Asset managers will forever be in the hunt for a sort of “holy grail” that could offer investors above-market returns at lower risk in an efficient and low-cost way. One of their latest efforts are commonly known as Smart Beta exchange-traded funds (ETFs). The investment approach implements a rule-based system for choosing and weighting assets based on factors associated with risk-premiums. From having practically zero dollars’ worth of assets under management in 2000, there are now more than $1 trillion in 2020 in the U.S. alone.
This thesis aims to provide an empirical study on US-listed Smart Beta equity ETFs and, thus, unmask some of the critical elements of the popular investment product. In a two-part analysis, the promise of outperformance and ability to provide intended factor exposures are investigated in the period between Jan-2007 and Mar-2020.
For the first part of the analysis, a sample of 60 domestic Smart Beta ETFs across six well-known and acknowledged factor-strategies is constructed. These strategies are Size, Value, Momentum, Low Volatility, Quality, and Multifactor. The Smart Beta ETFs are analyzed concerning both relative returns and risk-adjusted-performance over three-time frames; the entire period, “up” and “down” periods. For the second part of the analysis, a multivariate factor-based regression model is built by assessing factors from the AQR data library. From outputs of the regression model, it will be possible to judge whether the different Smart Beta portfolios have provided investors with the intended factor-exposures. The methodology is mostly based on previous studies on similar subjects as well as traditional financial theory, but have found the most inspiration in Glushkov (2015).
In line with Glushkov (2015), this thesis does not find any statistically significant evidence of Smart Beta ETFs outperforming either its benchmark indices nor a broad, cap-weighted market indices. Besides, Smart Beta ETFs are found to provide investors with statistically significant exposure to intended factor tilts. However, there is also evidence of significant unintended factor tilts that seem to more than offset the harvested risk-premiums from the intended factor exposures.
|Educations||MSc in Finance and Investments, (Graduate Programme) Final Thesis|
|Number of pages||86|