Word2Vec, a shallow machine learning approach. We find that first, BERT outperforms all other approaches, and second, pre-training BERT with financial texts further improves its performance. Using BERT, we show that conference call texts provide information to investors and that other less accurate approaches underestimate the economic significance of textual informativeness by at least 25%. Last, textual sentiments summarized by BERT can predict future earnings and capital expenditure, after controlling for financial statement based determinants commonly used in finance and accounting research.
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    The Devil is in the Details: Firm-Specific or Market Information in Shareholder Activism
    ( 2020-08-16) Pei, Duo
    This study measures how shareholder activism may change market participants' processing and incorporation of different types of information. Specifically, I examine the earnings response coefficient (ERC), price delay, and probability of informed trading (PIN), which capture the usage of firm-specific public information, public market-wide information, and firm-specific private information, respectively. I find an increase in ERC, price delay, and PIN during shareholder activism. I also find an influx of attention-based trading in the 2 quarters immediately after 13D filing, which is subsequently replaced by information-based trading. The findings are consistent with a slower reflection of publicly available market-wide information and investors engaging in more firm-specific information processing. Investors appear to substitute more general information with focused information about activist targets in their trading decisions.
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    Why do firms forecast earnings for multiple years simultaneously?
    ( 2020-08-15) Basu, Sudipta ; Lee, Caroline
    By issuing earnings forecasts for both current and future years simultaneously, managers provide the multi-year data required for many valuation models and help investors sort out transitory and permanent shocks. We find that firms that are overpriced and have more transitory earnings tend to issue multi-year forecasts simultaneously. Overpriced firms are more likely to issue both short- and long-term bad news than only short-term bad news forecasts. Mispricing tends to be corrected after firms' multi-year forecasts, especially when overpriced firms issue both long- and short-term bad news forecasts. We also find a more linear current period earnings—return relation when firms issue multi-year forecasts, which suggests that investors underreact less to extreme news because the future year forecasts embed earnings persistence information.
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    Disclosure Speed: Evidence from Nonpublic SEC Investigations
    ( 2020-08-15) Blackburne, Terrence ; Quinn, Phillip
    We examine cross-sectional variation in how quickly managers disclose private information. We use novel data on SEC investigations that allow us to measure a shock to managers' private information sets and the time lag until subsequent disclosures. We measure the associations between 1) the time to disclose and 2) auditor quality, shareholder monitoring, analyst coverage, corporate governance, CEO compensation incentives, and ex ante litigation costs. We document that institutional ownership, changes in auditors, CEO power, and CEO equity vega are associated with faster disclosure. We document that analyst following is associated with slower disclosure. Our findings generate insights on the relation between institutional and firm characteristics and the timely disclosure of private information.
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    Hiding or Helping? Determinants and Consequences of the Timing of Earnings Conference Calls
    ( 2020-08-15) Basu, Sudipta ; Xiang, Zhongnan
    Open conference calls are an important information source because of their forward-looking discussion, interactive nature, and easy accessibility. Using Bloomberg data, we investigate why firms time earnings conference calls differently and how the stock market interprets and reacts to firms' timing choices. We directly measure retrospective and prospective news that derives from earnings calls by earnings surprise and the tone of forward-looking statements. We find that firms with more extreme news (both good and bad) tend to hold calls outside trading hours, especially in the evening. To test whether the market understands the information of "timing," we conduct an event study around the date when firms schedule the calls. We find a higher trading volume when the market is notified of an upcoming switching from outside to during trading hours. Overall, our results suggest that firms strategically time conference calls and investors infer news from the timing.