• Room: D310, 2023-4
• Website: ASAM
• 00-asam-syllabus-2023.html: Full ASAM Syllabus

1. Handover (Receiver)
2. Learn how to program (Python, perhaps R)
3. *Mentor one another learning.*
4. Some Speakers
5. Role Selection
6. Annual Meeting

• 01-asam-summer.html: Programming and Data for ASAM (30m).
  Very important to start working on this!

1. Learn how to program (Python, perhaps R)
2. Learn about and download finance equity data sets (CRSP, Compustat)
3. *Mentor one another learning.*

• 01-asam-summer.html: Programming and Data for ASAM (30m).
  Very, very important to this get done. I mean it.

2023: Classroom is D310

Topics Covered

1. Learn about portfolio formation and portfolio returns
2. Learn about finance backtesting and performance evaluation methods (Fama-Macbeth; Black-Jensen-Scholes/Fama-French)
3. Read about finance strategies
4. Select, design, and backtest your own strategy
5. Incept your strategy (around the turn of the calendar year)

Topics Covered In Class

• 01-asam-summer.html: Programming and Data for ASAM (30m).
  I really, really mean it. You are running out of time.
• 01-asam-finintro.html: Finance and Portfolio Basics (1h)
• 02-asam-famamacbeth.html: Fama-Macbeth X-Sect Method
• 03-asam-bjsff.html: Black-Jensen-Scholes/Fama-French TS Method
• 04-asam-minvar.html: Low-Variance Portfolios
• asam-attribution.html: ASAM Attribution
• asam-checklist.html: ASAM Checklist
• asam-finalreport.html: ASAM Final Report Advice

1. Strategy Monitoring
2. Evaluation
3. Industry Speakers
4. Selection of Next Year's Cohort (Successors)
5. Maintenance and Improvement of ASAM website. (asamsiteplan.txt.)

1. Transition Handover to Next Cohort
2. Industry Speakers
3. Final Report
4. Annual Meeting

I will update the following slides as the quarter progresses and move them up.

• bt-sig2inv.html: Translating Signals Into Investments
• bt-worldsim.html: Simulating A World For BJSFF Tests
• bt-conclusion.html: Further Considerations and Conclusion

• asam-performance-updates.html: Performance Updates Guidelines
• asam-performanceeval.pdf: Basic Performance Evaluation

I have decided to change the ASAM course project this year from those of years past, where each group decided on its own strategy to maximize a mean.

This year, each group will be asked to maximize the daily mean return of the portfolio minus the daily standard deviation of the portfolio, both annualized, with no less than 10 stocks and no more than 30 stocks.  (This is not the Sharpe ratio, which is the mean divided by the standard deviation.)  Because the standard deviation is a lot more predictable than the mean, this means a big aspect of your strategy will have to be minimizing the standard deviation of the rate of return on your portfolio.

For example, if the average daily rate of return is 0.1% and the average standard deviation is 1%, you first annualize this (255*0.1% and sqrt(255)*1%), and your score is 25.5 - 16.0 = 9.5 percent. This is your score.

Unlike in earlier years, I plan to give some extra grade weight (about 20%) to the group that performs the best on this metric.  So, this time, you have more at stake than just your money and your pride.

Of course, you are welcome to use any variables as information to achieve the best score possible.

See occasional announcements on Bruinlearn.

Per vote, ASAM is henceforth meeting on Wednesdays, 7 pm to 10 pm. The classroom has not been set yet.

To pass this course, everyone will have to take and pass a simple python test. If you flunk, you can take a test again --- except that your costudents will have to write yet another test, so if you flunk 5 times, they will get pissed with you.