The course is an upper-level undergraduate elective on trading strategies. I have developed the course independently during my fourth year with UGA. This course has not been offered before in Terry College, but similar courses were offered by peer and aspirant schools. The course was well received by the students and has been offered in every semester since Spring 2012, when the course was launched. In total, over 200 students have taken the course since then and provided very positive feedback.

The course is a hands-on course that teaches the students the cutting-edge results from my field of expertise and makes these results applicable to real life. The students trade on a simulation trading platform (StockTrak) following the strategies explored in the research literature. The trading platform uses real-life stock prices, thus showing the students exactly what would have come out of their investment decisions if they had been trading with real money. I also provide the students with the tools of analyzing the risks and trading costs of the strategies, as well as performance evaluation techniques for analyzing the performance of passive and actively managed portfolios.

To facilitate the students’ learning, I have put together a series of class notes that have evolved into a textbook manuscript, currently being considered for potential publication by John Wiley and Sons. I have also developed innovative homework assignments that involve trading on a simulation trading platform using my directions, analyzing historical market data, and working on relevant case studies from Harvard Business Schools.

I feel that my textbook will be able to fill the current void in the textbook market, where a big gap exists between undergraduate/MBA textbooks and master/PhD textbooks. If an instructor wants to venture off the script in the standard investments course or create an advanced investments elective that would discuss such topics as Conditional CAPM or the momentum anomaly, the instructor has to choose between very cursory conceptual overview in undergraduate/MBA textbooks and really rigorous treatment involving derivations, advanced statistics, and advanced econometrics in PhD-level readings. What is missing from the textbook market is the textbook that would explain, say, the Conditional CAPM in plain English rather than in formulas and then would teach the students to apply it to historical data using a simple linear regression.

A number of students managing the student-managed investment fund (SMIF) have taken my Trading Strategies class and found it useful in guiding them in their investment decisions for the fund.

I taught the undergraduate course "Survey of Investments" in Spring 2009, Spring 2010, Spring 2011, and Spring 2012. I am trying to make this course follow the recent developments in the understanding of risk and market efficiency. In addition to teaching the basic textbook things, I introduce students to the new results in these topics that are not covered by standard textbooks, at the same time trying to keep the material simple and intuitive and not to overwhelm the students with technical details. That makes my course quite unique, since parts of it introduce the students to graduate-level ideas at the undergraduate technical level.

Another important part of my course that is very popular with students is trading in StockTrak. StockTrak is the trading environment where students trade with imaginary balances, but using real-life quotes. I use StockTrak in a variety of ways. First, I require that students perform several trades each week, which helps them learn the ropes and understand how trading happens in real life. Second, I require students submit weekly reports with a short justification of their trades, which helps the students link the concepts from the course with the trading they do and also helps the students develop the skills of working with several sources of information about financial markets. Third, I include the competition components with bonuses to the trading teams that beat the passive portfolio (at the end of the course and during several separate three-weeks periods within the course) and bonuses to the trading teams that end up in the top three. Fourth, at the end of trading I ask students to evaluate their own performance using the performance measures and performance evaluation techniques they learn during the course.

The last part of my course that makes my course quite unique is the empirical projects. I supply students with historical data about stocks and mutual funds and ask them to use these data in performing the analysis we discuss during the course. For example, after I teach portfolio theory and show the students how to find the mean-variance portfolio, I assign the project that asks to take the given series of historical returns to two stocks and construct a mean-variance efficient portfolio out of them. After I teach the CAPM, I assign the project that asks to take the historical returns to a mutual fund, estimate its alpha and beta, and conclude on the risk of the fund and its desirability as an investment vehicle. I think the projects are an essential part of the class, since, for example, the formula for portfolio variance is quite useless to the one who cannot compute returns from the closing prices and then compute the inputs for the formula using the historical returns. Both steps sometimes prove to be challenging compared to how easy similar things looked on a multiple-choice test, but I feel that the final learning outcome justifies the effort.

