Robust and Efficient Strategies to Track and Outperform a Benchmark

32 Pages Posted: 19 Feb 2009

See all articles by Paskalis Glabadanidis

Paskalis Glabadanidis

Essential Services Commission of South Australia

Multiple version iconThere are 3 versions of this paper

Date Written: February 15, 2009

Abstract

I investigate the question of how to construct a portfolio consisting of a few securities that an investor can use to track a benchmark. I consider two approaches: a sequential stepwise regression and another method based on factor models of security returns. The rst approach produces the standard hedge portfolio that has the maximum feasible correlation with the benchmark. The second approach produces weights that are proportional to a "signal-to-noise" ratio of factor beta to idiosyncratic volatility. I also consider a second objective that maximizes expected returns subject to minimizing the variance of tracking error. The security selection criterion naturally extends to the product of the information ratio and the signal-to-noise ratio. I implement the algorithms presented in the paper using three widely followed stock indices with very good results.

Keywords: Optimal Portfolio Weights, Benchmarking

JEL Classification: G11, G12

Suggested Citation

Glabadanidis, Paskalis, Robust and Efficient Strategies to Track and Outperform a Benchmark (February 15, 2009). Available at SSRN: https://ssrn.com/abstract=1343488 or http://dx.doi.org/10.2139/ssrn.1343488

Paskalis Glabadanidis (Contact Author)

Essential Services Commission of South Australia ( email )

Level 1, 151 Pirie Street
Adelaide, SA 5001
Australia

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