Asset Allocation

show aggressive, moderate, conservative etc.

make asset allocation and MPT into links?

Asset allocation is the process of choosing among possible asset classes.

A large part of financial planning consists of finding an asset allocation that is appropriate for a given investor in terms of their appetite for and ability to shoulder risk. This can depend on various factors; see investor profile. Asset allocation is the product of an examination of an investor's needs and objectives. Asset allocation, done well, is a plan to invest in assets or asset classes which will best meet the needs and objectives of the investor. Investors seeking high returns and willing to expose their investments to an elevated amount of risk will allocate to equity (ownership) investments. Investors seeking stability and income will allocate to debt investments. Most investors, particularly personal investors, will find mixtures of equity and debt investments most nearly meets their needs. Asset allocation can be practiced by optimization techniques, minimizing risk for a given level of return or maximizing return for a given level of risk. It also can be accomplished as goal based investing.

■1 Justification
■2 Asset allocation strategies
■3 Examples of asset classes
■4 Academic studies
■4.1 Performance indicators
■5 Return versus risk trade-off
■6 See also
■7 References
■8 External links

Asset allocation is based on the idea that in different years a different asset is the best-performing one. It is difficult to predict which asset will perform best in a given year. Therefore, although it is psychologically appealing to try to predict the "best" asset, proponents of asset allocation consider it risky. Experts in the field note[citation needed] that someone who "jumps" from one asset to another, according to whim, may easily end up with worse results than does someone following any consistent plan.

A fundamental justification for asset allocation is the notion that different asset classes offer returns that are not perfectly correlated, hence diversification reduces the overall risk in terms of the variability of returns for a given level of expected return. Therefore, having a mixture of asset classes is more likely to meet the investor's wishes in terms of amount of risk and possible returns.

In this respect, diversification has been described as "the only free lunch you will find in the investment game". [1] Academic research has painstakingly explained the importance of asset allocation and the problems of active management (see academic studies section below). This explains the steadily rising popularity of passive investment styles using index funds.

Although risk is reduced as long as correlations are not perfect, it is typically forecast (wholly or in part) based on statistical relationships (like correlation and variance) that existed over some past period. Expectations for return are often derived in the same way.

When such backward-looking approaches are used to forecast future returns or risks using the traditional mean-variance optimization approach to asset allocation of modern portfolio theory, the strategy is, in fact, predicting future risks and returns based on past history. As there is no guarantee that past relationships will continue in the future, this is one of the "weak links" in traditional asset allocation strategies as derived from MPT. Other, more subtle weaknesses include the "butterfly effect", by which seemingly minor errors in forecasting lead to recommended allocations that are grossly skewed from investment mandates and/or impractical—often even violating an investment manager's "common sense" understanding of a tenable portfolio-allocation strategy.

Asset allocation strategies
There are four basic types of asset allocation strategies based on investment goals, risk tolerance, time frames and diversification: strategic, tactical, and core-satellite.

Strategic Asset Allocation — the primary goal of a strategic asset allocation is to create an asset mix that will provide the optimal balance between expected risk and return for a long-term investment horizon.

Tactical Asset Allocation — method in which an investor takes a more active approach that tries to position a portfolio into those assets, sectors, or individual stocks that show the most potential for gains.

Core-Satellite Asset Allocation — is more or less a hybrid of both the strategic and tactical allocations mentioned above.

Systematic Asset Allocation is another approach which depends on three assumptions. These are-

■The markets provide explicit information about the available returns.
■The relative expected returns reflect consensus.
■Expected returns provide clues to actual returns.
Examples of asset classes
■cash and cash equivalents (e.g., certificate of deposit, money market funds)
■fixed interest securities such as Bonds: investment-grade or junk (high-yield); government or corporate; short-term, intermediate, long-term; domestic, foreign, emerging markets; or Convertible security
■stocks: value, dividend, growth, sector specific or preferred (or a "blend" of any two or more of the preceding); large-cap versus mid-cap, small-cap or micro-cap; public equities versus private equities, domestic, foreign (developed), emerging or frontier markets
■commercial or residential real estate (also REITs)
■natural resources: agriculture, forestry and livestock; energy or oil and gas distribution; carbon or water
■precious metals
■industrial metals and infrastructure
■collectibles such as art, coins, or stamps
■insurance products (annuity, life settlements, catastrophe bonds, personal life insurance products, etc.)
■derivatives such as long-short or market neutral strategies, options, collateralized debt and futures
■foreign currency
■venture capital, leveraged buyout, merger arbitrage or distressed securities
Note that 'funds' are not an asset class; funds are filed under what they own, e.g. stocks for stock funds, bonds for bond funds, et cetera.

