Information Analysts Research Articles

Cross-border regulatory cooperation and analyst forecasts

Using the staggered entry of foreign countries into the Multilateral Memorandum of Understanding (MMoU) as a shock to the cross-border regulatory cooperation and information exchange, we find that foreign firms cross-listed in the U.S. show a greater level of analyst following and a significant reduction in analyst forecast error and dispersion after their foreign home countries join the MMoU. Furthermore, the effect of the MMoU on analysts tends to be stronger for cross-listed firms with a greater level of information opacity and for analysts with greater difficulty forecasting cross-listed firms. To examine the potential channel, we find an increase in information/financial statement comparability after the entry of the MMoU by a firm's foreign home country. Collectively, our findings suggest that strengthened cross-border regulatory cooperation has a significant effect on reducing analyst information processing costs.

The Impact of Corporate Political Connections on Analyst Forecast Quality

Abstract The purpose of this study is to investigate whether the companies have significant impact of political connections on analyst forecasts quality. Refer to the literature on relevant analyst information in the past, especially distinguish the information obtained by analysts into public information and private information, and check whether it will affect the quality of analysts’ forecast information. Our empirical results show that there is a significant negative correlation between companies with political ties and the accuracy of analysts' public information. It indicates that when companies have political connections, companies are only willing to disclose information to specific stakeholders, resulting in information asymmetry. Meanwhile, the accuracy of public information obtained by analysts is relatively low, while the accuracy of private information is relatively high for the companies with political connections. JEL classification numbers: G30, M41. Keywords: Political Connections, Analyst Forecast Quality.

Cooperation of Librarians and Educators as a Direction of the University Library’s Work (on the Example of Creating and Filling in a Scientist's Scientometric Profile).

The purpose of the article is to analyze and summarize the experience of cooperation between library specialists and scientists and lecturers within the framework of the university library on the example of creating and filling in the author’s profile in various scientometric databases. The research methods are based on the information approach, which made it possible to identify problems related to the need of scientists for information support in the processes of creating and filling in their scientometric profiles; to determine the role of library specialists as information (digital) analysts in the methodological support and intensification of research in a modern university. The paper also applies general scientific methods of analysis and synthesis that have allowed us to analyze the subject of the study properly. The scientific novelty of the results obtained is to establish a correlation between the information and consulting support for the process of creating and filling the scientometric scientist’s profile and the formation of new competence models of library and information specialists within the research infrastructure of a modern university. Conclusions. It has been emphasized that current trends in information support and research support indicate a change in the role and place of academic libraries in the university research infrastructure, whose staff is constantly striving to provide the faculty with expanded services and facilities. This actualizes the problem of university libraries’ modernization in the scientific communication system, which is a condition for the development of new competence models for library and information professionals. It has been proved that registration, creation and filling of profiles in ORCID, Google Scholar, Scopus, Web of Science, etc., synchronization and exchange of data in these systems, using the potential of social networks (ResearchGate) require from library staff proper information and analytical support, advanced training in scientometrics, development of new proposals, expansion of the range of information products and services offered, which makes scientometrics one of the most relevant areas of activity of the university library.

Earnings Performance Targets in Annual Incentive Plans and Management Earnings Guidance

ABSTRACTWe study how corporate boards set earnings performance targets in CEOs’ annual incentive plans (AIPs) and the implications for strategic management earning guidance. We find that corporate boards rely on management and analyst information in setting the earnings performance targets, and the weight placed on each signal increases with its precision. We also find that management earnings guidance issued before compensation committee meetings (“event-window management forecast (MF)”) is more pessimistic than that issued by the same firm at other times. The pessimism in the event-window MF is more pronounced when the expected managerial benefits of having lower performance targets are greater. Ex post, the event-window MF pessimism is associated with higher bonus payouts to CEOs. We use a theoretical framework to illustrate how the use of earnings performance targets might drive our findings. This study highlights boards’ tradeoffs in designing executive compensation and the resulting managerial strategic disclosure.JEL Classifications: G34; M41; M52.

Audit Quality and Investment Efficiency with Endogenous Analyst Information

ABSTRACT We study audit quality and investment efficiency when an analyst’s information can curb overvaluation and auditors are subject to legal liability following audit failure. With the auditor’s damage payment based on price inflation after audit failure, the analyst’s information brings prices closer to fundamentals and provides a hedge to the auditor against legal liability risk. This weakens incentives for audit quality, and the analyst responds with more information production due to the penalty for mispricing. Consequently, in equilibrium, stricter legal liability leads to higher audit quality that reduces overinvestment but also less information production that increases underinvestment. Thus, stricter liability has a nonmonotonic effect on firm value; it increases the value of firms with a high valued growth option but reduces the value of firms with a low valued growth option. The results have implications for the optimal level of legal liability that maximizes the expected value of the firm. JEL Classifications: M41; M42; G14; G23; G32.

