5 Year Return Forecasts

COVID-19 Challenges Investment Portfolios

Factors First: A Risk-based Approach to Harnessing Alternative Sources of Income

Image by Arek Socha from Pixabay 

Staff Report From NUVEEN/Nuveen

The income-generating potential of alternatives seems to be largely underappreciated, despite the trend toward larger allocations to alternative asset classes in institutional portfolios and the quest for yield in a low-rate environment. When we ask institutional investors what roles they look for alternatives to play in a multi-asset portfolio, diversification is the top priority, followed closely by total return. Income generation usually is a distant third.

Institutional investors can enhance their ability to capitalize on the yield and diversification benefits of alternatives by focusing on the risks that drive returns in each specific segment of the alternatives universe. This approach allows investors to stitch together multi-asset portfolios in a more efficient, coherent way.

Executing this, however, is no simple task. If done incorrectly, investors risk negating some of the diversification benefits that make alternatives such valuable contributors to stronger, more resilient portfolios.

Know what risk factors drive return

Alternative asset classes such as private credit, real assets (farmland, timberland and private equity infrastructure investments) as well as non-traditional sectors of fixed income (preferred securities, emerging markets debt, high yield corporate debt and leveraged loans) present attractive income-generating potential.

Idiosyncratic risks play a vital role in driving returns in each of these asset classes — and these risks are what institutional investors should be trying to harness in an income-generating multi-asset portfolio. But it is important to note that each of these asset classes has significant exposure, in varying degrees, to the core, broad-based risk factors: equity, credit spread and rate duration.

As the chart below illustrates, idiosyncratic risks account for less than 60% of the contribution to total risk in all of the alternative asset classes included in the chart, except for real estate. With emerging markets debt, for example, equity risk accounts for 36% of the total risk and credit risk accounts for an additional 33%.

Preferreds are also an interesting case. Some investors consider them to be more like an equity instrument while others consider them to be more like fixed income. This debate is easily settled when viewed through a risk decomposition lens, which shows that equity risk and idiosyncratic risk account for the totality of risk for preferreds.

Click chart to enlarge.

This isn’t to imply that emerging markets debt and infrastructure aren’t valuable diversifiers. Rather, it is to highlight that unless an investor decomposes the risk contributors, a portfolio could end up with significantly more exposure to equity, credit or rate risk than the investor bargained for.

Allocate to risk factors, not asset classes

Investors are compensated for owning risk, not asset classes. We believe that their portfolio construction processes should reflect this and we have developed a five-step approach to do just that:

1. Decompose risk factors driving the performance of asset classes
2. Analyze how the market is compensating those risk factors
3. Determine which risks need to be owned to fulfill investment objectives and constraints
4. Determine which asset classes and vehicles will achieve the desired risk exposures
5. Monitor risk and asset class relationships and how the market is compensating risks

The benefits of a risk-first approach

This framework puts risk at the heart of constructing multi-asset portfolios and delivers multiple benefits to investors. As already noted, it reduces the risk of overconcentration of risk factors in a portfolio, which could undermine the diversification benefits investors seek from alternatives.

It also encourages a more nimble approach to pursuing yield. The relationships among the risk factors and thus the relationships among the asset classes are constantly evolving — and the degree to which the market is compensating various risks is always changing. Predefined asset allocation constraints limit an investor’s ability to exploit these changes and manage risk.

The framework fosters a more nuanced approach to managing liquidity. Liquidity risk is just one of the idiosyncratic risks of an investment. But when using alternatives to generate income and cash flows needed to fund a set liability, liquidity becomes the idiosyncratic risk that institutions need to understand the best. Taking a risk-first approach to multi-asset portfolio construction frees an investor to take a more nuanced and sophisticated approach to managing liquidity risk — not just with alternatives, but across the entire portfolio.

Learn more about harnessing alternative sources of income

The full paper with complete disclosures can be found at Nuveen.com. 

Nuveen is an Associate Member of TEXPERS. The views expressed in this article are those of the authors and not necessarily Nuveen nor TEXPERS. 

Sources
All market and economic data from Bloomberg, FactSet and Morningstar.

The Mega-Cap Conundrum

Image by Arek Socha from Pixabay

By EDWARD J. RAKHAM, PH.D./Los Angeles Capital

2019 was an unusually challenging year for quantitative equity strategies with a large proportion of quantitative strategies underperforming their stated benchmark on a rolling one year basis. There has, therefore, been a great deal of interest in understanding the shortcomings of quantitative portfolios over the same calendar year.

The struggles faced by quants in 2019 were related not just to the efficacy of quantitative signals but also to the ability of systematic strategies to access these signals over that 12-month period – an issue exacerbated by the outperformance of large cap stocks over smaller names over the course of the year.

