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In December 2000,  SMIF membership voted to implement a strategy called the January Effect.  The strategy works like this:  According to historical data, stocks which have had poor year to date returns tend to get sold during the month of December, possibly due to tax-loss selling.  The impact is most pronounced for small company stocks with high book–to-market ratios.   In January, many of these same stocks have tended to rebound, offering superior performance during the month of January relative to the overall market as measured by the S&P 500.  

SMIF implemented the January Effect by analyzing historical stock return data using SAS programs developed by our faculty advisor, Dr. Mike Cooper. The goal of the program was to identify which criteria had been successful in the past at selecting stocks likely to experience the rebound in January.

The data indicated that (since 1959) the January Effect strategy has outperformed the S&P 500 in EVERY January.

Team Members :  Brian Randolph (Leader), et al.

Similar to the January effect, the SMIF teams also invest in the November effect. This effect attempts to take advantage of the mutual fund generated tax-loss selling which occurs in October. In principle, stocks sold off by funds in October should rebound in November. This effect is not historically as strong as the January effect, but still has a relatively large positive expected return.

Team Members :  Brian Randolph (Leader), et al.

According to one study, there is a window of time, between the public announcement of a stock to the S&P 500 index and the actual addition of the stock, when the stock will earn above average returns. The window of time is anywhere from 2 to 10 days and the returns on average are approximately 8% in that short period of time. The rationale for the above average returns is attributed to the many different mutual funds, and ETF’s, that attempt to mimic the S&P 500 purchasing the stock to update their portfolio to match the S&P 500, thus driving up the price.

Team Members :                      

Justin Banner (Leader), Sarah Biegert, Joe Cerri, Carlton Getz, Ben Hollenshead, Kavita Mehta

Very similar to a mutual fund, Electronically Traded Funds (ETF) are baskets of stocks that typically track an index such as the S&P 500 and can be traded by investors.  However, ETF’s can be distinguished from mutual funds because they are traded during regular market hours and can deviate from Net Asset Value.  Using ETF’s that track an index or a specific sector of the economy, an investor can create a trading strategy that will take advantage of the divergence from the index.  By shorting ETF's that are trending above the index and buying ETF’s that are trading below the same index, an investor can potentially profit.

Team Members :

Karen Asadourian, Chris Boik (Co-Leader), Mike Gremelspacher (Co-Leader), Charles Lawson, Karan Malla, Kavita Mehta, Jon Stanner

This strategy seeks to selectively time the market using a variety of widely available macroeconomic variables.  Prior to implementation, these variables are back-tested against historical data to determine the statistical significance of their predictive power.  Primary investments are index funds or linked to overall market upswings. 

 Team Members :        

Justin Puttock (Leader), Brenton Clark, Zhe Zhou, Srinivasan Iyer

This team strives to identify potential new investment opportunities that can be quantitatively back-tested against historical data.  Published papers, current strategies, and whispered “dusty corners” serve as potential sources for these new strategies.

Team Members :      

Natalia Batalova, Karan Bhalla (Leader), Eric Larson, Jonathan Mata, Quyet Tran, Eric Werth, Maria Zagrodzka