PulseWave Trading: Modern portfolio theory

Saturday, May 15, 2010

Modern portfolio theory

Capital Market Line

Modern portfolio theory (MPT) is a theory of investment which tries to maximize return and minimize risk by carefully choosing different assets. Although MPT is widely used in practice in the financial industry and several of its creators won a Nobel prize for the theory, in recent years the basic assumptions of MPT have been widely challenged by fields such as behavioral economics.
MPT is a mathematical formulation of the concept of diversification in investing, with the aim of selecting a collection of investment assets that has collectively lower risk than any individual asset. This is possible, in theory, because different types of assets often change in value in opposite ways. For example, when the prices in the stock market fall, the prices in the bond market often increase, and vice versa. A collection of both types of assets can therefore have lower overall risk than either individually.
More technically, MPT models an asset's return as a normally distributed random variable, defines risk as the standard deviation of return, and models a portfolio as a weighted combination of assets so that the return of a portfolio is the weighted combination of the assets' returns. By combining different assets whose returns are not correlated, MPT seeks to reduce the total variance of the portfolio. MPT also assumes that investors are rational and markets are efficient.
MPT was developed in the 1950s through the early 1970s and was considered an important advance in the mathematical modeling of finance. Since then, much theoretical and practical criticism has been leveled against it. These include the fact that financial returns do not follow a Gaussian distribution and that correlations between asset classes are not fixed but can vary depending on external events (especially in crises). Further, there is growing evidence that investors are not rational and markets are not efficient.^{[citation needed]}

[edit] Concept

The fundamental concept behind MPT is that the assets in an investment portfolio cannot be selected individually, each on their own merits. Rather, it is important to consider how each asset changes in price relative to how every other asset in the portfolio changes in price.
Investing is a tradeoff between risk and return. In general, assets with higher returns are riskier. For a given amount of risk, MPT describes how to select a portfolio with the highest possible return. Or, for a given return, MPT explains how to select a portfolio with the lowest possible risk (the desired return cannot be more than the highest-returning available security, of course.)^[1]
MPT is therefore a form of diversification. Under certain assumptions and for specific quantitative definitions of risk and return, MPT explains how to find the best possible diversification strategy.

[edit] History

Harry Markowitz introduced MPT in a 1952 article and a 1959 book.^[1]

[edit] Mathematical model

In some sense the mathematical derivation below is MPT, although the basic concepts behind the model have also been very influential.^[1]
This section develops the "classic" MPT model. There have been many extensions since.

[edit] Risk and return

MPT assumes that investors are risk averse, meaning that given two assets that offer the same expected return, investors will prefer the less risky one. Thus, an investor will take on increased risk only if compensated by higher expected returns. Conversely, an investor who wants higher returns must accept more risk. The exact trade-off will differ by investor based on individual risk aversion characteristics. The implication is that a rational investor will not invest in a portfolio if a second portfolio exists with a more favorable risk-return profile – i.e., if for that level of risk an alternative portfolio exists which has better expected returns.
MPT further assumes that the investor's risk / reward preference can be described via a quadratic utility function. The effect of this assumption is that only the expected return and the volatility (i.e., mean return and standard deviation) matter to the investor. The investor is indifferent to other characteristics of the distribution of returns, such as its skew (measures the level of asymmetry in the distribution) or kurtosis (measure of the thickness or so-called "fat tail").
Note that the theory uses a parameter, volatility, as a proxy for risk, while return is an expectation on the future. This is in line with the efficient market hypothesis and most of the classical findings in finance. There are problems with this, see criticism.
Under the model:

Portfolio return is the proportion-weighted combination of the constituent assets' returns.
Portfolio volatility is a function of the correlation ρ of the component assets. The change in volatility is non-linear as the weighting of the component assets changes.