To further understand the difficulties faced by quants in 2019 it is informative to consider the returns to common quantitative strategies over recent years. The compounded active performance of five simulated portfolios from the end of 2003 to the end of 2019 is given in Figure 1. The portfolios have been constructed using the assets within the developed world opportunity set to have a fixed ex-ante tracking error of 1% relative to a standard world index and, subject to constraints on risk exposures as well as the requirement to be long only and fully invested, maximized exposure to one of the following investment signals: value, momentum, quality, residual reversal and earnings revision. The signals shown represent common quantitative investment factors and the portfolios can be thought of as investable applications that maintain exposure to the listed investment factors at all times.

Figure 1

Click chart to enlarge.

What is immediately clear from Figure 1 is that a quantitative manager implementing one or multiple of these factor portfolios would have added value over the last 15 years. What is also clear, however, is that 2019 saw a downturn in all but one of five of the common quant strategies shown in the figure. Such consistent under performance across almost all of the strategies is unusual. Moreover, at first glance, the results of Figure 1 are somewhat unintuitive. Certainly there were many high quality companies with durable cash flows that performed well in 2019. Residual reversal and earnings revisions factors consistently have proven powerful signals in many different market environments and many of the “winners” in 2019 continued to win throughout the year suggesting that momentum based strategies should also have fared well. Indeed, only the value portfolio’s underperformance in 2019 is in line with expectation as it continues the trend of negative returns to such strategies that have been observed over the last five years.

To further understand the challenges faced by quants and to rationalize investment intuition with Figure 1, it is helpful to consider the performance of the largest names versus the smallest names within the developed world opportunity set over 2019.

Overall, the compounded payoff to the size factor – the return associated with characteristic of being large capitalization – is negative over the last 15 years, consistent with the idea that there is a premium to holding small cap names over larger sized stocks. Performance from the end of 2017 to the end of 2019, however, has been reversed with the largest names in the world developed index outperforming their smaller cap counterparts over the last two years.

Interestingly, the preference for large capitalization names over mid or small capitalization names was even more pronounced in 2019 conditional on those stocks also being, for example, of high momentum or high quality. That is, high quality stocks did well in 2019 if those stocks were also mega caps; high momentum stocks added value if those stocks were also amongst the largest names in the index. Indeed, this was the case for almost all of the factors in Figure 1 throughout last year.

The observation that signal efficacy is concentrated in the largest names for most of the systematic investment signals is of particular relevance to the question of quant performance.

Being overweight the largest names within the global benchmark requires managers to build portfolios with concentrated positions in a handful of large cap names. Many quantitative investment processes tend to result in long only diversified portfolios that hold large numbers of names so as to exploit breadth. Quant portfolios are, as a result, frequently underweight the largest names in the index irrespective of investment style.

Given that quant portfolios tend to access the smaller end of the capitalization spectrum, the performance of the investable quant portfolios of Figure 1 can now be understood. The stark underperformance of investment strategies constructed around these signals was as much related to the portfolio’s ability to access the mega caps within the opportunity set as it was to the performance of the signals themselves. That is, for a given quantitative investment signal in 2019, it was almost always necessary to buy exposure to these factors amongst the largest names in the opportunity set while simultaneously avoiding the smaller end of the market cap spectrum – an allocation that simply does not come naturally to most quantitative portfolios.

Given the market environment of 2019, and the consistent performance of the largest names within the opportunity set, it is tempting to conclude that quant managers should adjust their investment processes to account for continued mega cap performance. While lessons must be learned from the events of 2019 there is also risk in assuming that market environments and factor behavior continues indefinitely. Investor preferences for particular factors and assets tends to be cyclical and while large cap growth assets may continue to perform well in the near future, a change in economic fortunes could result in a change in their outlook.

In the short term, however, challenges to performance may continue to plague quant investment processes if factor efficacy remains focused within the largest end of the market. Nevertheless such an environment does not, on an ongoing basis, preclude the existence of alternative investment ideas within the broad opportunity set that are unrelated to size. Systematic managers who can find such ideas and, importantly, express those ideas efficiently within an investment portfolio may still be able to overcome the headwinds of mega cap outperformance.

Los Angeles Capital is an Associate Member of TEXPERS.The views expressed in this article are those of the author and not necessarily Los Angeles Capital nor TEXPERS.

About the Author

Ed Rackham, Ph.D., is Co-director of Research at Los Angeles Capital and is responsible for overseeing all functions of the Research department which includes: model development, risk management and factor research. Alongside these broad responsibilities, Rackham specializes in the development of Investment Risk Management tools and he and the Risk Management group focus on the research and development of portfolio construction techniques that are designed to forecast and control the investment risk of the firm’s portfolios. In addition, Rackham and the Risk Management group look at ways to embed the firm’s stock selection views into investment portfolios in a cost-controlled fashion while simultaneously controlling for forecast uncertainty in both portfolio’s expected performance and the portfolio’s forward-looking risk. Prior to joining Los Angeles Capital, he spent six years at Wilshire Associates researching and developing risk and portfolio analytics tools, most recently as the Head of Research and Development of their Equity Analytics group. Previously, Rackham was an instructor in mathematics and physical chemistry at the University of Oxford, where he also earned his doctorate.