Asymmetric Information Problems Research Articles

Foreign Experienced CFOs’ and Financial Reporting Quality

This paper aims to empirically examine whether foreign experienced CFOs (FCFOs) affect financial reporting quality (FRQ). Analyzing Chinese listed firms from 2005 to 2018, this study employs methods such as ordinary least square, fixed effect model (FEM), generalized method of moment (GMM), two-stage least square (2SLS), propensity score matching method (PSM), and change analysis to find the relationship between FCFOs and FRQ. The results show that FCFOs significantly positively impact FRQ, which means that FCFOs improve FRQ in Chinese firms. We argue that international experience imprints CFO cognition, elevates his moral standards, and makes him more transparent in dealings, leading to a better information environment and robust corporate governance mechanism. In addition, this study indicates that the relationship between FCFOs and FRQ is significant in non-state-owned enterprises (NSOEs), suggesting that NSOEs outperform state-owned enterprises (SOEs) in terms of FRQ. Finally, we find that both foreign work and study experiences of CFOs affect FRQ. The findings suggest that asymmetric information problems can be alleviated by encouraging foreign experienced individuals to the firm’s CFO position. The study provides empirical evidence that a CFO’s foreign experience determines a firm’s FRQ.

Analyst coverage and default risk

This study explores the causal effect of analyst coverage on corporate default risk. Using the exogenous drop of analyst coverage due to brokerage mergers and closuresas a natural experiment, we observe an increase in the probability of default following the coverage termination. This result is driven by firms having large asymmetric information problems (few analysts following and intangible-intensive firms), higher financing constraints, lower stock liquidity and firms operating in countries with a larger domestic stock market size. We explore agency costs and stock liquidity as potentialchannels, finding empirical support only for this latter. Finally, we observe that firms react to the analyst loss by adopting conservative investment and financing policies in the aim to mitigate the increase in default risk.

Algorithmic Decision-Making, Agency Costs, and Institution-Based Trust

Algorithm Decision Making (ADM) systems designed to augment or automate human decision-making have the potential to produce better decisions while also freeing up human time and attention for other pursuits. For this potential to be realised, however, algorithmic decisions must be sufficiently aligned with human goals and interests. We take a Principal-Agent (P-A) approach to the questions of ADM alignment and trust. In a broad sense, ADM is beneficial if and only if human principals can trust algorithmic agents to act faithfully on their behalf. This mirrors the challenge of facilitating P-A relationships among humans, but the peculiar nature of human-machine interaction also raises unique issues. The problem of asymmetric information is omnipresent but takes a different form in the context of ADM. Although the decision-making machinery of an algorithmic agent can in principle be laid bare for all to see, the sheer complexity of ADM systems based on deep learning models prevents straightforward monitoring. We draw on literature from economics and political science to argue that the problem of trust in ADM systems should be addressed at the level of institutions. Although the dyadic relationship between human principals and algorithmic agents is our ultimate concern, cooperation at this level must rest against an institutional environment which allows humans to effectively evaluate and choose among algorithmic alternatives.

Cash holding, economic uncertainty and investment: Evidence from ASEAN countries

This research observes the effect of uncertainty on corporate cash holding. Uncertainty drives up asymmetric information problems, increasing companies' external capital costs. As a result, companies prefer to accumulate cash to protect their investment capabilities in the future. The study uses a sample of non-financial public companies in six ASEAN countries, such as Malaysia, the Philippines, Thailand, Singapore, Indonesia, and Vietnam, from 2008 to 2021. The study uses a total of 34,359 firm-year observations. The research method employs a fixed-effect panel data model with Driscoll-Kraay standard errors. The findings of this study indicate that economic uncertainty can cause companies to accumulate their cash holdings. Furthermore, cash holding can reduce the impact of economic uncertainty on decreasing corporate investment decisions. This research contributes to the literature by showing the relationship between economic uncertainty, cash holdings, and corporate investment in the context of ASEAN countries.

The Insurance Sector and the Problem of Asymmetric Information

The Insurance Sector and the Problem of Asymmetric Information

Role of Machine Learning in Cybersecurity: Techniques and Challenges

Abstract: Machine learning (ML) plays a crucial role in cybersecurity across various aspects of threat detection, prevention, and response. Here are specific roles and applications of machine learning in cybersecurity. Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such a discrepancy has its root cause in the current state of the art, which does not allow us to identify the role of ML in cybersecurity. The full potential of ML will never be unleashed unless its pros and cons are understood by a broad audience. In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting data-bases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators

