When contemplating a move into the MAC arena, you might ask, “What’s the big deal? We already have some models and processes that work pretty well, can’t I just add a couple of new classes to the SAA and run with the results?” Not likely. There are several characteristics inherent to MAC that raise the bar relative to traditional investment processes:

  • It’s decidedly more complex. To realize the benefits that initially make MAC attractive on both sides of the return/risk equation, it’s essential to draw from a more sophisticated playbook. For example, as highlighted in our last installment, static SWAG allocation weights leave money on the table and introduce unnecessary risk.
  • Benchmarking is a challenge. In the absence of a published MAC index, managers are forced to confront the prospect of bespoke benchmark construction and blending. If illiquid classes are part of the mix, then simply selecting and sourcing the underlying indices can present additional, unique issues.
  • Each additional class adds a new level of complexity. Skills, expertise, and methods developed to manage single-class strategies rarely easily transfer over to new ones. Each new market has its own, unique set of characteristics regarding structure, liquidity, valuation, performance, and correlation that demand the manager develop or acquire the requisite expertise, infrastructure, and data.


We have yet to meet a single MAC manager who does not employ an allocation model that is more sophisticated by several degrees than those used elsewhere. “60/40” is, in this context, neither quantitative nor multi-asset. It also accentuates that “Quantitative Allocation” comprises two separate components. “Quantitative:” a thorough grounding in correlation, estimation error, confidence in intervals, non-stationary and the like is vital to managing a strategy based on diversifying uncorrelated risky assets. Second, MAC (as we’ve seen it practice), is foremost an allocation game. Within specific classes, notably the illiquid ones, selection becomes an inseparable part of the process, and must be practiced equally well. Nevertheless, primary focus is generally directed toward risk-efficient, strategic, and tactical allocation.


One cannot automatically infer that the MAC selection process consists solely of buying market-cap weighted indices. Indeed, the objective is to efficiently capture market meta – whilst minimizing the marginal risk spent to do so. Consequently, intra-class construction can often utilize techniques including equal-weighting, minimum volatility, Sharpe-ratio optimization, and other similar techniques. MAC managers, as a rule, focus much more on diversification and absolute volatility than on tracking error.


As we’ve mentioned before, designing, sourcing, and maintaining benchmarks for MAC strategies isn’t an off-the-shelf affair. Complex blending techniques need to be applied across multiple indices, often with differing valuation, rebalancing, and reporting lag parameters. Out-of-benchmark bets – and zero-weighted blend components – often need to be considered. Similarly, splitting weights of a single blend component can be a useful technique when multiple managers are operating within the same asset class.


As we suggested above, running a successful listed equity strategy doesn’t guarantee that one will be able to transfer that expertise over to, for example, private equity markets. The most successful MAC teams have a thorough understanding where their core competencies are adequate and utilize external managers to access markets in which they are not.

While an effective tool for implementing MAC strategy, outsourcing parcels of asset management is not a complete absolution of effort and accountability, however. To be successful, external managers need to be selected and managed based on the techniques of internal managers, and developed from a solid foundation of skill, relationship, transparency and measurement.


For those not already managing assets in a currency other than base, this prospect offers an attractive diversification opportunity; but comes with a steep learning curve. Exposure to foreign exchange and interest rates is not something that can be casually implemented. A thorough understanding of interest rate parity is essential. An application of the principles of Karnosky-Singer attribution is usually advisable, though in its vanilla implementation can lack the necessary precision, (more on this in a later post).


While not for everyone, it’s hard to ignore the return and diversification benefits available from expanding into private equity, infrastructure, timber, real estate, etc. Some of the issues arising from investments in illiquid classes are well known: illiquidity (obviously), “chunkiness,” valuation infrequency and lag, and lack of comparables or benchmarks.

Follow-on effects that derive from these characteristics might be less so. For one, owning illiquid assets is like having a boulder in your swimming pool – everyone else in the tactical allocation party must swim around it. Further muddying the allocation waters is that correlations with other classes are harder to estimate in the presence of valuation infrequency and lag, which can be significant.

Finally, much of the illiquid investment process is a selection one; notwithstanding one’s adherence to a purely allocative approach, embracing illiquid investments necessitates some considerable expertise in selecting managers and deals.


It can’t be stressed enough: thoughtfully integrated performance and risk measures are critical to a successful MAC program, with an emphasis on the integration. The entire premise of MAC investing is that allocation to multiple, uncorrelated risky asset classes both lowers overall risk and increases expected return. If these effects are not measured in combination, the benefits will remain elusive.

Often, you’ll see a few ex-post risk stats thrown onto the bottom of a contribution or attribution report. This does not constitute, by any measure, an integrated approach. Volatility and Sharpe ratio (or some variant) are essential; but going further to measure contribution and marginal contribution to these — at least at the asset class level — provide further insight into the efficiency of the return/risk strategy. Given the long time frames required to estimate these quantities, effort should also be expended to track estimation error, confidence bands and the like.


Though not all the above are relevant to every strategy and manager, it’s apparent that the set of disciplines indispensable to a MAC manager can be a formidable one. In the prior installment, we explored aspects of multi-class investing that make it an attractive approach — it is clear, however, that doing so cannot be done casually or without thoughtful planning and application.

And, it doesn’t stop there: measuring the impact of these considerations on the return/risk proposition is also a significant challenge. In the next and final installment, we’ll analyze the complex implications of MAC on performance/risk measurement and attribution and have a close look at two radically different approaches taken by practitioners in pursuit of that goal.

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Mark R. David

Mark R. David, CFA, is the Director of Performance, Risk & Analytics for Meradia. He and his team of subject matter experts begin by working with front office practitioners: eliciting, informing, and refining their business requirements to obtain consensus on a detailed analytic solution. They then pivot to providing and managing the hands-on implementation team – operations, data stewardship, vendors and IT – bringing the client’s business vision to reality at the highest standards of quality. Mark brings 32 years of experience in portfolio performance, construction, and analytics. He delivers a competitive advantage by combining technical know-how and solution development with deep subject matter expertise. As a thought leader in his field, Mark speaks regularly at industry forums, and has published several ground-breaking articles on performance attribution.

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