In general:

Expected return:-

$\operatorname{E}(R_p) = \sum_i w_i \operatorname{E}(R_i) \quad$
Where $R i$ is return and $w i$ is the weighting of component asset $i$ .
Portfolio variance:-

$\sigma_p^2 = \sum_i w_i^2 \sigma_{i}^2 + \sum_i \sum_j w_i w_j \sigma_i \sigma_j \rho_{ij}$ ,
where $i \neq j$ . Alternatively the expression can be written as:
$\sigma_p^2 = \sum_i \sum_j w_i w_j \sigma_i \sigma_j \rho_{ij}$ ,
where $ρ i j = 1$ for i=j.
Portfolio volatility:-

$\sigma_p = \sqrt {\sigma_p^2}$
For a two asset portfolio:-

Portfolio return: $\operatorname{E}(R_p) = w_A \operatorname{E}(R_A) + (1 - w_A) \operatorname{E}(R_B) = w_A \operatorname{E}(R_A) + w_B \operatorname{E}(R_B)$

Portfolio variance: $\sigma_p^2 = w_A^2 \sigma_A^2 + w_B^2 \sigma_B^2 + 2w_Aw_B \sigma_{A} \sigma_{B} \rho_{AB}$

For a three asset portfolio:-

Portfolio return: $w_A \operatorname{E}(R_A) + w_B \operatorname{E}(R_B) + w_C \operatorname{E}(R_C)$

Portfolio variance: $\sigma_p^2 = w_A^2 \sigma_A^2 + w_B^2 \sigma_B^2 + w_C^2 \sigma_C^2 + 2w_Aw_B \sigma_{A} \sigma_{B} \rho_{AB} + 2w_Aw_C \sigma_{A} \sigma_{C} \rho_{AC} + 2w_Bw_C \sigma_{B} \sigma_{C} \rho_{BC}$

[edit] Diversification

An investor can reduce portfolio risk simply by holding combinations of instruments which are not perfectly positively correlated (correlation coefficient -1<(r)<1)). In other words, investors can reduce their exposure to individual asset risk by holding a diversified portfolio of assets. Diversification may allow for the same portfolio return with reduced risk.
If all the assets of a portfolio have a correlation of +1, i.e., perfect positive correlation, the portfolio volatility (standard deviation) will be equal to the weighted sum of the individual asset volatilities. Hence the portfolio variance will be equal to the square of the total weighted sum of the individual asset volatilities.^[2]
If all the assets have a correlation of 0, i.e., perfectly uncorrelated, the portfolio variance is the sum of the individual asset weights squared times the individual asset variance (and the standard deviation is the square root of this sum).
If correlation coefficient is less than zero (r=0), i.e., the assets are inversely correlated, the portfolio variance and hence volatility will be less than if the correlation coefficient is 0.

[edit] The risk-free asset

The risk-free asset is the (hypothetical) asset which pays a risk-free rate. In practice, short-term Government securities (such as US treasury bills) are used as a risk-free asset, because they pay a fixed rate of interest and have exceptionally low default risk. The risk-free asset has zero variance in returns (hence is risk-free); it is also uncorrelated with any other asset (by definition: since its variance is zero). As a result, when it is combined with any other asset, or portfolio of assets, the change in return and also in risk is linear.
Because both risk and return change linearly as the risk-free asset is introduced into a portfolio, this combination will plot a straight line in risk-return space. The line starts at 100% in the risk-free asset and weight of the risky portfolio = 0 (i.e., intercepting the return axis at the risk-free rate) and goes through the portfolio in question where risk-free asset holding = 0 and portfolio weight = 1.

Using the formulae for a two asset portfolio as above:

Return is the weighted average of the risk free asset, $f$ , and the risky portfolio, p, and is therefore linear:

$\text{Return} = w_{f} \operatorname{E}(R_{f}) + w_p \operatorname{E}(R_p) \quad$
Since the asset is risk free, portfolio standard deviation is simply a function of the weight of the risky portfolio in the position. This relationship is linear.

