Optimal omega-ratio portfolio performance constrained by tracking error

Leveraged Exchange Traded Funds (ETFs) (LETFs) are a recent and highly successful financial innovation; yet, investors and several studies criticized them for not performing as advertised, especially in the long term. Τhis paper discusses their unique characteristics and their path-dependent price dynamics, which may result in unexpected returns. Furthermore, the authors evaluate the performance of a large sample of European and American leveraged ETFs since each fund’s inception and show that they perform as intended for daily holding periods. Leveraged ETFs are also successful in delivering the promised performance over holding periods of up to one week, their performance starts to deviate when the holding period increases to one month. Empirical evidence suggests that bear (short) ETFs deviate from their target return more quickly than their bull (long) counterparts as the holding period lengthens. A possible explanation for this is that transaction costs, which are related to daily re-balancing activity, are higher for bear funds. When comparing the daily performance of European vs American funds, the authors find them both to be equally efficient in replicating their benchmarks, although European leveraged ETFs are much smaller in their Assets Under Management (AUM) compared to US LETFs.

Tracking error is a ubiquitous tool among active and passive portfolio managers, widely used for fund selection, risk management, and manager compensation. This paper shows that traditional measures of the tracking error are incapable of detecting variations in skewness and kurtosis. As a solution, this paper introduces a new class of Quantile Tracking Errors (QuTE), which measures differences in the quantiles of return distributions between a tracking portfolio and its benchmark. Through an extensive simulation study, this paper shows that QuTE is six times more sensitive than traditional tracking measures to skewness and three times more sensitive to kurtosis. The QuTE statistic is robust to various calibrations and can easily be customized. By using the QuTE tracking measure during the Dot Com bubble and the Great Recession, this paper finds differences between the DIA and its benchmark, the DJIA, that otherwise would have gone undetected. Quantile based tracking provides a robust method for relative performance measurement and index portfolio construction.

The COVID-19 pandemic has caused significant disruption in financial markets worldwide and impacted the performance of investment avenues like mutual funds. It has been a challenging scenario for all mutual funds to sustain the pre-pandemic performance. To understand the mutual fund investment scenario further, this study focused on examining the post-pandemic performance in the year 2021 of various categories of mutual funds, the significance of scheme characteristics in determining the performance, risk-adjusted performance, and outperformance of various categories of funds. Out of 4,305 mutual fund schemes, tax planning funds (58%), sectoral funds (57%), and equity diversified funds (55%) achieved better returns. Further, using the ordinary least squares (OLS) regression, the study estimated the effect of fund characteristics like scheme category, scheme type, scheme access type along with the fund’s tracking error and corpus size on funds’ return. The results show that tax planning, sectoral, and equity diversified funds significantly outperform. Tracking error significantly reduces the fund return by 4.52%. Scheme type, scheme access type, and corpus size were not significant. Equity, index, pension, and balanced category funds exhibit risk-adjusted performance, and only bond funds were able to outperform the respective benchmarks. The study adds to the existing literature by investigating the post-pandemic performance determinants of mutual funds.