Adverse Selection Risk Research Articles

Time to End the Use of Genetic Test Results in Life Insurance Underwriting.

The decreasing cost of genome and exome sequencing, including direct-to-consumer testing, produces information asymmetry between applicants and life insurance companies, which increases the risk of adverse selection. Rather than requiring applicants for life insurance to undergo genetic testing, life insurers ought to revise their policies and not use the results of genetic tests in the medical records of applicants for underwriting. Prohibitions on access to genetic test results already have been adopted without great upheaval in several other countries. There are two main reasons why this new approach should be used in the United States. First, genetic test results do not provide essential information that is not already available through traditional underwriting measures. Second, because many individuals at genetically increased risk of serious disorders currently decline testing out of fear of the economic consequences, policies prohibiting the use of genetic test results in life insurance will encourage genetic testing and facilitate timely surveillance and necessary medical intervention, thereby saving lives.

Adverse-Selection Considerations in the Market-Making of Corporate Bonds

We examine the presence of adverse selection considerations in the market-making of corporate bonds. We find that institutional-sized customer trades are more informative than retail-sized ones. Dealers seem aware of the informational asymmetry between the two customer segments, as they update their quotes with greater intensity after days dominated by institutional-sized orders. Despite the lower adverse-selection risk they pose to market-makers, we also find that retail customers are charged relatively higher transaction costs, perhaps because of their low bargaining power. Our evidence suggests that a significant portion of the dealers’ profits stems from their ability to exploit efficiently the arrival of public information.

Foreign ownership and stock market liquidity

In this study we analyze how the price impact of trades and the bid-ask spread are related to foreign stock ownership using data from 20 emerging markets. We show that while the price impact of trades increases with the percentage of shares held by foreign investors, the bid-ask spread decreases with foreign ownership. We interpret these results as evidence that although foreign investors increase adverse selection risks for liquidity providers, they bring net benefit to the market in terms of lower trading costs by increasing competition in the price discovery process. The general increase in foreign ownership in emerging markets after the global financial crisis resulted in higher price impacts and lower spreads. The two-stage least squares regression analysis suggests that our results are unlikely to be driven by reverse causality.

An Extended MRR Model for Transaction-Level Analysis of High Frequency Trading Processes

Transaction-level analysis of security price change due to Madhavan, Richardson and Roomans (1997, hereafter MRR) has been a useful framework in financial analysis. The one order Markov property of the trade indicator variables is a key assumption in the MRR model, which contradicts the information lag empirically evidenced in high frequency trading processes. In this paper, a non-parametric test is employed, showing that the Markov property of the trade indicator variables is rejected in most of trading days. Based on the spread decomposed structure, an extended MRR model is proposed with a moving average structure adopted to absorb the information lag as an extension. Empirical results show that the information lag plays an important role and the difference of the adverse selection risk parameter between the original and the extended is significant. Further, our analysis suggests that the information lag parameter can be a useful measure of the average speed at which the information integrates into the price.

Dark Trading Volume at Earnings Announcements

We examine how abnormal dark market share changes at earnings announcements and find a statistically and economically significant increase in abnormal dark market share in the weeks prior to, during, and following the earnings announcement. The increase in dark market share is larger for firms with a relatively high quality of information environment and a relatively low level of informed trading. These findings are consistent with informed (uninformed) traders facing lower execution (adverse selection) risk in dark venues for firms with a high quality information environment and for firms with low levels of informed trading.

Instantaneous Order Impact and High-Frequency Strategy Optimization in Limit Order Books

We propose a limit order book (LOB) model with dynamics that account for both the impact of the most recent order and volume imbalance. To model these effects jointly we introduce a discrete Markov chain model. We then find the policy for optimal order choice and control. The optimal policy derived uses limit orders, cancellations and market orders. It looks to avoid non-execution and adverse selection risk simultaneously. Using ultra high-frequency data from the NASDAQ stock exchange we compare our policy with other submission strategies that use a subset of all available order types and show that ours significantly outperforms.