$\begin{align} \text{Standard deviation} &= \sqrt{ w_{f}^2 \sigma_{f}^2 + w_p^2 \sigma_{p}^2 + 2 w_{f} w_p \sigma_{fp} }\\ &= \sqrt{ w_{f}^2 \cdot 0 + w_p^2 \sigma_{p}^2 + 2 w_{f} w_p \cdot 0 }\\ &= \sqrt{ w_p^2 \sigma_{p}^2 }\\ &= w_p \sigma_p \quad \end{align}$

[edit] Capital allocation line

The capital allocation line (CAL) is the line of expected return plotted against risk (standard deviation) that connects all portfolios that can be formed using a risky asset and a riskless asset. It can be proven that it is a straight line and that it has the following equation.

$\mathrm{CAL} : E(r_{C}) = r_F + \sigma_C \frac{E(r_P) - r_F}{\sigma_P}$

In this formula P is the risky portfolio, F is the riskless portfolio, and C is a combination of portfolios P and F.

[edit] The efficient frontier

Efficient Frontier. The hyperbola is sometimes referred to as the 'Markowitz Bullet'

Every possible asset combination can be plotted in risk-return space, and the collection of all such possible portfolios defines a region in this space. The line along the upper edge of this region is known as the efficient frontier (sometimes "the Markowitz frontier"). Combinations along this line represent portfolios (explicitly excluding the risk-free alternative) for which there is lowest risk for a given level of return. Conversely, for a given amount of risk, the portfolio lying on the efficient frontier represents the combination offering the best possible return. Mathematically the Efficient Frontier is the intersection of the Set of Portfolios with Minimum Variance (MVS) and the Set of Portfolios with Maximum Return. Formally, the efficient frontier is the set of maximal elements with respect to the partial order of product order on risk and return, the set of portfolios for which one cannot improve both risk and return.
The efficient frontier is illustrated above, with return μ_p on the y-axis, and risk σ_p on the x-axis; an alternative illustration from the diagram in the CAPM article is at right.
The efficient frontier will be convex – this is because the risk-return characteristics of a portfolio change in a non-linear fashion as its component weightings are changed. (As described above, portfolio risk is a function of the correlation of the component assets, and thus changes in a non-linear fashion as the weighting of component assets changes.) The efficient frontier is a parabola (hyperbola) when expected return is plotted against variance (standard deviation).
The region above the frontier is unachievable by holding risky assets alone. No portfolios can be constructed corresponding to the points in this region. Points below the frontier are suboptimal. A rational investor will hold a portfolio only on the frontier.

Matrices are preferred for calculations of the efficient frontier. In matrix form, for a given "risk tolerance" $q \in [0,\infty)$ , the efficient front is found by minimizing the following expression:

$w T Σ w - q * R T w$
where

$w$ is a vector of portfolio weights. Each $w_i \ge 0$ and

∑ w_i = 1

i

$Σ$ is the covariance matrix for the assets in the portfolio

$q$ is a "risk tolerance" factor, where 0 results in the portfolio with minimal risk and $\infty$ results in the portfolio with maximal return

$R$ is a vector of expected returns

The front is calculated by repeating the optimization for various $q \ge 0$ .
Many software packages, including Microsoft Excel, MATLAB, Mathematica and R, provide optimization routines suitable for the above problem.

[edit] Portfolio leverage

An investor adds leverage to the portfolio by borrowing the risk-free asset. The addition of the risk-free asset allows for a position in the region above the efficient frontier. Thus, by combining a risk-free asset with risky assets, it is possible to construct portfolios whose risk-return profiles are superior to those on the efficient frontier.

An investor holding a portfolio of risky assets, with a holding in cash, has a positive risk-free weighting (a de-leveraged portfolio). The return and standard deviation will be lower than the portfolio alone, but since the efficient frontier is concave, this combination will sit above the efficient frontier – i.e., offering a higher return for the same risk as the point below it on the frontier.
The investor who borrows money to fund his/her purchase of the risky assets has a negative risk-free weighting – i.e., a leveraged portfolio. Here the return is geared to the risky portfolio. This combination will again offer a return superior to those on the frontier.