Academic studies
In 1986, Gary P. Brinson, L. Randolph Hood, and SEI's Gilbert L. Beebower (BHB) published a study about asset allocation of 91 large pension funds measured from 1974 to 1983.[2] They replaced the pension funds' stock, bond, and cash selections with corresponding market indexes. The indexed quarterly return were found to be higher than pension plan's actual quarterly return. The two quarterly return series' linear correlation was measured at 96.7%, with shared variance of 93.6%. A 1991 follow-up study by Brinson, Singer, and Beebower measured a variance of 91.5%.[3] The conclusion of the study was that replacing active choices with simple asset classes worked just as well as, if not even better than, professional pension managers. Also, a small number of asset classes was sufficient for financial planning. Financial advisors often pointed to this study to support the idea that asset allocation is more important than all other concerns, which the BHB study lumped together as "market timing".[4] One problem with the Brinson study was that the cost factor in the two return series was not clearly discussed. However, in response to a letter to the editor, Hood noted that the returns series were gross of management fees.[5]

In 1997, William Jahnke initiated debate on this topic, attacking the BHB study in a paper titled The Asset Allocation Hoax.[6] The Jahnke discussion appeared in the Journal of Financial Planning as an opinion piece, not a peer reviewed article. Jahnke's main criticism, still undisputed, was that BHB's use of quarterly data dampens the impact of compounding slight portfolio disparities over time, relative to the benchmark. One could compound 2% and 2.15% quarterly over 20 years and see the sizable difference in cumulative return. However, the difference is still 15 basis points (hundredths of a percent) per quarter; the difference is one of perception, not fact.

In 2000, Ibbotson and Kaplan used five asset classes in their study Does Asset Allocation Policy Explain 40, 90, or 100 Percent of Performance?[7] The asset classes included were large-cap US stock, small-cap US stock, non-US stock, US bonds, and cash. Ibbotson and Kaplan examined the 10 year return of 94 US balanced mutual funds versus the corresponding indexed returns. This time, after properly adjusting for the cost of running index funds, the actual returns again failed to beat index returns. The linear correlation between monthly index return series and the actual monthly actual return series was measured at 90.2%, with shared variance of 81.4%. Ibbotson concluded 1) that asset allocation explained 40% of the variation of returns across funds, and 2) that it explained virtually 100% of the level of fund returns. Gary Brinson has expressed his general agreement with the Ibbotson-Kaplan conclusions.

In both studies, it is misleading to make statements such as "asset allocation explains 93.6% of investment return".[8] Even "asset allocation explains 93.6% of quarterly performance variance" leaves much to be desired, because the shared variance could be from pension funds' operating structure.[7] Hood, however, rejects this interpretation on the grounds that pension plans in particular cannot cross-share risks and that they are explicitly singular entities, rendering shared variance irrelevant.[5] The statistics were most helpful when used to demonstrate the similarity of the index return series and the actual return series.

A 2000 paper by Meir Statman found that using the same parameters that explained BHB's 93.6% variance result, a hypothetical financial advisor with perfect foresight in tactical asset allocation performed 8.1% better per year, yet the strategic asset allocation still explained 89.4% of the variance.[4] Thus, explaining variance does not explain performance. Statman says that strategic asset allocation is movement along the efficient frontier, whereas tactical asset allocation involves movement of the efficient frontier. A more common sense explanation of the Brinson, Hood, and Beebower study is that asset allocation explains more than 90% of the volatility of returns of an overall portfolio, but will not explain the ending results of your portfolio over long periods of time. Hood notes in his review of the material over 20 years, however, that explaining performance over time is possible with the BHB approach but was not the focus of the original paper.[9]