Role of Machine Learning in Cybersecurity: Techniques and Challenges

Abstract: Machine learning (ML) plays a crucial role in cybersecurity across various aspects of threat detection, prevention, and response. Here are specific roles and applications of machine learning in cybersecurity. Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such a discrepancy has its root cause in the current state of the art, which does not allow us to identify the role of ML in cybersecurity. The full potential of ML will never be unleashed unless its pros and cons are understood by a broad audience. In the computer world, data science is the force behind the recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated and intelligent is to extract patterns or insights related to security incidents from cybersecurity data and construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, and systems, is the study of actual occurrences via the use of data. Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting data-bases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators

Regression for Asymmetric Information Problem: The Comparison of Different Methods

ABSTRACT In previous decades, seminal theories were advanced addressing the problems of asymmetric information. These scholarly works confirmed a significant positive correlation between insurance coverage and risk in insurance markets. We showed that when regression models were used to analyze the asymmetric information problem, the sample selection bias and chosen regression methods could produce distinct results. Moreover, samples at different quantiles may lead to considerably different levels of information asymmetry. The present research suggests that quantile regression models be employed to better reveal the situation of the insurance market and reduce estimation errors.

Insurance Contracting with Adverse Selection and Moral Hazard

Most of the previous research in the asymmetric information problem treats adverse selection and moral hazard separately, though they may coexist and interact with each other. We build a principal-agent model to examine optimal contracts in a competitive insurance market facing adverse selection and moral hazard simultaneously. We apply the change-of-variable method and the Kuhn-Tucker conditions to solve the optimization programs. Our model yields richer separating Nash equilibria than pure moral hazard and pure adverse selection models, although separating Nash equilibria may not exist in some cases. It also retains some properties, for example, no full insurance and the positive correlation between insurance coverage and risk type, in those benchmark models. Our study on comparative statics indicates that, under some conditions and with some exceptions, the optimal indemnity and premium decrease with disutility from effort, increase with potential loss, and decrease with the initial wealth of the insured.

Information asymmetry and dividend payout in an emerging market: Does corporate governance quality matter?

Numerous studies have explored the impact of information asymmetry on firms’ dividend policies. These studies have generally focused on advanced capital markets and have provided conflicting evidence on the quality of the information environment and dividend policy. Our paper, thus, tries to address this gap in current understanding by examining the connection between asymmetric information and dividend payout policies and whether this connection is moderated by corporate governance quality (CGQ) in an emerging economy, the United Arab Emirates (UAE). Using a panel sample of non-financial firms traded on the UAE stock exchanges over the period from 2009 to 2022, we document that dividend payments are negatively influenced by information asymmetry problems. We also document that the negative connection between information asymmetry and dividend policy is less pronounced in firms with strong corporate governance systems, consistent with the conjecture that such firms face lower agency and asymmetric information problems and hence pay higher dividends.

The Impact of Information Asymmetry on Investment Behavior in the Stock Market

The stock market is the main component of the capital market and plays an important role in promoting the development of the national economy. Information asymmetry, as a typical issue in China’s stock market, triggers stock price fluctuations, increases stock market risks and largely influences investors’ investment behavior in stock market. Therefore, improving the information asymmetry problem is of great significance for the stable development of the stock market. This paper depends on the nature of information asymmetry, explores its specific manifestation in the stock market, and then analyses the process of information asymmetry issues on investment behavior. Studies indicate that information asymmetry leads to misdirected investment from three aspects, which include judgment on individual stock, assessment of the market value of the company and expectations of stock market developments, resulting in investor losses. Based on the above conclusions, this paper puts forward recommendations for improvement in terms of market regulation and the legal system: to improve the transparency of stock market transactions through the disclosure of information by regulators and companies. To complete the legislation of the stock market and establish strict punishment mechanisms to ease the problem of asymmetric information and maintain the stable operation of the stock market.

DyCause: Crowdsourcing to Diagnose Microservice Kernel Failure

Today many web applications in the cloud (apps) are built based on microservices. However, as the anomaly propagates in a highly dynamic and complex way, troubleshooting them becomes full of challenges. Existing diagnostic methods are mostly designed based on monitoring metrics retrieved from the microservice system kernel. Therefore, application owners and even site reliability engineers (SREs) cannot effectively resort to those methods when the microservice systems lack such a comprehensive monitoring infrastructure. In this paper, we develop DyCause, a crowdsourcing solution to the asymmetric diagnostic information problem. Our solution collects the operational status of kernel services collaboratively from the user space and initiates diagnosis on demand. Without the requirement of any architectural or functional infrastructure, it is both fast and lightweight to deploy DyCause in a microservice system. In order to discover the fine-grained dynamic causalities between services during the anomaly, we also design an efficient algorithm based on statistical analysis. Based on this algorithm, we can also analyze the anomaly propagation paths within the microservice system and generate a better interpretable diagnosis. In our evaluation, we test DyCause in a controlled simulation environment and a real-world cloud system. Our results have shown that DyCause has the best accuracy and efficiency among several state-of-the-art methods and is more robust in terms of parameters.

