This article is a follow-up to my earlier article which focused on the tax-loss harvesting benefits of direct indexing. This article covers the two other reasons that investors utilize direct indexing in addition to the tax benefits:
A “factor tilt” involves intentionally deviating from benchmark index weights in order to emphasize or de-emphasize certain characteristics:
1. ESG (Environmental, Social, Governance) factor tilts. Several companies provide screened indexes that provide rankings of stocks based on ESG factors, including “sustainable” practices such as pledging to eliminate use of fossil fuels, reduce or eliminate practices that generate greenhouse gases, support of LGBTQ+ rights, diversity of boards of directors, etc.
2. Alpha factor tilts. Many passively managed ETFs tilt on various factors, and this practice is often referred to as “smart beta.” Such factors include value, smallness, dividend yield, momentum, low volatility, and high quality. Some evidence indicates that alpha factor returns exhibit enough return momentum to make using trailing 12-month return an effective factor-weighting methodology. (See “A Smarter Way to Use Smart Beta ETFs” on our website.)
Tilting on ESG Factors
ESG is the most recent version of “Socially Responsible Investing” or SRI. In the second half of the 20th century, most SRI investing was motivated by religious conviction, although later on it was more often motivated by political conviction. The investment screens used prototypically eliminated stocks deemed to be associated with alcohol, tobacco, firearms, weapons, abortion, pornography, the apartheid regime in South Africa, or the state of Israel. Firms that provided investment funds using such screens did not try to sell them as providing a superior return/risk opportunity.
The rise of ESG investing in the 21st century is closely tied to the rise of progressive political values. Often, the purveyors of ESG indexes and funds have asserted that ESG investing leads to superior return/risk performance results. This is self-serving nonsense.
It is an irrefutable truth of finance that a portfolio subject to one or more constraints cannot dominate the return/risk results of a portfolio with no constraints over the long-term. Any time period that purports to show that it does is based on time period dependent happenstance. This is the type of “proof” put forward by ESG advocates, most of whom have a financial, political, or ideological stake in the matter.
In fact, the overwhelming consensus among sophisticated academic studies of ESG investing concludes that, although there may have been some ephemeral profit in jumping on the ESG bandwagon early as its popularity swelled, it is impossible for a restricted set of securities to dominate the return/risk opportunity set of the full universe of securities permanently. Actually, short-term association of ESG factors with higher return/risk performance results mostly has to do with other factors, such as quality, size, and sector- and industry-specific effects.
Among the myriad of scholarly articles on ESG investing, I would like to highlight one written by Cliff Asness, PhD., managing partner of AQR. It is short, entertaining, and compelling: “Virtue is its Own Reward Or One Man's Ceiling is Another Man's Floor.”
Asness explains that if the investing public is divided into the “virtuous” who will only invest in virtuous companies and “sinners” who will invest in the entire universe of companies, then the only way to entice the sinners to over-weight the non-virtuous companies (to make up for the under-weighting by the virtuous) is to increase their expected returns by lowering their stock prices. While the popularity of virtuous investing builds, it may appear for a time as though “virtue is its own reward” because non-virtuous companies will be having their stock prices reduced, boosting the relative returns of the virtuous investors who avoid them. This happy circumstance (for the virtuous) will continue to play out until virtuous wave crests.
The higher expected returns of non-virtuous companies increases their cost of capital (or “discount rate”), shrinking their investment opportunities in non-virtuous projects. Higher expected return = higher discount rate. This is exactly the mechanism by which ESG investing has its intended effect on the economy. To adapt the famous Paul Simon song, “one man’s discount rate is another man’s expected return.”
Sometimes ESG proponents argue that virtuous companies have lower risk, which more than compensates for the lower expected return. It is true that, even though the various ESG ratings are vastly different and often conflict, there is some evidence that high ESG scores are correlated with the common “quality” factor, which emphasizes companies with higher profitability and lower debt. However, ESG funds are an expensive and diluted way to obtain a tilt on the quality factor. iShares MSCI USA Quality Factor ETF (QUAL) is a factor-based ETF with an expense ratio of only .15.
Finally, for those who are not investing their own money, there is the fiduciary issue. A fiduciary is legally and ethically compelled to focus solely on the economic benefits of an investment made on behalf of clients. Of course, if a client instructs the fiduciary to invest in a different way, that is the client’s prerogative. But a responsible fiduciary will make it clear that imposing ESG constraints comes with a return/risk cost.
Tilting on Alpha Factors
Most direct indexation is focused on two objectives:
- Minimizing taxes
- Minimizing tracking error (return deviation relative to the index)
Tilting on alpha factors introduces a third objective:
- Maximizing risk-adjusted return (“alpha”)
In a sense, three balls are being juggled. There is some natural tension among these three competing objectives. Managing the complex tradeoffs requires sophisticated and expensive software that will incorporate the tax basis of each security tax lot. (A “tax lot” arises each time that a security is purchased on a given day at a given price.) Other key categories of information for a tax-sensitive optimization include long-term and short-term marginal tax rates, expected risks (for estimating tracking error to the index), expected returns (for estimating alpha), and expected transaction costs. It is quite complex!
