Hybrid Portfolio Theory (HPT) is a model of financial management intended for use in client portfolio asset allocations and investment decision making by investment managers.
The term Hybrid Portfolio Theory (HPT) has multiple meanings ranging from diversified asset allocation with risk and reward optimization in mind to investment selection based on algorithms and quantified investment data. This article will outline and assess hybrid portfolio theory in terms of its meaning, benefits, disadvantages and validity.
What Hybrid-Portfolio Theory is
In simple terms, hybrid means the conversion of two financial methods; portfolio means the distribution of assets via investment and/or asset allocation, and theory means, a tested but not absolutely proven financial hypothesis. Thus, HPT is essentially an investment management optimization theory. Below are two examples of HPT, both of which are quite differentiated from one another.
1. Hybrid Portfolio Theory using algorithmic data compression:
The application of Hybrid Portfolio Theory ideally produces financial consistency and returns with higher returns on investment with less risk making it an investment selection model. An example of Hybrid Portfolio Theory is described by Paul Bao and Hakman Wong uses several key components including the following:
• Fuzzification: Data classification
• Compression techniques: Simplification of information
• Genetic algorithm: Analysis of changing data for use in a formula
Since 'fuzzification', 'data compression' and 'algorithms' can vary, so can a Hybrid Portfolio Theory. In other words, HPT can be viewed in terms of 1) data in, 2) data processed, and 3) data out. All three steps in the HPT model can vary making the actual data, its processing and outcome important to the validity of the model.
2. Hybrid portfolio using non-sequential data analysis:
This second model of Hybrid Portfolio Theory is more static and presumes conditions don't change, but rather follow general principles that are consistent enough to be incorporated into the theory. This particular version of Hybrid Portfolio Theory is attributed to Jeff Joseph of Prescient Advisors and makes use of a split portfolio technique. The two portfolios combine low risk and average return in the majority of the split portfolio and high return with optimized risk in the minority portion.
Essentially, this theory goes without saying, but does provide financial planners with a method by which to allocate assets. The question with this HPT is which particular investments provide the portfolio with its intended results, i.e. one can easily state the conditions by which an objective can be achieved without actually defining how those conditions can be met.
Benefits of Hybrid-Portfolio Theory
The benefits of Hybrid-Portfolio Theory are unique to the actual performance of the application of the theory. In other words, both the above theories must be demonstrated to work to be considered beneficial to an investor or investment manager. Until that is done, the HPT theory is just another theory that sounds interesting, but may not actually be any better than simpler quantitative techniques of investment analysis combined with intuitive insight, existing knowledge and traditional analysis.
The benefits of traditional analysis techniques such a technical analysis and fundamental analysis is that they are time tested to be accurate within a certain range. The goal of new models and theories such as Hybrid Portfolio Theory is to improve upon the accuracy of the predictive capacity of existing investment analytics, investment management models and financial planning policies.
Disadvantages of Hybrid-Portfolio Theory
The Hybrid Portfolio Theory put forth in the second example above is a reaction to what it refers to as "black swan" market conditions. In this reaction, the call for lower risk investment techniques is emphasized in order to reduce the probability of highly volatile asset value swings. Essentially the 'theory' states by aggressively reducing risk while simultaneously increasing return an investment portfolio can achieve an 'asymmetrical' return i.e. a ROI that is not symmetrical with risk.
The disadvantages of both the above techniques are 1) opportunity cost, 2) validity verification, 3) fiscal and temporal cost. In other words, the theory needs to be proven, and the theory needs to be proven as being better than other investments in terms of both risk and return. Additionally, especially in the case of the first HPT example, the quantitative and information technology consulting and upfront costs for such a model, especially in the case of asset managers, may be high, making the necessity of value for the premium to be worthwhile.
Validity of Hybrid-Portfolio Theory
"In theory", there's nothing invalid with the assertions made by either Jeff Joseph's, Paul Bao and Hakman Wong's HPT. However, financial models are not financial theories, and financial hypotheses are not financial models. On first impression, it seems as though HPT may encompass a little of all three of these terms of validity potentially giving it more credibility than might be justified.
As with any hypothesis, model or theory, until the data is demonstrated to be both statistically significant, and quantitatively proven, it is nothing more than financial idealism packaged within fiscal lingo. This is not to say Hybrid Portfolio Theory doesn't work, but it is to say, HPT must first be demonstrated to work, and the burden of proof is on the proponents of the theory.
