The Smart Beta Mirage
Shiyang Huang (University of Hong Kong), et al.
We document sharp performance deterioration of smart beta indexes after the corresponding smart beta ETFs are listed for investments. Adjusted by aggregate market return, the average return of smart beta indexes drops from 2.77% per year “on paper” before ETF listing to −0.44% per year after ETF listing. This performance deterioration cannot be explained by strategic timing in ETF listing nor explained by time trend in factor premia. We find evidence of data mining in constructing smart beta indexes as the post-ETF-listing performance decline is much sharper for indexes that are more susceptible to data mining in backtests. Our results caution the risk of data mining in the proliferation of ETF offerings as investors respond strongly to the stellar performance in backtests.
Do Smart Beta ETFs Deliver Persistent Performance?
Marco Soggiu (consultant), et al.
This paper analyses Smart Beta ETF performance and provides the first evidence on the funds’ performance persistence. Our sample is comprised of 152 US equity smart beta ETFs over the period June 2000 to May 2017. We found that as per the risk-adjusted performance about 40% of Smart Beta ETFs outperformed their related traditional ETFs after expenses. The analysis of performance persistence conducted based on the relative performance of Smart Beta ETFs showed that the performance of winners and losers does persists in the year ahead. The persistence in performance was documented in 7 out of 9 peer categories.
Smart Beta Made Smart
Andreas Johansson (Stockholm School of Economics), et al.
We construct synthetic, tradable risk factors (e.g., tradable HML and MOM) and individual factor legs (e.g., growth and value) using optimal combinations of large and liquid mutual funds and ETFs based on their holdings. We show that a large fraction of existing smart beta funds are simply market funds, and that both retail and institutional investors are not able to harvest the unconditional factor risk premia, with the exception of the value premium. We conclude that the investable set of strategies available to investors may be smaller than previously thought. We also show that smart beta funds’ names might not be indicative of the actual fund strategy, although daily flows to smart beta strategies suggest that naive investors tend to get exposure to smart beta strategies based on funds’ names. Our analysis has several important implications, including how we evaluate portfolio managers and cross-sectional returns’ anomalies.
Factor Tilts and Asset Allocation
Javier Estrada (IESE Business School)
Factor investing has received much attention from academics and practitioners, as well as from individual and institutional investors. It has become usual for investors that aim to enhance returns to add to the core of their portfolios a factor satellite, thus tilting their portfolios toward factors that have produced a long-term risk premium. However, in most cases, investors behaving this way are not fully invested in stocks, which begs an interesting question: Should an investor with a two-asset portfolio of broadly-diversified stocks and bonds tilt the stocks slice of his portfolio toward (small-cap and value) factors, or would he be better off by simply increasing the allocation to broadly-diversified stocks in his two-asset portfolio? The results discussed here, based on different samples and sample periods, support the notion of factor-tilting portfolios.
The Drivers and Inhibitors of Factor Investing
Dunhong Jin (University of Oxford)
I model the equilibrium asset allocations when households can invest directly, search for factor (smart-beta and ETFs) investments or fundamental (stock-picking) investments. Managers endogenously choose to specialize in factor or fundamental information given the equilibrium fee structure. Fundamental managers can opt to be opportunistic “closet indexers.” I show that wealth inequality increases demand for factor investing: fundamental investing attracts the wealthiest households, who are more willing to detect closet indexing. Fundamental managers have to compete more aggressively through information acquisition, which lowers their excess returns and thus delegation fees. The reduced fees earned by fundamental managers force factor managers to lower their fees, making factor investing more attractive. However, the equilibrium fraction of capital allocated to factor investing can never reach 100 percent: the ceiling is determined by the endogenous level of opportunism in the fundamental investment industry.
How Smart Is the Real Estate Smart Beta? Evidence from Optimal Style Factor Strategies for REITs
Massimo Guidolin and Manuela Pedio (Bocconi University)
This paper has a twofold objective. First, we contribute to the stream of literature that investigates whether traditional asset pricing factors show any predictive power for the cross-section of Real Estate Investment Trust (REIT) returns. In particular, we investigate the existence of a premium associated to the Value, Size, Momentum, Investment, and Profitability factors over the period 1993-2018. We find support for all the pricing factors but for the Profitability one. Second, we investigate whether a set of smart beta strategies, based on the combination of the identified factors, may outperform similar allocation techniques that do not exploit factors. We find that all the proposed factor-based strategies display a higher risk-adjusted out-of-sample performance than a simple buy-and-hold investment in the real estate market (proxied by the FTSE NAREIT All REITs Index). In addition, we find that when factor-based strategies are implemented, REIT-only portfolios display risk-adjusted performances comparable to those of diversified portfolios that include equity, bond, and commodities.
Smart Beta and Statistical Significance
Paskalis Glabadanidis (University of Adelaide Business School)
I propose an alternative weighting mechanism for equity mandates based on the statistical significance of the factor loading on the benchmark. Specifically, the weight of each security entering the active portfolio is directly proportional to the t-statistic of the factor loading ($\beta$) with the benchmark. I show that this amounts to overweighting securities with high correlations with the benchmark and vice versa. The t-statistic of market beta within a single-factor model is a monotonic transformation of its correlation with the benchmark. I test the out-of-sample performance of this alternative weighing scheme with industry portfolios as well as individual US stocks. I find that this strategy has higher correlations with the benchmark compared to other popular alternatives, has lower tracking error, unitary exposure to the benchmark, very good Sharpe ratios and substantial ex-post realized returns relative to the underlying benchmark.
Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno