Updating Modern Portfolio Theory for Investor Behavior

by Greg B. Davies and Arnaud de Servigny

Updating Modern Portfolio Theory For Investor Behavior Splash image

To construct an optimal portfolio for any investor requires knowledge of two quite different types.

Most obviously, we need to have some knowledge of investments: What is the expected risk and return of all the assets we could use to build the portfolio, and to what degree are they likely to rise or fall together? Secondly, if we are to succeed in combining these into optimal portfolios, we need to understand investors: In particular, we need to know exactly what trade-offs between risk and return each investor is prepared to make. Without this knowledge we may well design a portfolio that is optimal ... but optimal for whom?

The core model used by the financial services industry to construct optimal portfolios of risky assets, known as modern portfolio theory (MPT), was developed almost 60 years ago. This model embodies a number of brilliant insights, still relevant today, about how investors should combine assets in an efficient way to simultaneously reduce expected risk and maximize expected return to attain a portfolio that displays the optimal trade-off between the two for each individual investor. However, 60 years ago our state of knowledge was considerably lower than it is today, in many crucial areas:

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Greg B. Davies is head of global behavioral and quantitative finance at Barclays Wealth.
Arnaud de Servigny is managing director and the global head of discretionary portfolio management and investment strategy at Deutsche Bank Private Wealth Management.


Per from Illinois posted over 2 years ago:

It seems to me this article could be improved by:
1)Eliminate the concept of "Risk Free Asset", there is no such thing.Give it a name,UUP,GLD or whatever and consider it part of the portfolio.
2)Eliminate leverage. Most people don't use it and you cannot use it in IRA's.

Now you have a fully invested portfolio and there is no optimal point. The only question is where on the Efficient Frontier you want to be. To make that choice it is helpful to look at other risk measures such as Maximum Drawdown and value-at-risk.
The Efficient Frontier is based on historical data and will change based on the historical period used, i.e. 10 years, 5 years,2 years.
Running all 3 periods help show how the near term is different from the longer term.

Ted from California posted over 2 years ago:

An academic treatise such as this leaves me with virtually no useful take home message guiding my investing. To me, risk is essentially the possibility of loss. If I invest in whatever with no chance of loss, then I consider it risk free. Realizing that a single investment goes up and down in value over time is easily understood by mastering standard deviation of that investment. That will quantify the frequency of expected gains and losses and tell you (as much as possible) what volatility to expect while you hold the investment. You can then decide if you can stomach those swings. If not, choose another investment vehicle. Phrases such as, "...reap the full benefits of dynamically optimal portfolios in a changing world" don't help me with day to day decisions.

Bernard from California posted over 2 years ago:

Maybe it's the water out here but I agree with Ted from California '...leaves me with virtually no useful take home message guiding my investing.'

William from Massachusetts posted about 1 year ago:

I agree with Ted and Bernard. How might this be applied - wheres the beef..

Bernard from California posted about 1 year ago:

There's a posting attributed to me here. But I never posted it. I opened the article for the first time right now. What to do next to fix this?

Dale from Tennessee posted about 1 year ago:

I've done quite a bit of work in this area because I believe that:
1. Asset allocation is more important than anything else, to long-term performance;
2. Dynamic Asset allocation is the best way to do risk management when markets change.

Fundamentally, I use the approach based on the idea that recent (3-6 month) behavior of the various asset classes is all I need to design a portfolio that roughly balances risk of financial loss due to any particular asset class with the risk of all other asset classes. Once that "approximately equal risk" portfolio is calculated, I can then decide if I want to overweight or underweight certain asset classes (stocks, bonds, real assets).

The key benefit to this approach, IMHO, is that when asset classes become more volatile (and more risky), the "approximately equal risk" algorithm automatically, gradually, reduces the exposure to that asset class, relative to the other asset classes. There is no market timing or all in/all out nonsense.

Finally, I add a risk tolerance "red line" for the fluctuations of the overall portfolio, which allows me to gradually raise cash when all asset classes start to go crazy (like during the crash of 2008). Interestingly this "red line" also allows for the use of leverage, when appropriate to the account situation, to keep the total portfolio risk from falling too far below the risk level the originally selected for the portfolio.

If you are interested in more detail, feel free to send me a note or check my website at

Dale from Tennessee posted about 1 year ago:

Bernard, there may be another Bernard from California.

Dale from Tennessee posted about 1 year ago:

Oh... Full Disclosure...
I manage money for others as a Fee-Only Registered Investment Advisor in TN. However, my blog and tools at are designed for the individual investor.

Frank from Washington posted about 1 year ago:

The authors are right as far as they go - MPT needs expanding and updating. Among other defects almost any optimization approach has problems. 1) MPT tends to narrow choices defying the diversification dictum. In practical applications using the single index "approximation" ones's choices get narrowed as market risk increases and to an intolerable extent. 2) Despite the fact the game theory is unwieldy, to say the least, there are down and dirty methods melding MPT and mixed strategy game theory which can restore diversification. 3) Quick reaction investors will always find MPT inadequate to "...reap the full benefits .... " and "... help with day to day decisions ..." does anything? How about encouraging a practical brand of the creative thinking which gave us MPT in the first place!

Pete A from California posted about 1 year ago:

MPT's assumption of normality makes it less useful at best, and dangerous at worst. Black swans are all too real, as evedenced by the fat tails that are WAY outside of normality predicted values. Thus normality assumptions cause MPT (and options models, etc.) to sometimes be WAY off.

The second observation, that measuring volatility is not an adequate measure of risk is spot on. The SD was chosen because it is computationally easy. The argument about "good side" and "bad side volatility" is a useful insight.

I'm hopeful that the authors will carry forward their ideas into workable tools.

William from Massachusetts posted about 1 year ago:

How do you calculate "Behavioral Volatility?" Is the Ulcer index better than the standard deviation? Maximum Drawdown? Beta? Other?

Jim from Michigan posted about 1 year ago:

I too would like to see more detail on what's actually proposed. Otherwise it sounds like an invitation to rear-view-mirror market timing.

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