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This paper aims to examine the relationship between monetary policy and market interest rates. This paper examines the efficiency of interest rate channel used in monetary regulation as well as implementation of monetary policy under low interest rates. This paper examines and reviews the scientific literature published over the past 30 years to determine primary research areas, to summarize their results and to identify appropriate measures of monetary policy to be used in practice in changing economic environment.

This paper reviews 94 studies focused on the relationship between monetary policy and market interest rates in terms of meeting the goals of macroeconomic regulation. The articles are selected on the basis of Scopus citation and bibliometric analysis. A major feature of this paper is the use of text analysis (data preparation, frequency of terms and collocations use, examination of relationships between terms, use of principal component analysis to determine research thematic areas). Using the method of principal component analysis while studying abstracts this paper reveals thematic areas of the research. Thus, the conducted text analysis provides unbiased results.

First, this paper examines the whole complex of relationships between monetary policy of central banks and market interest rates. Second, this research reviews a wide range of literature including recent studies focused on specific features of monetary policy under low and negative rates. Third, this study identifies and summarizes the thematic areas of all the researches using text analysis (transmission mechanism of monetary policy, efficiency of zero interest rate policy, monetary policy and term structure of interest rates, monetary policy and interest rate risk of banks, monetary policy of central banks and financial stability). Finally, this paper presents the most important findings of the studied articles related to the current situation and trends on the financial market as well as further research opportunities. This paper finds the principal results of studies on significant issues of monetary policy in terms of its efficiency under low interest rates, influence of its instruments on term structure of interest rates and role of banking sector in implementation of transmission mechanism of monetary policy.

The limitation of the review is examining articles for the study period of 30 years.

Central banks of emerging economies should apply the instruments and results of the countries' monetary policies reviewed in this paper. Using text analysis this paper reveals the main thematic areas and summarizes findings of the articles under study. The analysis allows presenting the main ideas related to current economic situation.

The findings are of great value for adjusting the monetary policy of central banks. Also, these are important for people because these show the significant role of monetary policy for the economic growth.

Using text analysis this paper reveals the main thematic areas (transmission mechanism of monetary policy, efficiency of zero interest rate policy, monetary policy and term structure of interest rates, monetary policy and interest rate risk of banks, monetary policy of central banks and financial stability) and summarizes findings of the articles under study. The analysis allows defining the current ideas relevant to the monetary policy of developing countries. It is important for central banks because it examines the monetary policy problems and proposes optimal solutions.

The paper aims to estimate how corporate philanthropy expenditures and corporate philanthropy disclosure (in general and in different spheres) affect investment attractiveness of Russian companies.

To assess the degree of corporate philanthropy disclosure the authors compiled lexicons based on a set of techniques: text and frequency analysis, correlations, principal component analysis. To adjust the existing classifications of corporate philanthropic activities to the Russian market the authors employed expert analysis. The empirical research base includes 83 Russian publicly traded companies for the period 2013–2019. To estimate the impact of indicators of corporate philanthropy disclosure on company's investment attractiveness the authors utilized panel data regression and random forest algorithm.

We compiled 2 Russian lexicons: one on general issues of corporate philanthropy and another one on philanthropic activities in various spheres (sports and healthcare; support for certain groups of people; social infrastructure; children protection and youth policy; culture, education and science). 2. The paper observes that the disclosure of non-financial data including that related to general issues of corporate philanthropy as well as to different spheres affects the market capitalization of the largest Russian companies. The results of regression analysis suggest that disclosure of altruism-driven philanthropic activities (such as corporate philanthropy in the sphere of culture, education and science) has a lesser impact on company's investment attractiveness than that of activities driven by business-related motives (sports and healthcare, children protection and youth policy).

Our findings are important to management, investors, financial analysts, regulators and various agencies providing guidance on corporate governance and sustainability reporting. However, the authors acknowledge that the research results may lack generalizability due to the sample covering a single national context. Researchers are encouraged to test the proposed approach further on other countries' data by using the authors’ compiled lexicons.

