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 Announcements on Bruinlearn.

Per vote, ASAM will henceforth meet on Wednesdays, rather than on Mondays. The time is, as before, 7 pm to 10 pm. The classroom has not been set yet.

Unfortunately, I will be away giving seminars on Oct 11 and Oct 18. Therefore, please plan to meet on Oct 9 (Mon) and Oct 20 (Fri) instead. We will record the classes for students who have conflicting classes.

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.

• Room: [TBA] 2023
• Old "Covid" Cohort: 2022-23 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

• Programming and Data for ASAM (30m)

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

• Programming and Data for ASAM (30m)

2022: Classroom is D310

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)

• Finance and Portfolio Basics (1h)
• Fama-Macbeth X-Sect Method
• Black-Jensen-Scholes/Fama-French TS Method
• ASAM Attribution
• ASAM Checklist
• ASAM Final Report Advice

1. Strategy Monitoring
2. Evaluation
3. Industry Speakers
4. Selection of Successors
5. PLUS ASAM website, based on some CMS (Drupal, WordPress, Joomla, MS). Login. Hosted elsewhere (for sequestered security and non-bureaucratic approvals)
   • Public: Posting of Current Members, with CVs and (ideally) Picts. Ideally, member-controlled.
   • Public: Short Posting of Bases for Current Investment Holdings
   • Public: Posting of Current Investment Holdings, ideally with nightly and since-incept rates of returns, plus current value.
   • Public: Posting of all Past Investment Reports
   • Public: Posting of ASAM Application Process
   • Public: Names of ASAM Alumni by Year
   • Public: Names of Past Class Speakers by Year
   • Public: Python Resources. Other useful Resources.
   • Private (Log-In Only): External Speaker List, Upcoming
   • Private (Log-In Only): Important URLs: Chats, GDrive, etc.
   • Private (Log-In Only): Posting of Members by Year
   • Private (Log-In Only): Long-Term Membership Directory (Alumni, etc.)
   • Private (Log-In Only): Admin Access, Transfer to Next Class.
   Once up and running, we need to inform ASAM alumni of the existence.

1. Handover (Sender)
2. Industry Speakers
3. Final Report
4. Annual Meeting

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

• Translating Signals Into Investments
• Simulating A World For BJSFF Tests
• Further Considerations and Conclusion

• Performance Updates Guidelines
• Basic Performance Evaluation

• annual: umd-ann.csv

  > t(subset(d, d>1990))
  
  yyyy    1991    1992    1993    1994    1995    1996   1997    1998    1999    2000    2001    2002    2003    2004    2005
  umd    14.56    3.47   23.72    3.14   18.07    6.39   11.5   23.14   35.18   15.37    4.44   25.69  -24.48   -0.42   14.81
  
  yyyy    2006    2007    2008    2009    2010    2011    2012   2013    2014    2015    2016    2017    2018    2019    2020
  umd    -7.84   22.06   13.56  -83.16    6.85    7.16    1.28    7.5    1.01   20.64  -21.38    4.62    9.74    -1.8    7.79
  
  > iaw$summary(subset(d, d>1990))
       nok pctna    mean     sd   tstat      0%      10%     50%    90%   pmost
  yyyy  30    -1 2005.50  8.803 1247.76    1991 1993.900 2005.50 2017.1 2020.00
  umd   30    -1    5.42 21.001    1.41  -83.16   -9.194    7.33   23.2   35.18
  

• monthly: umd-mo.csv.
• please update the above. you can do this! this is an easy task.