Portfolio Risk Management

Vertex Pharmaceuticals, Inc. is a global biotechnology company based out of Cambridge, MA. The Company's strategy is to commercialize its products both independently and in collaboration with major pharmaceutical companies. Vertex's product pipeline is focused on viral diseases, cystic fibrosis, inflammation, autoimmune diseases, cancer, and pain.

Given the uncertainty of outcomes in the biotech industry, consideration of variability is an inherent part of the decision process. Often, the mean (average) is not a relevant decision criteria. This is especially true for smaller biotech companies like Vertex – the opportunity costs are extremely high because scarce capital resources would be invested elsewhere, with a higher probability of realistic return. For example, a company may reject a project which is profitable on average (positive Net Present Value) because some of the possible outcomes are unacceptable to the decision maker. Consideration of variability allows a decision maker to bring in their own risk tolerance into the decision. A similar argument applies when estimating a safety margin above a base case (e.g. in cost budgeting).

Vertex’s strategy and analytics group within the corporate finance division seeks to provide the senior management with dynamic revenue and profit forecasting methodology that helps to identify types of drugs that should be developed given a finite amount of cash and resources. A traditional financial view allows the user to identify scenarios and potential outcomes, but lacks the ability to show the range of potential values within each and every outcome. Vertex’s team uses the DecisonTools Suite to establish the average outcome, the variability of outcomes and to pressure-test risk and uncertainty of a particular scenario throughout the decision process.

Vertex’s team built a complex financial risk analysis model using @RISK to enhance its portfolio process. Monte Carlo simulation and optimization are used to analyze and optimize project and portfolio decisions, given short and long-term corporate strategy. @RISK is also frequently used throughout the business development process: simulating across multiple sales forecasts provides BD team with a range of potential outcomes, making it easy to pinpoint a particular scenario on a curve, along with its probability and value. TopRank turns the sensitivity analysis into a quick and seamless exercise, answering multiple what-if questions within minutes. Franchise and program leaders can now see a dollar effect of their program being delayed or advanced, adding supplementary indications to the development plan and even addressing the price uncertainties all at the same time. The simple interface of PrecisionTree along with tornado chart outputs makes it easy to explain the effect and importance of a particular assumption / decision to an audience with no finance background.

As the company continues to grow, adding more drugs and collaborations to its development pipeline, we will see in this free live webcast how the DecisionsTools Suite remains one of Vertex’s analytical tools of choice to enhance and guide the decision making process.

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One of the side-effects of the recession appeared to be a reduction in the demand for electricity as businesses and consumers alike looked to make savings on their outgoings. However, economic recovery seems to render this trend as temporary, meaning that the global need to tackle energy-consumption is as pressing as ever.

BC Hydro, Canada's third largest electrical utility provides an interesting case study in how to  ascertain the most effective ways to tackle the gap between supply and demand of electricity in British Columbia. Trends such as an expanding population growth, and the increase in energy usage per customer, have led to a rise in the demand for electricity across the region. By legislation, BC Hydro must aim to meet these energy needs through implementing cost-effective energy conservation approaches before it can turn to increasing the supply. 

The company has set itself one of the most aggressive targets in North America, with a plan to meet almost 75 percent of its incremental load through Demand Side Management (DSM) over the next 20 years. DSM projects include compact fluorescent light promotions; subsidies for energy efficient appliances; variable speed motor promotions (for home furnaces); and promotional activity aimed at motivating customers to use less energy.

BC Hydro uses @RISK risk analysis software to measure the uncertainty around its energy conservation efforts, both at the project stage and at a higher portfolio level. Around 60 projects were analysed on a case-by-case basis, and a probability distribution around the forecast outcome was developed. @RISK helps BC Hydro to capture the level of uncertainty of the estimated savings for each individual DSM venture.

In recognition that projects do not operate in isolation, BC Hydro also uses @RISK to explore the interrelationships between key uncertainties: the participation and savings per participant, and the participation across projects. The analysis showed that if a 'conservation culture' was developed in the province, it would result in an increase in energy savings across all programmes. However, it also illustrated that, if this culture failed to materialise, the performance of all programmes will be dragged down.