The study aims to expand the domains of signaling and agency theories. First, this subject has not been widely examined in terms of emerging markets, the authors’ study is the first to focus on the Russian market. Secondly, the majority of scholars use text analysis to examine not only the impact of charitable donations but also the effect of corporate philanthropy disclosure. Thirdly, the authors provided the authors’ own lexicon of corporate philanthropy disclosure based on machine learning technique and expert analysis. Fourthly, to estimate the impact of corporate philanthropy on company's investment attractiveness the authors used the original approach based on combination of linear (regression), and non-linear methods (permutation importance. The authors’ findings extend the theoretical concept of Peterson et al. (2021): corporate philanthropy is viewed as the company strategy to reinforce its reputation, it helps to establish more efficient relationships with stakeholders which, in its turn, results in the increased business value.

The purpose of this study is to assess how the information disclosed in prospectuses impacted the initial public offering (IPO) underpricing at a time of high government interference amid the ongoing pandemic.

The design of this study has several tracks, namely, a macro-level track, which is represented by the government measures to halt the pandemic; a micro-level track, which is followed by textual analysis of IPO prospectuses; and, finally, a machine learning track, in which the authors use state-of-the-art tools to improve their linear regression model.

The authors found that strict government anti-COVID-19 measures indeed contribute to the reduction of the IPO underpricing. Interestingly, the mere fact of such measures taking place is enough to take effect on financial markets, regardless of the resulting efficiency of such measures. At the micro-level, the authors show that prospectus sentiments and their significance differ across prospectus sections. Using linear regression and machine learning models, the authors find robust evidence that such sections as “Risk factors”, “Prospectus summary”, “Financial Information” and “Business” play a crucial role in explaining the underpricing. Their effect is different, namely, it turns out that the more negative “Risk factors” and “Financial Information” sentiment, the higher the resulting underpricing. Conversely, the more positive “Prospectus summary” and “Business” sentiments appear, the lower the resulting underpricing is. In addition, we used machine learning methods. Consisting of more than 580 IPO prospectuses, the study sample required modern and powerful machine learning tools like Isolation Forest for pre-processing or Random Forest Regressor and Light Gradient Boosting Model for modelling purposes, which enabled the authors to gain better results compared to the classic linear regression model.

At the micro level, this study is not confined to 2020, but also embraces 2021, the year of the record number of IPOs held. Moreover, in this paper, these were prospectuses that served as a source of management sentiment. In addition, the authors used a tailor-made government stringency index. At the micro level, basing the study on behavioural finance hypotheses, the authors conducted both separate and holistic analysis of prospectuses to assess investors’ reaction to different aspects of IPO companies as well as to the characteristics of the IPOs themselves. Lastly, the authors introduced a few innovations to the research methodology. Textual analysis was conducted on a corpus of prospectuses included in a study sample. However, the authors did not use pre-trained dictionaries, but instead opted for FLAIR, a modern open-source framework for natural language processing.

The purpose of our study is to study the impact of chief executive officer (CEO) optimism and narcissism on the company's capital structure.

(1) The authors opt for regression, machine learning and text analysis to explore the impact of narcissism and optimism on the capital structure. (2) We analyze CEO interviews and employ three methods to evaluate narcissism: the dictionary proposed by Anglin, which enabled us to assess the following components: authority, superiority, vanity and exhibitionism; count of first-person singular and plural pronouns and count of CEO photos displayed. Following this approach, we were able to make a more thorough assessment of corporate narcissism. (3) Latent Dirichlet allocation (LDA) technique helped to find the differences in the corporate rhetoric of narcissistic and non-narcissistic CEOs and to find differences between the topics of interviews and letters provided by narcissistic and non-narcissistic CEOs.

Our research demonstrates that narcissism has a slight and nonlinear impact on capital structure. However, our findings suggest that there is an impact of pessimism and uncertainty under pandemic conditions when managers predicted doom and completely changed their strategies. We applied various approaches to estimate the gender distribution of CEOs and found that the median values of optimism and narcissism do not depend on sex. Using LDA, we examined the content and key topics of CEO interviews, defined as positive and negative. There are some differences in the topics: narcissistic CEOs are more likely to speak about long-term goals, projects and problems; they often talk about their brand and business processes.