The central question related to tilting on alpha factors is, “which factors?” I have spent my entire investment career focusing on determining which factors are most likely to generate alpha and when they are most likely to be productive. In addition to reading quite a lot of secondary research in journals, white papers, and textbooks, I also confirm the research of others with my own primary research. In other words, factor-based investing is my thing.
There are many potential alpha factors (some have called it a “factor zoo”), and many different ways of measuring and quantifying a factor. For example, the “value” factor could be related to price/earnings, price/book value, price/sales, or some measure of intermediate-term to long-term return underperformance. These “descriptors” (as they are called) can be absolute, relative to their own history, relative to industry or sector, or relative to the overall stock market. Again, quite complex.
The consensus of the best academic research on alpha factors, and my own primary research, indicates that there three type types of factors that tend to provide the strongest and most consistent payoffs in terms of risk-adjusted excess return:
These are the three types of alpha factors used at Sapient Investments. Having identified the three types of factors on which we want to focus, as well as the descriptors we will use to quantify their factor exposures, we then regress historical risk-adjusted monthly returns for securities (mostly ETFs) against their beginning-of-month factor exposures to quantify the monthly payoffs to those factors. We use time-series analysis to estimate the future payoffs for the factors. We are able to update these factor return forecast daily, but in practice we usually update and rebalance portfolios monthly.
For most of our clients, we invest in ETFs. While there are some ETFs that can provide exposure to these three factors somewhat directly, we can also measure the value, momentum, and quality characteristics of any ETF. Through FactSet (another very expensive software on top of the tax-aware optimization software we use!), we are able to look at an ETF just as if it were an individual stock, with all of the same descriptors. This allows us to apply our alpha factor return forecasts to any ETF.
How We Do Direct Indexation at Sapient Investments
Portfolio optimization that seeks to emphasize certain alpha factors while also controlling benchmark index tracking error must utilize alpha factor return forecasts that have been carefully “scrubbed” of risk effects so that what is being forecasted is “pure” alpha, not related to any risk exposure. The best way to accomplish this is to construct the risk model and the alpha factor model simultaneously, using the same securities universe and the same regression analysis. For this, we use the Northfield XRD Risk Model.
Northfield Information Services is a leader in risk management, portfolio construction, and wealth management software. Their XRD Risk Model provides us with the ability to predict the tracking error of portfolios relative to their benchmarks. Their risk factors also include nine “style factors” that allow us to tilt the portfolio with respect to these factors, while at the same time minimizing the risks from other factors, such as currencies, countries, and industries.
Four of the nine style factors in the Northfield XRD Risk Model we call “alpha factors,” and we intentionally tilt portfolios towards these factors to the extent that our time-series modelling indicates that they have been adding significant and consistent excess return:
- Dividend yield – a factor measuring trailing 12-month dividends paid divided by price.
- Value – a factor combining normalized book/price, cash-flow/price, and earnings/price.
- Quality – a factor combining normalized sustainable growth, cash flow/sales, return on equity, and return on assets.
- Short-term price momentum – a factor (also known as “specific return reversal”) that measures the extent to which a stock has recently outperformed its expected return based on its factor exposures. (It has a negative return, so we tilt away from this factor.)
The graph below depicts the cumulative returns of the nine Northfield XRD Model style factors within the U.S. stock market since the inception of the model in January 2003 through June 2023. The four factors that we are including in our tactical alpha factor forecasting methodology have had very high average returns, as noted to the right of the graph. (Short-term price momentum has had a very high negative average return.) We use these same four factors in international portfolios as well, but illustrating those other markets would require too many return lines for a single graph. We find that these four factors are the most powerful of the nine outside of the U.S. as well as within the U.S.
Note that the returns depicted above are net of all risk factor effects in the XRD Risk Model. That is, these are “pure” alpha factor returns and are by construction uncorrelated with any risk factors. While none of these alpha factors has achieved a payoff each and every month, over time the consistency of these factors has been quite impressive.
Summary and Conclusions
- Read “Is Direct Indexing Right for Me?” first.
- Other than tax minimization, investors sometimes also use direct indexation for factor tilting.
- The two most common types of factor tilts are ESG factors and alpha factors.
- Contrary to some claims, ESG factors do not improve the return/risk profile of a portfolio.
- While a fiduciary is legally and ethically barred from imposing their own social and political values on client assets, clients themselves can certainly do so—at a cost.
- Tilting on alpha factors is meant to improve the return/risk profile of a portfolio.
- At Sapient Investments, we use the Northfield Optimizer and XRD Risk Model to optimize directly indexed portfolios.
- The XRD alpha factors towards which we tilt are similar to those we emphasize in our ETF portfolios: