FAME EXECUTIVE COURSES IN FINANCE

Performance Evaluation and Attribution (PEVA)

Theory and Practical Application

Laboratory Exercises

Updated: September 10, 2006

Professor Russ Wermers                                                                                             

Copyright 2006 R. Wermers                                                                                                                            

Lab Exercise #1, Part A:  Computing and Comparing Basic Performance Measures

     Datasets:

        A. “net_returns_for_crystal_ball.xls”

        B. “t-bills and s&p 500 (monthly).xls”

        C. “msp500.xls”

Lab Exercise #1, Part B:  Computing and Comparing Bond Fund Styles, and Measuring the Performance of Bond Funds

     Datasets:

        A. "Lab 1B.xls"

Lab Exercise #1, Part C:  Conditional Performance Evaluation

     Datasets:

        A. “net_returns_for_crystal_ball.xls”

        B. “t-bills and s&p 500 (monthly).xls”

        C. "Ferson Schadt Conditional Carhart Regressors.xls"

Lab Exercise #2:  Dimensional Fund Advisors - An Exercise in Performance Decomposition

     Datasets:

        A. "dfa net returns.xls"       

        B. "Fama and French Factors (RMRF, SMB, HML, UMD).xls"

        C. “CRSP Cap-Based Decile Returns.xls”      

        D. “dgtw.xls” 

        E.  “styleavg.xls”    

        F.  “styletim.xls”

        G. “msp500.xls”      

Lab Exercise #3:  Bootstrapping the Cross-Section of Manager Alphas

     Datasets:

        A. "net_returns_for_optquest.xls"

Lab Exercise #4:  Maximizing Fund-of-Fund Alphas Using the Bootstrap with Value-at-Risk Constraints

     Datasets:

        A. "Optquest Mutual Fund Choice (Student version).opt"   Note: for this file, please right-click, then "Save Target As", then choose "Save as type: 'All Files'", then add the extension ".opt" before saving.

Solutions