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Table 3 Summary of recent research studies on some text-mining applications for corporate finance

From: Comprehensive review of text-mining applications in finance

Study

Datasets

Techniques/algorithms used

Evaluation parameters

Performance (on the basis of evaluation parameters)

References

Identifying text patterns for financial performance

Annual reports of US-listed companies (10-K)

Clustering, sentiment analysis

Correlations between text patterns in company’s reports and its sales performance

–

Lee et al. (2018)

Company sustainability report analysis

Corporate disclosures

n-grams, content analysis

Variations in disclosure

− 8.89 to 1.57%

Aureli (2017)

Competitive analysis from social media

Social media data from Social Mention, SABI database

Social mention tool

Pearson correlation, F-ratio

F-ratio: 3.361 (Good fit)

Gemar and Jiménez-Quintero (2015)

Automatic classification of accounting literature

Articles from EbscoHost online academic database

Bayes classifier, decision tree, rule-based classifier

Accuracy

87.27%

Chakraborty et al. (2014)

Financial reports analysis

Quarterly reports of telecom manufacturers

Prototype-matching text clustering

Change in tone of financial reports with respect to the company’s performance

–

Kloptchenko et al. (2004)

Computer-aided analysis of corporate disclosures

Annual reports of publicly listed DAX companies

Dictionary model, topic extraction, link analysis

Frequency of keywords, trend analysis

–

Matthies and Coners (2015)

Deviation detection in financial statements

Financial statements

Link Grammar Parser, conceptual graphs

Cumulative similarity

–

Kamaruddin et al. (2007)

Corporate bankruptcy prediction

Japanese annual reports

Conditional probability, chi-square test

Word frequency statistical metrics

–

Shirata et al. (2011)

Financial statement fraud detection

Financial statements

Bag-of-words, SVM

Accuracy, recall, precision, purity, F-measure

Conceptual paper; hence, performance not evaluated

Gupta and Gill (2012)

Financial footnote analysis

Income tax footnotes from financial reports

NB, k-means, KNN, SVM, decision tree

Runtime, accuracy, RMSE, absolute error

NB performed best

Runtime: 4 s

Accuracy: 82.86%

Absolute error: 0.171

RMSE: 0.414

Heidari and Felden (2015)