Exploring uncertainty using @RISK allowed BC Hydro to find the best balance between the uncertainties of supply side resources and those of relying heavily on energy conservation. Employing Decision Making Under Uncertainty helps BC Hydro to meet both financial risk analysis and environmental risk analysis goals. As a result, it expects to meet the majority of its incremental load growth through conservation measures.

» Read the BC Hydro case study

Craig Ferri
EMEA Managing Director of Risk & Decision Analysis

So.  Is it time to shut down your Monte Carlo software?  I don't think so. . . .   

Part 2 of this blog ended with me very quickly stating that the MotoGP tipping comp optimiser was identical in structure to a portfolio optimisation problem, where the portfolio could contain stock or other assets, or even projects. Let’s look at this in a little more detail as I’m sure you’re reading this to find how to optimise your own decisions rather than wondering how I went in the tipping competition!

In my model there was a fixed budget (though less could be spent if desired) to spend on riders, with the aim of maximising their total points haul. In the real world you may have a total budget of say $100m to invest in a range of projects perhaps many hundreds of millions of dollars in total value each of which have certain expected returns. At its simplest this decision evaluation will find the most (expected) profitable portfolio of the projects. This is an inclusion/exclusion grouping model, but it is very simple to optimise assets with a continuous level e.g. the amount of money invested in various shares etc. Another real example I have seen when working with an investment company here in Australia was a model whose goal was simply to find the portfolio mix that came closest to the total allowable spend without exceeding it.

Further realism can be included by using constraints should there be the need. A resource constraint may mean there has to be a limit to the number of projects that can be run simultaneously. There may also be a minimum number of projects determined by management as a mitigation strategy. Such constraints are very simple to employ using Evolver and add value to the decision analysis without the need to provide specific risk analysis/Monte Carlo simulation information for the model.

A slightly more sophisticated method of turning an optimisation into a useful portfolio risk management tool where uncertainty hasn’t been specifically modelled is to estimate the possible downside of each asset and include it in the calculation of the portfolio’s ‘score’. The Evolver software comes standard with over twenty example spreadsheets for your educational pleasure, of which “Portfolio Mix.xls” gives one method for doing just this.
In the next (and final) instalment of the Making Optimal Choices blog I will explore the idea that not all optimisations no matter how mathematically correct will produce the same results in good time, and that elegant modelling should always be the goal prior to firing up Evolver.

And so you know, I came second in the competition. Next year I’m hoping to go one better!

» Making Optimal Choices, Part 1
» Making Optimal Choices, Part 2

Rishi Prabhakar
Trainer/Consultant

In my last blog entry I introduced the notion that optimal decision making wasn’t ‘on the radar’ for many clients in Australasia, and laid out a couple of ideas why. I too once focussed on Monte Carlo simulation rather than decision evaluation, but last year the most obscure event changed that.

Call me a nerd of you will, but I like modelling problems in Excel. There is skill involved in setting up a problem such that the model assumptions aren’t too gross, and an art to making the model elegant. This elegance can be very important to optimisation problems, but more on that later. My first homemade optimisation problem was generated by motorcycle racing! MotoGP, to be precise. A friendly tipping competition with friends was formed at the start of the 2009 season with the following structure:

• Entrants played the role of Team Manager.
• Team Managers had a fixed budget to spend on riders.
• Either a few good riders could be purchased, or many lesser riders, or something in between.
• The team that had accumulated the most points at the end of the season was the winner and received kudos!

Although the future results could not be known of course so I set up and ran the optimisation with Evolver after the event to see what the optimal team selection would have been. Historical data could have been used to discover the type of rider mix that tended to be optimal and thus make an informed decision for this competition. The risk in having only a few riders was that any misfortune would have a big negative impact on the points won, whereas a team consisting of many (cheaper) riders was less likely to suffer such a fate. This downside scenario will be modelled into the 2010 MotoGP Team Manager predictive, optimised model (currently in production)!

What has this to do with the corporate world? Replace “team” with portfolio and “riders” with “assets”, “shares” or “projects” and you have a classic portfolio optimisation model. I hadn’t created this model with business applications in mind but I realised that was precisely what I was doing. An instant later I realised just how useful Evolver would be in many decision scenarios even though it doesn’t incorporate uncertainty (RISKOptimizer does).

