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Journal articles on the topic 'Adverse selection costs'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Heflin, Frank, and Kenneth W. Shaw. "ADVERSE SELECTION, INVENTORY-HOLDING COSTS, AND DEPTH." Journal of Financial Research 24, no. 1 (March 2001): 65–82. http://dx.doi.org/10.1111/j.1475-6803.2001.tb00818.x.

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2

Desiraju, Ramarao, and David E. M. Sappington. "Equity and adverse selection with correlated costs." Economics Letters 95, no. 3 (June 2007): 402–7. http://dx.doi.org/10.1016/j.econlet.2006.11.015.

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3

Palazzo, Francesco. "Search costs and the severity of adverse selection." Research in Economics 71, no. 1 (March 2017): 171–97. http://dx.doi.org/10.1016/j.rie.2016.09.001.

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4

Detragiache, Enrica. "Adverse selection and the costs of financial distress." Journal of Corporate Finance 1, no. 3-4 (April 1995): 347–65. http://dx.doi.org/10.1016/0929-1199(94)00009-j.

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5

Ayadi, O. "Adverse selection, search costs and sticky credit card rates." Financial Services Review 6, no. 1 (1997): 53–67. http://dx.doi.org/10.1016/s1057-0810(97)90032-9.

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6

Jiang, Christine X., Jang-Chul Kim, and Robert A. Wood. "Adverse selection costs for NASDAQ and NYSE after decimalization." International Review of Financial Analysis 18, no. 4 (September 2009): 205–11. http://dx.doi.org/10.1016/j.irfa.2009.03.001.

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7

Kalra, Rajiv. "Government Intervention and Adverse Selection Costs in Foreign Exchange Markets." CFA Digest 30, no. 4 (November 2000): 16–18. http://dx.doi.org/10.2469/dig.v30.n4.757.

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8

Naranjo, Andy, and M. Nimalendran. "Government Intervention and Adverse Selection Costs in Foreign Exchange Markets." Review of Financial Studies 13, no. 2 (April 2000): 453–77. http://dx.doi.org/10.1093/rfs/13.2.453.

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9

Chung, Kee H., and Mingsheng Li. "Adverse-Selection Costs and the Probability of Information-Based Trading." Financial Review 38, no. 2 (May 2003): 257–72. http://dx.doi.org/10.1111/1540-6288.00045.

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10

He, Chengying, Zonghui Lu, Xingqiang He, and Jun Chai. "Adverse selection costs: A study on the Chinese stock market." Frontiers of Business Research in China 4, no. 2 (May 23, 2010): 209–30. http://dx.doi.org/10.1007/s11782-010-0010-6.

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11

Philippon, Thomas, and Vasiliki Skreta. "Optimal Interventions in Markets with Adverse Selection." American Economic Review 102, no. 1 (February 1, 2012): 1–28. http://dx.doi.org/10.1257/aer.102.1.1.

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We study the design of interventions to stabilize financial markets plagued by adverse selection. Our contribution is to analyze the information revealed by participation decisions. Taking part in a government program carries a stigma, and outside options are mechanism dependent. We show that the efficiency of an intervention can be assessed by its impact on the market interest rate. The presence of an outside market determines the nature of optimal interventions and the choice of financial instruments (debt guarantees in our model), but it does not affect implementation costs. (JEL D82, D86, G01, G20, G31)

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12

Harris, Katherine M., and Roland Sturm. "Adverse Selection and Generosity of Alcohol Treatment Benefits." INQUIRY: The Journal of Health Care Organization, Provision, and Financing 39, no. 4 (November 2002): 413–28. http://dx.doi.org/10.5034/inquiryjrnl_39.4.413.

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Concerns about attracting disproportionate numbers of employees with alcohol problems limit employers' willingness to offer health plans with generous alcohol treatment benefits. This paper analyzes two potential avenues of adverse selection, namely biased enrollment into plans and biased exit from plans offered by 57 employers between 1991 and 1997. We compare alcohol treatment use rates and costs of new and old enrollees between more generous and less generous plans; we also analyze disenrollment rates and enrollment duration by plan generosity for users and nonusers of alcohol treatment services. To avoid confounding benefit generosity with other plan features, in particular the use of managed care mechanisms, we compare plans that were administered in the same way by a large managed behavioral health care organization. Overall, we find no evidence of adverse selection into more generous plans. Contrary to the selection hypothesis, treatment costs of new members compared to old members are lower in firms with more generous treatment benefits than in firms with more limited benefits. Also, users of alcohol treatment services do not remain disproportionately enrolled longer in plans with generous benefits.