State-Contingent Debt Instruments for Sovereigns

Background. The case for sovereign state-contingent debt instruments (SCDIs) as a countercyclical and risk-sharing tool has been around for some time and remains appealing; but take-up has been limited. Earlier staff work had advocated the use of growth-indexed bonds in emerging markets and contingent financial instruments in low-income countries. In light of recent renewed interest among academics, policymakers, and market participants—staff has analyzed the conceptual and practical issues SCDIs raise with a view to accelerate the development of self-sustaining markets in these instruments. The analysis has benefited from broad consultations with both private market participants and policymakers. The economic case for SCDIs. By linking debt service to a measure of the sovereign’s capacity to pay, SCDIs can increase fiscal space, and thus allow greater policy flexibility in bad times. They can also broaden the sovereign’s investor base, open opportunities for risk diversification for investors, and enhance the resilience of the international financial system. Should SCDI issuance rise to account for a large share of public debt, it could also significantly reduce the incidence and cost of sovereign debt crises. Some potential complications require mitigation: a high novelty and liquidity premium demanded by investors in the early stage of market development; adverse selection and moral hazard risks; undesirable pricing effects on conventional debt; pro-cyclical investor demand; migration of excessive risk to the private sector; and adverse political economy incentives.

Un modelo de creación de mercado con trading de alta frecuencia

En este artículo se hace una presentación del trading de alta frecuencia, junto con sus características y estrategias. Posteriormente, bajo el contexto de transacciones de alta frecuencia (HFT), se desarrolla un modelo de creación de mercado, conducido por un agente cuyas posibilidades de negociación en el mercado bursátil se desarrollan a través de órdenes límite y órdenes de mercado; también puede presentar órdenes agresivas con el objetivo de enfrentar los riesgos de inventario, de selección adversa y de ejecución, los cuales son los riesgos a los que el agente se encuentra expuesto.

Does willingness-to-pay for weather index-based insurance follow covariant shocks?

PurposeThe purpose of this paper is to investigate the role that weather shocks can play in the livestock mortality microinsurance take-up when the insured risk has a prevalent covariant component.Design/methodology/approachThe sample consists of 360 rural Ethiopian households. Data were collected in a panel-structure at the end of three agricultural seasons (2011-2013). In the questionnaire, a specific section on insurance was meant to collect information on the farmer’s willingness-to-pay (WTP) for a set of insurance products, including livestock mortality insurance. Two OLS regression models and a quantile regression model were employed to estimate the impact of weather anomalies on the WTP for the insurance product.FindingsThe authors find that weather anomalies contribute to changes in the WTP to a large extent. Negative (positive) changes in precipitation (temperature) anomalies can lead to more than a 30 percent reduction in the WTP. This general finding is complemented with the analysis of the conditional distribution of the WTP, which shows that other elements can prevail for low values of the conditional distribution. In this case, the WTP seems to be represented more by the interviewee’s age and basic knowledge of insurance, and village fixed-effects. Basic knowledge of insurance, in particular, can increase WTP by about 60 percent.Practical implicationsThis paper has straightforward implications from a policy perspective. It suggests that farmers would prefer an insurance premium that follows the changes in the systemic component. On the contrary, insurance as well as reinsurance companies are usually reluctant to frequently revise their premiums. Financial education programs, farmer-driven design, trust building, and bundling insurance with other financial and non-financial products can increase the value proposition perceived by the farmers. From a marketing perspective, the overall findings suggest that continuous fine-tuning of the contract, transparency, and targeted information campaigns can contribute to increase and stabilize potential customers’ WTP.Originality/valueTo the best of the authors’ knowledge, this is the first paper that considers the impact of weather shocks on the WTP for a livestock mortality insurance product. Livestock is one of the most strategic assets of poor rural households in Africa. This study contributes to the theoretical and empirical literature on the determinants of weather insurance take-up in developing countries and, in particular, the role of spatiotemporal adverse selection and basis risk (e.g. Jensen et al., 2016).

How is the Trade-Off between Adverse Selection and Discrimination Risk Affected by Genetic Testing? Theory and Experiment

We compare two genetic testing regulations, Disclosure Duty (DD) and Consent Law (CL), in an environment where individuals choose to take a genetic test or not. DD forces agents to reveal the test results to their insurers, resulting in a discrimination risk. CL allows agents to withhold that information, generating adverse selection. We complement our model with an experiment. We obtain that a larger fraction of agents test under CL than under DD, and that the proportion of individuals preferring CL to DD is non-monotone in the test cost when adverse selection is set endogenously at its steady state level.

