Portfolio Practicum Courses – About the Courses and How to Apply for the Fall 2023 Semester


Baylor has two student-managed investment funds: A large-cap stock fund currently valued at approximately $14.2 million, and a small-cap stock fund currently valued at approximately $1.1 million. Students in the Portfolio Practicum courses are directly responsible for managing the portfolios, 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 Classes:

Small-Cap: Mondays, 2:30-4:00pm, for a total of 16 weeks spread across both the Fall and Spring semesters
Large-Cap: Mondays, 5:00-7:30pm, for the Fall Semester only
Location: Hodges Financial Markets Center (Foster 116)
Structure: Designed after the format of an investment management firm and built around student participation.

Designed to cover two-semesters, the Small-Cap Practicum gives students experience researching, analyzing, and managing a portfolio of small capitalization (small-cap) stocks. The Fall course introduces equity research methods, including valuation, modeling, fundamental analysis, and cultivating resources. Student analysts, in teams, complete an initiation-of-coverage research report on a firm. Their research may include the team talking to company management and utilizing various information sources including financial documents, trade associations, and competitors, customers, and suppliers of the firm. In the Spring, one team will compete in the CFA Investment Research Challenge, while other student teams will continue to research and present new opportunities for the portfolio.

The Large-Cap Practicum is a one-semester course. The class structure is designed after the operational format of a funds management firm and is built around student participation. Specifically, two-person teams are assigned to cover each sector of the S&P 500. Although there are course readings, the course primarily consists of teams preparing and presenting to the class detailed reports on stocks in their sector. Every class member is involved in a discussion of each stock. Following the presentation and discussion, the team makes a recommendation on each stock. The class votes and the recommendations of the class are implemented.

For a better understanding of either course, you are welcome to attend all or part of a class session this semester! Both classes meet on Monday. The Small-Cap class begins this semester at 2:30 pm and the Large-Cap class begins at 5:00. The available Mondays this semester are March 20 (Large-Cap) and 27 (Small-Cap and Large-Cap).

Professors (Small Cap):

  • David Morehead, CFA, Chief Investment Officer at Baylor University – Office of Investments.
  • Renee Hanna, CFA, Managing Director of Investments at Baylor University – Office of Investments.

Professors (Large Cap):

  • Brandon Troegle, CFA, CAIA, Senior Portfolio Advisor at Northern Trust Corporation.
  • Taylor Finch, CFA, Managing Member at Finch Capital Management.

How to Apply:

Complete the online application at https://www.baylor.edu/business/financialmarkets/apply. The application will be open beginning at 8:00 am on Monday, March 20.

In addition to the usual grades, contact, and background information you will need to provide:

  1. Statements of why you wish to take the course and your career plans/goals
  2. Description(s) of any investment and/or finance-related experience
  3. Uploaded copies of your current resume and current unofficial transcript

The deadline for submission is Midnight, Monday, March 27.

Each course is open to both graduate and undergraduate business students with a minimum 3.2 GPA, a strong academic record, and an interest in investments*. Applicants will be evaluated by a Finance faculty committee.

For More Information go to: http://www.baylor.edu/business/financial_markets

Rothschild-Stiglitz model (numerical and graphical illustration)

According to the Rothschild-Stiglitz model (referenced in the Adverse Selection – a definition, some examples, and some solutions blog posting, and on page 22 of the Asymmetric Information: Moral Hazard and Adverse Selection lecture note), insurers will limit contract choices such that there is no adverse selection.  To see this, assume there are equal numbers of high-risk and low-risk insureds, all of whom have an initial wealth of $125 and square root utility.  There are two states of the world – loss and no loss, and the probabilities of loss are 75% for high-risk types and 25% for low-risk types.  By offering high-risk types full coverage at their actually fair price of $75 and offering low-risk types partial (10%) coverage at their actuarially fair price of $2.50, both types of risks buy insurance and there is no adverse selection.

