Victor DeMiguel

Professor of Management Science and Operations

MSc (Madrid) MSc PhD (Stanford)

Professor Victor DeMiguel’s main research interest is portfolio optimization in the presence of estimation error, transaction costs, and taxes. More generally, he is interested in applications of optimization in finance and management.

He teaches MBA courses on Business and Financial Analytics as well as the Decision Making Strategies for Leaders and the Advanced Development Programmes of Executive Education. He is the recipient of the Junior Faculty Teaching Award for 2003/2004 and the Outstanding Core Course Teaching Award for 2008/2009 at London Business School.

Professor DeMiguel’s papers have been published in most of the top journals of his field, including Management Science and Operations Research. One of his most popular papers received the Best Paper Award from the Institute for Quantitative Investment Research and was published in The Review of Financial Studies.

Professor DeMiguel is a regular speaker at academic and practitioner conferences on quantitative investment management and he has consulted for several financial institutions. He is a member of the editorial boards of Operations Research and Management Science.

  • Portfolio optimisation
  • Investment management
  • Operations research


Cover-up of vehicle defects: the role of regulator investigation announcements

Cho S-H; DeMiguel V; Hwang W

Management Science 2020 Vol 67:6 p 3834-3582 Online first


A transaction-cost perspective on the multitude of firm characteristics

DeMiguel V; Martin-Utrera A; Nogales F J; Uppal R

Review of Financial Studies 2019 Vol 33:5 p 2180-2222


Technical note: a robust perspective on transaction costs in portfolio optimization

Olivares-Nadal A V; DeMiguel V

Operations Research 2018 Vol 66:3 p 733-739

Wholesale price contracts for reliable supply

DeMiquel V; Hwang W; Bakshi N

Production and Operations Management 2018 Vol 27:6 p 1021-1037


Multiperiod portfolio optimization with multiple risky assets and general transaction costs

Mei X; DeMiguel V; Nogales F

Journal of Banking and Finance 2016 Vol 69:August p 108-120

Supplier capacity and intermediary profits: can less be more?

DeMiguel V; Adida E; Bakshi N

Production and Operations Management 2016 Vol 25:4 p 630-646


Parameter uncertainty in multiperiod portfolio optimization with transaction costs

DeMiguel V; Martin-Utrera A; Nogales F J

Journal of Financial and Quantitative Analysis 2015 Vol 50:6 p 1443-1471


Stock return serial dependence and out-of-sample portfolio

DeMiguel V; Nogales F J; Uppal R

Review of Financial Studies 2014 Vol 27:4 p 1031-1073


Improving portfolio selection using option-implied volatility and skewness

De Miguel V; Plyakha Y; Uppal R; Vilkov G

Journal of Financial and Quantitative Analysis 2013 Vol 48:6 p 1813-1845

Size matters: Optimal calibration of shrinkage estimators for portfolio selection

DeMiguel V; Martin-Utrera A; Nogales F J

Journal of Banking and Finance 2013 Vol 37:8 p 3018-3034


Supply chain competition with multiple manufacturers and retailers

DeMiguel V; Adida E

Operations Research 2011 Vol 59:1 p 156-172


A generalized approach to portfolio optimization: Improving performance by constraining portfolio norms

DeMiguel V; Garlappi L; Nogales F J; Uppal R

Management Science 2009 Vol 55:5 p 798-812

A stochastic multiple leader Stackelberg model: Analysis, computation, and application

DeMiguel V; Xu H

Operations Research 2009 Vol 57:5 p 1220-1235

Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy?

DeMiguel V; Garlappi L; Uppal R

Review of Financial Studies 2009 Vol 22:5 p 1915-1953

Portfolio selection with robust estimation

DeMiguel V; Nogales F J

Operations Research 2009 Vol 57:3 p 560-577


On decomposition methods for a class of partially seperable nonlinear programs

De Miguel V; Nogales F J

Mathematics of Operations Research 2008 Vol 33:1 p 119-139


A local convergence analysis of bilevel decomposition algorithms

DeMiguel V; Murray W

Optimization and Engineering 2006 Vol 7:2 p 99-133

A two-sided relaxation scheme for mathematical programs with equilibrium constraints.

DeMiguel V; et al.

SIAM Journal on Optimization 2006 Vol 16:2 p 587-609

Portfolio investment with the exact tax basis via nonlinear programming

DeMiguel V; Uppal R

Management Science 2006 Vol 51:2 p 277-290


Revenue management with correlated demand forecasting

DeMiguel V; Fridgeirsdottir K; Stefanescu C; et al.

in Proceedings of the American Statistical Association, Business and Economics Statistics Section, Alexandria, VA, 2004


Class of quadratic programming rest problems with global variables

DeMiguel V; Murray W

Technical report SOL 01-2 Dept of MS&E, Stanford University, 2001

Two decomposition algorithms for nonconvex optimization problems with global variables

DeMiguel V; Murray W

Technical report SOL 01-1, Dept of MS&E, Stanford University, 2001


An analysis of collaborative optimization methods

DeMiguel V; Murray W

in Proceedings of the Eighth AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 2000


Cover-Up of vehicle defects: the role of regulator investigation announcements

Cho S-H; DeMiguel V; Hwang W

HEC Paris Research Paper

Optimal portfolio diversification via independent component analysis

Lassance N; DeMiguel V; Vrins F D

Social Sciences Research Network


A portfolio perspective on the multitude of firm characteristics

DeMiquel V; Martin-Utrera A; Nogales FJ; Uppal R

Social Sciences Research Network


Simple contracts for reliable supply

Bakshi N; Hwang W; DeMiguel V

Working Paper


What multistage stochastic programming can do for network revenue management

DeMiguel V; Mishra N

Decision Sciences Working Paper

Teaching portfolio

Our teaching offering is updated annually. Faculty and programme material are subject to change.

  • Masters Degrees core courses

    A key part of our Masters programmes curriculum.

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  • Masters Degrees electives

    Optional courses providing a deep dive into specialist areas.

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    • Financial Analytics
      In today's information age, managers increasingly rely on quantitative models to make financial decisions that have a profound impact on the performance of their organizations. Often business analysts produce the quantitative models that top management use to support their decision making, but top management must also understand the strengths and weaknesses of the models if they are to use them effectively to support their decisions. The objective of this course is to equip you with the frameworks, tools, and methodologies necessary to build and/or be an educated user of quantitative models for financial decision making. The course is suitable for students seeking a career in finance, but also for students with broader interests who wish to strengthen their general modelling skills, and it does not require any quantitative background other than what is covered in the MBA core courses.
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  • Executive Education

    Short programmes offering academic excellence, global focus and exceptional diversity of perspective.

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