A Contract-Theory-Based Incentive Mechanism for UAV-Enabled VR-Based Services in 5G and Beyond

The proliferation of novel infotainment services such as Virtual Reality(VR)-based services has fundamentally changed the existing mobile networks. These bandwidth-hungry services expanded at a tremendously rapid pace, thus, generating a burden of data traffic in the mobile networks. To cope with this issue, one can use Multi-access Edge Computing (MEC) to bring the resource to the edge. By doing so, we can release the burden of the core network by taking the communication, computation, and caching resources nearby the end-users (UEs). Nevertheless, due to the vast adoption of VR-enabled devices, MEC resources might be insufficient in peak times or dense settings. To overcome these challenges, we propose a system model where the service provider (SP) might rent Unmanned Area Vehicles (UAVs) from UAV service providers (USPs) to serve as micro-based stations (UBSs) that expand the service area and improve the spectrum efficiency. In which, UAV can pre-cached certain sets of VR-based contents and serve UEs via air-to-ground (A2G) communication. Furthermore, future intelligent devices are capable of 5G and B5G communication interfaces, and thus, they can communicate with UAVs via A2G links. By doing so, we can significantly reduce a considerable amount of data traffic in mobile networks. In order to successfully enable such kinds of services, an attractive incentive mechanism is required. Therefore, we propose a contract theory-based incentive mechanism for UAV-assisted MEC in VR-based infotainment services, in which the MEC offers an amount reward to a UAV for serving as a UBS in a specific location for certain time slots. We then derive an optimal contract-based scheme with individual rationality and incentive compatibility conditions. The numerical findings show that our proposed approach outperforms the Linear Pricing (LP) technique and is close to the optimal solution in terms of social welfare. Additionally, our proposed scheme significantly enhanced the fairness of utility for UAVs in asymmetric information problems.

The impact of corruption on SMEs’ access to finance: Evidence using firm-level survey data from developing countries

The present paper estimates the impact of bureaucratic corruption on access to finance for small and medium enterprises (SMEs) in the manufacturing sectors of 79 developing countries. Corruption can make it difficult for businesses to obtain financing by reducing profits, increasing credit demand, increasing the likelihood of bankruptcy, creating uncertainty about future profits, and exacerbating the asymmetric information problem between borrowers and lenders. Consistent with this viewpoint, our findings show that corruption significantly increases the likelihood of a manufacturing SME being financially constrained. A one standard deviation increase in the prevalence of corruption leads to a 3.5–4.9% point increase in the likelihood or probability of a manufacturing SME being financially constrained. In countries with credit bureaus and more freely functioning credit markets, the increase in the likelihood of being financially constrained due to higher corruption is much smaller. We argue and provide evidence that these heterogeneities stem from the specific ways in which corruption affects SMEs’ access to finance. Thus, the heterogeneities help to raise confidence against the omitted variable bias and reverse causality concerns with our estimation. We also find that higher corruption makes it more difficult for SMEs to access finance in more competitive product markets, as well as for small compared to medium-sized firms. The findings have important policy implications for anti-corruption initiatives, financial development, and the level of competition in product markets.

Enduring lending relationships and european firms default

This paper investigates the link between close banking relationships and European manufacturing firms’ defaults. Based on binary outcomes (i.e., Logit, Probit, Complementary log-log) and discrete-time models, the empirical analysis reveals that enduring lending relationships present benefits in reducing firms’ temporary and permanent defaults. This evidence suggests that, in a framework of limited credit access that may compromise firms’ survival, relationship lending might represent an effective tool for overcoming asymmetric information problems, helping firms relax credit constraints, encouraging greater discipline in their managers, and preventing failure.

Marketing the Self vs. Preserving the Self: Resisting Downward Mobility in the New Economy

AbstractHow do dislocated workers try to avoid downward mobility as they navigate insecure, nonstandard, and precarious work in the contemporary American economy? Should they embrace flexibility, or follow their passions? Drawing on in-depth, qualitative interviews with 56 displaced job seekers, we extend the job searching literature to distinguish two kinds of job searching: self-marketing and self-preservation. Self-marketers are willing to reinvent themselves to pursue opportunities wherever they perceive the best payoffs. By contrast, self-preservers, drawing on affective commitments to particular kinds of work, limit their searches to their current occupation. In this paper, we show how the neoliberal economy buffets and preys on both types of job seekers and how bounded rationality and asymmetric information problems leave self-marketers vulnerable to downward mobility via cons, scams, and predatory business models. But self-preservation searches contain their own pathways to downward mobility: descents into low-wage work that begin as temporary measures often become permanent. Thus, even though self-marketers and self-preservers embrace very different job search strategies, neither flexibility nor passion offers protection against downward mobility in the post-Great Recession economy. We conclude with some reflections on how these two job search orientations may help us better understand current labor market upheavals.

