All posts by jgarven

Adverse Selection – a definition, some examples, and some solutions

During last Thursday’s Finance 4335 class meeting, I introduced the topic of adverse selection. Adverse selection is often referred to as the “hidden information” problem. This concept is particularly easy to understand in an insurance market setting; if you are an insurer, you have to be concerned that the worst possible risks are the ones that want to purchase insurance. However, it is important to note that adverse selection occurs in many market settings other than insurance markets. Adverse selection occurs whenever one party to a contract has superior information compared with his or her counter-party. When this occurs, often the party with the information advantage is tempted to take advantage of the uninformed party.

In an insurance setting, adverse selection is an issue whenever insurers know less about the actual risk characteristics of their policyholders than the policyholders themselves. In lending markets, banks have limited information about their clients’ willingness and ability to pay back on their loan commitments. In the used car market, the seller of a used car has more information about the car that is for sale than potential buyers. In the labor market, employers typically know less than the worker does about his or her abilities. In product markets, the product’s manufacturer often knows more about product failure rates than the consumer, and so forth…

The problem with adverse selection is that if left unchecked, it can undermine the ability of firms and consumers to enter into contractual relationships, and in extreme cases, may even give rise to so-called market failures. For example, in the used car market, since the seller has more information than the buyer about the condition of the vehicle, the buyer cannot help but be naturally suspicious concerning product quality. Consequently, he or she may not be willing to pay as much for the car as it is worth (assuming that it is not a lemon). Similarly, insurers may be reticent about selling policies to bad risks, banks may be worried about loaning money to poor credit risks, employers may be concerned about hiring poor quality workers, consumers may be worried about buying poor quality products, and so forth…

A number of different strategies exist for mitigating adverse selection. In financial services markets, risk classification represents an important strategy. The reason insurers and banks want to know your credit score is because consumers with bad credit not only often lack the willingness and ability to pay their debts, but they also tend to have more accidents than consumers with good credit. Signaling is used in various settings; for example, one solution to the “lemons” problem in the market for used cars is for the seller to “signal” by providing credible third party certification; e.g., by paying for Carfax reports or vehicle inspections by an independent third party. Students “signal” their quality by selecting a high-quality university (e.g., like Baylor! :-)). Here the university provides potential employers with credible third-party certification concerning the quality of human capital. In product markets, if a manufacturer provides a long-term warranty, this may indicate that quality is better than average.

Sometimes it’s not possible to fully mitigate adverse selection via the methods described above. Thus, insurers commonly employ pricing and contract design strategies which incentivize policyholders to reveal their actual risk characteristics according to their contract choices. Thus, we obtain a “separating” (AKA Rothschild-Stiglitz) equilibrium in which high-risk insureds select full coverage “high-risk” contracts whereas low-risk insureds select partial coverage “low risk” contracts:

Rothschild-Stiglitz

The Rothschild-Stiglitz equilibrium cleverly restricts the menu of available choices in such a way that the insurer induces self-selection. Here, the insurer offers contract L, which involves partial coverage at an actuarially fair price (based upon the loss probability of the low risk insured), and contract H, which provides full coverage at an actuarially fair price (based upon the loss probability of the high risk insured). The differences in the shapes of the indifference curves are due to the different accident probabilities, with a lower accident probability resulting in a more steeply sloped indifference curve. Here, the high-risk policyholder optimally chooses contract H and the low-risk policyholder optimally chooses contract L. The high-risk policyholder prefers H to L because L would represent a point of intersection with a marginally lower indifference curve (here, the Ih curve lies slightly above contract L, which implies that contract H provides the high-risk policyholder with higher expected utility than contract L). The low-risk policyholder will prefer L, but would prefer a full coverage contract at the point of intersection of APl line with the full insurance (45 degrees) line. However, such a contract is not offered since both the low and high-risk policyholders would choose it, and this would cause the insurer to lose money. Thus, one of the inefficiencies related to adverse selection is that insurance opportunities available to low-risk policyholders are limited compared with the world where there is no adverse selection.

There is a very practical implication of this model. If you are a good risk, you owe it to yourself to select high-deductible insurance. The problem with a low deductible is that you will unnecessarily bear adverse selection costs if you follow this strategy.

Case studies of how (poorly designed) insurance creates moral hazard

During last week’s class meetings, we discussed how contract designs and pricing strategies can “fix” the moral hazard that insurance might otherwise create. Insurance is “good” to the extent that it enables firms and individuals to manage the risks that they face. However, we also saw insurance has a potential “dark side.” The dark side is that too much insurance and/or incorrectly priced insurance can create moral hazard by insulating firms and individuals from the financial consequences of their decision-making. Thus, in real world insurance markets, we commonly observe partial rather than full insurance coverage. Partial insurance ensures that policyholders still have incentives to mitigate risk. Furthermore, real world insurance markets are characterized by pricing strategies such as loss-sensitive premiums (commonly referred to as “experience rated” premiums), as well as premiums that are contingent upon the extent to which policyholders invest in safety.

In competitively structured private insurance markets, we expect that the market price for insurance will (on average) be greater than or equal to its actuarially fair value. Under normal circumstances, one does not to observe negative premium loadings in the real world. Negative premium loadings are incompatible with the survival of a private insurance market since this would imply that insurers are not able to cover capital costs and would, therefore, have incentives not to supply such a market.

