The Information Content of Forward-Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach

By Feng Li. 2010

1,140 citations recorded by the source corpus.

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Citations in the corpus

Textual Sentiment in Finance: A Survey of Methods and Models (2014, methodology): “The dictionary-based approach uses a mapping algorithm in which a computer program reads text and classifies the words, phrases or sentences into groups based on pre-defined dictionary categories (Li (2010)).”
Good debt or bad debt: Detecting semantic orientations in economic texts (2013, methodology): “The corpus used in this article is made out of English news on all listed companies in OMX Helsinki. The news has been downloaded from the LexisNexis database using an automated web scraper. Out of this news database, a random subset of 10,000 articles was selected to obtain good coverage across sma”
Measuring Qualitative Information in Capital Markets Research: Comparison of Alternative Methodologies to Measure Disclosure Tone (2016, methodology): “Further, inverse document frequency weighting, advocated in Loughran and McDonald (2011), provides little improvement to the alternative approach of equal weighting.”
The Economics of Disclosure and Financial Reporting Regulation: Evidence and Suggestions for Future Research (2015, background): “Thus, it is ex ante unclear how SOX affects corporate investment. Bargeron et al. (2010) and Kang et al. (2010) analyze this effect and present evidence consistent with a decline in corporate investment by U.S. firms relative to non-U.S. firms in the period after SOX. In contrast, Albuquerque and Zh”
Text mining for market prediction: A systematic review (2014, methodology): “Top 30 concepts Binary, TF-IDF Rachlin et al. (2007) Bag-of-words, commonly used financial values Most influential keywords list (Automatic extraction) TF, Boolean, Extractor software output Tetlock et al. (2008) Bag-of-words for negative words Pre-defined dictionary.”
The perceived credibility of forward-looking performance disclosures (2014, neutral): “Li (2010a) finds that firms with better current performance, smaller size, less return volatility and lower market-to-book ratios issue more positive forward-looking disclosures.”
The Impact of Audit Committee Financial Expertise on Management Discussion and Analysis (MD&A) Tone (2019, methodology): “…explore the informativeness of qualitative disclosures in corporate SEC filings (Campbell, Chen, Dhaliwal, Lu, & Steele, 2014; Feldman et al., 2010; Li, 2008, 2010; Mayew, Sethuraman, & Venkatachalam, 2015; Muslu, Radhakrishnan, Subramanyam, & Lim, 2015), media articles (Tetlock, 2007; Tetlock,…”
Informational Content and Assurance of Textual Disclosures: Evidence on Integrated Reporting (2019, neutral): “…and finance examine whether and to what extent textual or linguistic attributes of financial disclosures (e.g., readability, sentiment and style) affect the decision-making of investors and information intermediaries (Lang & Stice-Lawrence, 2015; Li, 2008, 2010; Loughran & McDonald, 2016).”
Aggregated, Voluntary and Mandatory Risk Disclosure Incentives: Evidence from UK FTSE All Share Companies (2013, background): “First, within a UK context, an opportunity exists to explore issues regarding investors’ perceived risk (e.g., Linsmeier et al., 2002) or the information content of risk disclosures (e.g., Ryan and Taffler, 2004; Bajo, 2010; Li, 2010).”
The Role of the Media in Takeovers: Theory and Evidence (2015, positive): “This section builds upon the classical Bagnoli and Lipman (1988) model by adding asymmetric information and a signaling stage in the spirit of Milgrom and Roberts (1986).”
Firm and Investor Responses to Uncertain Tax Benefit Disclosure Requirements (2013, neutral): “We also include BUS_SEG and GEO_SEG to control for the complexity of the firms underlying business (Li 2008, 2010).”
Manager sentiment and stock returns (2019, methodology): “…opinions and beliefs about why their firms performed as they did over the recent fiscal period and their expectations for future firm performance (Li, 2008, 2010; Henry, 2008; Blau, DeLisle, and Price, 2015; Brochet, Kolev, 1The latest Google article citations of Baker and Wurgler (2006)…”
Textual analysis in finance (2019, methodology): “…to predict the pessimism and optimism of internet messages (Antweiler and Frank, 2004), the tone in analyst reports (Huang et al., 2014) and corporate filings (Li, 2010), among other applications (Antweiler and Frank, 2006; Das and Chen, 2007; Agarwal et al., 2016; Buehlmaier and Whited, 2018a).”
Textual Analysis of Corporate Disclosures: A Survey of the Literature (2011, methodology): “2.0.3 Variables of interest from textual information Conceptually, there are at least three disclosure characteristics that are potentially interesting to researchers [Li, 2010a]: the amount of disclosure, the tone, and the transparency (or readability).”