Bekkers, Doeswijk and Lam (2009) investigate the diversification benefits for a portfolio by distinguishing ten different investment categories simultaneously in a mean-variance analysis as well as a market portfolio approach. The results suggest that real estate, commodities, and high yield add most value to the traditional asset mix of stocks, bonds, and cash. A study with such a broad coverage of asset classes has not been conducted before, not in the context of determining capital market expectations and performing a mean-variance analysis, neither in assessing the global market portfolio.[10]

Performance indicators
McGuigan described an examination of funds that were in the top quartile of performance during 1983 to 1993.[11] During the second measurement period of 1993 to 2003, only 28.57% of the funds remained in the top quartile. 33.33% of the funds dropped to the second quartile. The rest of the funds dropped to the third or fourth quartile.

In fact, low cost was a more reliable indicator of performance. Bogle noted that an examination of five-year performance data of large-cap blend funds revealed that the lowest cost quartile funds had the best performance, and the highest cost quartile funds had the worst performance.[12]

Return versus risk trade-off
In asset allocation planning, the decision on the amount of stocks versus bonds in one's portfolio is a very important decision. Simply buying stocks without regard of a possible bear market can result in panic selling later. One's true risk tolerance can be hard to gauge until having experienced a real bear market with money invested in the market. Finding the proper balance is key.

Cumulative return after inflation from 2000-to-2002 bear market[13]
80% stock / 20% bond −34.35%
70% stock / 30% bond −25.81%
60% stock / 40% bond −19.99%
50% stock / 50% bond −13.87%
40% stock / 60% bond −7.46%
30% stock / 70% bond −0.74%
20% stock / 80% bond +6.29%
Projected 10 year Cumulative return after inflation
(stock return 8% yearly, bond return 4.5% yearly, inflation 3% yearly[14]
80% stock / 20% bond 52%
70% stock / 30% bond 47%
60% stock / 40% bond 42%
50% stock / 50% bond 38%
40% stock / 60% bond 33%
30% stock / 70% bond 29%
20% stock / 80% bond 24%

The tables show why asset allocation is important. It determines an investor's future return, as well as the bear market burden that he or she will have to carry successfully to realize the returns.

See also
■Efficient-market hypothesis
■Tactical asset allocation
■Mutual fund
■Index fund
■Asset location
■Performance attribution
■Economic capital

Modern portfolio theoryFrom Wikipedia, the free encyclopediaJump to: navigation, search
Portfolio analysis redirects here. For theorems about the mean-variance efficient frontier, see Mutual fund separation theorem. For non-mean-variance portfolio analysis, see Marginal conditional stochastic dominance.
Modern portfolio theory (MPT) is a theory of investment which attempts to maximize portfolio expected return for a given amount of portfolio risk, or equivalently minimize risk for a given level of expected return, by carefully choosing the proportions of various assets. Although MPT is widely used in practice in the financial industry and several of its creators won a Nobel memorial prize[1] 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. That this is possible can be seen intuitively because different types of assets often change in value in opposite ways. For example, as prices in the stock market tend to move independently from prices in the bond market, a collection of both types of assets can therefore have lower overall risk than either individually. But diversification lowers risk even if assets' returns are not negatively correlated—indeed, even if they are positively correlated.

More technically, MPT models an asset's return as a normally distributed function (or more generally as an elliptically 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 perfectly positively correlated, MPT seeks to reduce the total variance of the portfolio return. 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, many theoretical and practical criticisms have been leveled against it. These include the fact that financial returns do not follow a Gaussian distribution or indeed any symmetric 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.[2][3]

ConceptThe fundamental concept behind MPT is that the assets in an investment portfolio should not 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 expected return. In general, assets with higher expected returns are riskier. For a given amount of risk, MPT describes how to select a portfolio with the highest possible expected return. Or, for a given expected return, MPT explains how to select a portfolio with the lowest possible risk (the targeted expected return cannot be more than the highest-returning available security, of course, unless negative holdings of assets are possible.)[4]

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] HistoryHarry Markowitz introduced MPT in a 1952 article[5] and a 1959 book.[6] Markowitz classifies it simply as "Portfolio Theory," because "There's nothing modern about it." See also this[4] survey of the history.

