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EspañolPOST-MODERN PORTFOLIO THEORY
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Please help Wikipedia by adding references. See the for details.
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This article discusses in detail the application of 'post-modern portfolio theory1 (PMPT)' to risk/return analysis and describes its theoretical and practical benefits over Modern Portfolio Theory (“MPT”), also referred to as Mean-Variance Analysis (“MVA”). And like MPT, PMPT proposes how rational investors will use diversification to optimize their portfolios, and how a risky asset should be priced.
It has been a generation since Harry Markowitz laid the foundations and built much of the structure of what we now know as MPT, the greatest contribution of which is the establishment of a formal risk/return framework for investment decision-making. By defining investment risk in quantitative terms, Markowitz gave investors a mathematical approach to asset selection and portfolio management. But as Markowitz himself and William F. Sharpe, the other giant of MPT acknowledge, there are important limitations to the original MPT formulation.
"Under certain conditions the MVA can be shown to lead to unsatisfactory predictions of (investor) behavior. Markowitz suggests that a model based on the semi-variance would be preferable; in light of the formidable computational problems, however, he bases his (MVA) analysis on the mean and the standard deviation2."
The causes of these “unsatisfactory” aspects of MPT are the assumptions that 1) variance of portfolio returns is the correct measure of investment risk, and 2) the investment returns of all securities and portfolios can be adequately represented by the normal distribution. Stated another way, MPT is limited by measures of risk and return that do not always represent the realities of the investment markets.
Recent advances in portfolio and financial theory, coupled with today’s increased electronic computing power, have overcome these limitations. The resulting expanded risk/return paradigm is known as Post-Modern Portfolio Theory, or PMPT. Thus, MPT becomes nothing more than a (symmetrical) special case of PMPT.
As already stated, standard deviation3 and the normal distribution are a major practical limitation: they are symmetrical. Using standard deviation implies that better-than-expected returns are just as risky as those returns that are worse than expected. Furthermore, using the normal distribution to model the pattern of investment returns makes investment results with more upside than downside returns appear more risky than they really are, and vice-versa for returns with more a predominance of downside returns. The result is that using traditional MPT techniques for measuring investment portfolio construction and evaluation frequently distorts investment reality.
It has long been recognized that investors typically do not view as risky those returns ''above'' the minimum they must earn in order to achieve their investment objectives. They believe that risk has to do with the bad outcomes (i.e., returns below a required target), not the good outcomes (i.e., returns in excess of the target) and that losses weigh more heavily than gains. This view has been noted by researchers in finance, economics and psychology, including Sharpe (1964). "Under certain conditions, the mean-variance approach can be shown to lead to unsatisfactory predictions of behavior. Markowitz suggests that models based on semi-variance would be preferable; in light of the formidable computational problems, however, he bases his analysis on the variance and standard deviation."
In 1987 The Pension Research Institute at San Francisco State University developed the practical mathematical algorithms of PMPT that are in use today. These methods provide a framework that recognizes investors' preferences for upside over downside volatility. At the same time, a more robust model for the pattern of investment returns, the three-parameter lognormal distribution4, was introduced.
Downside risk (DR) is measured by target semi-deviation (the square root of target semi-variance) and is termed downside deviation. It is expressed in percentages and therefore allows for rankings in the same way as standard deviation.
An intuitive way to view downside risk is the annualized standard deviation of returns below the target. Another is the square root of the probability-weighted squared below-target returns. The squaring of the below-target returns has the effect of penalizing failures at an exponential rate. This is consistent with observations made on the behavior of individual decision-making under
:
where
''d'' is downside deviation (commonly known in the finacial community as 'downside risk'). Note: By extension, ''d''2 = downside variance.
''t'' is the annual target return, or MAR
''r'' is the random variable representing the return for the distribution of annual returns ''f''(''r''),
''f''(''r'') is a the three-parameter lognormal distribution
For the reasons provided below, this ''continuous'' formula is preferred over a simpler ''discrete'' version that determines the standard deviation of below-target periodic returns taken from the return series.
