THE SPILLOVER, RISK AND LEVERAGE EFFECTS OF SMART BETA MANAGEMENT EXCHANGE-TRADED FUND (ETF)

This research uses two different GARCH models to measure spillover, risk, and leverage effects of active, passive, and smart beta management Exchange-traded Funds (ETFs). The increase in popularity of ETFs and new categories within them, specifically the growth of smart beta management, means asset managers and investors have new metrics to account for when determining portfolio exposure following the Adaptive Investment Approach (AIA). The results show significant relationships among all groups regarding the spillover. A trend of positive multi-lateral spillover of returns among the three management types including passive, active and small beta is observed with smart beta showing the highest percentage of a bi-lateral positive effect. The strongest spillover of volatility effects is among the actively managed ETFs. The testing of risk results is insignificant, but the leverage effect results are consistent with the past studies showing the significant negative bi-lateral effect.

Financial Risk and Better Returns through Smart Beta Exchange-Traded Funds?

Smart beta exchange-traded funds (SB ETFs) have caught the attention of investors due to their supposed ability to offer a better risk–return trade-off than traditionally structured passive indices. Yet, research covering the performance of SB ETFs benchmarked to traditional cap-weighted market indices remains relatively scarce. There is a lack of empirical evidence enforcing this phenomenon. Extending the work of Glushkov (“How Smart are “Smart Beta” ETFs? …”, 2016), we provide a quantitative analysis of the performance of 145 EU-domicile SB ETFs over a 12 year period, from 30 December 2005 to 31 December 2017, belonging to 9 sub-categories. We outline which criteria were retained such that the investigated ETFs had at least 12 consecutive monthly returns data. We consider three models: the Sharpe–Lintner capital asset pricing model, the Fama–French three-factor model, and the Carhart four-factor model, discussed in the literature review sections, in order to assess the factor exposure of each fund to market, size, value, and momentum factors, according to the pertinent model. In order to do so, the sample of SB ETFs and benchmarks underwent a series of numerical assessments in order to aim at explaining both performance and risk. The measures chosen are the Annualised Total Return, the Annualised Volatility, the Annualised Sharpe Ratio, and the Annualised Relative Return (ARR). Of the sub-categories that achieved greater ARRs, only two SB categories, equal and momentum, are able to certify better risk-adjusted returns.

Smart Beta Allocation and Macroeconomic Variables: The Impact of COVID-19

Smart beta strategies across economic regimes seek to address inefficiencies created by market-based indices, thereby enhancing portfolio returns above traditional benchmarks. Our goal is to develop a strategy for re-hedging smart beta portfolios that shows the connection between multi-factor strategies and macroeconomic variables. This is done, first, by analyzing finite correlations between the portfolio weights and macroeconomic variables and, more remarkably, by defining an investment tilting variable. The latter is analyzed with a discriminant analysis approach with a twofold application. The first is the selection of the crucial re-hedging thresholds which generate a strong connection between factors and macroeconomic variables. The second is forecasting portfolio dynamics (gain and loss). The capability of forecasting is even more evident in the COVID-19 period. Analysis is carried out on the iShares US exchange traded fund (ETF) market using monthly data in the period December 2013–May 2020, thereby highlighting the impact of COVID-19.