scholarly journals Mutual Fund Trades: Timing Sentiment and Managing Tracking Error Variance

2012 ◽  
Author(s):  
Dominic Gasbarro ◽  
Grant Stewart Cullen ◽  
Gary S. Monroe ◽  
J. Kenton Zumwalt
Keyword(s):  
Mutual Fund ◽  
Tracking Error ◽  
Error Variance

Fund Manager Performance in Emerging Market: Factor Specialisation and Financial Crisis Impact

2018 ◽  
Vol 17 (1) ◽  
pp. 130-158
Author(s):  
Giuseppe Galloppo ◽  
Mauro Aliano
Keyword(s):  
Mutual Fund ◽  
Emerging Market ◽  
Explanatory Power ◽  
Tracking Error ◽  
Active Management ◽  
Market Model ◽  
Good Representation ◽  
Local Factor ◽  
Equity Funds ◽  
Fama And French

In the branch of literature dealing with analysis of the consistency of management styles, this article investigates the relation between portfolio concentration and the performance of emerging market equity funds. Unlike previous studies, on global and US mutual fund, we focus on emerging markets equity, finding funds with higher levels of tracking error, display lower performance than funds with less diversified portfolios when we do not take into account specific concentration in holdings in different multifactor style. The explanatory power of local models that use local explanatory returns is recently investigated by De Groot, Pang and Swinkels (2012), Cakici, Fabozzi and Tan (2013) and Fama and French (2012). Following the same research line, the most remarkable finding of this article is that the fund-picking process, only based on the level of track error from a broad benchmark, can contribute to disappointing results when it is not also accompanied by information about the fund concentration in multiple market segment. According to the previous work, overall, we found that local factor market model provides quite good representation of local average returns for portfolios formed on size and style factors. The contribution of this research is two-fold. First, we examined emerging market funds from the perspective of active management and second, under the effect of strategies mentioned in Huij and Derwall (2011). Moreover, as additional analysis with respect to most of the previous papers, we also tested the effects of the crisis that we found to have not affected the main result.

Tracking Efficiency of Exchange Traded Funds (ETFs)

Paradigm ◽  
2016 ◽  
Vol 20 (2) ◽  
pp. 176-190 ◽  
Author(s):  
Jaspal Singh ◽  
Prabhdeep Kaur
Keyword(s):  
Regression Analysis ◽  
Present Article ◽  
Mutual Fund ◽  
Negative Impact ◽  
Tracking Error ◽  
Evaluation Criteria ◽  
Panel Regression ◽  
Exchange Traded Funds ◽  
Tracking Ability ◽  
Managed Funds

Exchange traded funds (ETFs) have emerged as a new investment vehicle in the mutual fund industry providing investors with the ability to trade the entire market through a single transaction executed at the exchange. Using a sample of 12 equity ETFs from 1 April 2011 to 31 March 2015, the present article attempts to examine the performance efficiency of ETFs in India and explore factors that drive the performance of ETFs away from their target indices. The study reveals that ETFs exhibit significant tracking error while trying to replicate the returns of their benchmark indices. The results of panel regression analysis further reveal that the assets under management and volume positively affected the tracking ability of ETFs whereas volatility is reported to have negative impact on the tracking efficiency of ETFs. The results will have important implications for investors, managers as well as for the evaluation criteria involved in assessing the performance of actively managed funds.

scholarly journals Serial correlation and TEV bias in index funds

2003 ◽  
Vol 34 (2) ◽  
pp. 45-53 ◽  
Author(s):  
H. Raubenheimer
Keyword(s):  
High Frequency ◽  
Serial Correlation ◽  
Tracking Error ◽  
Error Variance ◽  
Value Proposition ◽  
Fund Manager ◽  
Index Funds ◽  
Fund Managers ◽  
The Difference

