Creating better tracking portfolios with quantiles

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.

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.

The mean-variance framework coupled with the Sharpe ratio identifies optimal portfolios under the passive investment style. Optimal portfolio identification under active investment approaches, where performance is measured relative to a benchmark, is less well-known. Active portfolios subject to tracking error (TE) constraints lie on distorted elliptical frontiers in return/risk space. Identifying optimal active portfolios, however defined, have only recently begun to be explored. The Ω – ratio considers both down and upside portfolio potential. Recent work has established a technique to determine optimal Ω – ratio portfolios under the passive investment approach. The authors apply the identification of optimal Ω – ratio portfolios to the active arena (i.e., to portfolios constrained by a TE) and find that while passive managers should always invest in maximum Ω – ratio portfolios, active managers should first establish market conditions (which determine the sign of the main axis slope of the constant TE frontier). Maximum Sharpe ratio portfolios should be engaged when this slope is > 0 and maximum Ω – ratios when < 0.