[edit] Mathematical modelIn some sense the mathematical derivation below is MPT, although the basic concepts behind the model have also been very influential.[4]

This section develops the "classic" MPT model. There have been many extensions since.

[edit] Risk and expected returnMPT assumes that investors are risk averse, meaning that given two portfolios 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 expected returns must accept more risk. The exact trade-off will be the same for all investors, but different investors will evaluate the trade-off differently 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-expected return profile – i.e., if for that level of risk an alternative portfolio exists which has better expected returns.

Note that the theory uses standard deviation of return as a proxy for risk, which is valid if asset returns are jointly normally distributed or otherwise elliptically distributed. There are problems with this, however; see criticism.

Under the model:

Portfolio return is the proportion-weighted combination of the constituent assets' returns.
Portfolio volatility is a function of the correlations ρij of the component assets, for all asset pairs (i, j).

DiversificationAn investor can reduce portfolio risk simply by holding combinations of instruments which are not perfectly positively correlated (correlation coefficient )). 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 expected return with reduced risk.

If all the asset pairs have correlations of 0—they are perfectly uncorrelated—the portfolio's return variance is the sum over all assets of the square of the fraction held in the asset times the asset's return variance (and the portfolio standard deviation is the square root of this sum).

The efficient frontier with no risk-free asset
Efficient Frontier. The hyperbola is sometimes referred to as the 'Markowitz Bullet', and is the efficient frontier if no risk-free asset is available. With a risk-free asset, the straight line is the efficient frontier.

As shown in this graph, every possible combination of the risky assets, without including any holdings of the risk-free asset, can be plotted in risk-expected return space, and the collection of all such possible portfolios defines a region in this space. The left boundary of this region is a hyperbola,[7] and the upper edge of this region is the efficient frontier in the absence of a risk-free asset (sometimes called "the Markowitz bullet"). Combinations along this upper edge represent portfolios (including no holdings of the risk-free asset) for which there is lowest risk for a given level of expected return. Equivalently, a portfolio lying on the efficient frontier represents the combination offering the best possible expected return for given risk

The two mutual fund theoremOne key result of the above analysis is the two mutual fund theorem.[7] This theorem states that any portfolio on the efficient frontier can be generated by holding a combination of any two given portfolios on the frontier; the latter two given portfolios are the "mutual funds" in the theorem's name. So in the absence of a risk-free asset, an investor can achieve any desired efficient portfolio even if all that is accessible is a pair of efficient mutual funds. If the location of the desired portfolio on the frontier is between the locations of the two mutual funds, both mutual funds will be held in positive quantities. If the desired portfolio is outside the range spanned by the two mutual funds, then one of the mutual funds must be sold short (held in negative quantity) while the size of the investment in the other mutual fund must be greater than the amount available for investment (the excess being funded by the borrowing from the other fund).

[edit] The risk-free asset and the capital allocation lineMain article: Capital allocation line
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 is linearly related to the change in risk as the proportions in the combination vary.

When a risk-free asset is introduced, the half-line shown in the figure is the new efficient frontier. It is tangent to the hyperbola at the pure risky portfolio with the highest Sharpe ratio. Its horizontal intercept represents a portfolio with 100% of holdings in the risk-free asset; the tangency with the hyperbola represents a portfolio with no risk-free holdings and 100% of assets held in the portfolio occurring at the tangency point; points between those points are portfolios containing positive amounts of both the risky tangency portfolio and the risk-free asset; and points on the half-line beyond the tangency point are leveraged portfolios involving negative holdings of the risk-free asset (the latter has been sold short—in other words, the investor has borrowed at the risk-free rate) and an amount invested in the tangency portfolio equal to more than 100% of the investor's initial capital.

By the diagram, the introduction of the risk-free asset as a possible component of the portfolio has improved the range of risk-expected return combinations available, because everywhere except at the tangency portfolio the half-line gives a higher expected return than the hyperbola does at every possible risk level. The fact that all points on the linear efficient locus can be achieved by a combination of holdings of the risk-free asset and the tangency portfolio is known as the one mutual fund theorem,[7] where the mutual fund referred to is the tangency portfolio.