1. The continuous form permits all subsequent calculations to be made using annual returns which is the natural way for investors to specify their investment goals. The discrete form requires monthly returns for there to be sufficient data points to make a meaningful calculation, which in turn requires converting the annual target into a monthly target. This significantly affects the amount of risk that is identified. For example, a goal of earning 1% each and every month results in greater risk than the (apparently) equivalent goal of earning 12% each and every year.
2. A second reason for strongly preferring the continuous form to the discrete form has been proposed by Sortino & Forsey (1996):
"Before we make an investment, we don't know what the outcome will be... After the investment is made, and we want to measure its performance, all we know is what the outcome was, not what it could have been. To cope with this uncertainty, we assume that a reasonable estimate of the range of possible returns, as well as the probabilities associated with estimation of those returns...In statistical terms, the shape of [this] uncertainty is called a probability distribution. In other words, looking at just the discrete monthly or annual values does not tell the whole story."
Using the observed points to create a distribution is a staple of conventional performance measurement. For example, monthly returns are used to calculate a fund's mean and standard deviation. Using these values and the properties of the normal distribution, we can make statements such as the likelihood of losing money (even though no negative returns may actually have been observed), or the range within which two-thirds of all returns lies (even though the returns identified in this way do not necessarily have to have actually occurred). Our ability to make these statements comes from the process of assuming the continuous form of the normal distribution and certain of its well-known properties.
In PMPT an analogous process is followed:
1. Observe the monthly returns,
2. Fit a distribution that permits asymmetry to the observations,
3. Annualize the monthly returns, making sure the shape characteristics of the distribution are retained,
4. Apply integral calculus to the resultant distribution to calculate the appropriate statistics.
The Sortino ratio measures returns adjusted for the target and downside risk. It is defined as:
:
where,
''r'' = the annualized rate of return,
''t'' = the target return,
''d'' = downside risk.
This ratio replaces the traditional Sharpe ratio as a means for ranking investment results. The following table shows risk-adjusted ratios for several major indexes using both Sortino and Sharpe ratios. The data cover the five years 1992-1996 and are based on monthly total returns. The Sortino ratio is calculated against a 9.0% target.
{| class="wikitable"
|-
! Index
! Sortino Ratio
! Sharpe Ratio
|-
| 90-day T-bill
| -1.00
| 0.00
|-
| Lehman Aggregate
| -0.29
| 0.63
|-
| MSCI EAFE
| -0.05
| 0.30
|-
| Russell 2000
| 0.55
| 0.93
|-
| S&P 500
| 0.84
| 1.25
|}
As an example of the different conclusions that can be drawn using these two ratios, notice how the Lehman Aggregate and MSCI EAFE compare - the Lehman ranks higher using the Sharpe ratio whereas EAFE ranks higher using the Sortino ratio. In many cases, manager or index rankings will be different, depending on the risk-adjusted measure used. These patterns will change again for different values of t. For example, when t is close to the risk-free rate, the Sortino Ratio for T-Bill's will be higher than that for the S&P 500, while the Sharpe ratio remains unchanged.
Volatility skewness is another portfolio-analysis statistic introduced by Rom and Ferguson under the PMPT rubric. It measures the ratio of a distribution's percentage of total variance from returns above the mean, to the percentage of the distribution's total returns from returns below the mean. Thus, if a distribution is symmetrical (i.e., normal, as is assumed under MPT), it has a volatility skewness of 1.00. Values greater than 1.00 indicate positive skewness; values less than 1.00 indicate negative skewness. While closely correlated with the traditional statistical measure of skewness (viz., the third moment of a distribution), the authors of PMPT argue that their volatility skewness measure has the advantage of being intuitively more understandable to non-statisticians who are the primary practical users of these tools.