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13

Chakravarty, Sugato, Bonnie F. Van Ness, and Robert A. Van Ness. "The Effect of Decimalization on Trade Size and Adverse Selection Costs." Journal of Business Finance Accounting 32, no. 5-6 (June 2005): 1063–81. http://dx.doi.org/10.1111/j.0306-686x.2005.00622.x.

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14

Rebello, Michael J. "Adverse Selection Costs and the Firm′s Financing and Insurance Decisions." Journal of Financial Intermediation 4, no. 1 (January 1995): 21–47. http://dx.doi.org/10.1006/jfin.1995.1002.

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15

Bhattacharya, Jayanta, and William B. Vogt. "Employment and Adverse Selection in Health Insurance." Forum for Health Economics and Policy 17, no. 1 (January 1, 2014): 79–104. http://dx.doi.org/10.1515/fhep-2013-0017.

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Abstract We construct and test a new model of employer-provided health insurance provision in the presence of adverse selection in the health insurance market. In our model, employers cannot observe the health of their employees, but can decide whether to offer insurance. Employees sort themselves among employers who do and do not offer insurance on the basis of their current health status and the probability distribution over future health status changes. We show that there a pooling equilibrium is more likely when the costs of switching jobs are high or when health status is not persistent. We test and verify some of the key implications of our model using data from the Current Population Survey, linked to information provided by the US Department of Labor about the job-specific human capital requirements of jobs.

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16

Polyakova, Maria. "Regulation of Insurance with Adverse Selection and Switching Costs: Evidence from Medicare Part D." American Economic Journal: Applied Economics 8, no. 3 (July 1, 2016): 165–95. http://dx.doi.org/10.1257/app.20150004.

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I take advantage of regulatory and pricing dynamics in Medicare Part D to explore interactions among adverse selection, inertia, and regulation. I first document novel evidence of adverse selection and switching frictions within Part D using detailed administrative data. I then estimate a contract choice and pricing model that quantifies the importance of inertia for risk sorting. I find that in Part D switching costs help sustain an adversely-selected equilibrium. I also estimate that active decision making in the existing policy environment could lead to a substantial gain in annual consumer surplus of on average $400–$600 per capita—20 percent to 30 percent of average annual spending. (JEL D82, G22, H51, I13, I18)

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17

Datar, S. "Earnouts: The Effects of Adverse Selection and Agency Costs on Acquisition Techniques." Journal of Law, Economics, and Organization 17, no. 1 (April 1, 2001): 201–38. http://dx.doi.org/10.1093/jleo/17.1.201.

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18

Chung, Kee H., Chairat Chuwonganant, and D. Timothy McCormick. "Order preferencing, adverse-selection costs, and the probability of information-based trading." Review of Quantitative Finance and Accounting 27, no. 4 (December 2006): 343–64. http://dx.doi.org/10.1007/s11156-006-0042-3.

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19

Macdonald, Angus S. "How will improved forecasts of individual lifetimes affect underwriting?" Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 352, no. 1357 (August 29, 1997): 1067–75. http://dx.doi.org/10.1098/rstb.1997.0087.

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The combined effects of underwriting and adverse selection among heterogeneous populations are considered, using a simple Markov model. We illustrate the possible extent of the costs of adverse selection; in all cases, above–average sums assured is the most significant factor.

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20

Adams, C., C. Donnelly, and A. Macdonald. "Adverse selection in a start-up long-term care insurance market." British Actuarial Journal 20, no. 2 (November 19, 2014): 298–347. http://dx.doi.org/10.1017/s1357321714000270.