The Impact of Institutional Investors' Shareholding on The Efficiency of Corporate Investment

This study investigates the impact of institutional shareholding on corporate investment efficiency and identifies the role played by information asymmetry as well as agency costs. The results show that (1) institutional shareholding promotes corporate investment efficiency, with state-owned institutions playing a more pronounced role. (2) Institutional investors' shareholding is negatively related to information asymmetry. (3) Institutional investors' shareholding is negatively related to agency costs. (4) Institutional investors' shareholding can improve corporate investment efficiency by reducing corporate information asymmetry and agency costs, which are reflected in reducing corporate information opacity and analysts' forecast divergence.

A Trading Strategy Based on Analysts’ Industry Analyses – Evidence from Textual Analyses of Analyst Reports in Chinese Stock Market

ABSTRACT Applying a deep-learning method that is efficient in differentiating the order of words, we extract the tone of analysts’ industry analyses to measure analyst expertise and aggregate it at industry level as OPNI. Based on OPNI, we successfully construct the industry hedging portfolio that longs industries with highest OPNI and shorts industries with lowest OPNI, which generates significant and robust abnormal returns. Furthermore, we find that the industry hedging portfolio based on industry-level numerical forecasts cannot generate significant returns. Additionally, in mechanism analyses, we find that the informativeness of analysts’ industry analyses is driven by its predictability on industry-level unexpected revenues and earnings. Our findings suggest that industry analyses in analysts’ reports contain incremental value about their industry expertise, which is beyond analysts’ quantitative forecasts.

Trust and Risk Assessment in IoT Networks

The Internet of Things (IoT) is a large-scale, heterogeneous ecosystem of connected devices encompassing a range of purposes and computing capabilities. As IoT systems grow ubiquitous, new approaches to security are needed. This work proposes a method of risk assessment for devices that combines the use of trust models based on dynamic behaviors with static capability profiles drawn from immutable device characteristics to determine the level of risk each device poses to network security. A risk-based approach allows security mechanisms and monitoring activities to be more efficiently allocated across IoT networks. Simultaneously, devices can be allowed a greater degree of functionality while ensuring system availability and security. This paper presents a methodology and architecture to integrate risk assessment into IoT networks. This allows additional tailoring of security control application and provides higher-level, more human-readable information for security analysts.

Empirical Analysis of Global Markets Herding on COVID-19 Effect

This article investigates the presence of herding on global stock markets during COVID-19. Cross-Sectional Absolute Deviation developed by Chiang and Zheng (2010) and Chang et al. (2000) examine market-wide herding. Results show that herding is prevalent in Brazil, Germany, India, Italy, Korea, China-Shanghai, China-Shenzhen and Turkey. During the epidemic, no evidence of herding was detected in France, Hong Kong, Indonesia, Japan, Malaysia, Poland, the United Kingdom and the United States. It has been shown that the United States and Chinese cross-market herding substantially influences all markets. The impact of analyst information (target price, EPS forecasts and revenue predictions) on herding is shown before the pandemic and weakening during the pandemic. It indicates that investors disregard expert recommendations and liquidate equities during the pandemic. The findings shed light on the occurrence and determinants of herding that causes investors’ to behave irrationally during COVID-19. Policymakers, government and regulators should formulate a more robust policy to monitor, control and oversee the naïve herding that drives fundamentals away from stock prices.

Do Corporate Disclosures Constrain Strategic Analyst Behavior?

Abstract We show that analyst behavior changes in response to a randomly assigned shock that exogenously varies the timeliness and cost of accessing mandatory disclosures in the cross-section of investors: analysts reduce coverage and issue less optimistic, more accurate, less bold, and less informative forecasts. Our evidence indicates that analysts reduce a strategic component of their behavior: the changes are stronger among analysts with more strategic incentives like affiliated or retail-focused analysts. We conclude that mandatory disclosure can substitute for analyst information production, which is constrained by investors’ ability to verify forecasts using corporate filings. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Accounting estimation intensity, analyst following, and earnings forecast properties

We specify two measures of accounting estimation intensity (AEI) based on the textual analysis of the qualitative disclosures in the critical accounting policies (CAP) section of firms' MD&A. We then examine how these measures relate to financial analyst following and earnings forecast properties. Using a narrow dictionary definition of accounting estimates, we find AEI is positively associated with analyst following. It is also associated with increasing levels of private information in analysts' forecasts and the informativeness of analysts' reports when analysts engage in greater scrutiny of accounting estimates. Using a broader definition of accounting estimates yields a statistically significant relation with the informativeness of analysts' reports. Overall, our results are consistent with AEI stimulating investor demand for analysts' services and increasing the informativeness of these services.