The importance of skewness lies in the fact that the more non-normal (i.e., skewed) a return series is, the more its true risk will be distorted by traditional MPT measures such as the Sharpe ratio. Thus, with the recent advent of hedging and derivative strategies, which are asymmetrical by design, MPT measures are essentially useless, while PMPT is able to capture significantly more of the true information contained in the returns under consideration. This being said, as the following table shows, many of the common market indices and the returns of stock and bond mutual funds cannot themselves always be assumed to be accurately represented by the normal distribution. This fact is also not well understood by many practitioners.
{| class="wikitable"
|-
! Index
! Upside Skewness(%)
! Downside Skewness(%)
! Volatility Skewness
|-
| Lehman Aggregate
| 32.35
| 67.65
| 0.48
|-
| Russell 2000
| 37.19
| 62.81
| 0.59
|-
| S&P 500
| 38.63
| 61.37
| 0.63
|-
| 90-day T-Bill
| 48.26
| 51.74
| 0.93
|-
| MSCI EAFE
| 54.67
| 45.33
| 1.21
|}
PMPT is able to assist investment practitioners more accurately create optimal investment strategies and evaluate the true performance of investment managers, mutual funds and other portfolios, without the restrictions imposed by MPT.
For a comprehensive survey of the early literature, see R. Libby and P. Fishburn [1979].
1. The earliest citation of the term 'Post-Modern Portfolio Theory' in the literature appears in 1993 in the article "Post-Modern Portfolio Theory Comes of Age" by Brian M. Rom and Kathleen W. Ferguson, published in The Journal of Investing, Winter, 1993. Summarized versions of this article have been subsequently published in a number of other journals and websites.
2. See Sharpe [1964]. Markowitz recognized these limitations and proposed downside risk (which he called "semi-variance") as the preferred measure of investment risk. The complex calculations and the limited computational resources at his disposal, however, made practical implemetations of downside risk impossible. He therefore compromised and stayed with variance.
3. In MPT, the terms variance, variability, volatility and standard deviation are used interchangeably to represent investment risk.
4. The three-parameter lognormal distribution recommended for use in downside risk calculations permits both positive and negative skewness in return distributions. This is a more robust measure of portfolio returns than the normal distribution, which requires that the upsides and downside tails of the distribution be identical
★ Bawa, V.S. "Stochastic Dominance: A Research Bibliography." Management Science, June, 1982.
★ Balzer, L.A. "Measuring Investment Risk: A Review." The Journal of Investing, Fall 1994.
★ Clarkson, R.S. Presentation to the Faculty of Actuaries (British). february 20, 1989.
★ Fishburn, P.C. "Mean-Risk Analysis with Risk Associated Below Target Variance." American Economic Review, March 1977.
★ Hammond, D.R. "Risk Management Approaches in Endowment Portfolios in the 1990's" Journal of Investing, Summer, 1993.
★ Harlow, W.V. "Asset Allocation in a Downside Risk Framework." Financial Analysts Journal, Sept-Oct 1991.
★ "Investment Review." Brinson Partners, Inc. 1992.
★ Kaplan, P. and L. Siegel. "Portfolio Theory is Alive and Well," Journal of Investing, Fall 1994.
★ Lewis, A.L. "Semivariance and the Performance of Portfolios with Options." Financial Analysts Journal, July-August 1990.
★ Leibowitz, M.L. and S. Kogelman. "Asset Allocation under Shortfall Constraints." Salomon Brothers, 1987.
★ Leibowitz, M.L., and T.C. Langeteig. "Shortfall Risks and the Asset Allocation Decision." Journal of Portfolio management, Fall 1989.
★ Libby, R. and P. Fishburn. "Behavioral Models of Risk taking in Business decisions: A Survey and Evaluation," Journal of Accounting Research, Autumn 1977. See also D. Kahneman and A. Tversky, "Prospect Theory: An Analysis of Decision under Risk," Econometrica, March 1979.
★ Post-Modern Portfoloi Theory Spawns Post-Modern Optimizer." Money Management Letter, February 15, 1993.