IOT-based cyber security identification model through machine learning technique

Manual vulnerability evaluation tools produce erroneous data and lead to difficult analytical thinking. Such security concerns are exacerbated by the variety, imperfection, and redundancies of modern security repositories. These problems were common traits of producers and public vulnerability disclosures, which make it more difficult to identify security flaws through direct analysis through the Internet of Things (IoT). Recent breakthroughs in Machine Learning (ML) methods promise new solutions to each of these infamous diversification and asymmetric information problems throughout the constantly increasing vulnerability reporting databases. Due to their varied methodologies, those procedures themselves display varying levels of performance. The authors provide a method for cognitive cybersecurity that enhances human cognitive capacity in two ways. To create trustworthy data sets, initially reconcile competing vulnerability reports and then pre-process advanced embedded indicators. This proposed methodology's full potential has yet to be fulfilled, both in terms of its execution and its significance for security evaluation in application software. The study shows that the recommended mental security methodology works better when addressing the above inadequacies and the constraints of variation among cybersecurity alert mechanisms. Intriguing trade-offs are presented by the experimental analysis of our program, in particular the ensemble method that detects tendencies of computational security defects on data sources.

Financial development, interest rate pass-through and interest rate channel of monetary policy

The paper examined the interest rate operations and processes in Nigeria and examined the role of financial development in incentivizing central bank monetary policies from the monetary policy rate to the money market rates, lending rates, and deposit rates. The analysis covered the period from 1981 to 2021 with sub-samples for 1981 to 2011 and 1991 to 2021 to test the consistency of the findings. The findings confirmed that interest rate pass-through is incomplete for Nigeria, albeit to a lower degree in the short run compared to the long run. The reasons for this may be due to interest rate stickiness, problems of asymmetric information, and bank switching costs. Also, the findings confirmed that financial development weakens the impact of monetary policy on the interest rate pass-through process, while the analysis of the asymmetric mean adjustment lags confirmed that changes in the policy rate are transmitted to the deposit and lending rates within the year it was announced. The analysis confirmed that the results across each sub-sample and the robustness tests are consistent with the main analysis. Therefore, it is imperative that policymakers should account for financial development when designing monetary policy effectiveness since it can hinder or strengthen the interest rate monetary policy channel.

Insider Trading Crime in the Turkish Capital Market: A Qualitative Research on the Profiles of Perpetrators

Asymmetric information causes problems in the functioning of the capital markets. Insider Trading Crime is a market disruptive action that allows certain individuals or groups to gain an unfair advantage by using the specific information only they have, which shakes the trust of investors in the market and institutions. For this reason, while states are making various legal arrangements to struggle with that issue, international organizations are also trying to gain a broader collaboration opportunity for an “Insider Trading Clean” financial market. In the study, qualitative research was conducted on the profiles of those who committed the crime of Insider Trading, which is one of the asymmetric information problems. Data was obtained from Capital Markets Board bulletins, newspapers, and other media. Thus, it is aimed to draw a profile for the insider trading perpetrators.

Multinational Corporations and SEO Discounts

The fact that publicly traded companies incur substantial costs when issuing new equity has been extensively documented in the empirical finance literature. In particular, numerous studies show that seasoned equity offerings (SEOs) tend to be priced significantly below prevailing market prices, thereby causing issuers to leave money on the table. Offer price discounting is an indirect flotation cost borne by pre-SEO shareholders that, according to extant theoretical and empirical research, arises due to asymmetric information. A heretofore unrelated literature argues that corporate multinationalism, i.e., establishing operations in one or more foreign countries, exacerbates the asymmetric information problem via greater costs to shareholders of monitoring the activities and performance of foreign subsidiaries. Motivated by these lines of thought, I investigate the relation between SEO discounting and corporate multinationalism. Using a sample of SEOs completed by U.S. firms between 1998 and 2016, I show that offer price discounting is significantly higher in offerings conducted by multinational firms relative to those conducted by purely domestic firms after controlling for known determinants of SEO discounts. This effect is stronger for multinational firms with foreign subsidiaries spread across a greater number of countries and weaker for multinationals that hire a high-reputation underwriter to lead the underwriting syndicate. These findings suggest that asymmetric information costs borne by seasoned equity issuers are increasing in the geographic scope of a firm’s operations, which can be mitigated by certification from a highly reputed lead underwriter.