I am currently trying to form an overall asset allocation strategy which combines base strategic allocation and tactical shifts. My model already incorporates the tactical shifts using various factors like momentum,carry etc. But i am using a base 1/N strategic allocation for all my asset classes (equity, bonds and commodities). I would like to improve this base allocation using some strategic allocation model.

I have already read the following papers regarding this:

Strategic Asset Allocation
Karl Eychenne, Stéphane Martinetti, Thierry Roncalli, 2011

To implement strategic asset allocation, we must determine risk and return expectations for the various asset classes. Starting from the paradigm that long-run asset returns are determined by the long-run fundamentals of the economy, a fair value approach to building expectations is crucial. This paper proposes to formalize a quantitative and systematic methodology for optimizing portfolios, from the determination of long-run fundamental pillars through the modeling of asset returns and the assessment of market risks. We apply forecasting models and build in the specific of the main asset classes (equities, bonds and alternative investments) depending on the uncertainties they represent for the risk-averse investor. Our resulting allocations within the equity asset class, and with regard to the place of alternative investments, question the choices of long-term institutional investors such as pension funds that have shifted their long-run allocations in response to the recent financial crisis.



Dynamic Strategic Asset Allocation: Risk and Return Across Economic Regimes
David Blitz, Pim Van Vliet, 2011

We propose a practical investment framework for dynamic asset allocation across different economic regimes, which we illustrate using a sample of U.S. data from 1948 to 2007. We identify four regimes in the economic cycle and find that these regimes capture pronounced time-variation in the risk and return properties of asset classes. Time-variation is also observed in the risk of a traditional, static strategic asset allocation portfolio. In order to stabilize risk across the economic cycle we propose a dynamic strategic asset allocation approach, which has the potential to enhance expected return as well. The proposed approach is found to be robust to variations in the variable composition of the regime model and can easily be extended with different economic variables and/or additional assets.


They use forecasting of long run return using key fundamental and economic pillars.

I would appreciate if anyone has any relevant paper links to strategic allocation( recent research is preferred).

A little update on exactly what kind of research I am looking at:

I am not looking for research related to risk parity or MV optimized portfolios but on asset allocation strategies that combine views about the economy of different countries and how that will affect the expected returns across broad asset classes. Which i can use to fix the strategic allocations for a period of next 5-10 years and overlay monthly tactical shifts (models for which I have already developed) Two such kind of paper I have attached.

I am not sure why I am getting downvotes on a reference question, maybe my research objective is not clear. Here is another paper from BlackRock regarding this, ideally I would like to replicate this but again they have disclosed too little, so I need similar "academic research".

Building resilience: a framework for strategic asset allocation

Designing a suite of models that explicitly reflect cross-asset and macroeconomic linkages. These models are the key input to inform our views on returns across asset classes

Link to paper

  • $\begingroup$ What are your overlay or tactical asset allocations schemes? Do you just overweight/underweight parts of your SAA or is it totally independent? Do you allocate funds to TAA within SAA? Also, what are your capabilities and capacity (e.g. will you have access to private markets, do you have liability requirements, personal vs corporate setup, etc.)? $\endgroup$
    – AK88
    Jun 24, 2019 at 17:05
  • $\begingroup$ My tactical overlays are based on common factors across asset classes like value momentum carry and other indicators like vix for risk on risk off strategy. I wish to add a certain percentage of this overlay to the strategic weights. I want my SAA to be the base and tactically tilt by Max of +-10%. I have access to mostly public datasets using Bloomberg. $\endgroup$ Jun 24, 2019 at 17:25
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    $\begingroup$ Can somebody explain why a question asking for references is downvoted? $\endgroup$ Jun 24, 2019 at 22:57
  • $\begingroup$ Not the downvoter, but I have to admit I got pretty confused about what you're looking for as well... My initial impression was that you wanted methods to determine SSA weights for assets, but perhaps you're looking for references on setting Capital Market Assumptions instead? Can you clarify so that we can help you better? $\endgroup$
    – Helin
    Jun 24, 2019 at 23:45
  • $\begingroup$ Basically I need research about econometric research that involves predicting long term asset returns ( I need signals like gdp, inflation etc so I can backtest it using a panel regression on my data), after that I need a framework to allocate strategic weights according to these expectations, for example BlackRock has used an extension of black litterman model but have not described how exactly they do it. So basically instead of backward looking allocations like risk parity, I need forward looking allocations $\endgroup$ Jun 25, 2019 at 4:31

2 Answers 2


One thing I personally believe is that SAA should minimize forecasting the future. Ideally (though not possible), SAA is the optimal asset allocation when you have no views about the future at all. A weaker version of that statement is that SAA is the portfolio that's mostly likely to meet your risk/return-objectives (subject to constraints), based on assumptions that are as reliable as possible. Accordingly, I generally do not like introducing economic views and/or incorporating alphas into an SAA program. This is just a personal belief and I fully acknowledge other approaches might produce better results, but I thought I'd lay this out at the beginning to provide some context for the comments below.

SAA at its core involves just two steps:

  1. Setting Capital Market Assumptions ("CMA") for the asset classes/factors you plan to use in your SAA.
  2. Calculate the weights for these assets to meet your long-term return objective subject to constraints (such as risk/spending/illiquidity).