★ Rom, B. M. and K. Ferguson. "Post-Modern Portfolio Theory Comes of Age." Journal of Investing, Winter 1993.
★ Rom, B. M. and K. Ferguson. "Portfolio Theory is Alive and Well: A Response." Journal of Investing, Fall 1994.
★ Rom, B. M. and K. Ferguson. "A software developer's view: using Post-Modern Portfolio Theory to improve investment performance measurement." Managing downside risk in financial markets: Theory, practice and implementation; Butterworth-Heinemann Finance, 2001; p59.
★ Sharpe, W.F. "Capital Asset Prices: A Theory of Market Equilibrium under Consideration of Risk." Journal of Finance, Vol. XIX (1964)
★ Sortino, F. "Looking only at return is risky, obscuring real goal." Pensions and Investments magazine, November 25, 1997.
★ Sortino, F. and H. Forsey "On the Use and Misuse of Downside Risk." The Journal of Portfolio Management, Winter 1996.
★ Sortino, F. and L. Price. "Performance Measurement in a Downside Risk Framework." Journal of Investing, Fall 1994.
★ Sortino, F. and S. Satchell, editors. "Managing downside risk in financial markets: Theory, practice and implementation" Butterworth-Heinemann Finance, 2001.
★ Sortino, F. and R. van der Meer. "Downside Risk: Capturing What's at Stake." Journal of Portfolio Management, Summer 1991.
★ "Why Investors Make the Wrong Choices." Fortune Magazine, January 1987.
★ http://investmenttechnologies.com/publications.html
★ http://www.fpanet.org/journal/articles/2005_Issues/jfp0905-art7.cfm
★ http://www.bwater.com/PDFs/engineering_targeted_returns_and_risks_pmpt_060215.pdf
★ http://investmenttechnologies.com/about.html
|
?
| 'This {{{part| may contain or .'
Please help Wikipedia by adding references. See the for details.
|}
This article discusses in detail the application of 'post-modern portfolio theory1 (PMPT)' to risk/return analysis and describes its theoretical and practical benefits over Modern Portfolio Theory (“MPT”), also referred to as Mean-Variance Analysis (“MVA”). And like MPT, PMPT proposes how rational investors will use diversification to optimize their portfolios, and how a risky asset should be priced.
| Contents |
| Overview |
| Introduction to PMPT |
| The Tools of PMPT |
| Downside risk |
| Sortino ratio |
| Volatility skewness |
| Conclusion |
| Endnotes |
| References |
| External links |
Overview
It has been a generation since Harry Markowitz laid the foundations and built much of the structure of what we now know as MPT, the greatest contribution of which is the establishment of a formal risk/return framework for investment decision-making. By defining investment risk in quantitative terms, Markowitz gave investors a mathematical approach to asset selection and portfolio management. But as Markowitz himself and William F. Sharpe, the other giant of MPT acknowledge, there are important limitations to the original MPT formulation.
"Under certain conditions the MVA can be shown to lead to unsatisfactory predictions of (investor) behavior. Markowitz suggests that a model based on the semi-variance would be preferable; in light of the formidable computational problems, however, he bases his (MVA) analysis on the mean and the standard deviation2."
The causes of these “unsatisfactory” aspects of MPT are the assumptions that 1) variance of portfolio returns is the correct measure of investment risk, and 2) the investment returns of all securities and portfolios can be adequately represented by the normal distribution. Stated another way, MPT is limited by measures of risk and return that do not always represent the realities of the investment markets.
Recent advances in portfolio and financial theory, coupled with today’s increased electronic computing power, have overcome these limitations. The resulting expanded risk/return paradigm is known as Post-Modern Portfolio Theory, or PMPT. Thus, MPT becomes nothing more than a (symmetrical) special case of PMPT.
