1. Comment on: "The Road Not Taken" by C. French, Journal Of Investment Management 14(4): 4-13
    Dr. Michaud offers a thoughtful response to French's piece on the rejection of the Markowitz (1959) critical line algorithm by investment managers. 
    Author: Richard Michaud
  2. Reply to 'Reply to 'Comment on 'Markowitz versus Michaud: Portfolio Optimization Strategies Reconsidered,' Becker, Gürtler and Hibbeln, European Journal of Finance, 21(4): 2015.'''
    The continued academic exchange concerning a critique of Michaud optimization.
    Authors: Richard Michaud, Robert Michaud, David Esch.
  3. Deconstructing Black-Litterman (available through JOIM)
    Black-Litterman optimization claims to solve the problems of mean-variance optimization in practice, but our analysis demonstrates that it has limited investment value.
    Authors: Richard Michaud, David Esch, and Robert Michaud
    Publication: Journal Of Investment Management 1st quarter 2013
  4. Deconstructing Black-Litterman (draft)
    Black-Litterman optimization claims to solve the problems of mean-variance optimization in practice, but our analysis demonstrates that it has limited investment value.
    Authors: Richard Michaud, David Esch, and Robert Michaud
    Publication: Journal Of Investment Management 1st quarter 2013
  5. Comment on 'Markowitz versus Michaud: Portfolio Optimization Strategies Reconsidered,' Becker, Gürtler and Hibbeln, European Journal of Finance, 21(4): 2015.
    New Frontier responds to a critique of Michaud optimization.
    Authors: Richard Michaud, Robert Michaud, David Esch.
  6. Portfolio Monitoring in Theory and Practice (available through JOIM)
    The described algorithms improve the when-to-trade decision and allow for large-scale automatable, non-calendar-based portfolio monitoring.
    Author: Richard Michaud, David Esch, and Robert Michaud
    Publication: JOIM Vol 10, No. 4, 2012
  7. Portfolio Monitoring in Theory and Practice (draft)
    The described algorithms improve the when-to-trade decision and allow for large-scale automatable, non-calendar-based portfolio monitoring.
    Author: Richard Michaud, David Esch, and Robert Michaud
  8. Morningstar vs. Michaud Optimization
    Richard Michaud and David Esch present the first direct comparison as well as an analysis of key differences.
    Author: Richard Michaud and David Esch
    Publication: September 2012 Newsletter
  9. Non-Normality Facts and Fallacies (available through JOIM)
    A summary rejection of normal distributions is almost always ill-advised.
    Author: David Esch
    Publication: Journal Of Investment Management 1st quarter 2010
  10. Non-Normality Facts and Fallacies (draft)
    A summary rejection of normal distributions is almost always ill-advised.
    Author: David Esch
    Publication: September 2009
  11. Are Good Estimates Enough?
    Markowitz optimization has been the standard, but it does not correct for estimate uncertainty.
    Author: Richard Michaud and Robert Michaud
    Publication: Investment Management Consultants Association. January/February 2009
  12. Estimation Error and Portfolio Optimization (available through JOIM)
    Richard Michaud and Robert Michaud review and update the optimization information introduced in Efficient Asset Management.
    Author: Richard Michaud and Robert Michaud
    Publication: JOIM First Quarter 2008
  13. Estimation Error and Portfolio Optimization (draft)
    Richard Michaud and Robert Michaud review and update the optimization information introduced in Efficient Asset Management.
    Author: Richard Michaud and Robert Michaud
  14. Defense of Markowitz-Usmen
    Dr. Michaud and Robert Michaud respond to Harvey et al.'s criticism of the simulation tests used by Markowitz and Usmen.
    Author: Robert Michaud and Richard Michaud
    Publication: NFA March 2008
  15. Scherer's Errors
    Scherer published a critique of resampled efficiency. This paper provides corrections to the invalid conclusions found in Scherer.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: December 2005 Newsletter
  16. Resampled Efficiency Equity Portfolio Optimizer
    New Frontier describes the features and theory behind the new Equity Optimizer.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: October 2005 Newsletter
  17. The Information Ratio of Factor Based Alpha
    Misconceptions concerning the proper definition of breadth in Grinold’s Active Law of Management have suggested that the information ratio of optimized portfolios increases with the number of stocks in the portfolio. We show that when active return depends on factor bets, the IR has an upper bound independent of the number of stocks, but depending on the breadth of the strategy and some maximum information ratio of the joint factor bet.
    Authors: Noah Kraut, Robert O. Michaud & Richard O. Michaud
    Publication: October 2005 Newsletter.
  18. Equity Optimization Issues-V: Monte Carlo and Optimization Errors
    Improvements in optimization design and resolutions of fallacies in asset management practice are largely due to recent applications of Monte Carlo simulation technology.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: August 2005 Newsletter
  19. Equity Optimization Issues-IV: The Fundamental Law of Mismanagement
    The Grinold Law of Active Management is one of the most widely referenced and misused formulas in investment theory and practice.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: July 2005 Newsletter
  20. Equity Optimization Issues-III: Insignificant Alphas, Heterogeneous Errors
    Insignificant alphas and heterogeneous estimation error are two issues associated with performance limitations in mean variance equity portfolio optimization.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: March 2005 Newsletter
  21. Equity Optimization Issues-II: Large Stock Universes and Scaling Alphas
    In order to obtain the provable benefits of Resampled Efficiency, a number of common ad hoc equity portfolio optimization techniques need to be avoided or corrected. This article focuses on two: the use of large stock universes and incorrect alpha scaling.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: February 2005 Newsletter
  22. Equity Optimization Issues-I
    The first equity optimization article is a beginning discussion of difficulties of traditional optimizers and the solutions New Frontier was starting to explore.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: November 2004 Newsletter
  23. Optimization with Non-Normal Resampling
    In order to retain some of the normal distribution relevance for MC optimization, we use a multivariate distribution procedure that allows for exogenous specification of skewness and kurtosis.