In the next instalment I will further explore some practical applications for Evolver and you’ll see just how universally appropriate it can be.

» Making Optimal Choices, Part 1

Rishi Prabhakar
Trainer/Consultant

Risk analysis and decision-making tools are relevant to most organisations, in most industries around the world.  This is demonstrated by the speaker line-up at this year's European User Conference, an event at which we believe it is important to bring together customers from a wide range of market sectors.

We are holding 'New Approaches to Risk and Decision Analysis' at the Institute of Directors in central London on 14th and 15th April 2010.  As with previous years, the programme aims to provide everyone attending with practical advice to enhance the decision-making capabilities of their organisation.  Customer presentations, which offer insight into a wide variety of  business applications of risk and decision analysis, include:

• CapGemini: Faldo's folly or Monty's Carlo – The Ryder Cup and Monte Carlo simulation
• DTU Transport: New approaches to transport project assessment; reference scenario forecasting and quantitative risk analysis
• Georg-August University Research: Benefits from weather derivatives in agriculture: a portfolio optimisation using RISKOptimizer
• Graz University of Technology: Calculation of construction costs for building projects – application of the Monte Carlo method
• Halcrow: Risk-based water distribution rehabilitation planning – impact modelling and estimation
• Pricewaterhouse Coopers: PricewaterhouseCoopers and Palisade: an overview
• Noven: Use of Monte Carlo simulations for risk management in pharmaceuticals
• SLR Consulting: Risk sharing in waste management projects - @RISK and sensitivity analysis
• Statoil: Put more science into cost risk analysis
• Unilever: Succeeding in DecisionTools Suite 5 rollout – Unilever's story

We will also look at the recently-launched language versions of @RISK and DecisionTools Suite, which are now available in French, German, Spanish, Portuguese and Japanese.  Software training sessions will provide delegates with practical knowledge to ensure they can optimise their use of the tools and implement business best practise and methodologies.

With over 100 delegates from around the world attending, the event is also a good opportunity to network and knowledge-share with risk professionals from around the world.

» Complete programme schedule, more information on each presentation,
   and registration details

In Part 2 of this series I finished by asking what should be done with historical data, now that we have decided that storing it is probably a good idea. I won’t keep you waiting any longer.

Auditing and calibration of the model at both the micro and macro level. It’s as important as any other element of risk or statistical analysis, or indeed the model building itself. At the distribution level historical data helps to both parameterise the distributions and in fact select them in the first place. As a minimum a few data points will help you to understand possible central tendencies and variability for your risks, and also generate a list of feasible distributions to choose from. With a reasonable number of observations @RISK for Excel can be used to fit distributions to the data taking care of both distribution selection and parameterisation simultaneously. Only five data points are technically needed, but a reasonable fit will require either more than that or other holistic information to achieve validity.

At the macro level total project cost estimates are often ignored from the portfolio perspective. Commonly high percentiles are reported from such models to use in a ‘contingency’ calculation, such as the P90 or P95. Whilst a high percentile, the P90 (say) should still be exceeded 10% of the time! If your projects never go over this percentile then either there are some major mitigating factors not included in the model or the volatility is being consistently overstated. Likewise, the P10 for total cost (these ‘good’ percentiles are rarely if ever reported or considered in project cost estimation work) should be bettered in roughly 10% of projects. If this is not the case then the upside risk has been overstated. This may be due to misconceptions about the positive skewing present in most cost/delay risks or mistakes made in the parameterisation of the risks where the estimate (“most likely” etc.) is actually the “best case” or close to it, rather than a central tendency of the process over time. There could also be other possibilities.

No matter how you look at it, the collection and intelligent use of historical data is integral to effective and useful risk analysis and management, and critical to achieving valid Monte Carlo simulation results. If you aren’t currently recording everything you can get your hands on start right now!