[edit] The market portfolio

The efficient frontier is a collection of portfolios, each one optimal for a given amount of risk. A quantity known as the Sharpe ratio represents a measure of the amount of additional return (above the risk-free rate) a portfolio provides compared to the risk it carries. The portfolio on the efficient frontier with the highest Sharpe Ratio is known as the market portfolio, or sometimes the super-efficient portfolio; it is the tangency-portfolio in the above diagram. This portfolio has the property that any combination of it and the risk-free asset will produce a return that is above the efficient frontier—offering a larger return for a given amount of risk than a portfolio of risky assets on the frontier would.

[edit] Capital market line

When the market portfolio is combined with the risk-free asset, the result is the Capital Market Line. All points along the CML have superior risk-return profiles to any portfolio on the efficient frontier. Just the special case of the market portfolio with zero cash weighting is on the efficient frontier. Additions of cash or leverage with the risk-free asset in combination with the market portfolio are on the Capital Market Line. All of these portfolios represent the highest possible Sharpe ratio. The CML is illustrated above, with return μ_p on the y-axis, and risk σ_p on the x-axis.
One can prove that the CML is the optimal CAL and that its equation is

$\mathrm{CML} : E(r_{C}) = r_F + \sigma_C \frac{E(r_M) - r_F}{\sigma_M}.$

[edit] Asset pricing using MPT

A rational investor would not invest in an asset which does not improve the risk-return characteristics of his existing portfolio. Since a rational investor would hold the market portfolio, the asset in question will be added to the market portfolio. MPT derives the required return for a correctly priced asset in this context.

[edit] Systematic risk and specific risk

Specific risk is the risk associated with individual assets - within a portfolio these risks can be reduced through diversification (specific risks "cancel out"). Specific risk is also called diversifiable, unique, unsystematic, or idiosyncratic risk. Systematic risk (a.k.a. portfolio risk or market risk) refers to the risk common to all securities - except for selling short as noted below, systematic risk cannot be diversified away (within one market). Within the market portfolio, asset specific risk will be diversified away to the extent possible. Systematic risk is therefore equated with the risk (standard deviation) of the market portfolio.
Since a security will be purchased only if it improves the risk / return characteristics of the market portfolio, the risk of a security will be the risk it adds to the market portfolio. In this context, the volatility of the asset, and its correlation with the market portfolio, is historically observed and is therefore a given (there are several approaches to asset pricing that attempt to price assets by modelling the stochastic properties of the moments of assets' returns - these are broadly referred to as conditional asset pricing models). The (maximum) price paid for any particular asset (and hence the return it will generate) should also be determined based on its relationship with the market portfolio.
Systematic risks within one market can be managed through a strategy of using both long and short positions within one portfolio, creating a "market neutral" portfolio.

[edit] Security characteristic line

The security characteristic line (SCL) represents the relationship between the market excess return and the excess return of a given asset i at a given time t. In general, it is reasonable to assume that the SCL is a straight line and can be illustrated as a statistical equation:

$\mathrm{SCL} : R_{i,t} - R_{f} = \alpha_i + \beta_i\, ( R_{M,t} - R_{f} ) + \epsilon_{i,t} \frac{}{}$

where α_i is called the asset's alpha and β_i the asset's beta coefficient.

[edit] Capital asset pricing model

Main article: Capital Asset Pricing Model

The asset return depends on the amount for the asset today. The price paid must ensure that the market portfolio's risk / return characteristics improve when the asset is added to it. The CAPM is a model which derives the theoretical required return (i.e., discount rate) for an asset in a market, given the risk-free rate available to investors and the risk of the market as a whole. The CAPM is usually expressed:

$\operatorname{E}(R_i) = R_f + \beta_i (\operatorname{E}(R_m) - R_f)$

$β$ , Beta, is the measure of asset sensitivity to a movement in the overall market; Beta is usually found via regression on historical data. Betas exceeding one signify more than average "riskiness"; betas below one indicate lower than average.

