Designing and managing asset allocation strategies based on factors is promoted in some corners as a better way to build portfolios. Not surprisingly, there’s no shortage of studies that support this view. But the jury’s still out on whether it’s prudent to throw out the standard asset-class buckets. Factor-based investing can play a productive role in enhancing a conventionally designed asset allocation, but it’s debatable if a pure factor-only strategy is a true solution.
There are two problems to consider. First, real-world factor investing has a short history. There are exceptions, such as small-cap, value, and momentum strategies for the US equity market. Otherwise, the track record is short, at least in terms of mutual funds and ETFs that represent proxies for putting theory into practice. As I discussed recently, you can slice up the US stock market into eight different factor exposures via ETFs, but the common start date for this lot only stretches back to July 2013.
Four years is hardly enough time to pass judgment, but let’s ignore that glitch for now and create 1000 randomly allocated portfolios using the following eight ETFs:
* iShares Edge MSCI Min Vol USA (USMV) – low-volatility
* Vanguard High Dividend Yield ETF (VYM) – high-dividend yields
* Guggenheim S&P 500 Equal Weight ETF (RSP) – small-cap bias within large-cap space
* iShares Edge MSCI USA Quality Factor (QUAL) – so-called quality stocks
* iShares Edge MSCI USA Momentum Factor (MTUM) – price momentum
* iShares S&P Small-Cap 600 Value (IJS) – small-cap value stocks
* iShares S&P Mid-Cap 400 Value (IJJ) – mid-cap value stocks
* iShares S&P 500 Value (IVE) – large-cap value stocks
The benchmark for comparison: the S&P 500 Index, based on the SPDR S&P 500 (SPY). The question before the house: How do 1000 randomly designed factor strategies (using the eight ETFs above) compare? For some insight, I fired up R to generate 1000 wealth indexes based on randomly changing the initial allocations to the eight funds. Any one fund could have a weight in the portfolio ranging from 0% to 100%. Most of the weights were a mix across the funds. All the strategies were rebalanced at the end of each calendar year to the initial randomly generated weights. The results are shown in the graph below.
