risk factors outperform allocations based on asset classes? In theory, allocations based on risk factors should provide the same risk-adjusted return as allocations based on assets if the same information sets are used (Idzorek and Kowara 2013). It can be shown that in a perfect world, where all risk factors can be traded and all asset returns can be explained by risk factors, neither approach is inherently superior to the other. Research using real-world data has demonstrated that either approach may be superior over a given period of time (Idzorek and Kowara 2013). In particular, in some cases, the apparent superiority of risk factors is a simple result of the fact that risk factors can be conceptualized as being an alternative set of asset classes that can be identified when the long-only constraint is removed. For instance, a portion of the return earned by the value factor is due to the fact that it shorts growth stocks. Therefore, an asset allocation strategy that would permit the investor to short stocks should be able to match the performance of a portfolio that allocates to the value factor. If all risk factors were traded and short sale constraints were removed, then it would be difficult to imagine that a portfolio constructed using risk allocation could outperform a portfolio constructed using asset allocation on a consistent basis.
Several practical issues associated with risk-factor-based asset allocation must be highlighted (Idzorek and Kowara 2013). First, portfolio construction using risk factors is not likely to become globally adopted, because it implies allocation strategies that are not sustainable (i.e., not consistent on the macro level). That is, not everyone can be short growth stocks or commodities that are in contango. Therefore, the capacity is likely to be limited, and as more money is allocated to factor investing, the strategies will become expensive and the risk premium will shrink or disappear altogether. Second, risk allocation requires asset owners to take extreme positions in some asset classes. Many institutional investors are not allowed to make such allocations. For example, taking short positions in all growth stocks, including such names as Google, Amazon, and Facebook, is not something that most investors are prepared to do. Third, risk allocation is not a magic bullet that will automatically lead to asset allocations that will dominate those based on asset classes. Similar to other investment products, the cost of the strategy must be taken into account. Flows into some of these factors have already reduced the size of the premium (e.g., size and low volatility factors have not performed well in recent years). Finally, alternative investments are important vehicles for accessing some of these risk factors, but they typically represent a bundle of risk factors; as such, a pure risk allocation approach cannot be applied to alternatives. However, measuring factor exposures of alternative assets can provide valuable information about their risk-return profiles, which should be taken into account when allocations to traditional asset classes are considered. For instance, factor exposure analysis of private equity will highlight the fact that it has significant exposures to size and credit factors. Therefore, the investor may choose to tilt the exposure of the portfolio's traditional assets to other factors.
2.6 Conclusion
Chapter 1 introduced the basics of the asset allocation process and studied the mean-variance approach to asset allocation. This chapter extended the concepts discussed in Chapter 1 to take into account practical issues that arise while applying the asset allocation process. This chapter began with a discussion of tactical asset allocation (TAA), with a focus on the costs and benefits of this strategy when applied to portfolios of traditional and alternative investments. It was argued that because the rebalancing of alternative investments is costly, a higher level of skill is needed to make TAA a value-added activity.
Next, the chapter discussed several extensions to the mean-variance approach. The focus here was on those extensions that are important to alternative assets. For instance, we discussed how illiquidity and estimation risk can be taken into account when performing mean-variance optimization. While these extensions may not provide perfect answers, they represent excellent starting points to the asset allocation process and can serve as checks against other, perhaps more heuristic approaches that are adopted by asset allocators.
Risk budgeting and the risk parity approach were discussed next. Risk budgeting is a valuable tool for analyzing a portfolio's risk-return profile and imposing risk constraints desired by asset owners. Risk parity uses the results of risk budgets to create portfolios in which each asset contributes the same amount to the total risk of the portfolio. The chapter discussed the pros and cons of risk parity and pointed out that some of the reasons given in support of risk parity may not apply to alternative investments.
Finally, the chapter discussed factor investing. This is a relatively new topic in the investment community and, similar to other new ideas, has to be evaluated carefully when applied to alternative investments. The primary benefit of factor investing is that it informs investors that returns will come from being exposed to certain risk factors and allows asset owners to decide if earning returns through exposure to these certain risk factors is consistent with their objectives and constraints.
References
Ang, A. 2014. Asset Management: A Systematic Approach to Factor Investing. New York: Oxford University Press.
Asness, C., A. Frazzini, and L. H. Pedersen. 2012. “Leverage Aversion and Risk Parity.” Financial Analysts Journal 68 (1): 47–59.
Asness, C., T. Moskowitz, and L. H. Pedersen. 2010. “Value and Momentum Everywhere.” AFA 2010 Atlanta Meetings Paper, March 6. http://ssrn.com/abstract=1363476.
Baker, M., B. Bradley, and J. Wurgler. 2011. “Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly.” Financial Analysts Journal 67 (1): 1–15.
Dahlquist, M., and C. Harvey. 2001. “Global Tactical Asset Allocation.” Duke University Working Paper.
Faber, M. 2013. “A Quantitative Approach to Tactical Asset Allocation.” Mabane Faber Research White Paper.
Fabozzi, F., P. Kolm, D. Pachamanova, and S. Focardi. 2007. Robust Portfolio Optimization and Management. Hoboken, NJ: John Wiley & Sons.
Frazzini, A., and L. H. Pedersen. 2014. “Betting against Beta.” Journal of Financial Economics 111:1–25.
Hamilton, M., and A. de Longis. 2015. “Portfolio Manager Global Multi-Asset Group Dynamic Asset Allocation through the Business Cycle: A Macro Regime Approach.” Oppenheimer Funds White Paper.
Harvey, C., Y. Liu, and H. Zhu. 2016. “… and the Cross-Section of Expected Returns.” Review of Financial Studies 29 (1): 5–68.
Hayes, M., J. Primbs, and B. Chiquoine. 2015. “A Penalty Cost Approach to Strategic Asset Allocation with Illiquid Asset Classes.” Journal of Portfolio Management 41, no. 2 (Winter): 33–41.
Hibbert, J., A. Kirchner, G. Kretzschmar, R. Li, and A. McNeil. 2009. “Liquidity Premium: Literature Review of Theoretical and Empirical Evidence.” Barrie & Hibbert Research Report version 1.1.
Hull, B., and X. Qiao. 2015. “A Practitioner's Defense of Return Predictability.” July 22. http://ssrn.com/abstract=2609814.
Idzorek, T., and M. Kowara. 2013. “Factor-Based Asset Allocation vs. Asset-Class-Based Asset Allocation.” Financial Analysts Journal 69 (3): 1–11.
Lee, W. 2000. Theory and Methodology of Tactical Asset Allocation. New Hope, PA: Frank J. Fabozzi Associates.
Lo, A., C. Petrov, and M. Wierzbicki. 2003. “It's 11pm – Do You Know Where Your Liquidity Is?” Journal of Investment Management 1 (1): 55–93.
Pearson, N. D. 2002. Risk Budgeting: Portfolio Problem Solving with Value at Risk. New York: John Wiley & Sons.
Pesaran, H. 2010. “Predictability of Asset Returns and the Efficient Market Hypothesis.” CWPE Working Paper 1033, May.
Santos, T., and P. Veronesi. 2005. “Cash-Flow Risk, Discount Risk, and the Value Premium.” Working paper, Columbia University.
Schneider, P., C. Wagner, and J. Zechner. 2016. “Low Risk Anomalies.” February 1. http://ssrn.com/abstract=2593519.
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