There is a large literature on CMA out there. This answer summarizes the different techniques and provides some well-known practitioner examples. Because it was more fixed income oriented, some updates are needed:

  1. AQR has been publishing their CMAs for major asset classes for a few years ago. They use a building-block approach that's very popular in the industry. I'm a fan – the methodology is theoretically sound and makes relatively few assumptions.
  2. Research Affiliates also publishes extensive documentations on their website. The methodology is based on building blocks as well, but it incorporates additional assumptions such as valuation reversion. Unsurprisingly, it would have better in-sample forecasting performance than AQR's approach (certainly for equities). It can be argued that valuation reversion cannot be depended on, which precludes it from entering the SAA process (again, just an opinion).
  3. Simple risk parity ideas can be used to generate forward-looking assumptions as well. A quick example – let's assume that all assets over the long run will achieve the same Sharpe ratio of 0.3 (roughly what was realized and the equality assumption ensures investors won't favor one assets over another on an ex-ante basis), and let our forwad-looking volatility assumption for stocks be 15%, then the forward-looking excess return assumption is simply 0.3 * 15% = 4.5%. You can then add this to an expected cash return assumption to get the forward-looking total return. I like this approach too because of its simplicity. The principle assumption is that all assets are roughly the same on a risk-adjusted basis, which again fits well with the belief that SAA should have no strong views.
  4. Many other shops provide similar reports (e.g., JPMorgan). They all end up being variations of similar ideas.

Additional searches for "Capital Market Assumptions" will return many relevant answers, but as I mentioned in the CMA answer, empirical evidence suggests that simple approaches are remarkably difficult to beat. For example, starting yield levels are more than sufficient for forecasting next 10-year government bond returns. In equities, John Bogle's formula for forecasting long-term equity return is surprisingly effective (and the methods cited above can be considered its variations). I urge you to explore these simpler techniques before delving into complex econometric models. Keep in mind that the forecast horizon is very long and the range of outcomes is enormously wide, so any number will likely be wrong. I'd also comment that your desire to link returns to growth/inflation is understandable. But I hope I'm conveying that 1) the added complexity is not necessary, and 2) literature will disappoint (e.g., long-term growth and equity returns have poor linkage, think China...).

With regard to the second step, as others have pointed out, the traditional approach, such as MVO, remains the workhorse. I do think this is the step where extra work could yield dividends. For example, the shortcomings of MVO are well known, and volatility is a poor measure of risk in the long-run. Alternatives include resampling optimization and CVaR optimization. I recommend JPMorgan's excellent paper "Non-normality of market returns" for inspirations.

  • $\begingroup$ Hey helin thanks for the detailed answer, I would go through the resources in some time and would appreciate your help in the further comments if I have any confusion regarding it. But one point I want to make now is regarding the second step, wouldn't optimization using traditional MVO and CVaR optimization which uses past returns as a proxy for future beat the point of making capital market assumptions in the first place? Wouldn't a model like Black-Litterman that incorporates investor's view regarding future conditions be better? Thank you again for the detailed summary. $\endgroup$ Jun 25, 2019 at 7:55
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    $\begingroup$ Hi @DhruvMahajan. The inputs to your MVO / CVaR Optimizer would be forward-looking expected returns, volatilities, etc. $\endgroup$
    – Helin
    Jun 25, 2019 at 7:58
  • $\begingroup$ Hey @helin, I went through the papers you suggested and it is really good stuff. And i understand your point of keeping forecasts out of SAA but in specific cases like emerging markets, SAA on the basis of either past performance or on an equal basis would understate the weights,because it is highly agreed that EM would grow at much faster way than the DM in the next decade. Also all the CMA papers you suggested are for long run returns, but for SAA i need allocations based on risk adjusted returns, can you please provide some relevant research for forecasting long run vol and correlations? $\endgroup$ Jun 26, 2019 at 7:16
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    $\begingroup$ Hi @DhruvMahajan. I'd consider all the approaches proposed to be forward looking. Some of the approaches also reflect higher EM growth (e.g., AQR). But empirically, high growth rates don't translate into high equity returns, which is why I question whether the effort is worthwhile. Regarding vols/corrs, there's not a ton of literature because we all just use some variants of historical vols. Some winsorize returns to remove the impact of outliers (JPM); some use annual returns (instead of say monthly returns) to reduce impact of autocorrelations, but they're all historical vol-based estimates. $\endgroup$
    – Helin
    Jun 26, 2019 at 7:39

Markowity remains the stonghold, thus you must start reading that in my view. Afterwards, you can have a look at risk parity. Recent developments are based on hierachical clustering and neural Networks. I suggest reading Lopez de Prado on hierarchical clustering asset allocation.

  • $\begingroup$ Hey vitomir, thanks for the answer. I have read and applied those frameworks, but the main idea that i am getting at is quite different from risk parity and is used typically in big asset management firms like BlackRock for Strategic asset allocation, they use forecasts of long term economic growth cycle and other economic inputs as their school of thought revolves around that the market is governed by these economic cycles, one research in line with this is the one I attached in my question, I would like to read more similar kind of research. $\endgroup$ Jun 24, 2019 at 9:36

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