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The purpose of this paper is to find if erosion of value exists in grantor trust structured exchange traded funds. The author examines the performance of six currency exchange traded funds’ tracking errors and pricing deviations on intradaily-one-minute interval basis. All of these exchange traded funds are grantor trusts. The author also studies which metric is of more importance to investors in these exchange traded funds by examining how these performance metrics are related to the exchange traded funds’ arbitrage mechanism.

The Australian Dollar ETF (FXA) is designed to be 100 times the US Dollar (USD) value of the Australian Dollar, the British Pound ETF (FXB) is designed to be 100 times the USD value of the British Pound, the Canadian Dollar ETF (FXC) is designed to be 100 times the USD value of the Canadian Dollar, the Euro ETF (FXE) is designed to be 100 times the USD value of the Euro, the Swiss Franc ETF (FXF) is designed to be 100 times the USD value of the Swiss Franc and the Japanese Yen ETF (FXY) is designed to be 10,000 times the USD value of the Japanese Yen. The author uses these proportions to estimate pricing deviations. The author uses a moving average model based on an Elton et al. (2002) to estimate if tracking error or pricing deviation are more relevant in ETF arbitrage and thus to investors.

The author documents that the average intradaily tracking errors for the six currency ETFs are relatively small and stable. The tracking errors are highest for the FXF, 0.000311 percent and smallest for FXB, −0.000014 percent. FXB is the only ETF with a negative tracking error. All six ETFs average intradaily pricing deviations are negative with the exception of the FXA pricing deviation which is a positive $0.17; the rest of the ETFs pricing deviations are −0.3778 for FXB, −0.3231 for FXC, −0.2697 for FXC, −0.2697 for FXE, −0.6484 for FXF and −0.9273 for FXY. All exhibit skewness, kurtosis, very high levels of positive autocorrelation and negative trends, which suggests erosion of value. The author also found that these exchange traded funds’ arbitrage mechanism is more closely related to the exchange traded funds’ pricing deviation than tracking error.

The paper uses high-frequency one-minute interval data in the analysis of pricing deviation which might be artificially deflating standard errors and thus inflating the t-test significance values.

The paper is relevant to ETF investors and contributes to the continuing search in the finance literature of better ETF performance metric.

The purpose of this paper is to contribute to a better understanding of the impact of market liquidity on the daily tracking error of exchange-traded funds (ETFs). It puts a special focus on the liquidity cost of individual underlying stocks as well as the process of creation/redemption of ETF shares as key determinants of tracking ability.

The study is based on daily observations of fund data for eight fully replicating German equity ETFs for July 2001-October 2013. A regression model with fund fixed effects is chosen to determine the effect of liquidity cost, creation/redemption and other control variables on daily tracking error. Data were compiled from issuer websites and Datastream. Proprietary XETRA Liquidity Measure, which was used as proxy for liquidity cost was supplied by Deutsche Börse.

The study finds daily tracking error to significantly depend on the liquidity of underlying stocks. This finding emerges even though the ETFs in this study predominantly use in-kind creation/redemption. Even after controlling for creation/redemption, the liquidity impact remains basically unchanged. One reason might be imperfect replication of index weights: Either the in-kind-basket delivered in the course of creation/redemption does not perfectly match the benchmark-weights or the internal rebalancing of weights causes liquidity cost.

To the best of the authors’ knowledge, this is the first paper that uses a specific liquidity measure for each single stock underlying an ETF. The findings extend the literature by corroborating the view that liquidity of individual stocks in the underlying portfolio has an impact on tracking error.

The purpose of this paper is to analyse the performance of UK property funds using the dual sources of active management, Active Share and tracking error, to distinguish between the types of active management styles used by funds.

The authors use data on 38 UK real estate funds and classify them into five active management categories using the dual sources of active management, Active Share and tracking error. Then, the authors compare their return performance against Active Share, tracking error, fund size and leverage. Therefore the paper is able to answer two of the fundamental questions of investment: does active management add value and what form of active management, stock selection or factor risk, is better at adding value to the fund?