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AbstractCommon to all previous studies assessing the cost of adverse selection associated with genetics has been the assumption of an established market, i.e., the adverse selectors have been buying insurance at that rate for such a period that premiums have already absorbed it. Their analyses involve calculating the percentage difference between premiums in a market with adverse selection and one without adverse selection. They can shed no light on how the premiums would get to this stage over time and what losses might be incurred in the process. We take the modelling further by outlining a multiple state Markov model for a start-up market of long-term care insurance. With this model, we explicitly show the progression of adverse selection costs using the development of information that an insurer would gain from analysing the claims history of its existing business, to reprice premiums for new business. To overcome the complication of insurance benefit amounts, which depend on the value of previous benefit payments, we develop a simulation approach of estimating the expected present values of insurance benefits and premium payments. In applying our modelling to a UK setting, we find genetic testing of the apolipoprotein E gene (whose variants can cause a high risk of developing dementia) to be of a relatively small impact compared with our hypothetical state of intermediate dementia progression. Furthermore, we find that the government’s cap on care costs has little effect on adverse selection costs as it benefits only a small proportion of people.

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21

Jeziorski, Przemysław, Elena Krasnokutskaya, and Olivia Ceccarini. "Skimming from the Bottom: Empirical Evidence of Adverse Selection When Poaching Customers." Marketing Science 38, no. 4 (July 2019): 543–66. http://dx.doi.org/10.1287/mksc.2018.1134.

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22

Hackmann, Martin B., Jonathan T. Kolstad, and Amanda E. Kowalski. "Adverse Selection and an Individual Mandate: When Theory Meets Practice." American Economic Review 105, no. 3 (March 1, 2015): 1030–66. http://dx.doi.org/10.1257/aer.20130758.

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We develop a model of selection that incorporates a key element of recent health reforms: an individual mandate. Using data from Massachusetts, we estimate the parameters of the model. In the individual market for health insurance, we find that premiums and average costs decreased significantly in response to the individual mandate. We find an annual welfare gain of 4.1 percent per person or $51.1 million annually in Massachusetts as a result of the reduction in adverse selection. We also find smaller post-reform markups. (JEL D82, G22, H75, I13)

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23

Hackmann, Martin B., Jonathan T. Kolstad, and Amanda E. Kowalski. "Health Reform, Health Insurance, and Selection: Estimating Selection into Health Insurance Using the Massachusetts Health Reform." American Economic Review 102, no. 3 (May 1, 2012): 498–501. http://dx.doi.org/10.1257/aer.102.3.498.

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We implement an empirical test for selection into health insurance using changes in coverage induced by the introduction of mandated health insurance in Massachusetts. Our test examines changes in the cost of the newly insured relative to those who were insured prior to the reform. We find that counties with larger increases in insurance coverage over the reform period face the smallest increase in average hospital costs for the insured population, consistent with adverse selection into insurance before the reform. Additional results, incorporating cross-state variation and data on health measures, provide further evidence for adverse selection.

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24

Kugler, Adriana D., and Gilles Saint‐Paul. "How Do Firing Costs Affect Worker Flows in a World with Adverse Selection?" Journal of Labor Economics 22, no. 3 (July 2004): 553–84. http://dx.doi.org/10.1086/383107.

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25

Allard, Marie, Jean-Paul Cresta, and Jean-Charles Rochet. "Pooling and Separating Equilibria in Insurance Markets with Adverse Selection and Distribution Costs*." Geneva Papers on Risk and Insurance Theory 22, no. 2 (December 1997): 103–20. http://dx.doi.org/10.1023/a:1008664000457.

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26

Van Ness, Bonnie F., Robert A. Van Ness, and Richard S. Warr. "THE IMPACT OF MARKET MAKER CONCENTRATION ON ADVERSE-SELECTION COSTS FOR NASDAQ STOCKS." Journal of Financial Research 28, no. 3 (September 2005): 461–85. http://dx.doi.org/10.1111/j.1475-6803.2005.00134.x.

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27

Becker-Blease, John R., and Afshad J. Irani. "Do corporate governance attributes affect adverse selection costs? Evidence from seasoned equity offerings." Review of Quantitative Finance and Accounting 30, no. 3 (September 26, 2007): 281–96. http://dx.doi.org/10.1007/s11156-007-0051-x.

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28

Calem, Paul S., Michael B. Gordy, and Loretta J. Mester. "Switching costs and adverse selection in the market for credit cards: New evidence." Journal of Banking & Finance 30, no. 6 (June 2006): 1653–85. http://dx.doi.org/10.1016/j.jbankfin.2005.09.012.

