A Review of Artificial Intelligence and Biologically Inspired Computational Approaches to Solving Issues in Narrative Financial Disclosure

Minhas, Saliha; Poria, Soujanya; Hussain, Amir; Hussainey, Khalid

doi:10.1007/978-3-642-38786-9_36

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…In the pursuance of our literature review on textual analysis of corporate disclosure according to the evolution of different automated methods, we have extensively studied the other reviewing techniques of other literature review based papers (Kearney and Liu, 2014; Li, 2010; Loughran and Mcdonald, 2016; Fisher et al , 2010; Minhas et al , 2013; Hassan and Marston, 2019). Textual analysis of corporate disclosure is evolved chronologically from manual to automated with the progress of technology.…”

Section: Review Methodologymentioning

confidence: 99%

“…Prior researchers reviewed on automated textual analysis methodology in terms of the mechanism of the method where they studied on detailed mechanism of feature selection, as well as classification algorithm used in different automated methods and their shortcomings (Fisher et al , 2010; Minhas et al , 2013). We did not find in their review how the accuracy of the different automated methods has evolved over time.…”

Section: Introductionmentioning

confidence: 99%

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A review on textual analysis of corporate disclosure according to the evolution of different automated methods

Chakraborty

Bhattacharjee

2020

JFRA

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Purpose The purpose of this paper is to give a comprehensive review and synthesis of automated textual analysis of corporate disclosure to show how the accuracy of disclosure tone has been incremented with the evolution of developed automated methods that have been used to calculate tone in prior studies. Design/methodology/approach This study have conducted the survey on “automated textual analysis of corporate disclosure and its impact” by searching at Google Scholar and Scopus research database after the year 2000 to prepare the list of papers. After classifying the prior literature into a dictionary-based and machine learning-based approach, this study have again sub-classified those papers according to two other dimensions, namely, information sources of disclosure and the impact of tone on the market. Findings This study found literature on how value relevance of tone is varied with the use of different automated methods and using different information sources. This study also found literature on the impact of such tone on market. These are contributing to help investor’s decision-making and earnings and returns prediction by researchers. The literature survey shows that the research gap lies in the development of methodologies toward the calculation of tone more accurately. This study also mention how different information sources and methodologies can influence the change in disclosure tone for the same firm, which, in turn, may change market performance. The research gap also lies in finding the determinants of disclosure tone with large scale data. Originality/value After reviewing some papers based on automated textual analysis of corporate disclosure, this study shows how the accuracy of the result is incrementing according to the evolution of automated methodology. Apart from the methodological research gaps, this study also identify some other research gaps related to determinants (corporate governance, firm-level, macroeconomic factors, etc.) and transparency or credibility of disclosure which could stimulate new research agendas in the areas of automated textual analysis of corporate disclosure.

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Section: Review Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review on textual analysis of corporate disclosure according to the evolution of different automated methods

Chakraborty

Bhattacharjee

2020

JFRA

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“…Regarding the literature on sentic computing and finance, we identified 13 scientific papers that show the application of financial sentiment analysis ( Table 2). Papers Related to Sentic Computing The first study on the state of the art of sentic computing and finance was conducted in 2013 using a biologically inspired computational approach to solve the problems of narrative financial disclosures [35]. In 2017, Ahmed [34] proposed a lexicon useful to identify investor emotions in financial news.…”

Section: Sentic Computing and Financementioning

confidence: 99%

Does Twitter Affect Stock Market Decisions? Financial Sentiment Analysis During Pandemics: A Comparative Study of the H1N1 and the COVID-19 Periods

et al. 2021

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Investors are constantly aware of the behaviour of stock markets. This affects their emotions and motivates them to buy or sell shares. Financial sentiment analysis allows us to understand the effect of social media reactions and emotions on the stock market and vice versa. In this research, we analyse Twitter data and important worldwide financial indices to answer the following question: How does the polarity generated by Twitter posts influence the behaviour of financial indices during pandemics? This study is based on the financial sentiment analysis of influential Twitter accounts and its relationship with the behaviour of important financial indices. To carry out this analysis, we used fundamental and technical financial analysis combined with a lexicon-based approach on financial Twitter accounts. We calculated the correlations between the polarities of financial market indicators and posts on Twitter by applying a date shift on tweets. In addition, correlations were identified days before and after the existing posts on financial Twitter accounts. Our findings show that the markets reacted 0 to 10 days after the information was shared and disseminated on Twitter during the COVID-19 pandemic and 0 to 15 days after the information was shared and disseminated on Twitter during the H1N1 pandemic. We identified an inverse relationship: Twitter accounts presented reactions to financial market behaviour within a period of 0 to 11 days during the H1N1 pandemic and 0 to 6 days during the COVID-19 pandemic. We also found that our method is better at detecting highly shifted correlations by using SenticNet compared with other lexicons. With SenticNet, it is possible to detect correlations even on the same day as the Twitter posts. The most influential Twitter accounts during the period of the pandemic were The New York Times, Bloomberg, CNN News and Investing.com, presenting a very high correlation between sentiments on Twitter and stock market behaviour. The combination of a lexicon-based approach is enhanced by a shifted correlation analysis, as latent or hidden correlations can be found in data.

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Artificial Intelligence Literature in Accounting: A Panel Systematic Approach

Mardini

Alkurdi²

2021

Studies in Computational Intelligence

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A Review of Artificial Intelligence and Biologically Inspired Computational Approaches to Solving Issues in Narrative Financial Disclosure

Cited by 7 publications

References 36 publications

A review on textual analysis of corporate disclosure according to the evolution of different automated methods

A review on textual analysis of corporate disclosure according to the evolution of different automated methods

Does Twitter Affect Stock Market Decisions? Financial Sentiment Analysis During Pandemics: A Comparative Study of the H1N1 and the COVID-19 Periods

Artificial Intelligence Literature in Accounting: A Panel Systematic Approach

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