Harry M. Markowitz, Ph.D.

Quantitative Research Consultant

1

Ph.D. Economics, University of Chicago, 1954

M.A. Economics, University of Chicago, 1950

Ph.B., University of Chicago, 1947

Dr. Harry Markowitz, Ph.D., joined McKinley Capital in 2012 as a Quantitative Research Consultant.  In his distinguished career, Dr. Markowitz has served in various academic posts at many universities, including Baruch College, London School of Economics, London Business School, University of Tokyo, Rutgers University, Hebrew University, the Wharton School and UCLA. Dr. Markowitz has applied computer and mathematical techniques to various practical decision making areas.  In finance: in an article in 1952 and a book in 1959 he presented what is now referred to as MPT, “modern portfolio theory.”  This has become a standard topic in college courses and texts on investments, and is widely used for asset allocation, risk control and attribution analysis by institutional investors and financial planners.  In other areas: Dr. Markowitz developed “sparse matrix” techniques for solving very large mathematical optimization problems.  These techniques are now standard in production software for optimization programs.  Dr. Markowitz also designed and supervised the development of the SIMSCRIPT programming language. SIMSCRIPT has been widely used for programming computer simulations of systems such as factories, transportation systems and communication networks.  In 1989 Dr. Markowitz received The John von Neumann Award from the Operations Research Society of America for his work in portfolio theory, sparse matrix techniques and SIMSCRIPT.  In 1990 he shared The Nobel Prize in Economics for his work on portfolio theory.  Dr. Markowitz is best known for his pioneering work in Modern Portfolio Theory, studying the effects of asset risk, return, correlation and diversification on probable investment portfolio returns.

In his years on the MCM Scientific Advisory Board, Dr. Markowitz has published articles with Dr. Xu and Dr. Guerard on domestic and global stock selection and the role of earnings forecasting in stock selection modeling.