Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.

Distributed static linear Gaussian models using consensus

Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for static linear Gaussian models, including principal component analysis, factor analysis, and probabilistic principal component analysis. These algorithms do not rely on a fusion center, require only low-volume local (1-hop neighborhood) communications, and are thus efficient, scalable, and robust. We show how they are also guaranteed to asymptotically converge to the same solution as the corresponding existing centralized algorithms. Finally, we illustrate the functioning of our algorithms on two examples, and examine the inherent cost-performance tradeoff.

Highest return farming systems for Tama and Muscatine soils: an application of linear programming to 240- and 480- acre farms operated by two men/

Cover title.

Highest return farming systems for Drummer-Flanagan soils : an application of linear programming to farm planning /

Cover title.

Adaptive linear classifier by linear programming,

Mode of access: Internet.

Design of optimal one-bit adder networks by integer linear programming /

"July 15, 1971."

A linear programming code to determine whether a Boolean function is a threshold function,

Bibliography: p. 14.

A model for linear programming optimization of I/O-bound programs /

"June, 1969."

A code for zero-one integer linear programming by implicit enumeration, a programming manual for ILLIP.

Issued also as thesis (M.S.) University of Illinois.

An error analysis of solutions to sparse linear programming problems,

"Supported in part by the Advanced Research Projects Agency ... contract no. US AF 30(602) 4144."

Simplex method and theory; notes on linear programming and extension-----part 62.

"Contract no. AF 49(638)-700."

Economics of Douglas-fir tussock moth control : an analysis using the combined stand prognosis/Douglas-fir tussock moth outbreak model and the FORPLAN linear programming model on the Clearwater and Malheur National Forests /

"March 1984."

Regional adjustments in grain production : a linear programming analysis /

"U.S. Dept. of Agriculture, in cooperation with Iowa Agriculture and Home Economics Experiment Station Center for agricultural and economic adjustment."