Why the Modern Portfolio Theory Needs Modernisation

15 March 2016  |  Simon Miller
Why the Modern Portfolio Theory Needs Modernisation
Modern Portfolio Theory, developed by Harry Markowitz in the 1950s is a cornerstone theory of modern finance.
Traditionally, quantitative portfolio managers have relied on it to help them decide which assets they should place their money in.

Modern Portfolio Theory, developed by Harry Markowitz in the 1950s is a cornerstone theory of modern finance. Traditionally, quantitative portfolio managers have relied on it to help them decide which assets they should place their money in. At the time, the theory represented a major scientific breakthrough and Markowitz was honoured with the Alfred Nobel Memorial Prize in 1990 for his pioneering work helping the MPT to become ubiquitous in the investing world.

Over time, however, many theoretical and empirical shortcomings have been identified.

Essentially, the theory describes how risk-averse investors can construct portfolios to maximise expected return based on a given level of market risk, emphasising that risk is an inherent part of higher reward.

Markowitz Efficient Frontier

Markowitz Efficient Frontier

It suggests that the optimal portfolio should lie on the Efficient Frontier, a mathematically defined curve which offers the highest expected return among all portfolios having the same level of risk. An alternative perspective would be they have the lowest risk for a given return.

If the portfolio was to lie below the curve, it would imply that you are either taking more risk for the same level of return that you could achieve if you were on the curve. Or alternatively, you are receiving less return than you could achieve for the same risk level. The approach dictates that investors attempt to remove unsystematic risk, which can be reduced through diversification, even if assets’ returns are negatively correlated. Therefore, we can conclude that the perfect portfolio is not comprised of the perfect assets, but rather the perfect mix of assets, which balance one another.

It is Possible to Diversify Unsystematic Risk

 It is Possible to Diversify Unsystematic Risk

However, MPT is hardly as robust as it seems. Due to its simplistic assumptions, it facilitates mathematical tractability but also unfortunately hampers real-world applicability. Some of its rudimentary assumptions include:

  • Risk is defined by volatility
  • Financial assets’ returns are normally distributed
  • Correlations among different asset classes are constant over time
  • Investors are rational and risk averse
  • Markets are efficient

Firstly, when we use volatility or standard deviation to fully describe the risk of an asset or portfolio, we assume returns are normally distributed but unfortunately, empirical data suggests otherwise.

Historically, the distribution of returns has been subjected to skewness and fat tails. This means that returns are not symmetrical as assumed by MPT, instead real-life extreme outcomes are more likely to happen on the negative side than expected. This phenomenon explains rare events such as Black Monday (1987) or the Dot-com bubble and these events necessitate more complex risk measures than simply looking at volatility.

Secondly, in order to design a diversified portfolio, MPT assumes that asset correlations are constant in all market scenarios but historical data shows the opposite is true. The graph below indicates that correlations between various asset classes over a period of twenty years are constantly changing.

Correlations Between Asset Classes Constantly Change

Rolling 36-month correlations between the FTSE and other asset classes (in %)

Rolling 36-month correlations between the FTSE and other asset classes (in %)
Source: Bloomberg Data

This is because Markowitz assumed ordinary linear dependence, i.e. the prices of all assets move proportionally to one another however the systematic relationship between different assets does not remain constant in times of market stress making MPT increasingly useless during times of uncertainty.

And because financial markets are governed by much more complex, nonlinear dependence structures, the theory also fails to explain the increase in synchronisation of price movements in sharp downturns.

For example, over time the five-year correlation between the US stock market and five-year Treasury notes from the DFA returns programme swings from positive to negative and then back again indicating no correlation overall.

Thirdly, behavioural economists have debunked the assumption that ‘investors behave rationally’. If that were the case, no investor would be affected by cognitive or emotional biases and as a result there would be no speculation or herd behaviour.

In addition, MPT’s assumptions that investors are not limited by a time horizon, illiquidity or investment restrictions also falter as human beings have different requirements for when to access their money so ignoring time horizon is clearly an oversimplification.

Furthermore, we observe market risk is not constant over time. Instead, markets are governed by different volatility regimes, i.e., extended periods of low volatility can be followed by periods of high volatility, and vice versa and MPT fails to allow for such empirical regularities and does not tell us how to deal with complex risk dynamics.

Modern Portfolio Theory is an ubiquitous dogma in the investing world

The Solution?

Clearly, it would be wrong to dismiss the philosophy underlying Markowitz’s Modern Portfolio Theory, which provided the first rigorous framework for portfolio optimisation. The fact that so many investment managers continue to use the original assumptions of MPT for the basis of their investment models’ is indicative of the ground breaking research which Markowitz performed and even today it remains an elegant solution to a complex problem.

However, new insights into the behaviour of financial markets and advances in modelling financial risk-return dynamics mean it is no longer sufficient to use the basic MPT approach to manage investments and investors should expect more than a passive MPT derived portfolio.

Technological advances mean that it is no longer necessary to work with closed form mathematical solutions instead we can use Monte Carlo simulations to model the likely behaviour of different portfolios with a specified probability.

At Scalable Capital, we perform complex portfolio optimisation on an individual, investor-specific level for each of our clients by using cloud computing technology to run tens of thousands of Monte Carlo simulations. We use Value at Risk (VaR) instead of volatility as a measure of portfolio risk, since this gives investors more clarity on their exposure to downside price developments.

Our model makes use of pivotal advances in research and technology, replacing simplifications such as the normal distribution with more relevant empirically derived solutions which are adjusted to account for current market conditions. We use a dynamic asset allocation taking into account different volatility regimes which results in a portfolio that remains optimal over time taking it to a level that is well beyond the MPT.

To see a deeper explanation and our solutions, please view our dynamic risk management page.

Risk Warning – With investment comes risk. The value of your investment can go down as well as up and you may get back less than you invest. Past performance or future projections are not indicative of future performance. We do not provide any investment, legal and/or tax advice. If this website contains information regarding capital markets, financial instruments and/or other topics relevant for investments of assets, the exclusive purpose of this information is to give general guidance on investment management services provided by members of our group. Please note our Risk Warning and the Website Terms.

 

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Simon Miller
UK CEO & UK CO-FOUNDER
Formerly a derivatives trader at Barclays Capital, Simon merges capital markets knowledge and business development skills with an academic background in Economics, Business and Mathematics.