There are three main conclusions. First, the approach of Cremers and Petajisto (2009) and Petajisto (2010) is able to classify real estate funds in the UK on their management activity into categories that makes intuitive sense and seem stable over time. Second, balanced funds show relatively low Active Shares and particularly low tracking errors, due to the benefits of property-type diversification. In contrast, specialists funds display higher Active Shares and both low and high tracking errors depending on their stock-picking approach; diversified or concentrated. Third, an analysis over different time periods confirmed that funds in the sample essentially remained in the same categories within the sample period, even during markedly different market return periods. This implies that investors need to constantly monitor changes in the market and switch between fund management styles, if at all possible.

The analysis was only based on 38 funds with complete data over the sample period and the relationship between fees and active management was not examined, even though ultimately investors are concerned with returns after management fee. It would be instructive therefore if the number of funds and time period was expanded to see if the results are robust and to see whether management fees outweigh the benefits of active manager.

The findings should enable investors to make a more informed investment decisions in the future.

To the best of the author’s knowledge this is the first paper to apply the dual sources of active management, Active Share and tracking error, in the UK real estate market.

The purpose of this paper is to improve the tracking performance of the carrier phase lock loop (PLL) in the strapdown inertial navigation system/global positioning system (SINS/GPS) integrated system with an innovative scheme of ultra‐tight integration.

First, providing the Doppler frequency for PLL using SINS velocity could enlarge the loop equivalent bandwidth and reduce the dynamic effect on the carrier loop. Meanwhile, lowering the filter bandwidth could increase the immunity to noise. Second, the relationships between the PLL and SINS errors have been analyzed, and then the PLL error model is established to eliminate the correlation between the pseudo‐range‐rate error and SINS velocity error. Third, the carrier frequency is regulated to improve the tracking accuracy, according to the error estimations of Kalman filter.

The innovative ultra‐tightly integrated system could not only enhance the anti‐jamming capability and the dynamic tracking performance of the tracking loops, but also improve the pseudo‐range‐rate measurements accuracy for the integrated filter.

This paper provides further study on the method of enhancing the carrier‐tracking performance and improving the integration mode in the ultra‐tightly integrated system based on the software‐defined GPS receiver.

The purpose of this paper is to assess whether exchange‐traded funds (ETFs) can beat the market, as it is expressed by the Standard and Poor (S&P) 500 Index, examine the outperformance persistence, calculate tracking error, assess the tracking error persistence, investigate the factors that induce tracking error and assess whether there are predictable patterns in ETFs' performance.

The author uses a sample of 50 iShares during the period 2002‐2007 and calculates the simple raw return, the Sharpe ratio and the Sortino ratio, regresses the performance differences between ETFs and market index, calculates tracking error as the standard deviation in return differences between ETFs and benchmarks, assesses tracking error's persistence in the same fashion used to assess the ETFs' outperformance persistence, examines the impact of expenses, risk and age on tracking error and applies dummy regression analysis to study whether the performance of ETFs is predictable.

The results reveal that the majority of the selected iShares beat the S&P 500 Index, both at the annual and the aggregate levels while the return superiority of ETFs strongly persists at the short‐term level. The tracking error of ETFs also persists at the short‐term level. The regression analysis on tracking error reveals that the expenses charged by ETFs along with the age and risk of ETFs are some of the factors that can explain the persistence in tracking error. Finally, the dummy regression analysis indicates that the performance of ETFs can be somehow predictable.

The findings of this paper may be of help to investors seeking investment choices that will help them to gain above market returns. In addition, tracking error‐concerned investors will be helped by the findings of the paper. Finally, the findings on return predictability can also be helpful to investors.

The purpose is of this paper is to investigate whether the tracking ability of exchange traded funds (ETFs) is lower in highly volatile periods, and to shed more light on the factors behind the tracking error.