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29

MacMinn, R. D., P. L. Brockett, and J. A. Raeburn. "Health Insurance, Genetic Testing and Adverse Selection." Annals of Actuarial Science 2, no. 2 (September 2007): 327–47. http://dx.doi.org/10.1017/s1748499500000385.

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ABSTRACTThe implications of genetic testing information availability for society, medicine, employment, and individual privacy rights have generated much political debate, legislation and academic research. Part of this debate centres on the ethical and economic considerations resultant from this expanded knowledge, particularly for insurance practices. Within insurance economics, the possibility of adverse selection has been debated and the potential for a ban on an insurer's use of genetic testing has been studied with respect to whether or not such a ban might actually result in insurance market failure due to this adverse selection. Studies have examined the issue using expected loss cost (actuarial or ‘fair’) pricing models, and have not considered either equilibrium (supply and demand) price setting as is present in markets, or the potentially swamping effect of background health care risks facing the insured, having nothing to do with any particular genetic mutation. Here we construct a supply and demand function with both high and low risk individuals in the presence of background health care cost risks, and derive an equilibrium price and market composition to determine whether, if genetic information is allowed for individuals, but this same information is not shared with insurers: (1) is market failure inevitable? (it is not if the background risk is sufficiently high relative to potential genetic risk costs); (2) will equilibrium prices result in all low risk insured exiting the market? (not in the presence of significant background risk); and (3) how much would prices increase and market sales decrease if insurers do not have the same genetic information as the insured? (prices will increase, but not necessarily very much in the presence of background risk, and not as much as that previously estimated in the insurance literature).

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30

Pascual, Roberto, Alvaro Escribano, and Mikel Tapia. "Adverse selection costs, trading activity and price discovery in the NYSE: An empirical analysis." Journal of Banking & Finance 28, no. 1 (January 2004): 107–28. http://dx.doi.org/10.1016/s0378-4266(02)00400-4.

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31

Hamrouni, Amal, Anthony Miloudi, and Ramzi Benkraiem. "How does corporate voluntary disclosure affect asymmetric information and adverse selection?" Corporate Ownership and Control 12, no. 2 (2015): 413–25. http://dx.doi.org/10.22495/cocv12i2c4p1.

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This paper investigates whether the extent of corporate voluntary disclosure mitigates asymmetric information and adverse selection in the Euronext Paris stock exchange. We apply a disclosure index as a proxy for the extent of voluntary disclosure and use different spread measures to estimate both asymmetric information and adverse selection. Our findings show a negative relationship between the disclosure index and asymmetric information and adverse selection proxies. An analysis of sub-indexes provides additional mixed results. Several asymmetric information measures are negatively related to the volume of financial, non-financial and voluntary governance information in corporate annual reports. Nevertheless, the effect of strategic information volume is statistically significant only for effective bid-ask spreads. On the whole, these results are consistent with the view that high corporate voluntary disclosure is associated with narrow spreads and low adverse selection costs

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32

Attar, Andrea, Thomas Mariotti, and François Salanié. "The Social Costs of Side Trading." Economic Journal 130, no. 630 (May 1, 2020): 1608–22. http://dx.doi.org/10.1093/ej/ueaa041.

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Abstract We study resource allocation under private information when the planner cannot prevent bilateral side trading between consumers and firms. Adverse selection and side trading severely restrict feasible trades: each marginal quantity must be fairly priced given the consumer types who purchase it. The resulting social costs are twofold. First, second-best efficiency and robustness to side trading are in general irreconcilable requirements. Second, there actually exists only one budget-feasible allocation robust to side trading, which deprives the planner from any capacity to redistribute resources between different types of consumers. We discuss the relevance of our results for insurance and financial markets.

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33

Lewis, Gregory. "Asymmetric Information, Adverse Selection and Online Disclosure: The Case of eBay Motors." American Economic Review 101, no. 4 (June 1, 2011): 1535–46. http://dx.doi.org/10.1257/aer.101.4.1535.

