All Asset Allocation is Not Created Equal

We hold these truths to be self-evident: that all strategic asset allocation is not created equal; that they are endowed by their Creator with certain unalienable characteristics ; that among these are returns that come from Market Exposure, Skills, Illiquidity Premium and taking on rare Downside Risk.

Strategic Asset Allocation is responsible for the majority of long term variation in portfolio returns. Yet, it is glossed over and subsumed to making tactical calls, products and fund manager selection. Extending the not so modern portfolio theory and variants of mean variance optimization to create ‘model portfolios’ is undifferentiated and suboptimal; moreover, it just does not work in the real world.

Make Asset Allocation Great Again!

1.           All portfolio problems—strategic asset allocation, tactical asset allocation, manager selection, portable alpha, asset-liability management—are subtle variations of the same problem; they need to be approached:

–             in holistic and integrated manner.

–             from a practical perspective analyzed sequentially.

–             understood as being driven by choices on two dimensions: (i) how does one define risk? (ii) choice of constraints to arrive at tradeoffs?

2.           All asset allocation problems boil down to one of comparison—making valid comparisons between often hard-to-compare choices.

3.           There are four primary sources of returns that investors choose from: fundamental market exposure, skill based excess returns, unlocking illiquidity premium and taking on rare, but potentially large, sudden downside risks:

–             strategic asset allocation is about picking the right mix within and between each of these.

–             each investment out to be evaluated for its degree of exposure to each of these.

4.           Risks in many asset classes are understated; getting this right is the key to successful asset allocation:

–             valuation risk (volatility of valuations) is generally higher than reported within illiquid and alternative assets.

–             forecast risk (“the risk that you are wrong”) is generally higher in skill-based investments than fundamental-based ones (this includes sampling problems,    instability of characteristics, wrong modeling choices).

–             downside risk is generally higher in non-traditional assets.

5.           Using a broad set of investments (asset classes) is generally better—even if they are all equally efficient—than a more restricted set.

6.           Non-traded assets should and do attract a premium over the long-run:

–             this suggests that liquidity tolerance, and not risk tolerance, should be the first place one starts for asset allocation.

7.           Investors are generally overly wary of illiquidity:

–             diversification in various risks of illiquid investments (eg uncertainty about cash inflows, outflows, valuations, spending requirements, rebalancing      requirements, new investment opportunities) reduces the total risk.

–             volatility of portfolio weights from illiquidity should be offset by the non-tradability premium.

8.           Skill-based returns are attractive because of diversification:

–             many sources, given thousands of managers, relative to few fundamental returns sources ( equity, bonds, real assets..)

–             low correlation.

9.           Market exposure based returns are common and accessible cheaply using ETFs; skills based returns are expensive and rare.

–             Timeo Danaos et dona ferentes – “Beware of Greeks Bearing Gifts”:

–             Distinguish between market exposure (beta) masquerading as skill (alpha).

10.         Resolve to re-invent how you have traditionally approached strategic asset allocation in 2017;

–             calculate active risk adjusted returns and measure an active manager’s skill.

–             identify and isolate characteristics of a manager’s skill (alpha); calculate average alpha of managers, correlation among alphas, the effect of changes in the  number of managers and use Bayesian statistics to arrive at degrees of confidence in future performance.

–             forecast the probability distribution of degree of uncertainty in return, volatility, correlations of a fund manager.

–             select fund managers who produce high skills-based returns and optimally combine such managers into a portfolio to diversify active risk.

–             generate relative allocations across skilled fund managers.

–             include soft qualitative views and a degree of confidence within a quantitative manager allocation framework.

–             decide on appropriate allocations to expensive skilled managers versus to inexpensive passive benchmarks.

–             calculate the marginal impact of adding a fund manager to an existing portfolio.

–             compare manager performance in a like-for-like manner.

Author: Sameer_Jain

Partner. Sameer Jain is founder of FinTech, the world’s first portal that seamlessly integrates traditional, illiquid and alternative investments within portfolios. Prior to this he was Chief Economist & Managing Director at AR Capital. Before that he headed Investment Content & Strategy at UBS Alternative Investments. At UBS, he served as a non-voting member of the Wealth Management Research investment committee, and as a capital allocator was responsible for all illiquid investing including fund manager selection and due diligence across the platform. Prior to UBS he headed product development & investment research at Citigroup Alternative Investments that managed over $75 billion of alternative investments across hedge funds, managed futures, private equity, credit structures, infrastructure and real estate. Here he led a team that developed proprietary models for portfolio strategy and asset allocation with alternative investments, provided investment support and research to pension plans, sovereign wealth funds, endowments as well as internal clients including Citi Private Bank. Before this he was with Cambridge Alternative Investments and SunGard (System Access) where he travelled to over 80 countries for work across Europe, Asia, Middle-East and Africa. He has written over 30 academic and practitioner articles on alternative investments with thousands of downloads at SSRN, presented at over a hundred industry conferences and has coauthored a book, Active Equity Management. Mr. Jain has multiple degrees in engineering, management, public administration and policy and is a graduate of Massachusetts Institute of Technology and Harvard University. He is a recipient of the Alfred Sloan Fellowship and subsequently was a Fellow of Public Policy and Management at the Harvard Kennedy School of Government for a year. He holds Series 7 and 66 securities licenses.

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