The error of tracking error

Israelsen, Craig L.; Cogswell, Gary F

doi:10.1057/palgrave.jam.2250051

Cited by 18 publications

(3 citation statements)

References 6 publications

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“…1. Israelsen and Cogswell (2007) consider "differential from benchmark" to be more instructive and constructive term than "error".…”

Section: Notesmentioning

confidence: 99%

Real estate fund active management

Morri

2015

Journal of Property Investment &Amp; Finance

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link: Abstract:The traditional way to measure the active management of a fund is to calculate its tracking error: as measured by the standard deviation of the difference in a fund's returns versus its benchmark returns. However, tracking error alone is an inadequate measure of fund activity since even very actively managed funds can in fact generate rather low tracking errors. Therefore judging the activity level of a fund based solely on tracking error can be misleading. In addition, while tracking error volatility is easy to calculate it only infers what the manager is doing at the portfolio level and does tell you how the tracking errors were generated. For instance, Cremers and Petajisto (2009) argue that the two distinct approaches to active management, stock selection or factor risk, can produce significantly different tracking errors.Instead of using tracking error alone Cremers and Petajisto (2009) suggest that a more comprehensive picture of active management can be achieved by including Active Share into the calculations, where the Active Share of the fund is measured as half the sum of the absolute difference between the fund's holdings and those of the benchmark portfolio. In other words, Cremers and Petajisto (2009) claim that using Active Share and tracking error together enables investors to distinguish between the types of active management used by funds, stock selection and factor risk, and so classify funds by their investment management strategy: pure indexes, closet indexes, diversified stock pickers, concentrated stock pickers, and factor bets.

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“…1. Israelsen and Cogswell (2007) consider "differential from benchmark" to be more instructive and constructive term than "error".…”

Section: Notesmentioning

confidence: 99%

Real estate fund active management

Morri

2015

Journal of Property Investment &Amp; Finance

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“…In Experiments 1a and 2, which described index-tracking funds, σ e was 2% for both funds (per month), which matches the 90th percentile of index fund noise according to Petajisto (2013). While most index-tracking funds have very low noise relative to their benchmarks, it is not uncommon for poorly managed funds to achieve noise of 1% per month or higher (see also Frino et al, 2004; Frino & Gallagher, 2002; Israelsen & Cogswell, 2007). The 90th percentile for index funds was chosen to generate dispersion in the returns every month—by using the median the noise would not have been variant enough, and the differences in past performance might have been too small to reliably observe any behavioral biases.…”

Section: Methodsmentioning

confidence: 61%

Persistence is futile: Chasing of past performance in repeated investment choices.

Weiss‐Cohen¹,

Newall²,

Ayton³

2022

Journal of Experimental Psychology: Applied

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“…Various statistical techniques have been applied toward the objective of benchmark index replication ranging from time series clustering (Focardi and Fabozzi 2004) to cointegration (Dunis and Ho 2005). Many studies have also investigated the question of actively managing a portfolio that replicates the performance of a benchmark index subject to limits on the tracking error (Burmeister et al 2004;El-Hassan and Kofman 2003;Israelsen and Cogswell 2006). Corielli and Marcellino (2006) were among the first to introduce factor models in the analysis of the benchmark replicating problem.…”

Section: Introductionmentioning

confidence: 99%

Portfolio Strategies to Track and Outperform a Benchmark

Glabadanidis

2020

JRFM

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I investigate the question of how to construct a benchmark replicating portfolio consisting of a subset of the benchmark’s components. I consider two approaches: a sequential stepwise regression and another method based on factor models of security returns’ first and second moments. The first 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. Using a factor model of securities returns allows the use of a larger number of securities than the number of time periods used to estimate the parameters of the factor model. I also consider a second objective that maximizes expected returns subject to a target tracking error variance. The security selection criterion naturally extends to the product of the information ratio and the signal-to-noise ratio. The optimal tracking portfolio is either a one-fund or a two-fund portfolio rule consisting of the optimal hedging portfolio, the tangent portfolio or the global minimum variance portfolio, depending on what constraints are imposed on the objective function. I construct buy-and-hold replicating portfolios using the algorithms presented in the paper to track a widely followed stock index with very good results both in-sample and out-of-sample.

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The error of tracking error

Cited by 18 publications

References 6 publications

Real estate fund active management

Real estate fund active management

Persistence is futile: Chasing of past performance in repeated investment choices.

Portfolio Strategies to Track and Outperform a Benchmark

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