3. 3GEN Implementation Support
The main issues discussed around the building blocks for 3GEN asset allocation represent a paradigm shift in asset allocations in a post-crisis world. Risk mitigation, real diversification and multiple-objectives decision making are at its core.
3.1 On Investment Decision Making
As has often been noted (Lee, 1972; Lai and Hwang, 1994), a major concern in making decisions is that almost all decision problems have multiple and usually conflicting criteria. The soundness of decision making is thus measured by the degree to which the relevant goals are achieved. To that end, an application of the scientific approach is necessary, and this calls for systematic analysis of the decision system.
Systematic investigation enables the decision maker to consider all pertinent factors related to the decision so that the best ultimate course of action can be identified from among a set of alternatives.
There is a practical scientific approach to portfolio selection utilizing multiple objectives optimization, called, goal programming, an approach which is capable as far as is possible of achieving a required set of preferences.
Goal Programming (GP) is perhaps the most widely used approach in the field of multiple criteria decision making that enables the decision maker to incorporate numerous variations of constraints and goals. The original portfolio selection problem, with risk and return optimization, can be viewed as a case of Goal Programming with two objectives. Additional objectives representing other factors can be introduced for a more realistic approach to portfolio selection problems.
Although any Goal Programming problem of meaningful size would be solved on the computers, the notion of programming in GP is associated with the development of solutions, or programs, for a specific problem. Therefore, GP has nothing intrinsically to do with computer programming and the name GP is used to indicate seeking the (feasible) program for a mathematical model that is composed solely of goals (Ignizio, 1985).
Ignizio and Romero (2003) highlight that real-world decision problems are usually changeable, complex and resist treatment with conventional approaches. Therefore, the optimization of a single objective subject to a set of rigid constraints is in most cases unrealistic, and that is why Goal Programming was introduced, in an attempt to eliminate or at least mitigate this shortcoming.
The two philosophical concepts that serve to best distinguish Goal Programming from conventional methods of optimization (with a single objective) are the incorporation of flexibility in constraint functions and the adherence to the philosophy of Satisficing as opposed to Optimization.
Satisficing is an old Scots word that refers to the desire to find a practical and real-world solution to a problem, rather than an idealistic or optimal solution to a highly simplified model of that problem. In Goal Programming, the decision maker usually seeks a useful, practical, implementable and attainable solution rather than one satisfying the mathematician’s desire.
3.2 On Using Risk Measures
As outlined above, risk-management practices have become a central topic since the recent financial crisis. Risk management is no longer a compliance issue. Organisations/investors should identify and prepare for non-preventable risks that arise internally and externally to their investment strategy – see previous category 1-4 risks segmentation.
In implementation, risk is often under-measured. For example, many investment institutions use risk-distorting factors like standard deviation or Sharpe ratio as a measure of risk, due to its ease of use and the availability of the underlying data. However, many researchers provide analysis of risk measures that go beyond standard deviation, such as Pflug (2006), Balbas, Balbas and Mayoral (2009) and Rockafellar, Uryasev and Zabarankin (2006):
- Pflug (2006) researches measures of risk in two categories: risk capital measures (which serve to determine the necessary amount of risk capital in order to avoid damage if the outcomes of an economic activity are uncertain, and their negative values may be interpreted as: acceptability measures, safety measures, and pure risk measures) and risk deviation measures (which are natural generalisations of the standard deviation).
- Rockafellar, Uryasev and Zabarankin (2006) systematically study general deviation measures for their potential applications to risk measurement in areas such as portfolio optimisation and engineering.
- Balbas, Balbas and Mayoral (2009) emphasise that modern risk analysis must face two major drawbacks affecting most of the available securities and many investment strategies; namely: asymmetric returns and fat tails.
Other alternative approaches in creating a framework of risk management measures include Tobin´s Q (Tobin, 1968), Minsky Moments (Minsky, 1992) and Ineichen´s “FEI – Financial Explosivity Index” (Ineichen, 2012).
Part 4: Twelve 3GEN Imperatives for Practitioners
Dr. Rania Azmi
Rania Azmi is an adviser to one of the world’s largest Sovereign Wealth Funds. She enjoys a first-hand experience investing in Middle Eastern markets as well as global financial markets. Azmi enjoys 13+ years of private/institutional investing experience, and she believes that the best theory has no purpose unless it is applied in a practical manner. Azmi was chosen by aiCIO magazine as one of the forty under forty brightest stars in institutional investment. She received her Doctorate in Investment Decision Making from the University of Portsmouth and is the author of Making Investment Decisions for Portfolios (Cambridge Scholars, 2013).
Azmi is also an activist for women's positions in business, politics, and society. She has spoken for the World Bank on gender and economics, and was awarded the Google Prize for "Most Interesting and Creative Work." Most recently, she was designated an Egyptian Woman of Influence globally by the Women Speakers Association and contributed a multidimensional model in preparation for the new generation of the United Nations Millennium Development Goals beyond 2015 in support of women.
Mag. Markus Schuller, MBA, MScFE
Markus Schuller is the founder of Panthera Solutions, a Strategic Asset Allocation Consultancy in the Principality of Monaco. Panthera Solutions provides access to the third generation of portfolio optimization techniques to European institutional investors. Panthera´s monthly macro-newsletter (PSC) reaches 10.000+ finance professionals in Germany/Switzerland/Austria and is regularly published in German quality newspapers.
Markus has over 15 years experience in trading, structuring and managing standard and alternative investment products and was working at banks and asset management companies prior to Panthera Solutions. He graduated from his Master in Economics at Johannes Kepler University and University of Pittsburgh, his MBA at the International University of Monaco and his MSc in Financial Engineering degree at IUM. Since 2009 Markus is teaching the courses “Portfolio Theory & Alternative Assets” and “Investment Banking” at the International University of Monaco and established a 3-day workshop on “Third Generation Multi-Asset & Risk Management”, together with Deutsche Börse and Vienna Stock Exchange.