This is illustrated in the figure below and in the spreadsheet located at http://fin4335.garven.com/spring2023/rothschild-stiglitz-model.xls.  Clearly, neither the B (full coverage for low-risk insureds) nor C (based on the average cost of the actuarially fair prices for the low-risk and high-risk) contracts would ever be offered because both of these contracts incentivize high-risk types to adversely select against the insurer.

Rothschild-Stiglitz model (numerical and graphical illustration)

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

The adverse selection problem (also known as the “hidden information” problem) is especially easy to grasp in an insurance market setting; if you are an insurer, you need to be concerned that the worst potential risks may be the ones who wish to purchase insurance. However, it is important to note that adverse selection also occurs in many other market settings. Adverse selection occurs whenever one party to a contract has superior information compared with his or her counterparty. When this occurs, there is a risk that the more informed party may take advantage of the other, less informed party.

In an insurance setting, adverse selection is an issue whenever insurers know less about the actual risk characteristics of a potential client than the client herself.  In lending markets, banks have limited information about their potential clients’ willingness and ability to pay back 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…

If left unchecked, adverse selection can undermine the ability of firms and consumers to enter 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 suspicious about product quality. Thus, 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 on…

Several strategies exist for mitigating adverse selection. In financial services markets, risk classification represents an important strategy. Insurers and banks want to know your credit score because consumers with bad credit not only often lack the willingness and ability to pay their debts, but they also have more accidents on average 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., 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 signal that quality is better than average.

Sometimes it’s not possible to mitigate fully adverse selection via the methods described above. Thus, insurers commonly employ pricing and contract design strategies that financially reward policyholders for revealing their true risk characteristics according to the contract choices they make; i.e., they voluntarily reveal their preferences. Thus, we get what’s commonly referred to as a “separating” (Rothschild-Stiglitz) equilibrium in which high-risk insureds select full coverage “high-risk” contracts whereas low-risk insureds select partial coverage “low-risk” contracts:

The Rothschild-Stiglitz equilibrium cleverly restricts the menu of choices in such a way that the insurer induces self-selection; i.e., low-risk insureds select the (low-risk and profitable) partial coverage contract designed with them in mind, and the high-risk insureds select the (high-risk and profitable) full coverage contract designed for them.  Here, the insurer offers contract L, which involves partial coverage at an actuarially fair price (based on the loss probability of the low-risk insured), and contract H, which provides full coverage at an actuarially fair price (based on the loss probability of the high risk insured). The indifference curve slopes are steeper for the low-risk insureds than they are for the high-risk insureds.  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 prefers L to H, even though only provides partial coverage.   Thus, one inefficiency 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, then you owe it to yourself to select high-deductible insurance, since insurers price low-deductible insurance expecting high-risk policyholders will be the primary purchasers of such coverage (and therefore, low-deductible policies will be more costly per dollar of coverage than high-deductible policies).


Synopsis of the Moral Hazard topic…

In finance, the moral hazard problem is commonly referred to as the “agency” problem. Many, if not most real-world contracts involve two parties – a “principal” and an “agent”. Contracts formed by principals and agents also usually have two key features: 1) the principal delegates some decision-making authority to the agent, and 2) the principal and agent decide upon the extent to which they share risk.

The principal has good reason to be concerned that the agent may take actions that are not in her best interests. Consequently, the principal has strong incentives to monitor the agent’s actions. However, since it is costly to closely monitor and enforce contracts, some actions can be “hidden” from the principal in the sense that she is not willing to expend the resources necessary to discover them since the costs of discovery may exceed the benefits of obtaining this information. Thus, moral hazard is often described as a problem of “hidden action”.

Since it is not economically feasible to perfectly monitor all the agent’s actions, the principal needs to be concerned about whether the agent’s incentives line up, or are compatible with the principal’s objectives. This concern quickly becomes reflected in the contract terms defining the formal relationship between the principal and the agent. A contract is said to be incentive-compatible if it causes principal and agent incentives to coincide. In other words, actions taken by the agent usually also benefit the principal. In practice, contracts typically scale agent compensation to the benefit received by the principal. Thus, in insurance markets, insurers are not willing to offer full coverage contracts; instead, they offer partial insurance coverage which exposes policyholders to some of the risks that they wish to transfer. In turn, partial coverage reinforces incentives for policyholders to prevent/mitigate loss.