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Since Akerlof (1970), economists have understood the adverse selection problem that information asymmetries can create in used goods markets. The remarkable growth in online used goods auctions thus poses a puzzle. Part of the solution is that sellers voluntarily disclose their private information on the auction web page. This defines a precise contract -- to deliver the car shown for the closing price -- which helps protect the buyer from adverse selection. I test this theory using data from eBay Motors, finding that online disclosures are important price determinants, and that disclosure costs impact both the level of disclosure and prices. (JEL D44, D82, L81)

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34

Lin, Ji-Chai, Gary C. Sanger, and G. Geoffrey Booth. "External information costs and the adverse selection problem: A comparison of NASDAQ and NYSE stocks." International Review of Financial Analysis 7, no. 2 (1998): 113–36. http://dx.doi.org/10.1016/s1057-5219(99)80030-1.

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35

Senteney, David L. "Dealers' Adverse Selection Costs and the Evaluation of Alternative Measures of the Earnings Release Signal." Financial Review 25, no. 2 (May 1990): 199–209. http://dx.doi.org/10.1111/j.1540-6288.1990.tb00792.x.

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36

Brown, Jason, Mark Duggan, Ilyana Kuziemko, and William Woolston. "How Does Risk Selection Respond to Risk Adjustment? New Evidence from the Medicare Advantage Program." American Economic Review 104, no. 10 (October 1, 2014): 3335–64. http://dx.doi.org/10.1257/aer.104.10.3335.

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To combat adverse selection, governments increasingly base payments to health plans and providers on enrollees' scores from risk-adjustment formulae. In 2004, Medicare began to risk-adjust capitation payments to private Medicare Advantage (MA) plans to reduce selection-driven overpayments. But because the variance of medical costs increases with the predicted mean, incentivizing enrollment of individuals with higher scores can increase the scope for enrolling “overpriced” individuals with costs significantly below the formula's prediction. Indeed, after risk adjustment, MA plans enrolled individuals with higher scores but lower costs conditional on their score. We find no evidence that overpayments were on net reduced. (JEL G22, H51, I13, I18)

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37

Macdonald, Angus S., and Kenneth R. McIvor. "Modelling Adverse Selection in The Presence of a Common Genetic Disorder: the Breast Cancer Polygene." ASTIN Bulletin 39, no. 2 (November 2009): 373–402. http://dx.doi.org/10.2143/ast.39.2.2044640.

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AbstractThe cost of adverse selection in the life and critical illness (CI) insurance markets, brought about by restrictions on insurers' use of genetic test information, has been studied for a variety of rare single-gene disorders. Only now do we have a study of a common disorder (breast cancer) that accounts for the risk associated with multiple genes. Such a collection of genes is called a polygene. We take two approaches to modelling the severity of adverse selection which may result from insurers being unable to take account of tests for polygenes as well as major genes. First, we look at several genetic testing scenarios, with a corresponding range of possible insurance-buying behaviours, in a market model for CI insurance. Because a relatively large proportion of the population is exposed to adverse polygenic risk, the costs of adverse selection are potentially much greater than have been associated with rare single genes. Second, we use utility models to map out when adverse selection will appear, and which risk groups will cause it. Levels of risk aversion consistent with some empirical studies do not lead to significant adverse selection in our model, but lower levels of risk aversion could effectively eliminate the market.

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38

Viaggi, Davide, Francesco Galioto, and Alban Lika. "The Design of Pricing Policies for the Management of Water Resources in Agriculture Under Adverse Selection." Water 12, no. 8 (August 1, 2020): 2174. http://dx.doi.org/10.3390/w12082174.

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Water pricing policy for irrigated agriculture is considered as a key issue in the Water Framework Directive (WFD) implementation. The main obstacle is that a large part of the water used in agriculture is unmetered. The objective of this study is to assess the Water Authorities (WA)’s choices between different options of incentive pricing policies (IPP) and to evaluate their economic performance compared with flat rate (FR) solutions. The applied method relies on a principal-agent model under adverse selection, in which WAs are less informed than farmers about the water use costs and profits. In this respect, the paper provides a theoretical interpretation of how different information conditions, profit and cost structures contribute to affecting WAs’ pricing strategies and their ability to deal with some of the WFD principles. The study shows that, in the absence of water metering, WAs can still set up incentive pricing strategies by formulating menus of contracts that are more efficient than flat rate payments. Also, we show that, at least for cases in which there is only a small differentiation in water costs among farmers or no transaction costs, the first-best solution (the solution that yields the highest return from the use of the resource) can also be optimal under asymmetric information. The main policy recommendation is that, in the absence of water metering, a wider set of incentive pricing options should be considered, the performance of which, however, should be evaluated based on the specificities of each irrigated region.