Introduction to PMPT
As already stated, standard deviation3 and the normal distribution are a major practical limitation: they are symmetrical. Using standard deviation implies that better-than-expected returns are just as risky as those returns that are worse than expected. Furthermore, using the normal distribution to model the pattern of investment returns makes investment results with more upside than downside returns appear more risky than they really are, and vice-versa for returns with more a predominance of downside returns. The result is that using traditional MPT techniques for measuring investment portfolio construction and evaluation frequently distorts investment reality.
It has long been recognized that investors typically do not view as risky those returns ''above'' the minimum they must earn in order to achieve their investment objectives. They believe that risk has to do with the bad outcomes (i.e., returns below a required target), not the good outcomes (i.e., returns in excess of the target) and that losses weigh more heavily than gains. This view has been noted by researchers in finance, economics and psychology, including Sharpe (1964). "Under certain conditions, the mean-variance approach can be shown to lead to unsatisfactory predictions of behavior. Markowitz suggests that models based on semi-variance would be preferable; in light of the formidable computational problems, however, he bases his analysis on the variance and standard deviation."
The Tools of PMPT
In 1987 The Pension Research Institute at San Francisco State University developed the practical mathematical algorithms of PMPT that are in use today. These methods provide a framework that recognizes investors' preferences for upside over downside volatility. At the same time, a more robust model for the pattern of investment returns, the three-parameter lognormal distribution4, was introduced.
Downside risk
Downside risk (DR) is measured by target semi-deviation (the square root of target semi-variance) and is termed downside deviation. It is expressed in percentages and therefore allows for rankings in the same way as standard deviation.
An intuitive way to view downside risk is the annualized standard deviation of returns below the target. Another is the square root of the probability-weighted squared below-target returns. The squaring of the below-target returns has the effect of penalizing failures at an exponential rate. This is consistent with observations made on the behavior of individual decision-making under
:
where
''d'' is downside deviation (commonly known in the finacial community as 'downside risk'). Note: By extension, ''d''2 = downside variance.
''t'' is the annual target return, or MAR
''r'' is the random variable representing the return for the distribution of annual returns ''f''(''r''),
''f''(''r'') is a the three-parameter lognormal distribution
For the reasons provided below, this ''continuous'' formula is preferred over a simpler ''discrete'' version that determines the standard deviation of below-target periodic returns taken from the return series.
1. The continuous form permits all subsequent calculations to be made using annual returns which is the natural way for investors to specify their investment goals. The discrete form requires monthly returns for there to be sufficient data points to make a meaningful calculation, which in turn requires converting the annual target into a monthly target. This significantly affects the amount of risk that is identified. For example, a goal of earning 1% each and every month results in greater risk than the (apparently) equivalent goal of earning 12% each and every year.
2. A second reason for strongly preferring the continuous form to the discrete form has been proposed by Sortino & Forsey (1996):
"Before we make an investment, we don't know what the outcome will be... After the investment is made, and we want to measure its performance, all we know is what the outcome was, not what it could have been. To cope with this uncertainty, we assume that a reasonable estimate of the range of possible returns, as well as the probabilities associated with estimation of those returns...In statistical terms, the shape of [this] uncertainty is called a probability distribution. In other words, looking at just the discrete monthly or annual values does not tell the whole story."
Using the observed points to create a distribution is a staple of conventional performance measurement. For example, monthly returns are used to calculate a fund's mean and standard deviation. Using these values and the properties of the normal distribution, we can make statements such as the likelihood of losing money (even though no negative returns may actually have been observed), or the range within which two-thirds of all returns lies (even though the returns identified in this way do not necessarily have to have actually occurred). Our ability to make these statements comes from the process of assuming the continuous form of the normal distribution and certain of its well-known properties.
In PMPT an analogous process is followed:
1. Observe the monthly returns,
2. Fit a distribution that permits asymmetry to the observations,
3. Annualize the monthly returns, making sure the shape characteristics of the distribution are retained,
4. Apply integral calculus to the resultant distribution to calculate the appropriate statistics.
Sortino ratio
The Sortino ratio measures returns adjusted for the target and downside risk. It is defined as:
:
where,
''r'' = the annualized rate of return,
''t'' = the target return,
''d'' = downside risk.