Similarly, in a completely different setting, consider the principal/agent relationship which exists between the owner and manager of a business. If the manager’s effort level is high, then the owner may earn higher profits compared with when the manager’s effort level is low. However, if managerial pay consists of a fixed salary and lacks any form of incentive compensation (e.g., bonuses based upon meeting or beating specific earnings targets), then the manager may be inclined to not exert extra effort, which results in less corporate profit. Thus, compensation contracts can be made more incentive-compatible by including performance-based pay in addition to a fixed salary. This way, the owner and manager are both better off because incentives are better aligned.

Midterm 1 and Current Course Grades in Finance 4335

I just uploaded the midterm 1 grades, along with current attendance/participation, quiz, problem set, and Finance 4335 course grades, to Canvas.

As stated in the course syllabus, final numeric course grades will be determined according to the following equation:

Final Course Numeric Grade =.10(Attendance and Participation) +.10(Quizzes) +.20(Problem Sets) + Max{.20(Midterm Exam 1) +.20(Midterm Exam 2) +.20(Final Exam),.20(Midterm Exam 1) +.40(Final Exam),.20(Midterm Exam 2) +.40(Final Exam)}

As I noted in my February 25th blog posting entitled “Finance 4335 Grades on Canvas”, as the spring semester progresses and I continue to collect grades in the attendance, quiz, problem set, and exam categories, then the course grade listed on Canvas will dynamically incorporate that information on a timely basis for each student; now that we have Midterm 1 Exam grades, the equation that I am now using (until Midterm 2) is:

Course Numeric Grade after Midterm 1 = (.10(Attendance and Participation) +.10(Quizzes) +.20(Problem Sets) +.20(Midterm 1))/.6

There are n = 29 students enrolled in Finance 4335. Here are the current grade statistics, broken down by grade category:

As you can see from this table, more than half of the students enrolled in Finance 4335 have scored the mean or higher in all grade categories other than quizzes (since in all but the quiz category, the median is higher than the mean). Although actual letter grades won’t be assigned until after the final exam, hypothetically, you can determine where your course letter grade currently stands by comparing it with the course letter grade schedule that also appears in the course syllabus:

If you are disappointed by your performance to date in Finance 4335, keep in mind that the final exam grade automatically double counts in place of a lower midterm exam grade. In case both midterm exam grades are lower than the final exam grade, then the final exam grade replaces the lower of the two midterm exam grades.

If any of you would like to have a chat with me about your grades in Finance 4335, then by all means, stop by my office (Foster 320.39) 3:30-4:30 pm TR, or set up a Zoom appointment with me.

Your Tolerance for Investment Risk Is Probably Not What You Think

This WSJ article is authored by Professor Meir Statman, the Glenn Klimek Professor of Finance at Santa Clara University. Professor Statman’s research focuses on behavioral economics, an important topic that we covered briefly during last Thursday’s meeting of Finance 4335.

Your Tolerance for Investment Risk Is Probably Not What You Think

The questions financial advisers ask clients to get at the answer actually measure something completely different—often leading to misguided investment strategies.

VIX is back in the news (Page 1 feature article in today’s WSJ)!

It’s back!  At the beginning of this semester, I introduced our class to the CBOE’s Implied Volatility Index (VIX) in my blog posting entitled “On the relationship between the S&P 500 and the CBOE Volatility Index (VIX)“.  In that posting, I pointed out how over relatively short time intervals, percentage changes in VIX and SP500 indices move inversely.

VIX measures market expectations for stock market (S&P500) volatility over the coming 30 days.  It is commonly referred to as a “fear index”, and as such, it is indicative of the near-term degree of overall investor risk aversion.

This article mostly focuses on how investor fears of more aggressive Fed rate hikes and a possible recession are causing prices of options to be bid up, as investors “scurry for protection”.

Investors Are Bracing for Surge in Market Volatility
Bets on a rise in Wall Street’s fear gauge swell to most since March 2020