 

» Part 1
» Part 2

Rishi Prabhakar
Trainer/Consultant

Dr. Robert Ameo is principal of Market Modelers, LLC, with over 20 years’ experience in health care management, marketing and business development. Prior to founding Market Modelers, he served in the corporate development group at Johnson & Johnson. He is a recognized expert and innovator in the modeling and forecasting of new technology adoption and market share. Robert has extensive experience evaluating investment opportunities and their portfolio impact for mergers and acquisitions, venture investing, research development, and marketing efforts. Using his training as a psychologist and his extensive industry experience, he designs and executes targeted market and expert research experiments to quantify the defensible range of possibilities for new technology and product adoption. His forecasts are used both by start-up ventures to create a vision of their potential worth, and well-established biopharmaceutical and medical device companies to understand the true economic (uncertainty adjusted) value of their potential investments. Prior to his industry experience, Robert was VP of Clinical Operations and Utilization Management for a national managed care company. He holds a behavioral science PhD from the University of Miami.

Dr. Ameo will present a case study next week at the 2009 Palisade Conference: Risk Analysis, Applications, & Training,  21 - 22 October at the Hyatt Regency in Jersey City (10 minutes by PATH from Manhattan's Financial District).

See the abstract for his case study below, and see the full schedule for the Conference here.

Interpretive and Ethical Issues in using Monte Carlo Simulations to Support Executive Decision-Making: How to avoid giving your boss impressive, but misleading guidance

Simulations are proliferating throughout the business community powered by a troop of freshly minted MBAs armed with their requisite course on decision sciences and their student versions of @RISK.

Finance organizations are asking their analysts to “do a Monte Carlo.”  Dutifully, the analysts select a handful of “key” variables, assign triangular or Pert distributions, set iterations to 1000, push the simulate button. The laptop’s screen displays a colorful histogram and a sensitivity analysis to add to the PowerPoint.

Lo and behold, the simulation analysis supports the original scenario model showing the mean or median simulated output to be just about in the middle of the distribution. Mission accomplished. Senior leadership is assured that the model has been tested by simulating 1000 potential outcomes. Management moves forward in their pre-decided direction with confidence bolstered by a state of the art Monte Carlo analysis.

This scenario happens every day and for so many reasons it is very wrong.

Using simulations to support executive decision-making introduces ethical concerns that are not present in “most likely case” scenario modeling. In this presentation, Bob Ameo discusses the ethical responsibilities of using simulation models to inform executive decision-making. Specific recommendations are made how to appropriately conduct and present outcomes from simulation models.

Next Week: October 21-22 in NYC

Building on the success of last year’s record-breaking event, the conference will offer a wide range of software training, model building, and real-world case study sessions. Last year, the event drew over 150 practitioners and decision-makers from a broad spectrum of industries. The @RISK and DecisionTools software tracks were more popular than ever. This year, we’re expanding software training with sessions that let you walk through examples and try the tools directly. This will enable you to take some new tips back to the office. Please join us in October for a great opportunity to learn and connect with colleagues.

In an earlier blog I allowed myself some raw speculation as to whether holistic risk management thinking is being adequately applied when it comes to the Norwegian government’s management of the state pension fund. This fund represents one of the world’s largest exercises in risk analysis in “retirement planning.” The Fund invests the oil wealth generated in the country in a mix of global equities and % bonds, and whose performance is essentially currently measured against a global benchmark portfolio of bonds and equities.

I specifically asked the question as to whether the fund should be devoting far more significant efforts to invest in non-traditional assets, as a way to mitigate some potential scenarios that could adversely affect both equity and bond investments. Investments that could potentially mitigate some of these scenarios could perhaps include very large positions in alternative energy technologies, and I noted however that although the costs and risks of such an investment strategy could be large (particularly as the scenario which it mitigates may never materialize), it could nevertheless be a prudent one, given the already very large fund that already exists for a small population base of about 5m people. Could it be so bad if 5%-10% of that fund were invested in such technologies (with such investments arguably supporting some of the fund’s other goals – such as ethical or social investments)?

It is therefore with interest that the Financial Times reported last Saturday  that the Norwegian government is planning to review the operations of its sovereign wealth fund after it lost €75bn ($100bn, £68bn) on investments last year. I await eagerly the results of this review, specifically of course to see whether such fundamentally new investment strategies will be implemented.


Dr. Michael Rees
Director of Training and Consulting

An item from the Department of More Things Change, the More They Stay the Same.

Last week, speaking at a conference on managing retirement income, an executive with a U.S. division of Deutsche Bank announced that with the "failure" of diversified investing strategies, Modern Portfolio Theory was dead.  R.I.P. balanced portfolios.  R.I.P. the Nobel Prize-winning work of Harry Markowitz.  R.I.P. Monte Carlo simulation projections.

