Linear and Mixed Integer Programming for Portfolio Optimization [Paperback]

This book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features. Other linear models, such as models for portfolio rebalancing and index tracking, are also covered. The book discusses computational issues and provides a theoretical framework, including the concepts of risk-averse preferences, stochastic dominance and coherent risk measures. The material is presented in a style that requires no background in finance or in portfolio optimization; some experience in linear and mixed integer models, however, is required. The book is thoroughly didactic, supplementing the concepts with comments and illustrative examples.

Renata Mansini is Professor of Operations Research at the Department of Information Engineering at the University of Brescia, Italy. She received her M.S. degree in Business Economics at the University of Brescia (Italy) and got her PhD in Computational Methods for Financial Decisions at the University of Bergamo (Italy) spending one year at the Olin Business School, Washington University in St. Louis (U.S.A) and working as researcher in the Center for Optimization and Semantic Control, at the System Science Department. She is author of several scientific papers most of which published in international volumes and journals such as Computers and Operations Research, Discrete Applied Mathematics, European Journal of Operational Research, IIE Transactions, INFORMS Journal on Computing, Journal ol³"