On the predictive ability of narrative disclosures in annual reports

Balakrishnan, Ramji; Qiu, X.; Srinivasan, Padmini

doi:10.1016/j.ejor.2009.06.023

Cited by 86 publications

(61 citation statements)

References 36 publications

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“…Alternative approaches to feature selection common in finance and accounting tend to use significantly larger sets of features (e.g., measures of the presence or absence of individual words as in Antweiler and Frank, 2004, Li, 2010, Balakrishnan et al, 2010 or significantly fewer features (e.g., Loughran and McDonald, 2011, propose the six alternative single-feature measures of the "tone" of 10-K filings using the proportion of words falling into specific categories, namely, positive tone, negative tone, uncertainty, litigious, strong modal words, and weak modal words).…”

Section: B1 Features Consideredmentioning

confidence: 99%

CEO Personality and Firm Policies

Gow

Kaplan

Larcker

et al. 2016

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Based on two samples of high quality personality data for chief executive officers (CEOs), we use linguistic features extracted from conferences calls and statistical learning techniques to develop a measure of CEO personality in terms of the Big Five traits: agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience. These personality measures have strong out-of-sample predictive performance and are stable over time. Our measures of the Big Five personality traits are associated with financing choices, investment choices and firm operating performance.

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Section: B1 Features Consideredmentioning

confidence: 99%

CEO Personality and Firm Policies

Gow

Kaplan

Larcker

et al. 2016

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“…Rogers and Grant (1997) found that the texts in annual reports provide almost twice the information that is provided by the financial statements (Balakrishnan et al, 2010). A survey of the Association for Investment Management and Research (AIMR) among financial analysts revealed that 86% of the respondents found the management's discussion an important factor to assess the value of the company (Balakrishnan et al, 2010).…”

Section: The Management Discussion and Analysismentioning

confidence: 99%

“…The MD&A section is arguably the part of the annual reports that is the most read and cited by analysts (Balakrishnan et al, 2010;Li, 2010;Rogers and Grant, 1997). Rogers and Grant (1997) found that the texts in annual reports provide almost twice the information that is provided by the financial statements (Balakrishnan et al, 2010).…”

Section: The Management Discussion and Analysismentioning

confidence: 99%

“…Sun et al (2016) only use bigrams and trigrams to detect review spam. Balakrishnan et al (2010) conclude that their word unigrams model captures more information to predict market performance than the fog-index, a readability score explained in Section 5.2.4, and the psychological process features risk sentiment and tone. Dong et al (2016b) combine word unigrams with psychological process features, such as positive, negative, anger, anxiety and sadness words, in a collection of stock market opinions in social media messages to predict financial fraud.…”

Section: Word N-gramsmentioning

confidence: 99%

“…Smith and Taffler (1999) demonstrated that the chairman's statement in the annual report contains information about the future of the company, which allows a discrimination between bankrupt and the financially healthy companies, based on words. Balakrishnan et al (2010) used words to predict market performance. The results demonstrate that the text contains information regarding the under-performance and out-performance of companies.…”

Section: Financial Distressmentioning

confidence: 99%

See 2 more Smart Citations

Text mining to detect indications of fraud in annual reports worldwide

Fissette¹

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Supervised learning models to predict firm performance with annual reports: An empirical study

Qiu

Srinivasan

2013

Asso for Info Science & Tech

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Text mining and machine learning methodologies have been applied toward knowledge discovery in several domains, such as biomedicine and business. Interestingly, in the business domain, the text mining and machine learning community has minimally explored company annual reports with their mandatory disclosures. In this study, we explore the question “How can annual reports be used to predict change in company performance from one year to the next?” from a text mining perspective. Our article contributes a systematic study of the potential of company mandatory disclosures using a computational viewpoint in the following aspects: (a) We characterize our research problem along distinct dimensions to gain a reasonably comprehensive understanding of the capacity of supervised learning methods in predicting change in company performance using annual reports, and (b) our findings from unbiased systematic experiments provide further evidence about the economic incentives faced by analysts in their stock recommendations and speculations on analysts having access to more information in producing earnings forecast.

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On the predictive ability of narrative disclosures in annual reports

Cited by 86 publications

References 36 publications

CEO Personality and Firm Policies

CEO Personality and Firm Policies

Text mining to detect indications of fraud in annual reports worldwide

Supervised learning models to predict firm performance with annual reports: An empirical study

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