Instead, announced Phillip Hensler, "Advisors who offer predictability will prevail"-- isn't predictability the goal of all those portfolio managers who rely on statistical analysis techniques for risk assessment?  And he foresees that we will enter a new era of "Outcome Driven Investing"--isn't outcome what drives all investment activity?

In this new era financial planners will help their clients match their "health risks, market risks, and longevity risks with specific guaranteed and non-guaranteed" investment products.  Two questions: What else have financial planners been doing for the past decade?  And just how are they going to measure that risk?  

Maybe in this new era, sound investment advice won't be based on Modern Portfolio Theory and risk evaluation won't be the work of Monte Carlo software.  But just exactly what will be the era's guiding principles and analytical techniques?  Post-Modern Portfolio Theory and Las Vegas computational tools?

As a regular visitor to Norway, it is hard not to be impressed by the wealth generation in the country. Even more impressive is the discipline of the government and population to accept that the majority of the vast oil windfall of the country should be invested for the future (in a pension fund) and not spent today (high tax rates and price levels being one testament to that).

In this blog I allow myself some raw speculation as to whether holistic risk management thinking is being adequately applied when it comes to the government’s management of the wealth generated by this windfall.

In the spring of 1997, the Ministry of Finance decided that the Government Pension Fund–Global (previously known as the Government Petroleum Fund) should invest parts of its portfolio in equities. In January 1998 the fund consisted of bond investments worth NOK 113 billion (about USD 15 billion at current exchange rates). Since then inflows of new capital into the fund (also boosted by the high oil price) have been significant; in 2007, capital inflows averaged more than USD 300 million per trading day. By January 2008 the fund was worth over NOK 2000 billion (about USD 300 billion) and it is forecast to be worth over NOK 4000 billion (about USD 600 billion) by 2015 (according to the National Budget 2009)—the ultimate in retirement planning. Over time the fund’s investment guidelines have been relaxed, with the fund currently consisting of about 50% equities and 50% bonds, including government, corporate, securitized and inflation linked bonds.

To some extent there is a natural diversification in the fund. For example, to the extent that it is believed that global equities in aggregate are negatively affected by high oil prices, then there is a natural hedge in the portfolio, as increases in the oil price will reduce the equity value but lead to increased capital inflows (although the balance of this will change as the equity portfolio becomes larger). Similarly, oil-related new inflows and the investment in inflation-linked bonds could also provide some protection against long-term inflation (arguably, equities may or may not be a good long term inflation hedge). In addition, the fund of course also uses advanced tools of portfolio management, which are surely applied with rigor. However, as we know from the credit crisis, such tools can lead one to a false sense of confidence if they miss the big picture (deckchairs on the Titanic, etc!).

In this context, I allow myself to speculate (hypothesise?!) as to whether the fund should be devoting far more significant efforts to invest in non-traditional assets. (The fund’s performance is essentially currently measured against a benchmark portfolio of bonds and equities and so such efforts or investments would be hard to justify against these objectives).

The most obvious scenario in which the fund could lose out significantly would be a shift in the world’s energy sources (over the many decades of pension obligations), which could create an environment that is simultaneously largely unfavorable for most asset classes in the fund. Conceivably the potentially massive costs associated with creating a low-carbon global economy could produce a situation that is unfavorable for most global equity investments, that could unleash inflationary forces that reduce the value of many bond investments, and potentially reduce demand for oil (and its price). Such a “nightmare” scenario for the fund does not seem beyond the realms of reality. 

The most obvious strategy to mitigate the effects of this scenario would be for the fund to proactively take very large positions in alternative energy technologies. Such positions would presumably need to be very large (and possibly require the fund to itself create and support the development of new innovative companies in this area, not just to passively invest in existing ones). The costs and risks in doing so would be large (particularly as the scenario may never materialize), but it could be a prudent one, given the already very large fund that already exists for a small population base of about 5m people. Could it be so bad if 5%-10% of that fund were invested in such technologies? (Such investments would arguably support, or at least relate to, some of the fund’s other goals—such as ethical or social investments). A risk assessment would be a good idea. Now, back to the real world!

Dr. Michael Rees
Director of Training and Consulting