In Spring 2011 and Spring 2013, I taught the PhD-level course in empirical asset pricing. I developed the course from scratch using the most recent research, as I think always should be done in PhD-level courses.

In my mind, the PhD courses are different from undergraduate/MBA courses in one important way. Undergraduate/MBA courses are about learning specific things. There is always a list of concepts students have to understand by the end of the course and a list of things they should be able to do by the end of the course. Most importantly, there is always the correct way to understand what they are supposed to understand and to do what they are supposed to be able to do.

PhD courses are more like apprenticeship than learning. The point of a PhD course is to help the PhD students take the first steps towards being a researcher. Most importantly, they should switch their mindset from reading books/papers in order to learn to reading papers critically. Therefore, when you teach PhD students a model or a concept, one cannot stop at teaching it to them and saying "this is the right way to do it and the right way to think about it", though one should definitely do it as a starting point. But in addition to that, one should take care pointing out the strengths and weaknesses of the model/concept and one should show how to use the model/concept to critique existing work.

For example, if you teach Conditional CAPM to undergrads/MBAs, you should explain why changes in betas are important, and teach them how to estimate Conditional CAPM and how to interpret the output. Pointing out some limitations of Conditional CAPM may also be appropriate, depending on the time constraints. You should do all that when you teach Conditional CAPM to PhDs. But in addition to that, you have to take a good paper that uses Conditional CAPM and go under the hood, pointing out where the signs in the equation for the beta are wrong, or where the magnitude of the slopes in this equation is off, or pointing out the implausibly high estimate of the risk-free rate in a cross-sectional regression. etc. The last part, to me, is the most important part of the PhD education.

In my PhD course, I do three things I think are crucial for a PhD course. First, I devote significant time during the lecture to "going under the hood" discussed above. Effectively, I try to present the papers from the reading list in the conference format, by first putting on the author's hat and then putting on the discussant's hat.

Second, I require the students to present several papers from the reading list themselves. The final goal is to make their presentations similar to conference discussions (with a longer summary part for educational purposes). In addition to helping the students develop the skills of an effective discussant, which is a good thing in its own right, I think this experience will also make them better researchers and will help them with switching their mindset from passive learning to critical reading.

Third, I aim at giving the students hands-on experience with empirical research by assigning the homework that requires the use of the WRDS databases and SAS programming. I start with easy things, like asking the students to perform single sorts, to value-weight portfolio returns and to compute the alphas of decile portfolios. I then gradually increase the difficulty and introduce new databases, one per homework, with the final goal of bringing the students to the level at which they can reproduce the main results of a technically simple empirical paper. The culmination of this part of the course is the empirical project of the student's choice, where each of the students should develop a simple asset-pricing hypothesis and perform a few empirical tests of this hypothesis using the real data, thus producing a mini-paper or a research proposal with preliminary results.

The course is a one credit hour seminar for freshmen, taken in their first semester at UGA. The main goal is to introduce the freshmen, who normally have some background in economics, but little background in finance, to what it is like to be a finance major and what finance department (including both students and professors) does. I taught the course in Spring 2013, Fall 2013, and Fall 2014.

My goal in teaching this course is to provide students with some basic information about financial markets that is likely to be helpful to them in making their own investment decisions. We discuss basic financial instruments and their risk measures, retirement plans and taxation, several popular trading strategies and their refinements, market efficiency and its limitations, and ways to gauge the magnitude of trading costs. For a fast-paced ten-week course that meets once a week, it is a rich and exciting program.

The main challenge of the course is making the useful material above easily accessable to motivated students with little finance background, given the very limited time for setting up the scene and teaching the basics. The feedback I received from the students shows that I have been successful in this effort.

One important learning tool in the course is the use of the StockTrak simulation trading environment. The students receive the chance of implementing the suggested trading strategies (using supplied directions) and trying out their own strategies using real-time data. The students are also excited about the tournament that rewarded the best performers and trade actively in order to win.