Asset pricing using MPTThe above analysis describes optimal behavior of an individual investor. Asset pricing theory builds on this analysis in the following way. Since everyone holds the risky assets in identical proportions to each other—namely in the proportions given by the tangency portfolio—in market equilibrium the risky assets' prices, and therefore their expected returns, will adjust so that the ratios in the tangency portfolio are the same as the ratios in which the risky assets are supplied to the market. Thus relative supplies will equal relative demands. MPT derives the required expected return for a correctly priced asset in this context.

[edit] Systematic risk and specific riskSpecific 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-expected return characteristics of the market portfolio, the relevant measure of the risk of a security is the risk it adds to the market portfolio, and not its risk in isolation. In this context, the volatility of the asset, and its correlation with the market portfolio, are historically observed and are therefore 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.)

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.

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

[edit] AssumptionsThe 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.[8] While the model can also be justified by assuming any return distribution which is jointly elliptical,[9][10] all the joint elliptical distributions are symmetrical whereas asset returns empirically are not.
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).[11]
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 marketThe risk, return, and correlation measures used by MPT are based on 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 expected values fail to take account of new circumstances which did not exist when the historical data were generated.

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.

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 investors' true preferences.

In particular, variance is a symmetric measure that counts abnormally high returns as just as risky as abnormally low returns. Some would argue that, in reality, investors are only concerned about losses, and do not care about the dispersion or tightness of above-average returns. According to this view, our intuitive concept of risk is fundamentally asymmetric in nature.

MPT does not account for the personal, environmental, strategic, or social dimensions of investment decisions. It only attempts to maximize risk-adjusted 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.

Financial economist Nassim Nicholas Taleb has also criticized modern portfolio theory because it assumes a Gaussian distribution:

After the stock market crash (in 1987), they rewarded two theoreticians, Harry Markowitz and William Sharpe, who built beautifully Platonic models on a Gaussian base, contributing to what is called Modern Portfolio Theory. Simply, if you remove their Gaussian assumptions and treat prices as scalable, you are left with hot air. The Nobel Committee could have tested the Sharpe and Markowitz models – they work like quack remedies sold on the Internet – but nobody in Stockholm seems to have thought about it.[12]:p.279
[edit] The MPT does not take its own effect on asset prices into accountDiversification eliminates non-systematic risk, but at the cost of increasing the systematic risk. Diversification forces the portfolio manager to invest in assets without analyzing their fundamentals, solely for the benefit of eliminating the portfolio’s non-systematic risk (the CAPM assumes investment in all available assets). This artificially increased demand pushes up the price of assets that, when analyzed individually, would be of little fundamental value. The result is that the whole portfolio becomes more expensive and, as a result, the probability of a positive return decreases (i.e. the risk of the portfolio increases).

Empirical evidence for this is the price hike that stocks typically experience once they are included in major indices like the S&P 500.[citation needed]

[edit] ExtensionsSince 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.

Black-Litterman model optimization is an extension of unconstrained Markowitz optimization which incorporates relative and absolute `views' on inputs of risk and returns.

[edit] Other Applications[edit] Applications to project portfolios and other "non-financial" assetsSome experts apply MPT to portfolios of projects and other assets besides financial instruments.[13][14] When MPT is applied outside of traditional financial portfolios, some differences between the different types of portfolios must be considered.

1.The assets in financial portfolios are, for practical purposes, continuously divisible while portfolios of projects 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 a project portfolio may not allow us to simply change the amount spent on a project. 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 projects into account.
2.The assets of financial portfolios are liquid; they can be assessed or re-assessed at any point in time. But opportunities for launching new projects may be limited and may occur in limited windows of time. 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 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 may be applied to a variety of decision analysis problems. MPT uses historical variance as a measure of risk, but portfolios of assets like major projects don't have a well-defined "historical variance". 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.[13]

[edit] Application to other disciplinesIn the 1970s, concepts from Modern Portfolio Theory found their way into the field of regional science. In a series of seminal works, Michael Conroy[citation needed] 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.[15]

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.[16]

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.[17]

[edit] Comparison with arbitrage pricing theoryThe 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.

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