In a recent Insights article, we discussed how factor models are changing the way institutional investment managers analyze and measure portfolio risk. In this Insight, we’ll examine factor-based investment methods and challenges, and discuss how managers use Charles River Portfolio Analytics to construct smart beta and risk parity portfolios.
Asset classes have traditionally served as the building blocks of investment portfolios. By constructing portfolios from supposedly uncorrelated assets, portfolio managers aim to diversify portfolio risk. But longstanding correlations between asset classes can shift rapidly, especially during times of financial stress, leaving portfolios overexposed to unintended risk. This can result in fund under performance, driven by forced liquidation of assets to meet investor redemption requests.
Historical analysis has shown that some asset class definitions, such as investment grade vs high yield debt, are really sub-asset classes that exhibit far higher correlation than previously thought. This also holds true for geographic delineations, such as developed vs emerging markets. Besides correlated price movement, liquidity profiles can also move in synch, which is especially problematic for a fund needing to sell large holdings quickly.
FROM ASSET CLASSES TO FACTORS
Risk factors are granular attributes of one or more asset classes that explain risk and return. Examples of equity factors are company size (large cap vs. small cap) and style (growth vs. value). Fixed income factors include interest rate, credit and prepayment risk. Factors can also include macroeconomic variables such as inflation, GDP growth, productivity, and commodity prices that impact multiple asset classes.
By constructing portfolios based on established, well-researched factors, rather than asset classes, managers can theoretically improve portfolio diversification, minimize undesirable correlation risk, and deliver better risk-adjusted performance.
In practice, constructing a portfolio using factors proves to be more challenging than the traditional asset class approach. Considerable latitude exists in how best to incorporate a particular factor into a portfolio, using methods ranging from straightforward to difficult. For example, to gain exposure to real interest rates, a manager can simply buy the Barclays TIPS Index. Gaining exposure to GDP on the other hand, is virtually impossible. Three primary challenges exist.
First, not every factor has an investable proxy. GDP and productivity fall into this category. Secondly, indexes and ETFs that serve as proxies have to be deep and liquid enough to handle institutional-scale investment flows. Thirdly, some factors have to be implemented using derivatives or long/short positions to capture a spread. This can be problematic for institutions constrained by long-only mandates or firms lacking derivative expertise.
LEVERAGING FACTORS WITH SMART BETA AND RISK PARITY
Recognizing both the opportunities and challenges of factor-based investing has spurred significant innovation on behalf of buy-side firms and index providers. The growing number of smart beta products underscores the popularity of factor based investing for both retail and institutional investors.
Similar to index strategies, smart beta products use pre-defined rules for security selection, portfolio construction and rebalancing. This removes subjectivity from the investment process and provides investors with full transparency, at a cost comparable to passive funds. Typically long only, smart beta products are benchmark-driven factor strategies usually implemented within an asset class. An equity smart beta ETF, for example, can offer investors targeted exposure to value, momentum, and/or size.
The flow of money into actively managed funds has declined steadily, while flows into passive funds have ballooned. In fact, 2015 saw more than $207 billion flow out of actively managed funds, while inflows into passive funds soared for the second straight year by more than $410 billion. And half of all the ETFs launched in 2015 were smart beta ETFs.
Sebastian Ceria, CEO, Axioma
This user-friendly packaging removes the need for investors to implement long/short strategies or incorporate derivatives in their portfolio. Smart beta products help investors reduce specific risks, improve portfolio diversification, and potentially enhance returns; benefits previously available only through more expensive actively managed strategies. These products are also designed with capacity in mind, circumventing the crowded trades that periodically impact the performance of active managers.
Like smart beta, risk parity is a passively managed, rules-driven strategy. Risk parity allocates assets to a portfolio using a risk factor-based weighting, rather than on more conventional approaches such as sectors or currencies. Mirroring the growth of smart beta products, over $500 billion in AUM are now allocated to risk parity portfolios.
IMPLEMENTING FACTOR BASED PORTFOLIOS IN CHARLES RIVER
Charles River Portfolio Analytics provides full investment lifecycle support and the capabilities required to construct and manage smart beta and risk parity products.
CONSTRUCT: Portfolio managers require factor level exposures, risk contributions and the ability to categorize those exposures by investment style, industry, country or currency. Portfolio optimization then uses this information to maximize/minimize exposure to desired factors. Charles River supports both client-specific and 3rd party factor models.
MANAGE: The periodic rebalancing required by these strategies can be performed efficiently directly from the portfolio management workspace. Performance measurement and factor-based attribution allows analysts to validate that new products and strategies perform reasonably well ex-post. Additionally, scenario analysis using a factor covariance matrix representing stressed periods also provides valuable insight into how factors behave under different market and economic scenarios.
GROWING MARKET ACCEPTANCE
Although factor model-based portfolio construction began as a specialized niche for quantitatively focused hedge funds, innovations such as smart beta and risk parity have made factor modeling a required tool for institutional asset managers. Smart beta ETFs and risk parity products offered by iShares, Oppenheimer, Northern Trust and State Street underscore the growing popularity of factor-based investing. These products are also seeing growing uptake in robo advisor managed portfolios.
With actively managed funds under pressure to reduce fees and justify their performance, firms that can offer lower cost, passively managed products will outperform less innovative managers. Key to supporting these new product offerings is an end-to-end portfolio management and decision support solution