Evidential strategies in financial statement analysis: a corpus linguistic text mining approach to bankruptcy prediction

The qualitative information of companies’ financial statements provides useful information that can increase the accuracy of bankruptcy prediction models. In this research, a dataset of 924,903 financial statements from 355,704 German companies classified into solvent, financially distressed, and bankrupt companies using the Amadeus database from Bureau van Dijk was examined. The results provide empirical evidence that a corpus linguistic approach implementing evidential strategy analysis towards financial statements helps to distinguish between companies’ financial situations. They show that companies use different approaches and confidence assessments when evaluating their financial statements based on solvency and vary their use of evidential strategies accordingly. This leads to the proposition of a procedure to quantify and generate features based on the analysis of evidential strategies that can be used to improve corporate bankruptcy prediction. The results presented here stem from an interdisciplinary adaptation of linguistic findings and provide future research with another means of analysis in the area of text mining

Auditing Symposium IX: Proceedings of the 1988 Touche Ross/University of Kansas Symposium on Auditing Problems

Auditor evidential planning judgments / Arnold Wright, Theodore J. Mock; Discussant\u27s response to Auditor evidential planning judgments / Robert H. Temkin; Relative importance of auditing to the accounting profession: Is auditing a profit center? / Norman R. Walker, Michael D. Doll; Using and evaluating audit decision aids / Robert H. Ashton, John J. Willingham; Discussant\u27s response to The relative importance of auditing to the accounting profession: Is auditing a profit center? / Zoe-Vonna Palmrose; Accounting standards and professional ethics / Arthur R. Wyatt; Discussant\u27s response to Using and evaluating audit decision aids / Stephen J. Aldersley; Audit theory paradigms / Jack C. Robertson; Discussant\u27s response to Audit theory paradigms / Donald L. Neebes; Why the auditing standards on evaluating internal control needed to be replaced / Jerry D. Sullivan; Discussant\u27s response to Why the auditing standards on evaluating internal control needed to be replaced / William R. Kinney; AUDITOR\u27S ASSISTANT: A knowledge engineering tool for audit decisions / Glenn Shafer, Prakash P. Shenoy, Rajendra P. Srivastava; Discussant\u27s response to AUDITOR\u27S ASSISTANT: A knowledge engineering tool for audit decisions / John B. Sullivan; Reports on the application of accounting Principles -- A Review of SAS 50 / James A. Johnson; Discussant\u27s response to Reports on the application of accounting Principles -- A Review of SAS 50 / Gary L. Holstrumhttps://egrove.olemiss.edu/dl_proceedings/1008/thumbnail.jp

A survey on financial applications of metaheuristics

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program (E-2015-36)

TEXTUAL DATA MINING FOR NEXT GENERATION INTELLIGENT DECISION MAKING IN INDUSTRIAL ENVIRONMENT: A SURVEY

This paper proposes textual data mining as a next generation intelligent decision making technology for sustainable knowledge management solutions in any industrial environment. A detailed survey of applications of Data Mining techniques for exploiting information from different data formats and transforming this information into knowledge is presented in the literature survey. The focus of the survey is to show the power of different data mining techniques for exploiting information from data. The literature surveyed in this paper shows that intelligent decision making is of great importance in many contexts within manufacturing, construction and business generally. Business intelligence tools, which can be interpreted as decision support tools, are of increasing importance to companies for their success within competitive global markets. However, these tools are dependent on the relevancy, accuracy and overall quality of the knowledge on which they are based and which they use. Thus the research work presented in the paper uncover the importance and power of different data mining techniques supported by text mining methods used to exploit information from semi-structured or un-structured data formats. A great source of information is available in these formats and when exploited by combined efforts of data and text mining tools help the decision maker to take effective decision for the enhancement of business of industry and discovery of useful knowledge is made for next generation of intelligent decision making. Thus the survey shows the power of textual data mining as the next generation technology for intelligent decision making in the industrial environment

Auditing Symposium X: Proceedings of the 1990 Deloitte & Touche/University of Kansas Symposium on Auditing Problems

Discussant\u27s response to Analytical procedure results as substantive evidence / Abraham D. Akresh; Assessing control risk: Effects of procedural differences on auditor consensus / Jane E. Morton, William L. Felix; Discussant\u27s response to Assessing control risk: Effects of procedural differences on auditor consensus / Richard W. Kruetzfeldt; Illegal acts: What is the auditor\u27s responsibility? / Dan M. Guy, Ray O. Whittington, Donald L. Neebes; Discussants\u27 response no 1 to Illegal acts: What is the auditor\u27s responsibility? / Tim Damewood, Susan Harshberger, Russ Jones; Discussant\u27s response no 2 to Illegal acts: What is the auditor\u27s responsibility? / Frances M. McNair; Panel discussion on The impact of mergers of accounting firms on the auditing profession / Stephen J. Aldersley, David W. Hunerberg, Jonathan E. Kilner, Julia A. Lelik, Roger R. Nelson; New global realities and their impact on the accounting profession / Edward A. Kangas; Discussant\u27s response to With firmness in the right / Theodore F. Bluey; Neural nets versus logistic regression: A comparison of each model\u27s ability to predict commercial bank failures / Timothy B. Bell, Gary S. Ribar, Jennifer Verichio; Discussant\u27s response to Neural nets versus logistic regression: A comparison of each model\u27s ability to predict commercial bank failures / Miklos A. Vasarhelyi; Expert systems and AI-based decision support in auditing: Progress and perspectives / William E. McCarth, Eric Denna, Graham Gal; Discussant\u27s response to Expert systems and AI-based decision support in auditing: Progress and perspectives / Dana A. Madalon, Frederick W. Rook; Analytical procedure results as substantive evidence / William R. Kinney, Christine M. Hanes; With firmness in the right / Frederick L. Neumannhttps://egrove.olemiss.edu/dl_proceedings/1009/thumbnail.jp

Diversity v. Colorblindness

There seems to be broad social consensus that racial diversity is generally a good thing. Disagreements tend to focus on the constraints we should observe in bringing such diversity about. Evaluating the justification of such constraints requires understanding the kind of good that racial diversity is supposed to constitute. In this article, I draw on concepts familiar from philosophical discussions in value theory to analyze the value of racial diversity on the one hand and that of colorblindness on the other. Using several opinions from Parents Involved in Community Schools v. Seattle School District No. 1 to stake out some of the central positions, I argue first that the value of racial diversity is conditional or extrinsic, not intrinsic, but that we have both instrumental and non-instrumental reasons to regard it as something worth caring about. I further argue that the constraint of colorblindness is, strictly speaking, orthogonal to the value of racial diversity, because what colorblindness opposes is selecting for diversity, not diversity itself. The two values come into opposition, of course, in circumstances where such selection is the only feasible way of bringing diversity about. I consider the case for adhering to the constraint of colorblindness in such circumstances. I contend that the strongest positive argument for a deontological conception of colorblindness - viz., that it expresses a constitutional principle of equal respect - is obsolete. I further suggest that the strongest negative argument - viz., that selecting for diversity expresses disrespect for persons - ignores the possibility that such selection measures, when adopted in response to unjust social conditions, might more plausibly be regarded as an expression of a commitment to substantive principles of equality