Limit Order Strategic Placement with Adverse Selection Risk and the Role of Latency

This paper is split in three parts: first, we use labeled trade data to exhibit how market participants’ decisions depend on liquidity imbalance; then, we develop a stochastic control framework where agents monitor limit orders, by exploiting liquidity imbalance, to reduce adverse selection. For limit orders, we need optimal strategies essentially to find a balance between fast execution and avoiding adverse selection: if the price has chances to go down, the probability to be filled is high, but it is better to wait a little more to get a better price. In a third part, we show how the added value of exploiting liquidity imbalance is eroded by latency: being able to predict future liquidity consuming flows is of less use if you do not have enough time to cancel and reinsert your limit orders. There is thus a rationale for market makers to be as fast as possible to reduce adverse selection. Latency costs of our limit order driven strategy can be measured numerically. To authors’ knowledge, this paper is the first to make the connection between empirical evidences, a stochastic framework for limit orders including adverse selection, and the cost of latency. Our work is a first step to shed light on the role played by latency and adverse selection in optimal limit order placement.

Initial Conditions and the Private Debt Renegotiation Process

I investigate whether and how initial conditions around loan origination influence private debt renegotiation process. I model the renegotiation likelihood, and the conditional probability of multiple renegotiation rounds or multiple amended terms using a sequential logit model. I use a large sample of 15,000 loans on the European credit market. I find that contractual (covenants and collateral) and organizational (lenders pool size, reputation and relationship) mechanisms mitigating adverse selection and moral hazard risks have the largest and positive economic impacts. Lenders financial conditions (capitalization and credit portfolio exposure) and institutional arrangement aiming at creditors protection significantly impact the renegotiation process.

Who Provides Liquidity and When: An Analysis of Price vs. Speed Competition on Liquidity and Welfare

I model the interaction between buy-side algorithmic traders (BATs) and high-frequency traders (HFTs). When the minimum price variation (tick size) is small, BATs dominate liquidity provision by establishing price priority over HFTs in the limit order book (LOB), because providing liquidity is less costly than demanding liquidity from HFTs. A large tick size, however, constrains price competition and encourages HFTs to provide liquidity by establishing time priority. An increase in adverse selection risk raises the unconstrained bid-ask spread, reduces tick size constraints, and discourages HFTs’ liquidity provision. An increase in tick size increases transaction costs and harms liquidity demanders, but it does not benefit liquidity providers because the costs of speed investments dissipate the rents resulting from the tick size. I predict that mini-flash crashes are more likely to occur for stocks with a smaller tick size and higher adverse selection risk. I suggest that the literature should not use the message-to-trade ratio as a cross-sectional proxy for HFTs’ liquidity provision because stocks with more liquidity provided by HFTs have a lower message-to-trade ratio.

How is the Trade-Off between Adverse Selection and Discrimination Risk Affected by Genetic Testing? Theory and Experiment

We compare two genetic testing regulations, Disclosure Duty (DD) and Consent Law (CL), in an environment where individuals choose to take a genetic test or not. DD forces agents to reveal the test results to their insurers, resulting in a discrimination risk. CL allows agents to withhold that information, generating adverse selection. We complement our model with an experiment. We obtain that a larger fraction of agents test under CL than under DD, and that the proportion of individuals preferring CL to DD is non-monotone in the test cost when adverse selection is set endogenously at its steady state level.

Retraction Notice: Preventing Adverse Selection Risk of Construction Project Based on Signaling

Retraction Notice: Preventing Adverse Selection Risk of Construction Project Based on Signaling

Real Earnings Management and Information Asymmetry in the Equity Market

The literature suggests that real earnings management (REM) activities can increase adverse selection risk in capital markets. Due to their opacity and the difficulties in understanding their implications, REM strategies may increase the level of information asymmetry among investors. This paper examines the association between earnings management through real activities manipulation and information asymmetry in the equity market. To estimate the level of adverse selection risk we use a comprehensive index of information asymmetry measures proposed by the market microstructure literature. For a sample of Spanish listed firms, we find that firms’ strategies of increasing earnings through REM are associated with higher information asymmetry in those firms that meet last year’s earnings. Our findings are consistent with the hypothesis that earnings management through real activities manipulation garbles the market, enhances private information production, and exacerbates information asymmetry in the stock market.

Do Information Releases Diminish Equity Bid-Offer Spreads?