How Incongruence Between Managers’ Tone of Voice and Word Sentiment Influences Security Analysts

Maintaining favorable analyst recommendations is important to organizations and their management, as these recommendations provide stakeholders with expert opinions about organizations’ well-being. Hence, there is a strong impetus for managers to prevent analysts downgrading their organization. In verbal communication, such as in earnings calls, managers not only try to influence analysts via the words that they use, but also via the tone in which they speak (i.e., paraverbal communication). Paraverbal communication is both an extra channel of information for analysts, and a strategic tool managers may use to exert influence. On the one hand, management wants to signal trustworthiness through congruency in their communication, but on the other hand is inclined to offset negative information by speaking positively, or downplay expectations by using a negative tone when discussing positive information. Leveraging several state-of-the-art Natural Language Processing algorithms, using earnings calls’ audio and textual data, we analyze the incongruence in managers’ communication. We find that paraverbal communication affects analysts’ recommendations and show which paraverbal strategies characterize organizations that are least likely to get downgraded. Our study therefore contributes to the literature on corporate communication, demonstrating the importance of verbal tone in managing the relationship with market intermediaries (analysts). Furthermore, this is one of the first studies in management that utilizes audio data quantitatively.

Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents

AbstractResearch SummaryResearchers increasingly use unstructured text data to construct quantitative variables for analysis. This goal has traditionally been achieved using keyword‐based approaches, which require researchers to specify a dictionary of keywords mapped to the theoretical concepts of interest. However, recent machine learning (ML) tools for text classification and natural language processing can be used to construct quantitative variables and to classify unstructured text documents. In this paper, we demonstrate how to employ ML tools for this purpose and discuss one application for identifying artificial intelligence (AI) technologies in patents. We compare and contrast various ML methods with the keyword‐based approach, demonstrating the advantages of the ML approach. We also leverage the classification outcomes generated by ML models to demonstrate general patterns of AI technological innovation development.Managerial SummaryText‐based documents offer a wealth of information for researchers and business analysts. However, researchers often need to find a way to classify these documents to use in subsequent research projects. In this paper, we demonstrate how supervised ML methods can be used to automate the process of classifying textual documents into pre‐defined categories or groups. We provide an overview of when such techniques may be used in comparison to other methods, and the considerations and tradeoffs associated with each method. We apply these methods to identify AI‐based technologies from all patents in the United States, based on patent abstract text. This allows us to show interesting patterns of AI innovation development in the United States. We also provide the code and data used in this paper for future research.

Institutional investors, political connections and stock recommendations from sell-side analysts: the case of Malaysia

PurposeThe purpose of this study is to investigate the interplay between institutional investors' ownership (IIO), politically connected firms (POC) and sell-side analysts' stock recommendations (ASR).Design/methodology/approachThis study employs ordinary least square (OLS) regression to test the hypotheses. The sample comprises 280 Malaysian public listed companies (PLC) and encompasses the 2008–2013 time frame (a total of 735 observations).FindingsThe results show a significant and positive link between IIO and ASR. In addition, a negative association is found between POC and ASR. Moreover, the POC weakens the positive relationship between the IIO and ASR.Research limitations/implicationsOne important implication of this study is that political involvement in corporate decisions is a prominent characteristic of the Malaysian market, which can significantly affect the information environment and analysts' reactions.Practical implicationsThe findings of this study provide useful empirical guidance to the regulators in evaluating the efficacy of recent regulatory initiatives. Investors may also gain useful insights from this study, specifically in recognising the crucial monitoring role played by institutional investors and how politically patronised firms are viewed unfavourably by equity analysts.Originality/valueThis study is one of the first to examine the joint influence of IIO and POC, on ASR.

Do Brand-Affiliated Hotels Have Lower Cash-Flow Risk?

Hotels are generally perceived as the riskiest type of commercial real estate (CRE) investment because hotel “leases” have relatively high turnover. Existing literature regarding CRE investment risk and return lacks investigation of hotels at the unit level—which is the level of analysis undertaken by existing and prospective hotel investors. Two major types of hotels are branded and independent ones. The purpose of this study is to investigate the variability (risk) of key performance indicators (KPIs) such as occupancy rate, and revenues and profit of branded versus independent hotels. Using a large sample of performance data regarding over 4,000 U.S. hotel properties from 2000 to 2019, we examine the extent to which branding affects the volatility of KPIs. We find that brand-affiliated hotels have lower cash flow risk measured as lower volatilities of KPIs compared with independent ones. Furthermore, the level of volatility reduction of branded hotels is greater for profit than for revenue, and profit may be the most important KPI for hotel investors. The magnitude of volatility reduction also increases as the measurement window length (number of years) increases. We also study the long-term returns of branded versus independent hotels. This study contributes to the understanding regarding the relationships between investment risk of branded versus independent hotels, extends the literature regarding hotel investment, and provides hotel investors and analysts information regarding risk to aid decisions such as developing, purchasing, holding, or disposing of hotel assets.