    Author: Noah Kraut
    Publication: September 2004 Newsletter
  24. Forecast Confidence Level and Portfolio Optimization
    This report focuses on the role and importance of the uncertainty in forecast information in constructing portfolios with the optimal performance.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: July 2004 Newsletter
  25. Resampled Efficiency Fallacies
    This report responds to critiques of Resampled Efficiency.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: April 2004 Newsletter
  26. Resampled Efficiency vs. Bayes: Implications for Asset Management
    Good inputs, prepared with Bayesian statistics, are no better than bad inputs if the portfolio construction process misuses investment information.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: February 2004 Newsletter
  27. Why Mean-Variance Optimization Isn't Useful for Investment Management
    The logic of mean variance optimization is seductive, but the seduction unravels in the investment period.
    Author: Richard O. Michaud
    Publication: January 2004 Newsletter
  28. Are Good Inputs Enough? No.
    Investment institutions tend to focus the bulk of their human and capital resources on developing reliable forecasts of asset risks and return while ignoring the optimization technology they use to transform their information into investors' portfolios. These good inputs are no better than bad if the portfolios that represent the information to the investor have no investment value.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: October 2003 Newsletter.
  29. Resampled Efficiency For Financial Planning and Return Forecasting
    An examination of the effect of forecast certainty level indicate that the enormous effort focused on input estimation by many managers and institutions without Resampled Efficient Optimization is misplaced and likely to be ineffective.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: August 2003 Newsletter
  30. Optimal and Investable Portfolios
    Optimal portfolios typically include inconvenient and insignificant asset weights, make for impractical investment. This article introduces some of New Frontier's compute-efficient solutions for finding an investable portfolio from the optimal portfolio.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: June 2003 Newsletter
  31. LETTERS TO THE EDITOR: Portfolio Resampling: Review and Critique
    The authors responded immediately to Bernd Scherer’s critique of Resampled Efficient Optimization.
    Authors: Richard O. Michaud & Robert O. Michaud.
    Publication :Financial Analysts Journal. May/June 2003.
  32. Resampled Efficiency Issues
    Once resampled efficiency gained prominence, misinterpretations and misunderstandings arose. This 2003 article addresses the most frequently asked questions and misunderstandings of that time period.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: February 2003 Newsletter.
  33. Liquidity and Portfolio Optimization
    Liquidity, within the context of defining an optimal portfolio of risky assets, may be viewed as a non-linear return penalty factor that depends on the level of investment and asset size or float.
    Authors: Richard O. Michaud & Robert O. Michaud
    Publication: Second Quarter 2003 Newsletter
  34. LETTERS TO THE EDITOR: 'An Examination of Resampled Portfolio Efficiency': A Comment
    Dr. Michaud responds to the Fletcher & Hillier article (2001) that compared the performance of mean variance efficient asset allocations to resampled efficient asset allocations by means of a back test.
    Author: Richard O. Michaud
    Publication: Financial Analysts Journal. January/February 2003
  35. Resampled Portfolio Rebalancing and Monitoring
    An enhancement to the original rebalancing procedure is now available that dramatically increases the uniformity and discrimination power of the original portfolio rebalancing and asset weight range procedures.
    Author: Richard O. Michaud
    Publication: Fourth Quarter 2002 Newsletter
  36. An Introduction to Resampled Efficiency
    Resampled Efficiency provides the solution to using uncertain information in portfolio optimization.
    Author: Richard O. Michaud.
    Publication: Investment Management Consulting Association's Monitor, September 2002
    Third Quarter 2002 Newsletter
  37. A Better Way to Use Information
    In the second resampled efficiency article in the European Pensions News, Richard Michaud argues for the importance of taking statistical errors into account in asset allocation decisions.
    Author: Richard O. Michaud.
    Publication:European Pensions & Investment NewsJuly 9, 2001.
  38. Out-of-Sample Tests of Resampled Efficiency
    Resampled Efficient Optimization improves the average reward-to-risk ratio of classical asset allocation portfolios.
    Author: Richard O. Michaud
    Publication: European Pensions & Investment News. June 25, 2001
  39. Aspects: Resampled Efficient Asset Allocation
    This article introduces the advantages and functionality of the newly patented Resampled Efficiency.
    Author: Richard O. Michaud
    Publication: Frontier News. Second Quarter 2001
  40. A New Design for Portfolios
    Given all the problems associated with the inferior investment technology currently being used, it is little wonder that capital markets appear to be efficient. Only when asset management practice has achieved a level of sophistication consistent with the thoughtful use of investment information is it likely to provide statistically significant risk-adjusted performance.
    Author: Richard O. Michaud
    Publication: Bloomberg Personal Finance. July/August 2000
  41. New View of Mean Variance
    This article discusses five methods other than mean variance optimization for defining portfolio optimality: non-variance risk measures, utility function optimization, multi-period objectives, Monte Carlo financial planning, or linear programming.
    Author: Richard O. Michaud
    Publication: Financial Planning Magazine. November 1, 1998
  42. The Markowitz Optimization Enigma: Is Optimized Optimal?
    The major problem with mean variance optimization is its tendency to maximize the effects of errors in the input assumptions.  Unconstrained mean variance optimization can yield results that are inferior to those of simple equal-weighting schemes. Author: Richard O. Michaud
    Publication: Financial Analysts Journal. January/February 1989