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39

Qiuping, Guo, Tian Cunzhi, and Xia ZiXiang. "Company Characteristictrading Activity and Adverse Selection Costs Empirical Evidences from the Shanghai and Shenzhen Stock Markets." Information Technology Journal 12, no. 14 (July 1, 2013): 2838–44. http://dx.doi.org/10.3923/itj.2013.2838.2844.

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40

Shmueli, Amir. "Switching sickness funds in Israel: Adverse selection or risk selection? Some insights from the analysis of the relative costs of switchers." Health Policy 102, no. 2-3 (October 2011): 247–54. http://dx.doi.org/10.1016/j.healthpol.2011.07.008.

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41

Owusu-Manu, De-Graft, David John Edwards, A. S. Kukah, Erika Anneli Parn, Hatem El-Gohary, and M. Reza Hosseini. "An empirical examination of moral hazards and adverse selection on PPP projects." Journal of Engineering, Design and Technology 16, no. 6 (December 4, 2018): 910–24. http://dx.doi.org/10.1108/jedt-01-2018-0001.

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Purpose Failures of public–private partnership (PPP) projects are often ascribed to the agency problem, which arise under conditions of inadequate and asymmetric information when a principal (the client) hires an agent (the contractor). This paper aims to identify the causes and effects of moral hazard and adverse selection on PPP construction projects using a synthesis of extant literature (to determine key variables) and analysis of survey questionnaire data collected. Design/methodology/approach Mean score ranking was used to rank the causes and effects of moral hazard and adverse selection problems in PPP construction projects. One sample t-test was conducted to establish the relative significance of these variables. Findings Effort dimensions (which are not verifiable), low transfer of risk, lack of accurate information about project conditions, wrong party chosen to execute project and renegotiation of contracts were the most significant causes of moral hazard and adverse selection problems in PPP construction projects. In addition, reduction of competition, high transaction costs, consequences on profitability of project, siphoning of funds and negative implications on enforceability of contract were the most significant effects of moral hazard and adverse selection problems in PPP construction projects. Practical implications Application of these findings will help mitigating moral hazard and adverse selection problems occurring when undertaking PPP construction projects. Originality/value Research findings provide guidance to construction stakeholders in the PPP sector on the different causes and effects of adverse selection and moral hazard. This pioneering study is the first to conduct an empirical assessment of the causes and effects of moral hazard and adverse selection of PPP construction projects in a developing country.

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42

Back, Kerry, Ruomeng Liu, and Alberto Teguia. "Signaling in OTC Markets: Benefits and Costs of Transparency." Journal of Financial and Quantitative Analysis 55, no. 1 (October 16, 2018): 47–75. http://dx.doi.org/10.1017/s0022109018001394.

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We provide a theoretical rationale for dealer objections to ex post transparency in over-the-counter markets. Disclosure of the terms of a transaction conveys information possessed by the dealer about the asset quality and reduces the dealer’s rents when she disposes of the inventory in a second transaction. We show that costly signaling in a transparent market benefits investors through lower spreads and higher volume. Dealers may also gain from transparency despite lower spreads when potential gains from trade are small or adverse selection is high, because in those circumstances higher volume offsets smaller spreads for dealer profits.

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43

Dionne, Georges. "Le risque moral et la sélection adverse : une revue critique de la littérature." Articles 57, no. 2 (January 21, 2009): 193–224. http://dx.doi.org/10.7202/600971ar.

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ABSTRACT In this paper, we present a survey of the two main problems of information in insurance markets: moral hazard and adverse selection. Both arise because the insurers are less informed than the insureds. Moral hazard is explained by the fact that the insurer cannot observe, ex ante, the activities of the insured who may have the incentive to change the state of the world in response to insurance coverage. Adverse selection arises since the insurer cannot determine without costs, the risks inherent in the individuals. After defining formally these two problems, we shall present different insurance strategies (private and public) with a view to correct them. In conclusion we shall propose some avenues of research.