The authors apply the Error Correction Model that incorporates a short‐run adjustment mechanism on domestic US ETFs that follow industrial indices.

It was found that tracking errors attained pronounced levels during 2008 compared to 2006 and 2007, mainly in ETFs from the real estate and banking and finance sectors. In addition, tracking error is positively correlated with the daily volatility of the ETF, while trading volume has a limited effect on reducing tracking errors.

The paper sheds more light on the relationship between securities and their fundamentals, and contributes to the literature on the information transmission mechanism for dually‐listed securities.

The paper uses the co‐integration tests and the error correction model (ECM) to test the long‐run relationship between returns on domestic exchange trade funds (ETFs) and the returns on the underlying indices. In particular, the ECM is used for ETFs for the first time.

The purpose of this paper is to provide a brief review of three strands of the literature on exchange‐traded funds.

The paper starts with a review of the history of the growth of exchange‐traded funds and their characteristics. The paper then examines the key factors and findings of the existing studies on, respectively, the pricing efficiency, the tracking ability/performance, and the impact on underlying securities of exchange‐traded funds.

Although there has been a substantial amount of research conducted to advance our knowledge on the trading, management, and effect of exchange‐traded funds, the findings are still far from conclusive in addressing a number of research questions.

Investors and other market participants will find this review informative in enhancing the understanding of exchange‐traded funds.

By highlighting the general theme of the related research findings, the paper provides a systematic review of the existing literature that future researchers can utilize in developing their research agenda.

To reduce the effect of parameter uncertainties and input saturation on path tracking control for autonomous combine harvester, a path tracking controller is proposed, which integrates an adaptive neural network estimator and a saturation-aided system.

First, to analyze and compensate the influence of external factors, the vehicle model is established combining a dynamic model and a kinematic model. Meanwhile, to make the model simple, a comprehensive error is used, weighting heading error and position error simultaneously. Second, an adaptive neural network estimator is presented to calculate uncertain parameters which eventually improve the dynamic model. Then, the path tracking controller based on the improved dynamic model is designed by using the backstepping method, and its stability is proved by the Lyapunov theorem. Third, to mitigate round-trip operation of the actuator due to input saturation, a saturation-aided variable is presented during the control design process.

To verify the tracking accuracy and environmental adaptability of the proposed controller, numerical simulations are carried out under three different cases, and field experiments are performed in harvesting wheat and paddy. The experimental results demonstrate the tracking errors of the proposed controller that are reduced by more than 28% with contrast to the conventional controllers.

An adaptive neural network-based path tracking control is proposed, which considers both parameter uncertainties and input saturation. As far as we know, this is the first time a path tracking controller is specifically designed for the combine harvester with full consideration of working characteristics.

This paper aims to solve the problems of the synchronization between branches and the uncertainties such as joint friction, load variation and external interference of a hybrid mechanism. The controller is used to improve the synchronization and robustness of the hybrid mechanism system and achieve both finite time convergence and chattering-free sliding mode.

First, the dynamic model of hybrid mechanism containing lumped uncertainties is formulated by the Lagrange method, and a composite error based on coupling synchronization error and the end-effector tracking error is set up in the task space. Then, by combining the finite time super twisting sliding mode control algorithm, a composite error-based finite time super twisting sliding mode synchronous control law is designed to make the end-effector tracking error and coupling synchronization error achieve better tracking performance and convergence performance. Finally, the Lyapunov stability of the control law and the finite-time convergence of the composite error are proved theoretically.

To verify the effectiveness of the proposed control method, simulations and experiments for the prototype system of the hybrid mechanism are conducted. The results show that the proposed control method can achieve better tracking performance and convergence performance.

This is a new innovation for a hybrid mechanism containing lumped uncertainties to improve the robustness, convergence performance, tracking performance and synchronization of the system.

The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion.

A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction.

Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction.

The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.