This ratio replaces the traditional Sharpe ratio as a means for ranking investment results. The following table shows risk-adjusted ratios for several major indexes using both Sortino and Sharpe ratios. The data cover the five years 1992-1996 and are based on monthly total returns. The Sortino ratio is calculated against a 9.0% target.
{| class="wikitable"
|-
! Index
! Sortino Ratio
! Sharpe Ratio
|-
| 90-day T-bill
| -1.00
| 0.00
|-
| Lehman Aggregate
| -0.29
| 0.63
|-
| MSCI EAFE
| -0.05
| 0.30
|-
| Russell 2000
| 0.55
| 0.93
|-
| S&P 500
| 0.84
| 1.25
|}
As an example of the different conclusions that can be drawn using these two ratios, notice how the Lehman Aggregate and MSCI EAFE compare - the Lehman ranks higher using the Sharpe ratio whereas EAFE ranks higher using the Sortino ratio. In many cases, manager or index rankings will be different, depending on the risk-adjusted measure used. These patterns will change again for different values of t. For example, when t is close to the risk-free rate, the Sortino Ratio for T-Bill's will be higher than that for the S&P 500, while the Sharpe ratio remains unchanged.
Volatility skewness
Volatility skewness is another portfolio-analysis statistic introduced by Rom and Ferguson under the PMPT rubric. It measures the ratio of a distribution's percentage of total variance from returns above the mean, to the percentage of the distribution's total returns from returns below the mean. Thus, if a distribution is symmetrical (i.e., normal, as is assumed under MPT), it has a volatility skewness of 1.00. Values greater than 1.00 indicate positive skewness; values less than 1.00 indicate negative skewness. While closely correlated with the traditional statistical measure of skewness (viz., the third moment of a distribution), the authors of PMPT argue that their volatility skewness measure has the advantage of being intuitively more understandable to non-statisticians who are the primary practical users of these tools.
The importance of skewness lies in the fact that the more non-normal (i.e., skewed) a return series is, the more its true risk will be distorted by traditional MPT measures such as the Sharpe ratio. Thus, with the recent advent of hedging and derivative strategies, which are asymmetrical by design, MPT measures are essentially useless, while PMPT is able to capture significantly more of the true information contained in the returns under consideration. This being said, as the following table shows, many of the common market indices and the returns of stock and bond mutual funds cannot themselves always be assumed to be accurately represented by the normal distribution. This fact is also not well understood by many practitioners.
{| class="wikitable"
|-
! Index
! Upside Skewness(%)
! Downside Skewness(%)
! Volatility Skewness
|-
| Lehman Aggregate
| 32.35
| 67.65
| 0.48
|-
| Russell 2000
| 37.19
| 62.81
| 0.59
|-
| S&P 500
| 38.63
| 61.37
| 0.63
|-
| 90-day T-Bill
| 48.26
| 51.74
| 0.93
|-
| MSCI EAFE
| 54.67
| 45.33
| 1.21
|}
Conclusion
PMPT is able to assist investment practitioners more accurately create optimal investment strategies and evaluate the true performance of investment managers, mutual funds and other portfolios, without the restrictions imposed by MPT.
Endnotes
For a comprehensive survey of the early literature, see R. Libby and P. Fishburn [1979].
1. The earliest citation of the term 'Post-Modern Portfolio Theory' in the literature appears in 1993 in the article "Post-Modern Portfolio Theory Comes of Age" by Brian M. Rom and Kathleen W. Ferguson, published in The Journal of Investing, Winter, 1993. Summarized versions of this article have been subsequently published in a number of other journals and websites.
2. See Sharpe [1964]. Markowitz recognized these limitations and proposed downside risk (which he called "semi-variance") as the preferred measure of investment risk. The complex calculations and the limited computational resources at his disposal, however, made practical implemetations of downside risk impossible. He therefore compromised and stayed with variance.