Stock market makers are afraid that informed insiders will take advantage of them in trade. To protect themselves, they may increase the bid-offer spread to include a fee for the adverse selection risk . If set correctly, market makers will share in profits from others trading on private information and can distribute the remaining costs among other market participants. If market makers protect themselves this way, then when the risk of informed trading is relatively low, the bid-offer spread should decline. The risk of informed trading will be relatively low when the difference in public and private information shrinks. Filings with the Securities and Exchange Commission (SEC) and conference calls where corporate earnings are announced and discussed should be events that diminish this difference. Because smaller companies attract less scrutiny, they may experience relatively larger changes in this information distance after these releases. This paper finds weak evidence that spreads diminish when this information is released and a weak size effect. It hypothesizes that the bid-offer spread seems to the spread is smaller than has previously been estimated does or possibly does not exist. Estimates of this spread are actually a statistical illusion created by the structural form of earlier estimation techniques.

Evaluating credit rating prediction by using the KMV model and random forest

Purpose An increasing number of investors have begun using financial data to develop optimal investment portfolios; therefore, the public financial data shared in the capital market plays a critical role in credit ratings. These data enable investors to understand the credit levels of debtors from a bank perspective; this facilitates predicting the debtor default rate to efficiently evaluate investment risks. The paper aims to discuss these issues. Design/methodology/approach A credit rating model can be developed to reduce the risk of adverse selection and moral hazard caused by information asymmetry in the loan market. In this study, a random forest (RF) was used to evaluate financial variables and construct credit rating prediction models. Data-mining techniques, including an RF, decision tree, neural networks, and support vector machine, were used to search for suitable credit rating forecasting methods. The distance to default from the KMV model was then incorporated into the credit rating model as a research variable to increase predictive power of various data-mining techniques. In addition, four-level and nine-level classification were set to investigate the accuracy rates of various models. Findings The experimental results indicated that applying the RF in the variable feature selection process and developing a forecasting model was the most effective method of predicting credit ratings; the four-level and nine-level feature-selection settings achieved 95.5 and 87.8 percent accuracy rates, respectively, indicating that RF demonstrated outstanding feature selection and forecasting capacity. Research limitations/implications The experimental cases were based on financial data from public companies in North America. Practical implications Practical implication of this study indicates the most effective financial variables were dividends common/ordinary, cash dividends, volatility assumption, and risk-free rate assumption. Originality/value The RF model can be used to perform feature selection and efficiently filter numerous financial variables to obtain crediting rating information instantly.

Determinants of Voluntary National Health Insurance Drop-Out in Eastern Sudan

Low enrolment and high drop-out rates are common problems in voluntary health insurance schemes. Yet, most studies in this research area focus on community-based health insurance and enrolment, rather than drop-out. This study examines what causes informal sector families not to renew their voluntary National Health Insurance Fund (NHIF) health insurance membership in Eastern Sudan. Primary data from about 600 informal sector households that dropped out or remained insured, collected through a household survey conducted in March 2014, were used. Logistic regressions were employed to examine what determines drop-out of the voluntary NHIF scheme. The logistic regression results are consistent with the existing literature and confirm the importance of household head, household and community characteristics. Notably, worse family health status and higher health care utilization decrease the probability of drop-out, which requires further analysis as it may indicate the problem of adverse selection and insufficient risk management. Most importantly, the results consistently show that household heads who are satisfied with health services and those who understand the main features of the voluntary NHIF scheme are less likely to drop out. Also, 30% of drop-out households hold a social support card and reported that the social support scheme is the main reason for not renewing their voluntary NHIF health insurance membership as they qualify for sponsored NHIF health insurance membership. This study shows that satisfaction with health services and knowledge of the health insurance scheme are important factors explaining drop-out of a national health insurance programme. The results suggest that education and information campaigns should be developed further to raise understanding of the NHIF voluntary scheme. In addition, information systems and coordination between the main agencies should be strengthened to reduce administrative costs and ensure policy coherence.

THE STUDY OF INSURANCE PREMIUM RATE GIS MAPPING CONSIDERING THE STORM AND FLOOD HAZARD RISKS

Abstract. Recently, the number of natural disaster occurrence is increasing because of abnormal changes of weather in Korea. In Korea the storm and flood insurance system is in effect to prevent these natural disasters. The national storm and flood insurance Premium rate is very low and the risk of adverse selection resides because of choosing by who lives in high risk area. To solve these problems, the storm and flood insurance rate map are required. In this study, the prototype of storm and flood insurance premium rate map of the Ulsan, Korea was made and the method of GIS analysis for the insurance premium rate calculating and the procedure of the Ulsan storm and flood insurance rate map were researched.