Textual Analysis in Accounting and Finance: A Survey

Loughran, Tim; McDonald, Bill

doi:10.1111/1475-679x.12123

Cited by 1,524 publications

(899 citation statements)

References 125 publications

Supporting

Mentioning

786

Contrasting

Unclassified

Order By: Relevance

“…Previous research on textual analysis of company related texts was surveyed by Kearney and Liu (2014), Nassirtoussi et al (2014) and Loughran and McDonald (2016). Significant differences of word categories has been observed for firms with low/high earnings and stock returns (Li, 2008), stock market volatility (Loughran & McDonald, 2011), market-to-book ratio (Myskova & Hajek, 2016), return on assets (Davis et al, 2012), credit ratings (Hajek & Olej, 2013), Altman Z-score (Hajek et al, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Comprehensive assessment of firm financial performance using financial ratios and linguistic analysis of annual reports

Myšková¹,

Hájek²

2017

Journal of International Studies

View full text Add to dashboard Cite

Abstract. Indicators of financial performance, especially financial ratio analysis, have become important financial decision-support information used by firm management and other stakeholders to assess financial stability and growth potential. However, additional information may be hidden in management communication. The article deals with the analysis of the annual reports of U.S. firms from both points of view, a financial one based on a set of financial ratios, and a linguistic one based on the analysis of other information presented by firms in their annual reports. Spearman correlation coefficient is used to compare the values of financial and linguistic indicators. For the purpose of the comprehensive assessment, novel word lists are proposed, specifically designed for each category of financial analysis. The aim is to assess the information ability of annual reports and whether successful firms present their results precisely or not. The results show that the proposed topic dictionaries can be beneficial, especially for the assessment of cash flow and leverage ratios.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Comprehensive assessment of firm financial performance using financial ratios and linguistic analysis of annual reports

Myšková¹,

Hájek²

2017

Journal of International Studies

View full text Add to dashboard Cite

show abstract

“…One of the prominent examples is the negations of positive words to frame negative statements (Loughran and McDonald, 2016); moreover, we also find that the word "offset" is usually used to hide negative information with positive sentiment words. Therefore, to advance the understanding of financial textual information, this paper further constructs an information system based on sentence-level analysis to assist practitioners to capture more precise and meaningful insight within large amounts of textual information in finance.…”

Section: Introductionmentioning

confidence: 81%

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

Saphra¹,

Lopez²

2018

View full text Add to dashboard Cite

This paper presents NLP Lean Programming framework (NLPf), a new framework for creating custom natural language processing (NLP) models and pipelines by utilizing common software development build systems. This approach allows developers to train and integrate domain-specific NLP pipelines into their applications seamlessly. Additionally, NLPf provides an annotation tool which improves the annotation process significantly by providing a well-designed GUI and sophisticated way of using input devices. Due to NLPf's properties developers and domain experts are able to build domain-specific NLP applications more efficiently. NLPf is Opensource software and available at https:// gitlab.com/schrieveslaach/NLPf. IntroductionNowadays more and more business models rely on the processing of natural language data, e. g. companies extract relevant eCommerce data from domain-specific documents. The required eCommerce data could be related to various domains, e. g. life-science, public utilities, or social media, depending on the companies' business models.Furthermore, the World Wide Web (WWW) provides a huge amount of natural language data that provides a wide variety of knowledge to human readers. This amount of knowledge is unmanageable for humans and applications try to make this knowledge more accessible to humans, e. g. Treude and Robillard (2016) make natural language text about software programming more accessible through a natural language processing (NLP) application.All these approaches have in common that they require domain-specific NLP models that have been trained on a domain-specific and annotated corpus. These models will be trained by using different NLP frameworks and these models have to be evaluated for every annotation layer. For example, named entity recognition (NER) of Stanford CoreNLP (Manning et al., 2014) might work better than NER of OpenNLP (Reese, 2015, Chapter 1); the chosen segmentation tool, e. g. UDPipe (Straka and Straková, 2017), might work better than Stanford CoreNLP's segmentation tool, and so on. Existing studies show that domain specific training and evaluation is a common approach in the NLP community to determine the best-performing NLP pipeline (Buyko et al., 2006; Giesbrecht and Evert, 2009; Neunerdt et al., 2013; Omran and Treude, 2017).Developers of NLP applications are forced to create domain-specific corpora to determine the best-performing NLP pipeline among many NLP frameworks. During this process they face various obstacles:• The training and evaluation of different NLP frameworks requires a lot of effort of scripting or programming because of incompatible APIs.• Domain experts who annotate domainspecific documents with a GUI tool struggle with an insufficient user experience.• There are too many combinations how developers can combine these NLP tools into NLP pipelines.• The generated NLP models as a build artifact have to be integrated manually into the application code.NLP Lean Programming framework (NLPf) addresses these issues. NLPf provides a standardized p...

show abstract

“…In order to create reproducible sentiment scores [29] and due to the very robust results found by Liebmann [30], we use the Net-Optimism metric [31] combined with Henry's Finance-Specific Dictionary [32]. The sentiment scores S(A) in this method are calculated as the difference of positive W pos (A) and negative W neg (A) words, divided by the total number of words W tot (A) in a news announcement.…”

Section: Sentiment Analysis and Data Setmentioning

confidence: 99%

How The Market Can Detect Its Own Mispricing - A Sentiment Index To Detect Irrational Exuberance

Krinitz

Alfano

Neumann

2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

View full text Add to dashboard Cite

Abstract-The emergence of big data analytics enables real time news analysis. Such analysis offers the possibility to instantly extract the sentiment conveyed by any newly published, textual information source. This paper investigates the existence of a causal relationship between news sentiment and stock prices. As such, we apply news sentiment analysis for unstructured, textual data to extract sentiment scores and utilize the Granger-causality test to determine the causal relationship between daily news sentiment scores and the corresponding stock market returns. Upon successfully identifying such a causal relationship with a time lag, we develop a real-time news sentiment index. This news sentiment index serves as a decision-support system in detecting a potential over-or undervaluation of stock prices given the news sentiment of available news sources. Thus, as a novelty, the news sentiment index serves as an early-warning system to detect irrational exuberance.

show abstract

Textual Analysis in Accounting and Finance: A Survey

Cited by 1,524 publications

References 125 publications

Comprehensive assessment of firm financial performance using financial ratios and linguistic analysis of annual reports

Comprehensive assessment of firm financial performance using financial ratios and linguistic analysis of annual reports

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

How The Market Can Detect Its Own Mispricing - A Sentiment Index To Detect Irrational Exuberance

Contact Info

Product

Resources

About