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Although many theories aim to explain initial public offering (IPO) underpricing, initial-day returns of US Real Estate Investment Trust (REIT) IPOs remain a “puzzle”. The literature on REIT IPOs has focused on indirect quantitative proxies for information asymmetries between REITs and investors to determine IPO underpricing. This study, however, proposes textual analysis to exploit the qualitative information, revealed through one of the most important documents during the IPO process – Form S-11 – as a direct measure of information asymmetries.

This study determines the level of uncertain language in the prospectus, as well as its similarity to recently filed registration statements, to assess whether textual features can solve the underpricing puzzle. It assumes that uncertain language makes it more difficult for potential investors to price the issue and thus increases underpricing. Furthermore, it is hypothesized that a higher similarity to previous filings indicates that the prospectus provides little useful information and thus does not resolve existing information asymmetries, leading to increased underpricing.

Contrary to expectations, this research does not find a statistically significant association between uncertain language in Form S-11 and initial-day returns. This result is interpreted as suggesting that uncertain language in the prospectus does not reflect the issuer's expectations about the company's future prospects, but rather is necessary because of forecasting difficulties and litigation risk. Analyzing disclosure similarity instead, this study finds a statistically and economically significant impact of qualitative information on initial-day returns. Thus, REIT managers may reduce underpricing by voluntarily providing more information to potential investors in Form S-11.

The results demonstrate that textual analysis can in fact help to explain underpricing of US REIT IPOs, as qualitative information in Forms S-11 decreases information asymmetries between US REIT managers and investors, thus reducing underpricing. Consequently, REIT managers are incentivized to provide as much information as possible to reduce underpricing, while investors could use textual analysis to identify offerings that promise the highest returns.

This is the first study which applies textual analysis to corporate disclosures of US REITs in order to explain IPO underpricing.

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.

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.

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.

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.

The purpose of this paper is to determine systematically the broader relationship between news media sentiment, extracted through textual analysis of articles published by leading US newspapers, and the securitized real estate market.

The methodology is divided into two stages. First, roughly 125,000 US newspaper article headlines from Bloomberg, The Financial Times, Forbes and The Wall Street Journal are investigated with a dictionary-based approach, and different measures of sentiment are created. Second, a vector autoregressive framework is used to analyse the relationship between media-expressed sentiment and REIT market movements over the period 2005–2015.

The empirical results provide significant evidence for a leading relationship between media sentiment and future REIT market movements. Furthermore, applying the dictionary-based approach for textual analysis, the results exhibit that a domain-specific dictionary is superior to a general dictionary. In addition, better results are achieved by a sentiment measure incorporating both positive and negative sentiment, rather than just one polarity.

In connection with fundamentals of the REIT market, these findings can be utilised to further improve the understanding of securitized real estate market movements and investment decisions. Furthermore, this paper highlights the importance of paying attention to new media and digitalization. The results are robust for different REIT sectors and when conventional control variables are considered.

This paper demonstrates for the first time, that textual analysis is able to capture media sentiment from news relevant to the US securitized real estate market. Furthermore, the broad collection of newspaper articles from four different sources is unique.

This is a literature survey paper. The purpose of this paper is to focus on the latest developments in textual analysis on China’s financial markets, highlighting its differences from existing works in the US markets.

The authors review the literature and carry out an experiment of sentiment analysis based on a small sample of Chinese news articles.

Based on the experiment of sentiment analysis, there is limited evidence on the association between sentiment and other contemporaneous or future returns.

The supply of financial textual information has grown exponentially in the past decades. Technological advancements in recent years make the programming-based analysis an effective tool to digest such information. The authors highlight the use of credible textual information and discuss directions of research in this important field.

This paper seeks to provide a textual analysis of the anti money laundering practices of the central banks of Australia (Reserve Bank of Australia (RBA)) and Ukraine (National Bank of Ukraine (NBU)).

The analysis is performed two ways by both calculating a disclosure index and through use of textual analysis.

The results show very low levels of anti money laundering disclosures by both NBU and RBA with NBU usually showing more. Textual analysis reveals that the NBU is prepared to internalise its discussion on anti‐money laundering discussing wide‐ranging topics. There appears to be a concerted communication effort by NBU to tackle the issues of money laundering head‐on. Textual analysis of the RBA's four annual reports show a clipped discourse on anti‐money laundering, treating it as if it were a distant concern. Over the four year period, there is little acknowledgement in the way of RBA textual discourse that Australia is a jurisdiction of primary concern.

The value of this paper is that, it emphasizes that, if the globalised activity of money laundering is to be crushed further energies are needed to woo central banks from varied backgrounds into exerting their considerable resources toward anti‐money laundering enforcement.

In the past decade, the development of the global economy, the change in organizational structures and the maturing of new technologies have led to considerable changes in business structures. Emergency situations, such as the recent COVID-19 pandemic, have led many companies to declare bankruptcy. In this context, the present study aims to analyze strategic opinions of company executives in a declaration of bankruptcy.