$(\operatorname{E}(R_m) - R_f)$ is the market premium, the historically observed excess return of the market over the risk-free rate.

Once the expected return,

E (r i)

, is calculated using CAPM, the future cash flows of the asset can be discounted to their present value using this rate to establish the correct price for the asset. A more risky stock will have a higher beta and will be discounted at a higher rate; less sensitive stocks will have lower betas and be discounted at a lower rate. In theory, an asset is correctly priced when its observed price is the same as its value calculated using the CAPM derived discount rate. If the observed price is higher than the valuation, then the asset is overvalued; it is undervalued for a too low price.

(1) The incremental impact on risk and return when an additional risky asset, a, is added to the market portfolio, m, follows from the formulae for a two asset portfolio. These results are used to derive the asset appropriate discount rate.

Risk = $(w_m^2 \sigma_m ^2 + [ w_a^2 \sigma_a^2 + 2 w_m w_a \rho_{am} \sigma_a \sigma_m] )$
Hence, risk added to portfolio = $[ w_a^2 \sigma_a^2 + 2 w_m w_a \rho_{am} \sigma_a \sigma_m]$
but since the weight of the asset will be relatively low, $w_a^2 \approx 0$
i.e. additional risk = $[ 2 w_m w_a \rho_{am} \sigma_a \sigma_m] \quad$
Return = $( w_m \operatorname{E}(R_m) + [ w_a \operatorname{E}(R_a) ] )$
Hence additional return = $[ w_a \operatorname{E}(R_a) ]$
(2) If an asset, a, is correctly priced, the improvement in risk to return achieved by adding it to the market portfolio, m, will at least match the gains of spending that money on an increased stake in the market portfolio. The assumption is that the investor will purchase the asset with funds borrowed at the risk-free rate, $R f$ ; this is rational if $\operatorname{E}(R_a) > R_f$ .

Thus: $[ w_a ( \operatorname{E}(R_a) - R_f ) ] / [2 w_m w_a \rho_{am} \sigma_a \sigma_m] = [ w_a ( \operatorname{E}(R_m) - R_f ) ] / [2 w_m w_a \sigma_m \sigma_m ]$
i.e. : $[\operatorname{E}(R_a) ] = R_f + [\operatorname{E}(R_m) - R_f] * [ \rho_{am} \sigma_a \sigma_m] / [ \sigma_m \sigma_m ]$
i.e. : $[\operatorname{E}(R_a) ] = R_f + [\operatorname{E}(R_m) - R_f] * [\sigma_{am}] / [ \sigma_{mm}]$
$[\sigma_{am}] / [ \sigma_{mm}] \quad$ is the “beta”, $β$ -- the covariance between the asset and the market compared to the variance of the market, i.e. the sensitivity of the asset price to movement in the market portfolio.

[edit] Criticism

Despite its theoretical importance, some people question whether MPT is an ideal investing strategy, because its model of financial markets does not match the real world in many ways.

[edit] Assumptions

The mathematical framework of MPT makes many assumptions about investors and markets. Some are explicit in the equations, such as the use of Normal distributions to model returns. Others are implicit, such as the neglect of taxes and transaction fees. None of these assumptions are entirely true, and each of them compromises MPT to some degree.

Asset returns are (jointly) normally distributed random variables. In fact, it is frequently observed that returns in equity and other markets are not normally distributed. Large swings (3 to 6 standard deviations from the mean) occur in the market far more frequently than the normal distribution assumption would predict. ^[3]

Correlations between assets are fixed and constant forever. Correlations depend on systemic relationships between the underlying assets, and change when these relationships change. Examples include one country declaring war on another, or a general market crash. During times of financial crisis all assets tend to become positively correlated, because they all move (down) together. In other words, MPT breaks down precisely when investors are most in need of protection from risk.