The term Hybrid Portfolio Theory (HPT) has multiple meanings ranging from diversified asset allocation with risk and reward optimization in mind to investment selection based on algorithms and quantified investment data. This article will outline and assess hybrid portfolio theory in terms of its meaning, benefits, disadvantages and validity.
What Hybrid-Portfolio Theory is
In simple terms, hybrid means the conversion of two financial methods; portfolio means the distribution of assets via investment and/or asset allocation, and theory means, a tested but not absolutely proven financial hypothesis. Thus, HPT is essentially an investment management optimization theory. Below are two examples of HPT, both of which are quite differentiated from one another.
1. Hybrid Portfolio Theory using algorithmic data compression:
The application of Hybrid Portfolio Theory ideally produces financial consistency and returns with higher returns on investment with less risk making it an investment selection model. An example of Hybrid Portfolio Theory is described by Paul Bao and Hakman Wong uses several key components including the following:
• Fuzzification: Data classification
• Compression techniques: Simplification of information
• Genetic algorithm: Analysis of changing data for use in a formula
Since 'fuzzification', 'data compression' and 'algorithms' can vary, so can a Hybrid Portfolio Theory. In other words, HPT can be viewed in terms of 1) data in, 2) data processed, and 3) data out. All three steps in the HPT model can vary making the actual data, its processing and outcome important to the validity of the model.
2. Hybrid portfolio using non-sequential data analysis:
This second model of Hybrid Portfolio Theory is more static and presumes conditions don't change, but rather follow general principles that are consistent enough to be incorporated into the theory. This particular version of Hybrid Portfolio Theory is attributed to Jeff Joseph of Prescient Advisors and makes use of a split portfolio technique. The two portfolios combine low risk and average return in the majority of the split portfolio and high return with optimized risk in the minority portion.
Essentially, this theory goes without saying, but does provide financial planners with a method by which to allocate assets. The question with this HPT is which particular investments provide the portfolio with its intended results, i.e. one can easily state the conditions by which an objective can be achieved without actually defining how those conditions can be met.
Benefits of Hybrid-Portfolio Theory
The benefits of Hybrid-Portfolio Theory are unique to the actual performance of the application of the theory. In other words, both the above theories must be demonstrated to work to be considered beneficial to an investor or investment manager. Until that is done, the HPT theory is just another theory that sounds interesting, but may not actually be any better than simpler quantitative techniques of investment analysis combined with intuitive insight, existing knowledge and traditional analysis.
The benefits of traditional analysis techniques such a technical analysis and fundamental analysis is that they are time tested to be accurate within a certain range. The goal of new models and theories such as Hybrid Portfolio Theory is to improve upon the accuracy of the predictive capacity of existing investment analytics, investment management models and financial planning policies.
Disadvantages of Hybrid-Portfolio Theory
The Hybrid Portfolio Theory put forth in the second example above is a reaction to what it refers to as "black swan" market conditions. In this reaction, the call for lower risk investment techniques is emphasized in order to reduce the probability of highly volatile asset value swings. Essentially the 'theory' states by aggressively reducing risk while simultaneously increasing return an investment portfolio can achieve an 'asymmetrical' return i.e. a ROI that is not symmetrical with risk.
The disadvantages of both the above techniques are 1) opportunity cost, 2) validity verification, 3) fiscal and temporal cost. In other words, the theory needs to be proven, and the theory needs to be proven as being better than other investments in terms of both risk and return. Additionally, especially in the case of the first HPT example, the quantitative and information technology consulting and upfront costs for such a model, especially in the case of asset managers, may be high, making the necessity of value for the premium to be worthwhile.
Validity of Hybrid-Portfolio Theory
"In theory", there's nothing invalid with the assertions made by either Jeff Joseph's, Paul Bao and Hakman Wong's HPT. However, financial models are not financial theories, and financial hypotheses are not financial models. On first impression, it seems as though HPT may encompass a little of all three of these terms of validity potentially giving it more credibility than might be justified.
As with any hypothesis, model or theory, until the data is demonstrated to be both statistically significant, and quantitatively proven, it is nothing more than financial idealism packaged within fiscal lingo. This is not to say Hybrid Portfolio Theory doesn't work, but it is to say, HPT must first be demonstrated to work, and the burden of proof is on the proponents of the theory.
Sources:
1. http://tinyurl.com/4tv62e2
2. http://tinyurl.com/4ftwbr2
3. http://tinyurl.com/lrxfgn
4. http://www.badros.com/greg/papers/badros-dcc94.pdf
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