To this end, an innovative approach is applied to strategic management and business. First, the authors conducted 14 interviews with executives, and the interview data were transcribed. Second, using textual analysis and data mining techniques, the transcripts were analyzed to understand the importance of indicators identified as relevant in companies in a declaration of bankruptcy.

This resulted in identification of 10 relevant indicators perceived by executives to avoid or anticipate a state of bankruptcy, including innovation, business adaptability, room for improvement in production processes, time to react to situations of alarm, layoffs, support from public institutions, suppliers, international and national regulations, impact on the industry, credits and debts.

The paper concludes with a discussion of important theoretical and practical implications of these findings for the industry. Also, strategic management decision-making strategies are presented as a result of the innovative textual analysis approach used.

The purpose of this paper is to call researchers’ attention to cross-cultural research using online consumer reviews and multilingual textual analysis.

The authors discuss a selected literature review and the highlight of the four studies that show cross-cultural differences in online reviews on ethnic restaurants.

Applying multilingual textual analysis could prompt new venues to verify and expand future cross-cultural research in tourism and hospitality.

The paper introduces examples of multilingual textual analysis used for cross-cultural studies.

This chapter extends research on peer review by utilizing and assessing an emerging methodology: automated textual analysis. In a corpus of 38 papers successfully revised for publication in Administrative Science Quarterly, the authors found that measures based on exact wording (measured by plagiarism detection) and sentence similarity (measured by Word Mover’s Distance) performed well in capturing differences between original submissions and published papers. They identified the same overall pattern of revision that authors reported (intensive revision of Theory and Discussion sections, limited modification of Methods), and were strongly correlated with the turnover in references and hypotheses that occurred in the course of peer review. Automated textual analysis can usefully contribute to the study of manuscript change in peer review and other social scientific contexts, particularly as available textual corpora grow in size.

This paper aims to examine if Malaysian public listed companies have expressed any specific sentiment(s) when publishing their financial performance during the COVID-19 pandemic.

The disclosed sentiments contained in the management discussion and analysis section of the companies’ annual reports were extracted by means of computer-automated textual analysis through the linguistic inquiry and word counts and the Loughran–McDonald Financial Sentiment Dictionary. Next, a correlation analysis was conducted. Finally, a qualitative content analysis (QCA) was conducted to confirm these sentiments.

The analysis shows that companies adopted various tones of sentiments when communicating with their stakeholders. Most companies used negative sentiments to voice their concerns about how the COVID-19 pandemic has impacted upon their business operations. Only a few companies reflected positive sentiments, whilst those that experienced operating losses also expressed uncertainty.

This study may assist either the regulators or accounting bodies to introduce a reporting framework that public companies can adopt during natural hazards. It also provides useful insights to (potential) investors to enable them to better understand the business landscape. For future research, the same study could be conducted on more countries so that their experiences can be used to better understand the business phenomenon from a global perspective.

This study is one of few studies to adopt automated textual analysis and QCA to examine the exhibited sentiments when public companies reported their financial performance during the COVID-19 pandemic.

The authors investigate the association of the constructed corporate social responsibility (CSR) measures with the banks’ profitability, social contributions and CSR spending as well as the market reaction to CSR spending.

Using textual analysis of the CSR reports of banks listed on the Chinese market, the authors construct CSR measures in six domains: business, environment, human rights, corporate governance, charity and social capital. Our textual-based CSR measures contain substantial and valuable information beyond what Rankins CSR ratings offer.

The findings suggest that banks with stronger engagements and interests in the business-related CSR domain experience higher profitability, while those that are more committed to the corporate governance and charity-related domains create larger social contributions. Banks tend to incur higher CSR spending when they are more active in corporate governance. Although the stock market reacts positively to CSR expenditures, the reaction is less favorable for banks with CSR expenditures above the industry norm.

This study offers insights to policymakers of the regulatory bodies and the banks in China. To enhance the financial safety and soundness of the banking system, the regulatory bodies should encourage banks to strategically allocate corporate resources to achieve higher CSR ratings and engage more business-related CSR activities. To create larger social values, bank management should invest more in philanthropic CSR initiatives such as corporate governance and charity activities. To pursue higher corporate profits, they should engage more in self-centered business-related CSR activities. However, according to the reaction of the market, they should not over-invest in CSR activities.

While the use of textual analysis to evaluate CSR disclosure has recently emerged in the literature, few studies focus on banks in China. Using the term frequency–inverse data frequency (TF-IDF) method, the authors constructed a score for each of the six CSR domains: business (BUS), environment (ENV), human rights (HR), corporate governance (GOV), charity (CHY) and social capital (SCAP). To the best of our knowledge, no studies have adopted the textual approach to evaluate social reporting quality and CSR activities in the context of the banking industry in China.