All investors aim to maximize economic utility (in other words, to make as much money as possible, regardless of any other considerations). This is a key assumption of the efficient market hypothesis, upon which MPT relies.

All investors are rational and risk-averse. This is another assumption of the efficient market hypothesis, but we now know from behavioral economics that market participants are not rational. It does not allow for "herd behavior" or investors who will accept lower returns for higher risk. Casino gamblers clearly pay for risk, and it is possible that some stock traders will pay for risk as well.

All investors have access to the same information at the same time. This also comes from the efficient market hypothesis. In fact, real markets contain information asymmetry, insider trading, and those who are simply better informed than others.

Investors have an accurate conception of possible returns, i.e., the probability beliefs of investors match the true distribution of returns. A different possibility is that investors' expectations are biased, causing market prices to be informationally inefficient. This possibility is studied in the field of behavioral finance, which uses psychological assumptions to provide alternatives to the CAPM such as the overconfidence-based asset pricing model of Kent Daniel, David Hirshleifer, and Avanidhar Subrahmanyam (2001)^[4].

There are no taxes or transaction costs. Real financial products are subject both to taxes and transaction costs (such as broker fees), and taking these into account will alter the composition of the optimum portfolio. These assumptions can be relaxed with more complicated versions of the model.^{[citation needed]}

All investors are price takers, i.e., their actions do not influence prices. In reality, sufficiently large sales or purchases of individual assets can shift market prices for that asset and others (via cross-elasticity of demand.) An investor may not even be able to assemble the theoretically optimal portfolio if the market moves too much while they are buying the required securities.

Any investor can lend and borrow an unlimited amount at the risk free rate of interest. In reality, every investor has a credit limit.

All securities can be divided into parcels of any size. In reality, fractional shares usually cannot be bought or sold, and some assets have minimum orders sizes.

More complex versions of MPT can take into account a more sophisticated model of the world (such as one with non-normal distributions and taxes) but all mathematical models of finance still rely on many unrealistic premises.

[edit] MPT does not really model the market

The risk, return, and correlation measures used by MPT are expected values, which means that they are mathematical statements about the future (the expected value of returns is explicit in the above equations, and implicit in the definitions of variance and covariance.) In practice investors must substitute predictions based on historical measurements of asset return and volatility for these values in the equations. Very often such predictions are wrong, as captured in the classic disclaimer "past performance is not necessarily indicative of future results."
More fundamentally, investors are stuck with estimating key parameters from past market data because MPT attempts to model risk in terms of the likelihood of losses, but says nothing about why those losses might occur. The risk measurements used are probabilistic in nature, not structural. This is a major difference as compared to many engineering approaches to risk management.

Options theory and MPT have at least one important conceptual difference from the probabilistic risk assessment done by nuclear power [plants]. A PRA is what economists would call a structural model. The components of a system and their relationships are modeled in Monte Carlo simulations. If valve X fails, it causes a loss of back pressure on pump Y, causing a drop in flow to vessel Z, and so on.
But in the Black-Scholes equation and MPT, there is no attempt to explain an underlying structure to price changes. Various outcomes are simply given probabilities. And, unlike the PRA, if there is no history of a particular system-level event like a liquidity crisis, there is no way to compute the odds of it. If nuclear engineers ran risk management this way, they would never be able to compute the odds of a meltdown at a particular plant until several similar events occurred in the same reactor design.
—Douglas W. Hubbard, 'The Failure of Risk Management', p. 67, John Wiley & Sons, 2009. ISBN 978-0-470-38795-5

Essentially, the mathematics of MPT view the markets as a collection of dice. By examining past market data we can develop hypotheses about how the dice are weighted, but this isn't helpful if the markets are actually dependent upon a much bigger and more complicated chaotic system -- the world. For this reason, accurate structural models of real financial markets are unlikely to be forthcoming because they would essentially be structural models of the entire world. Nonetheless there is growing awareness of the concept of systemic risk in financial markets, which should lead to more sophisticated market models.