CDS trading and analyst optimism

This paper investigates whether and how the initiation of Credit Default Swaps (CDS) trading affects analyst forecast optimism. First, we document that the initiation of CDS trading curbs analyst forecasts optimism. Second, we find that the dampening effect of CDS on analyst optimism is stronger for firms with negative news and for firms with poorer financial performance or higher leverage, supporting a “correction effect” of CDS on non-strategic optimism. Moreover, we find that CDS also has a “disciplining effect” on strategic optimism that arises from incentives to cultivate relation with management or to please institutional investors. Overall, our evidence shows that the CDS market not only provides important information for analysts, but also alters analysts’ reporting incentives and enhances their objectivity. Additional analysis shows that this effect has disappeared after the Dodd-Frank Act.

Business intelligence systems: modern development trends

The purpose of the article is to determine the features of business intelligence systems, emphasizing their relationship with the professionalization of information analysts in the context of modern IT tools for the digitalization of society. Methodology of the research consists of the application of general scientific and special methods of cognition, in particular, methods of comparative analysis, terminological and other research methods, as well as methods of analytical Internet monitoring. The scientific novelty of this paper lies in the fact that the development trends are traced and the modern characteristics of business intelligence systems are determined in conjunction with the professional competencies of information analysts. The importance of the terminological aspect of defining business intelligence systems as "Business Intelligence" (BI) systems is emphasized. The interrelationships between information-analytical systems and businessanalytics systems at the content-functional level are revealed. Promising directions for the development ofbusiness intelligence systems in the structure of modern digital society are outlined. Conclusions. As a result of the study, it was revealed that modern trends in the development of business intelligence systems, taking into account foreign (American) experience, demonstrate the importance and necessity for the successful growth of enterprises and business structures the means of information and analytical activities integrated with information and communication technologies. Analytics and business analytics are seen not only as effective activities but also as a scientific approach to doing business. The recognition of the analytical profession as promising and in demand in the future is becoming obvious, as it is integrated into the information infrastructure of the economy and production in any sector of society. An important component of professional growth of the level of analysts is the improvement of knowledge and skills to work with information and communication technologies to effectively implement information-analytical and businessanalytical processes. Key words: business intelligence, business intelligence systems, information and analytical systems, business intelligence systems programs, information analytics.

Do Sell‐Side Analysts Play a Role in Hedge Fund Activism? Evidence from Textual Analysis*

ABSTRACTWe investigate variation in information production by sell‐side analysts and its potential role in hedge fund activist intervention, an important external corporate governance mechanism that creates shareholder value. Using textual analysis to derive an activism dictionary from intervention objectives and tactics, we find substantially more activism content in pre‐intervention analyst reports of target firms than propensity score matched control firms. Activism content is associated with more detailed reports containing more quantitative information. Target firm intervention‐date stock returns are significantly higher when activist intention (13D) filings are supported by reports with more general and objective‐specific activism content. Of activists' public letters to stakeholders, 31.9% directly mention sell‐side analysis, amplifying the association between target returns and analyst report information. The relationship between analyst information and activism returns is robust to using brokerage closures as an exogenous shock and is consistent with analyst incentives. Activist funds with no prior disclosed position in target firms and more experienced funds capture higher returns from sell‐side information. Overall, our results suggest sell‐side analysts play a significant informational role in supporting hedge fund activism.

Nondisclosure and analyst behavior: Evidence from redaction of proprietary information from public filings

This study explores how redacting proprietary information from public filings is related to analyst following and the properties of analysts' earnings forecasts. Using hand-collected data on firms' confidential treatment orders from the Securities and Exchange Commission's Electronic Data Gathering, Analysis, and Retrieval database, this study identifies firms that withhold information—redacting firms. Such firms are benchmarked to a matched set of firms not withholding information—non-redacting firms. The study predicts and demonstrates that compared to non-redacting firms, redacting firms have (i) a higher analyst following, (ii) more dispersed and less accurate analysts' earnings forecasts, and (iii) less precise public and private information in analysts' earnings forecasts. Further, additional analyses reveal that analysts covering redacting firms trade off accuracy for timeliness when issuing earnings forecasts, and the market reacts higher to the analyst reports of redacting firms. Moreover, for redacting firms, investors place a higher weight on the information derived from analysts than that obtained from the firm. Overall, the findings suggest that redacted disclosures are associated with greater investor demand for analyst outputs and a deterioration in the analysts' information environment. However, they are also associated with greater investor reliance on analyst outputs.

Herding behaviour and analyst information in global stock markets during COVID-19 pandemic

Herding behaviour and analyst information in global stock markets during COVID-19 pandemic