How much smart beta can you deliver?
Over the last five years, the style factor returns in Axioma’s Asia ex-Japan fundamental model for Profitability really stand out. If an asset manager were to show investors a cumulative return chart like the one in Figure 1 below, the client would be thinking, “I’ve got to get me some of that!” The question is, how much of “that” can a manager actually get them?
Factor returns are estimated via factor-mimicking portfolios, which are dollar-neutral long-short portfolios with high turnover and often not investable by the average investor. So, how much of the Profitability factor risk premium can you deliver to your clients in practice? If you were to launch—and market—a Profitability Smart Beta product, how much AUM can you realistically hope to raise and still deliver returns comparable to (or at least positively correlated with) the factor returns from Figure 1?
Figure 1: Cumulative Return for Profitability Factor in the AXAPxJP-MH Model
In our latest Applied Research paper entitled, “Is your smart beta strategy scalable?”, we use the back-tester capabilities of the Axioma Portfolio Optimizer to identify the maximum capacity of a smart beta strategy in the Asia ex-Japan market, based on the Profitability style factor in our Asia ex-Japan fundamental model (v4). We ran a total of 16 long-only back-tests rebalanced monthly from January 2014 through the end of March 2019 (64 rebalancings), using four different levels of AUM ($100M, $200M, $500M, and $1.0B), varying the levels of tracking error to the underlying benchmark (2% or 3%), and the maximum holding constraint (maximum 5% or 10% of the average daily volume (ADV) for each stock) as a way to manage liquidity and market impact risk. We then analyzed the quality of our results by focusing on three criteria: average exposure to the target factor; percent of active style risk from the achieved target factor exposure; and strategy return versus target factor return.
Figure 2 below shows the profitability factor exposures achieved on March 31, 2019 using a trading liquidity constraint of 5% of ADV for our four levels of AUM at both 2% and 3% tracking error to the underlying benchmark.
Figure 2: Profitability Factor Exposures
Source: Axioma Portfolio
Note the sharp deterioration in the factor exposure as the AUM level increases. At an AUM level of $500 million, we are already getting less than a 0.5 exposure, and past $800 million, our exposure can be considered statistically insignificant. Increasing the tracking error from 2% to 3% does not seem to help at higher AUM levels.
Figure 3 shows the percent of active style risk coming from the respective profitability factor exposures at each AUM levels. Again, we note a sharp deterioration in the contribution to active style risk from these exposures. Starting at 90% for the $100 million AUM and 2% of Active Risk strategy, we drop to under 50% past $400 million in AUM all the way down to 1% for the $1 billion strategy. We also note that relaxing the tracking error limit from 2% to 3% leads to a deterioration in the risk budget where the additional risk is not being allocated to the target factor.
Figure 3: Percent of Active Style Risk from Profitability Factor
Source: Axioma Portfolio
Finally, when we look at the realized cumulative active returns for each AUM variant in Figure 4 below and compare them to the ones for the profitability factor in Figure 1, we see a sharp deterioration in our ability to add active returns from our profitability tilt at higher AUM levels. At $1 billion, we actually deliver -1.2% cumulative active returns (-0.16% annualized) for the period. In contrast, both the $100 and $200 million AUM strategies delivered the additional active return we would expect from the factor returns in Figure 1. Above $500 million in AUM, we can only deliver about half the active returns that lower AUM levels can, and at $1 billion, we lose our ability to deliver any active returns from our (marginal) exposure to profitability.
Figure 4: Cumulative Active Returns at 2% TE & 5% of ADV
Source: Axioma Portfolio
In conclusion, when it comes to delivering on the promise of capturing alpha from factor returns via a quantitative strategy, scalability is a key consideration. Portfolio managers must investigate and determine what is the maximum capacity of each factor strategy by paying close attention to the impact of various constraints in their portfolio construction methodology before embarking on marketing programs designed to raise as much AUM as possible. In the example above, blindly raising a billion dollars in AUM for that strategy could lead to reputational risk for the asset management company as it fails to deliver on the promised factor return. Business owners should run capacity studies on all their smart beta products to align their marketing efforts with those strategies that are the most scalable.
 Since the target factor return is estimated using a dollar-neutral long-short factor mimicking portfolio approach, we are not expecting to get 100% of the profitability factor return in Figure 1, but we want to see a positive correlation with this return to give us confidence that the target factor is indeed driving the return of our strategy, despite our long-only and other constraints.
 Since the target factor return is estimated using a dollar-neutral long-short factor mimicking portfolio approach, we are not expecting to get 100% of the profitability factor return in Figure 1 but we want to see a positive correlation with this return to give us confidence that the target factor is indeed driving the return of our strategy despite our long-only and other constraints.
 The underlying benchmark, which doubles as our investment universe, is the All-Cap Axioma Market Portfolio for Asia Pacific ex-Japan (APxJP-LMS).
 Since contribution to active style risk is lower at 3% than 2% TE for the target factor.