First, we examine the COVID-19 pandemic period and evaluate how CEO optimism and pessimism affect their financial decisions under specific external conditions. The pandemic forced companies to shift the way they worked: either to switch to the remote work model or to interrupt operations; to lose or, on the contrary, attract clients. In addition, during this period, corporate management can have a different outlook on their company’s financial performance and goals. The LDA technique helped to find the differences in the corporate rhetoric of narcissistic and non-narcissistic CEOs. Second, we use three methods to evaluate narcissism. Third, the research is based on a set of advanced methods: machine learning techniques (random forest to reveal a nonlinear impact of CEO optimism and narcissism on capital structure).

The goal of this work is to evaluate how digital transformation disclosure in corporate news and press releases affects stock prices. We examine American and Chinese companies from the energy and industry sectors for two periods: pre-COVID-19 and during the COVID-19 pandemic.

To estimate the effects of disclosure of information related to digital transformation, we applied the bag-of-words (BOW) method. As the benchmark dictionary, we used Kindermann et al. (2021), with the addition of original dictionaries created via Latent Dirichlet allocation (LDA) analysis. We also employed panel regression analysis and random forest.

For USA energy sector, all aspects of digital transformation were insignificant in pre-COVID-19 period, while sustainability topics became significant during the pandemic. As for the Chinese energy sector, digital strategy implementation was significant in pre-pandemic period, while digital technologies adoption and business model innovation became relevant in COVID-19 period. The results show the greater significance of digital transformation aspects for industrials sectors compared to the energy sector. The result of random forest analysis proves the efficiency of the authors’ dictionary which could be applied in practice. The developed methodology can be considered relevant.

The research contributes to the existing literature in theoretical, empirical and methodological ways. It applies signaling and information asymmetry theories to the financial markets, digital transformation being used as an instrument. The methodological contribution of this article can be described in several ways. Firstly, our data collection process differs from that in previous papers, as the data are gathered “from investor’s point of view”, i.e. we use all public information published by the company. Secondly, in addition to the use of existing dictionaries based on Kindermann et al. (2021), with our own modifications, we apply the original methodology based on LDA analysis. The empirical contribution of this research is the following. Unlike past works, we do not focus on particular technologies (Hong et al., 2023) connected with digital transformation, but try to cover all multi-dimensional aspects of the transformational process and aim to discover the most significant one.

This paper aims to investigate the impact of climate change news on the dynamics of US stock indices.

The empirical basis of the study was 3,209 news articles. Sentiment analysis was performed by a pre-trained bidirectional FinBERT neural network. Thematic modeling is based on the neural network, BERTopic.

The results show that news sentiment can influence the dynamics of stock indices. In addition, five main news topics (finance and politics natural disasters and consequences industrial sector and Innovations activism and culture coronavirus pandemic) were identified, which showed a significant impact on the financial market.

First, we extend the theoretical concepts. This study applies signaling theory and overreaction theory to the US stock market in the context of climate change. Second, in addition to the news sentiment, the impact of major news topics on US stock market returns is examined. Third, we examine the impact of sentimental and thematic news variables on US stock market indicators of economic sectors. Previous works reveal the impact of climate change news on specific sectors of the economy. This paper includes stock indices of the economic sectors most related to the topic of climate change. Fourth, the research methodology consists of modern algorithms. An advanced textual analysis method for sentiment classification is applied: a pre-trained bidirectional FinBERT neural network. Modern thematic modeling is carried out using a model based on the neural network, BERTopic. The most extensive topics are “finance and politics of climate change” and “natural disasters and consequences.”

The goal of the study is to examine the effects of management discussion and analysis (MD&A) sentiment in public companies' annual reports on corporate investment incentives in developing economies.

The authors use sentiment analysis of MD&A texts based on Loughran and McDonald (2011) and combination of panel data regression, logit model and random forest. The text data consists of 3,511 annual reports of Chinese listed companies for the period from 2010 to 2019.

This paper provides empirical evidence of signaling theory that sentiment of annual reports and MD&A influences corporate decisions on both M&A and internal investments. The authors found that comparing to annual reports MD&A sentiment has more stable and significant explanatory and predictive power.

This paper confirms the importance of MD&A sentiment for corporate investment decision taking and provides practical techniques for analysts and researchers to study corporate investment incentives from the point of view of signaling theory.

