Tracking S&amp;P 500 Index Funds

Frino, Alex; Gallagher, David R.

doi:10.3905/jpm.2001.319822

Cited by 173 publications

(100 citation statements)

References 16 publications

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“…Chiang (1998) points out fund managers adopting an indexing approach can't guarantee that their funds' performance will be identical to the benchmark index, due to transaction costs, fund cash flows, the volatility of the benchmark, the treatment of income in index returns and changes in index composition through time. A view shared by Frino and Gallagher (2001) who argue that given market frictions tracking error is unavoidable, even in passively managed funds (see also Keim, 1999). Despite these issues Frino and Gallagher (2001) state that tracking error is a natural way to manage passive funds.…”

Section: Tracking Errormentioning

confidence: 99%

“…A view shared by Frino and Gallagher (2001) who argue that given market frictions tracking error is unavoidable, even in passively managed funds (see also Keim, 1999). Despite these issues Frino and Gallagher (2001) state that tracking error is a natural way to manage passive funds.…”

Section: Tracking Errormentioning

confidence: 99%

“…Incidental risk will occur since neither fund management strategy can ever hold assets that exactly match the benchmark portfolio due to market frictions such as transaction costs and liquidity constraints etc. (see inter alia, Chiang,1998;Keim, 1999;and Frino and Gallagher, 2001). Intentional risk arises in active fund strategies as they try to outperform a given benchmark by taking positions that differ from those in the benchmark.…”

Section: Active and Passive Managementmentioning

confidence: 99%

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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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Section: Tracking Errormentioning

confidence: 99%

Section: Tracking Errormentioning

confidence: 99%

Section: Active and Passive Managementmentioning

confidence: 99%

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Real estate fund active management

Morri

2015

Journal of Property Investment &Amp; Finance

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“…This research uses popular methodology of estimating tracking errors following previous studies such as Frino and Gallagher (2001), Rompotis (2006), Meinhardt et al (2014), Elia (2012). The first method (TE 1 ) is to estimate arithmetic mean of absolute differences between ETF returns and benchmark returns: …”

Section: Methodsmentioning

confidence: 99%

An Empirical Study on the Differences between Synthetic and Physical ETFs

Naumenko

Chystiakova²

2015

IJEF

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This research focuses on the differences between synthetic and traditional exchange-traded funds in benchmark replication process. It extends previous literature by empirical examining the tracking ability of traditional and synthetic ETFs traded at the Swiss Stock Exchange offered by the leading providers in Europe. 35 equity ETFs are used in the sample. For both types of funds the average tracking error is estimated in four ways. The research demonstrates that both traditional and synthetic ETFs have significant tracking errors. The findings also show that, contrary to popular opinion, synthetic ETFs have higher tracking errors than physical. These facts are of a significant importance to private investors and portfolio managers, especially if the performance of portfolio manager is based on a comparison to the performance of a benchmark.Additionally, factors that influence tracking errors are estimated. The results suggest that tracking errors are influenced by expense ratio, number of securities in the benchmark and type of replication for all estimation methods. Average daily trading volume doesn't influence dependent variables except for one tracking error estimation method.

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“…Similar conclusions have been reached after considering the risk assumed. The questionable success of many actively managed investment funds in outperforming the benchmark explains that index tracking is currently among the most popular techniques used by investment fund managers (Frino and Gallagher, 2001;Radisich, 2001, Coleman et al, 2006). This technique has become even more popular after the appearance of exchange traded funds (ETFs).…”

Section: Introductionmentioning

confidence: 99%

A MULTIOBJECTIVE MODEL FOR PASSIVE PORTFOLIO MANAGEMENT: AN APPLICATION ON THE S&P 100 INDEX

García

Guijarro

Clemente

2013

Journal of Business Economics and Management

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Index tracking seeks to minimize the unsystematic risk component by imitating the movements of a reference index. Partial index tracking only considers a subset of the stocks in the index, enabling a substantial cost reduction in comparison with full tracking. Nevertheless, when heterogeneous investment profiles are to be satisfied, traditional index tracking techniques may need different stocks to build the different portfolios. The aim of this paper is to propose a methodology that enables a fund's manager to satisfy different clients’ investment profiles but using in all cases the same subset of stocks, and considering not only one particular criterion but a compromise between several criteria. For this purpose we use a mathematical programming model that considers the tracking error variance, the excess return and the variance of the portfolio plus the curvature of the tracking frontier. The curvature is not defined for a particular portfolio, but for all the portfolios in the tracking frontier. This way funds’ managers can offer their clients a wide range of risk-return combinations just picking the appropriate portfolio in the frontier, all of these portfolios sharing the same shares but with different weights. An example of our proposal is applied on the S&P 100.

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Tracking S&P 500 Index Funds

Cited by 173 publications

References 16 publications

Real estate fund active management

Real estate fund active management

An Empirical Study on the Differences between Synthetic and Physical ETFs

A MULTIOBJECTIVE MODEL FOR PASSIVE PORTFOLIO MANAGEMENT: AN APPLICATION ON THE S&P 100 INDEX

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