Index or passive fund managers and investors analyse the interim volatility of the difference between their fund’s returns and the index’s returns, i.e. the fund’s tracking error variance** (TEV) in order to monitor the success with which tracker funds mimic their benchmark. The objective of a passive or index fund manager should be to keep TEV as close to zero as possible. Pope and Yadav (1994) show that an index fund that is overweight relative to it’s index in either relatively less or relatively more liquid stocks, is expected to exhibit negative serial correlation in its TE’s. Consequently, estimates of TEV will be upwardly biased, particularly when using high frequency (such as daily or weekly) data.This article finds evidence of negative serial correlation in the weekly, monthly and quarterly TE’s of domestic index funds. Consequently it is shown that TEV will likely be overestimated. There are two important implications of this upward bias in TEV estimation. Firstly, index funds, which are expected to offer close to zero benchmark-relative or active risk, may appear far more ‘risky’ than they actually are thus damaging their value-proposition to investors. Secondly, when funds appear to have greater TEV than they actually do, the manager may ‘churn’ the fund’s assets more than necessary in order to bring the fund back into alignment with its index thus incurring greater and unnecessary transaction costs.The analyses in this article therefore suggest that TE measurements should be examined for negative serial correlation before estimates of TEV are made. If serial correlation is detected, estimates of TEV should either be made from lower frequency, uncorrelated TE measurements, if they are available, or an adjustment technique such as the Lo-MacKinlay adjustment should be applied to correct for the bias in TEV estimation.

A Time-Varying Generalized Minimum Variance Controller for Servo Application

2013 ◽  
Vol 278-280 ◽  
pp. 1403-1408 ◽  
Author(s):  
Zheng Li
Keyword(s):  
Performance Index ◽  
Linear Time ◽  
Tracking Error ◽  
Error Variance ◽  
Minimum Variance ◽  
Time Varying ◽  
Output Tracking ◽  
Time Varying Systems ◽  
Control Objective ◽  
Process Disturbances

A generalized minimum variance controller is developed for linear time-varying systems for servo applications. The plants to be controlled is described using a SISO CARMA model and the control objective is to minimize a generalized minimum variance performance index, where the output tracking error variance is penalized by squared incremental of plant input in order to reduce fluctuation in plant input and attenuate process disturbances.

Assessing estimation error in a tracking error variance minimisation framework

1998 ◽  
Vol 6 (1-2) ◽  
pp. 175-192
Author(s):  
David M. Walsh ◽  
Kathleen D. Walsh ◽  
John P. Evans
Keyword(s):  
Estimation Error ◽  
Tracking Error ◽  
Error Variance

scholarly journals Performance Analysis of the Index Mutual Fund

2019 ◽  
Vol 8 (1) ◽  
pp. 1-5
Author(s):  
Yasmeen Bano ◽  
S. Vasantha
Keyword(s):  
Performance Analysis ◽  
Mutual Fund ◽  
Tracking Error ◽  
Individual Investors ◽  
Investment Fund ◽  
Index Funds ◽  
Secondary Sources ◽  
The Common ◽  
Managed Portfolios

A mutual fund is a professionally managed investment fund that pools together the savings of a number of investors who shares the common financial goals. These investors may be retail or institutional in nature. It offers small or individual investors access to professionally managed portfolios of equities, bonds and other securities. The paper is the study of the performance of Index fund. This is analyzed empirically since the period of 2012 – 2017. The main objective of this research is to evaluate the performance of Index funds. The study examined three parameters such as active returns, tracking error and Jensen’s alpha. In this paper the data has been collected from the secondary sources.

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

2013 ◽  
Vol 14 (4) ◽  
pp. 758-775 ◽  
Author(s):  
Fernando García ◽  
Francisco Guijarro ◽  
Ismael Moya
Keyword(s):  
Portfolio Management ◽  
Programming Model ◽  
Tracking Error ◽  
Error Variance ◽  
Index Tracking ◽  
Mathematical Programming Model ◽  
Substantial Cost ◽  
Risk Return ◽  
Wide Range ◽  
Reference Index

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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