The study aims to expand the domains of signaling theory and corporate investment valuation by including a broader range of data on companies' M&A and internal investments in developing economies. To explore the impact of MD&A sentiment on corporate investment, a state-of-the-art set of text mining and machine learning techniques is used. The authors' results confirm that MD&A has signaling effect and can get a positive market response. Furthermore, this study enhances the empirical evidence of overconfidence theory, i.e. optimistic management whose MD&A tend to positive overestimates the management's investments decision and also underestimate the potential risk to the firm.

The paper examines whether, along with the financial performance, the disclosure of research and development (R&D) expenses, patent portfolios, patent citations and innovation activities affect the market capitalization of Russian companies.

The paper opted for a set of techniques including bag-of-words (BoW) to retrieve additional innovation-related data from companies' annual reports, self-organizing maps (SOM) to perform visual exploratory analysis and panel data regression (PDR) to conduct confirmatory analysis using data on 74 Russian publicly traded companies for the period 2013–2019.

The paper observes that the disclosure of nonfinancial data on R&D, patents and primarily product and marketing innovations positively affects the market capitalization of the largest Russian companies, which are mainly focused on energy, raw materials and utilities and are operating on international markets. The study suggests that these companies are financially well-resourced to innovate at risk and thus to provide positive signals to stakeholders and external agents.

Our findings are important to management, investors, financial analysts, regulators and various agencies providing guidance on corporate governance and sustainability reporting. However, the authors acknowledge that the research results may lack generalizability due to the sample covering a single national context. Researchers are encouraged to test the proposed approach further on other countries' data by using the compiled lexicons.

The study aims to expand the domains of signaling theory and market valuation by providing new insights into the impact that companies' reporting on R&D, patents and innovation activities has on market capitalization. New nonfinancial factors that previous research does not investigate – innovation disclosure indicators (IDI) – are tested.

The goal of the study is to examine the effects of news sentiment and topics dominating in the news field prior to the initial public offering (IPO) on the IPO underpricing.

The authors’ approach has several steps. The first is textual analysis. To detect the dominating topics in the news, the authors use Latent Dirichlet allocation. The authors use bidirectional encoder representations from transformers (BERT) pretrained on financial news corpus to evaluate the tonality of articles. The second is evaluation of feature importance. To this end, a linear regression with robust estimators and Classification and Regression Tree and Random Forest are used. The third is data. The text data consists of 345,731 news articles from Thomson Reuters related to the USA in the date range from 1 January 2011 to 31 May 2018. The data contains all the possible topics from the website, excluding anything related to sports. The sample of 386 initial public offerings completed in the USA from 1 January 2011 to 31 May 2018 was collected from Bloomberg Database.

The authors found that sentiment of the media regarding the companies going public influences IPO underpricing. Some topics, namely, the climate change and environmental policies and the trade war between the US and China, have influence on IPO underpricing if they appear in the media prior to the IPO day.

The puzzle of IPO underpricing is studied from the point of Narrative Economics theory for the first time. While most of the works cover only some specific news segment, we use Thomson Reuters news aggregator, which uses such sources The New York Post, CNN, Fox, Atlantic, The Washington Post ? Buzzfeed. To evaluate the sentiment of the articles, a state-of-the-art approach BERT is used. The hypothesis that some common narratives or topics in the public discussion may impose influence on such example of biased behaviour like IPO underpricing has also found confirmation.

The purpose of this paper is to determine the impact of corporate investments in corporate social responsibility (CSR), measured by the environmental, social and government (ESG) rating, on the market valuation of a firm's stocks and to explain the regional differences in the degree of this influence.

The empirical study uses linear and non-linear panel regression models for a panel sample of 951 firms listed in Asia, North America and Europe operating in innovative industries.

The CSR score was found to be significant in terms of stock excess return on the regional level. However, this finding cannot be extrapolated to the global scale. ESG rating is priced by the European and North American markets negatively, while in the Asian market, it is positive. This penalty (negative influence) is greater than the reward for one point increase in ESG rating.

The results of this empirical study could be used by firms' managers to adjust strategies aimed at stock value growth and by investors to select an investment strategy to maximize return.

The impact of investments in CSR on stock excess return over a defined benchmark is assessed. The study reveals regional differences in the impact of CSR investment using a sample of Asian, European and North American firms. The authors apply a more advanced lagged CSR performance (d.ESG) assessment based on the methodology of Zhang and Rajagopalan (2010).