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44

Lehalle, Charles-Albert, and Othmane Mounjid. "Limit Order Strategic Placement with Adverse Selection Risk and the Role of Latency." Market Microstructure and Liquidity 03, no. 01 (March 2017): 1750009. http://dx.doi.org/10.1142/s2382626617500095.

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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.

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45

Andreev, Nikolay. "Robust Portfolio Optimization in an Illiquid Market in Discrete-Time." Mathematics 7, no. 12 (November 24, 2019): 1147. http://dx.doi.org/10.3390/math7121147.

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We present a robust dynamic programming approach to the general portfolio selection problem in the presence of transaction costs and trading limits. We formulate the problem as a dynamic infinite game against nature and obtain the corresponding Bellman-Isaacs equation. Under several additional assumptions, we get an alternative form of the equation, which is more feasible for a numerical solution. The framework covers a wide range of control problems, such as the estimation of the portfolio liquidation value, or portfolio selection in an adverse market. The results can be used in the presence of model errors, non-linear transaction costs and a price impact.

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Macdonald, Angus, and Delme Pritchard. "A Mathematical Model of Alzheimer's Disease and the Apoe Gene." ASTIN Bulletin 30, no. 1 (May 2000): 69–110. http://dx.doi.org/10.2143/ast.30.1.504627.

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AbstractAlzheimer's disease (AD) accounts for a significant proportion of long-term care costs. The recent discovery that the ε4 allele of the ApoE gene indicates a predisposition to earlier onset of AD raises questions about the potential for adverse selection in long-term care insurance, about long-term care costs in general, and about the potential effects on costs of gene therapy, or better targetted treatments for AD. This paper describes a simple Markov model for AD, and the estimation of the transition intensities from the medical and epidemiological literature.

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Flaherty, Susan, Joanne Li, and Kenneth Small. "Evidence on board size and information asymmetry: A capital markets perspective." Corporate Ownership and Control 4, no. 2 (2007): 248–56. http://dx.doi.org/10.22495/cocv4i2c2p1.

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We examine the relation of board size with market liquidity and adverse selection costs using a sample of Fortune 200 companies. After controlling for firm specifics, equity characteristics, and ratio of insiders, we find a direct relation between board size and equity market liquidity. Our findings indicate that board size is positively and significantly related to dollar depth but has no impact on bid-ask spreads. Furthermore, using the adverse selection component of the bid-ask spread as a proxy for transparency, we find that larger boards reduce information asymmetries, but the ratio of insiders to total board members has no impact on informational asymmetries

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Clemens, Jeffrey. "Regulatory Redistribution in the Market for Health Insurance." American Economic Journal: Applied Economics 7, no. 2 (April 1, 2015): 109–34. http://dx.doi.org/10.1257/app.20130169.

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Community-rating regulations equalize the insurance premiums faced by the healthy and the unhealthy. Intended reductions in the unhealthy's premiums can be undone, however, if the healthy forgo coverage. The severity of this adverse selection problem hinges largely on how health care costs are distributed across market participants. Theoretically, I show that Medicaid expansions can combat adverse selection by removing high cost individuals from the relevant risk pool. Empirically, I find that private coverage rates improved significantly in community-rated markets when states expanded Medicaid's coverage of relatively unhealthy adults. The effects of these health policy instruments are fundamentally linked. (JEL G22, G28, H51, H53, I13, I18, I38)

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Subramanian, Krupa, Jean Lemaire, John C. Hershey, Mark V. Pauly, Katrina Armstrong, and David A. Asch. "Estimating Adverse Selection Costs from Genetic Testing for Breast and Ovarian Cancer: The Case of Life Insurance." Journal of Risk and Insurance 66, no. 4 (December 1999): 531. http://dx.doi.org/10.2307/253862.

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Small, Kenneth, James Wansley, and Matthew Hood. "The impact of security concentration on adverse selection costs and liquidity: an examination of exchange traded funds." Journal of Economics and Finance 36, no. 2 (January 8, 2010): 261–81. http://dx.doi.org/10.1007/s12197-009-9117-z.

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