Which brings us to the National Flood Insurance Program (NFIP). The NFIP is a federal government insurance program managed by the Federal Emergency Management Agency (AKA “FEMA”). According to Cato senior fellow Doug Bandow’s blog posting entitled “Congress against Budget Reform: Voting to Hike Subsidies for People Who Build in Flood Plains”,

“…the federal government keeps insurance premiums low for people who choose to build where they otherwise wouldn’t. The Congressional Research Service figured that the government charges about one-third of the market rate for flood insurance. The second cost is environmental: Washington essentially pays participants to build on environmentally-fragile lands that tend to flood.”

Thus, the NFIP provides us with a fascinating case study concerning how subsidized flood insurance exacerbates moral hazard (i.e., makes moral hazard even worse) rather than mitigates moral hazard. It does this by encouraging property owners to take risks (in this case, building on environmentally fragile lands that tend to flood) that they otherwise might not take if they had to pay the full expected cost of these risks.

There are many other examples of moral hazard created by insurance subsidies. Consider the case of crop insurance provided to farmers by the U.S. Department of Agriculture. According to this Bloomberg article, the effective premium loading on federally provided crop insurance is more than -60%, thus putting crop insurance on a similar footing to flood insurance (in terms of its cost compared to its actuarially fair value). Once again, incorrect pricing encourages moral hazard. As the Bloomberg article notes,

“…subsidies give farmers an incentive to buy “Cadillac” policies that over-insure their holdings and drive up costs. Some policies protect as much as 85 percent of a farm’s average yield.”

Just as mis-priced flood insurance effectively encourages property owners to build in flood plains, mis-priced crop insurance incentivizes farmers to cultivate acreage that may or may not even be fertile.

I could go on (probably for several hundred more pages – there are innumerable egregious examples that I could cite), but I think I will stop for now…

On the definition for a “professional casino gambler”

WSJ columnist Holman Jenkins provides a superb definition of the term “professional casino gambler” in his article in today’s paper:

“Casino odds are designed to favor the house (otherwise there wouldn’t be a casino business). Anybody who plays for an extended period can expect to empty his bank account and fill the casino’s. “Professional casino gambler” is a polite term for somebody psychologically addicted to losing money.”

Hands-on Large-Cap Investment Course: Practicum in Portfolio Management (Spring 2018)

Overview:

Baylor has a student-managed investment fund comprised of large capitalization (large-cap) stocks which is now valued at approximately $7.5 million.  Students in the class are directly responsible for managing the portfolio, while learning the techniques used by professionals to analyze and select individual stocks. Each student will also learn how to use Bloomberg, FactSet, Thomson Eikon and other resources commonly used in the investment management industry.

The Class:

Time:              Mondays, 5:00-7:30pm
Location:         Hodges Financial Markets Center
Structure:        Designed after the operational format of a funds management firm and built around student participation.

The course primarily consists of market sector teams preparing and presenting to the class detailed reports on stocks in their sector.  Every class member is involved in the discussion of each stock.  Following the presentation and discussion, the team makes a recommendation on the stocks they presented.  The class votes and the decisions of the class are implemented.

For a better understanding of the course, you are welcome to sit through all or part of a class session this semester!  Just come to the Financial Markets Center before 5:00pm any Monday evening.

Professors:

  • Brandon Troegle, CFA®, is a Managing Director and portfolio manager with Hillcrest, focusing on the firm’s securities selections across various strategies. Before joining Hillcrest, Brandon was an equity analyst at Morningstar. Prior to Morningstar, he worked for Luther King Capital Management and Bank of America.
  • Wesley Wright, CFA®, is a Portfolio Manager at Hillcrest Asset Management focusing on the firm’s International Value Strategy. Prior to joining Hillcrest, Wesley was a Portfolio Manager at Dreman Value Management in New York where he managed the firm’s International Value product and U.S. All Cap Value product.

How to Apply:  By 5:00pm, Monday, October 23, submit the following:

  1. Cover letter stating why you wish to take the course
  2. Unofficial transcript ( Note:  Applicants must have completed an investments course (e.g., FIN 4365 or FIN 5365) or take it concurrently with the Practicum.)
  3. Resume

Submit documents to Dr. Bill Reichenstein, Department of Finance, Insurance & Real Estate, FOS 320.36.

Enrollment is limited to 15 graduate and undergraduate students with strong academic records and an interest in investments.  Applicants will be evaluated by a Finance faculty committee chaired by Dr. Bill Reichenstein, Professor of Finance and Chair of the Board of Trustees of the Phil Dorr Investment Fund.

For More Information:

Contact Dr. Bill Reichenstein at Bill_Reichenstein@baylor.edu or by phone at 710-6146, or go to: http://www.baylor.edu/business/financial_markets.

Extra Credit opportunity: Cyber Day Panel Discussion

Here is a very worthwhile extra credit opportunity for Finance 4335. You may earn extra credit by attending and reporting on the Cyber Day Panel Discussion described below.  In order to receive extra credit for this presentation, you must submit (via email sent to risk@garven.com) a 1-2 page executive summary of what you learn from this panel discussion. The executive summary is due by no later than 5 p.m. on Monday, October 9th.  This extra credit will replace your lowest quiz grade in Finance 4335 (assuming the extra credit grade is higher).