3. In MPT, the terms variance, variability, volatility and standard deviation are used interchangeably to represent investment risk.
4. The three-parameter lognormal distribution recommended for use in downside risk calculations permits both positive and negative skewness in return distributions. This is a more robust measure of portfolio returns than the normal distribution, which requires that the upsides and downside tails of the distribution be identical
References
★ Bawa, V.S. "Stochastic Dominance: A Research Bibliography." Management Science, June, 1982.
★ Balzer, L.A. "Measuring Investment Risk: A Review." The Journal of Investing, Fall 1994.
★ Clarkson, R.S. Presentation to the Faculty of Actuaries (British). february 20, 1989.
★ Fishburn, P.C. "Mean-Risk Analysis with Risk Associated Below Target Variance." American Economic Review, March 1977.
★ Hammond, D.R. "Risk Management Approaches in Endowment Portfolios in the 1990's" Journal of Investing, Summer, 1993.
★ Harlow, W.V. "Asset Allocation in a Downside Risk Framework." Financial Analysts Journal, Sept-Oct 1991.
★ "Investment Review." Brinson Partners, Inc. 1992.
★ Kaplan, P. and L. Siegel. "Portfolio Theory is Alive and Well," Journal of Investing, Fall 1994.
★ Lewis, A.L. "Semivariance and the Performance of Portfolios with Options." Financial Analysts Journal, July-August 1990.
★ Leibowitz, M.L. and S. Kogelman. "Asset Allocation under Shortfall Constraints." Salomon Brothers, 1987.
★ Leibowitz, M.L., and T.C. Langeteig. "Shortfall Risks and the Asset Allocation Decision." Journal of Portfolio management, Fall 1989.
★ Libby, R. and P. Fishburn. "Behavioral Models of Risk taking in Business decisions: A Survey and Evaluation," Journal of Accounting Research, Autumn 1977. See also D. Kahneman and A. Tversky, "Prospect Theory: An Analysis of Decision under Risk," Econometrica, March 1979.
★ Post-Modern Portfoloi Theory Spawns Post-Modern Optimizer." Money Management Letter, February 15, 1993.
★ Rom, B. M. and K. Ferguson. "Post-Modern Portfolio Theory Comes of Age." Journal of Investing, Winter 1993.
★ Rom, B. M. and K. Ferguson. "Portfolio Theory is Alive and Well: A Response." Journal of Investing, Fall 1994.
★ Rom, B. M. and K. Ferguson. "A software developer's view: using Post-Modern Portfolio Theory to improve investment performance measurement." Managing downside risk in financial markets: Theory, practice and implementation; Butterworth-Heinemann Finance, 2001; p59.
★ Sharpe, W.F. "Capital Asset Prices: A Theory of Market Equilibrium under Consideration of Risk." Journal of Finance, Vol. XIX (1964)
★ Sortino, F. "Looking only at return is risky, obscuring real goal." Pensions and Investments magazine, November 25, 1997.
★ Sortino, F. and H. Forsey "On the Use and Misuse of Downside Risk." The Journal of Portfolio Management, Winter 1996.
★ Sortino, F. and L. Price. "Performance Measurement in a Downside Risk Framework." Journal of Investing, Fall 1994.
★ Sortino, F. and S. Satchell, editors. "Managing downside risk in financial markets: Theory, practice and implementation" Butterworth-Heinemann Finance, 2001.
★ Sortino, F. and R. van der Meer. "Downside Risk: Capturing What's at Stake." Journal of Portfolio Management, Summer 1991.
★ "Why Investors Make the Wrong Choices." Fortune Magazine, January 1987.
External links
★ http://investmenttechnologies.com/publications.html
★ http://www.fpanet.org/journal/articles/2005_Issues/jfp0905-art7.cfm
★ http://www.bwater.com/PDFs/engineering_targeted_returns_and_risks_pmpt_060215.pdf
★ http://investmenttechnologies.com/about.html
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psst.. try this: add to faves