[edit] Variance is not a good measure of risk

Mathematical risk measurements are also useful only to the degree that they reflect investors' true concerns -- there is no point minimizing a variable that nobody cares about in practice. MPT uses the mathematical concept of variance to quantify risk, and this might be justified under the assumption of elliptically distributed returns such as normally distributed returns, but for general return distributions other risk measures (like coherent risk measures) might better reflect investor's true preferences.
In particular, variance is a symmetric measure that counts abnormally high returns as just as risky as abnormally low returns. In reality, investors are only concerned about losses, which shows that our intuitive concept of risk is fundamentally asymmetric in nature.

[edit] "Optimal" doesn't necessarily mean "most profitable"

MPT does not account for the social, environmental, strategic, or personal dimensions of investment decisions. It only attempts to maximize returns, without regard to other consequences. In a narrow sense, its complete reliance on asset prices makes it vulnerable to all the standard market failures such as those arising from information asymmetry, externalities, and public goods. It also rewards corporate fraud and dishonest accounting. More broadly, a firm may have strategic or social goals that shape its investment decisions, and an individual investor might have personal goals. In either case, information other than historical returns is relevant.
See also socially-responsible investing, fundamental analysis.

[edit] Extensions

Since MPT's introduction in 1952, many attempts have been made to improve the model, especially by using more realistic assumptions.
Post-modern portfolio theory extends MPT by adopting non-normally distributed, asymmetric measures of risk. This helps with some of these problems, but not others.

[edit] Other Applications

[edit] Applications to project portfolios and other "non-financial" assets

Some experts apply MPT to portfolios of projects and other assets besides financial instruments.^[5] When MPT is applied outside of traditional financial portfolios, some differences between the different types of portfolios must be considered.

The assets in financial portfolios are, for practical purposes, continuously divisible while portfolios of projects like new software development are "lumpy". For example, while we can compute that the optimal portfolio position for 3 stocks is, say, 44%, 35%, 21%, the optimal position for an IT portfolio may not allow us to simply change the amount spent on a project. IT projects might be all or nothing or, at least, have logical units that cannot be separated. A portfolio optimization method would have to take the discrete nature of some IT projects into account.
The assets of financial portfolios are liquid can be assessed or re-assessed at any point in time while opportunities for new projects may be limited and may appear in limited windows of time and projects that have already been initiated cannot be abandoned without the loss of the sunk costs (i.e., there is little or no recovery/salvage value of a half-complete IT project).

Neither of these necessarily eliminate the possibility of using MPT and such portfolios. They simply indicate the need to run the optimization with an additional set of mathematically-expressed constraints that would not normally apply to financial portfolios.
Furthermore, some of the simplest elements of Modern Portfolio Theory are applicable to virtually any kind of portfolio. The concept of capturing the risk tolerance of an investor by documenting how much risk is acceptable for a given return could be and is applied to a variety of decision analysis problems. MPT, however, uses historical variance as a measure of risk and portfolios of assets like IT projects don't usually have an "historical variance" for a new piece of software. In this case, the MPT investment boundary can be expressed in more general terms like "chance of an ROI less than cost of capital" or "chance of losing more than half of the investment". When risk is put in terms of uncertainty about forecasts and possible losses then the concept is transferable to various types of investment.^[5]

[edit] Application to other disciplines

In the 1970s, concepts from Modern Portfolio Theory found their way into the field of regional science. In a series of seminal works, Michael Conroy modeled the labor force in the economy using portfolio-theoretic methods to examine growth and variability in the labor force. This was followed by a long literature on the relationship between economic growth and volatility.^[6]
More recently, modern portfolio theory has been used to model the self-concept in social psychology. When the self attributes comprising the self-concept constitute a well-diversified portfolio, then psychological outcomes at the level of the individual such as mood and self-esteem should be more stable than when the self-concept is undiversified. This prediction has been confirmed in studies involving human subjects.^[7]
Recently, modern portfolio theory has been applied to modelling the uncertainty and correlation between documents in information retrieval. Given a query, the aim is to maximize the overall relevance of a ranked list of documents and at the same time minimize the overall uncertainty of the ranked list [1].

[edit] Comparison with arbitrage pricing theory

The SML and CAPM are often contrasted with the arbitrage pricing theory (APT), which holds that the expected return of a financial asset can be modeled as a linear function of various macro-economic factors, where sensitivity to changes in each factor is represented by a factor specific beta coefficient.
The APT is less restrictive in its assumptions: it allows for an explanatory (as opposed to statistical) model of asset returns, and assumes that each investor will hold a unique portfolio with its own particular array of betas, as opposed to the identical "market portfolio". Unlike the CAPM, the APT, however, does not itself reveal the identity of its priced factors - the number and nature of these factors is likely to change over time and between economies.

[edit] See also

[edit] References

[edit] Notes

^ ^a ^b ^c Edwin J. Elton and Martin J. Gruber, "Modern portfolio theory, 1950 to date", Journal of Banking & Finance 21 (1997) 1743-1759
^ Rumors Of MPT's Death Greatly Exaggerated
^ Mandelbrot, B., and Hudson, R. L. (2004). The (Mis)Behaviour of Markets: A Fractal View of Risk, Ruin, and Reward. London: Profile Books.
^ 'Overconfidence, Arbitrage, and Equilibrium Asset Pricing,' Kent D. Daniel, David Hirshleifer and Avanidhar Subrahmanyam, Journal of Finance, 56(3) (June, 2001), pp. 921-965
^ ^a ^b Hubbard, Douglas (2007). How to Measure Anything: Finding the Value of Intangibles in Business. Hoboken, NJ: John Wiley & Sons. ISBN 9780470110126.
^ Chandra, Siddharth (2003). "Regional Economy Size and the Growth-Instability Frontier: Evidence from Europe". Journal of Regional Science 43 (1): 95–122. doi:10.1111/1467-9787.00291.
^ Chandra, Siddharth; Shadel, William G. (2007). "Crossing disciplinary boundaries: Applying financial portfolio theory to model the organization of the self-concept". Journal of Research in Personality 41 (2): 346–373. doi:10.1016/j.jrp.2006.04.007.

[edit] Bibliography

Markowitz, Harry M. (1952). "Portfolio Selection". Journal of Finance 7 (1): 77–91.
Lintner, John (1965). "The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets". The Review of Economics and Statistics 47 (1): 13–39. doi:10.2307/1924119.
Sharpe, William F. (1964). "Capital asset prices: A theory of market equilibrium under conditions of risk". Journal of Finance 19 (3): 425–442.
Tobin, James (1958). "Liquidity preference as behavior towards risk". The Review of Economic Studies 25: 65–86.

http://en.wikipedia.org/wiki/Modern_portfolio_theory

Saturday, May 15, 2010

Modern portfolio theory

Modern portfolio theory

Contents

[edit] Concept

[edit] History

[edit] Mathematical model

[edit] Risk and return

[edit] Diversification

[edit] The risk-free asset

[edit] Capital allocation line

[edit] The efficient frontier

[edit] Portfolio leverage

[edit] The market portfolio

[edit] Capital market line

[edit] Asset pricing using MPT

[edit] Systematic risk and specific risk

[edit] Security characteristic line

[edit] Capital asset pricing model

[edit] Criticism

[edit] Assumptions

[edit] MPT does not really model the market

[edit] Variance is not a good measure of risk

[edit] "Optimal" doesn't necessarily mean "most profitable"

[edit] Extensions

[edit] Other Applications

[edit] Applications to project portfolios and other "non-financial" assets

[edit] Application to other disciplines

[edit] Comparison with arbitrage pricing theory

[edit] See also

[edit] References

[edit] Notes

[edit] Bibliography

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