Scenario-type Planning and Standard Forecasting
In the world of finance, and global macroeconomics at large, we find ourselves now more than ever where scenario planning is in desperate need. For years however, standard forecasting has ruled. As Kleiner articulates in his 2003 case study, “The Man Who Saw the Future” this challenge of following the trend versus charting the various possible outcomes is always at odds. This dichotomy dating back to the 1960s, still holds true today as we navigate the potential changing sees of the macroeconomic landscape.
Shell Oil, 1973 to 2002
The case study follows the impact of Pierre Wack, an executive at Royal Dutch Sell Oil company who pushed the use of scenario-type planning in the corporate sector. Wack’s strategy, based off of learnings from great thinkers like Sufi mystic G.I. Gurdjieff and the Futurist Herman Kahn, was a systematically examine every possible angle of a situation (Kleiner, 2003).
This approach in the early 1970s (as it often does today) stood in the face of the norm of following the trend. In the 1970s the general belief was that tensions in the Middle East between Arab states and Israel, would sort themselves out, as they always had since world war II. As the US dominated world power had always won out in the end, scenarios other than the norm were not taken to heart. Of course, this turned out to not be the case, and ever escalating wars fueled by the support (or interference) of Russia caused oil markets to swing wildly (USDOS, nd). Wack and his team had foreseen this scenario, as well as its escalation through the late 1970s, the collapse of the oil ‘bubble’ in the 1980s, as well as the eventual control of oil pricing by speculators on Wall Street.
Everything has a Probability
What Wack described as changing “the mental maps of managers” is the art of assigning a probability to scenario events which are not what managers typically have in their mental map. For most managers, the mental map is their base case which is either the same, or a rosier future than the present. In his final years at Shell, Wack developed a singular cryptic diagram labeled “the gentle art of reperceiving” (Kleiner, 2003; Chermack and Coons, 2015), shown in Figure 1.
Figure 1: Scenario Planning/Perceiving Diagram: Wick
Within this diagram there are four (4) key principals:
1. scenarios must be part of a larger strategy system and the elements are clarified
2. scenarios should not be positioned or sold as the product/outcome of scenario planning
3. the two-day workshop approach to scenario planning is not adequate
4. scenario planning should not primarily be practiced as group process
Developing strong scenarios however is just the first step. Arguably the most important part is after the scenarios are created, to provide them a probability. This is the most important part because it is the most difficult aspect of scenario planning, and the hardest to get buy-in from management to actually leverage in practice. What has added this buy-in, as well as its implementation is Value at Risk Modeling (Var).
VaR Modeling
Var Modeling was truly born in the corporate landscape after the 2008 Financial crisis, although there were analog versions as far back as the dot-com bubble. VaR modeling is essentially a mathematical way of assigning a probability to a scenario by working backwards. You run a Monte Carlo simulation, which is you take thousands of random-walks of economic variables over a specified time horizon called ‘paths’. The variables and their coefficients are generated using regression, then projected forward typically at random. VaR analysis is then taking the worst-case path, then assigning a narrative around that path to what had happened. Example could be when interest rates went above 5%, and unemployment went above 10%, and the stock market dropped 25%. Then you can weave a narrative to how that scenario will happen. This will usually be assigned a high confidence interval, say 95% or 99% and be fixed as a ‘severe scenario’ (CFI, 2022).
Economists will also lay other scenarios in the middle to help give a range or a path before one gets to ‘Severe.’ Many firms rely on a third party to provide models for these scenarios, such as those done by Moody’s (Moody’s, nd), which have scenarios ranging from ‘baseline’ to ‘stagflation’. Additionally, companies can leverage other percentile paths from the Monte Carlo simulations, to give more ‘Moderate’ results such as 75th or 80th percentile. Us Regulators also leverage these scenarios for required stress testing, as they can compare each firm’s projection under the same lens.
How Scenarios Support Innovation
In finance scenarios in forecasting and modeling help innovation by giving a glimpse into the potential benefits and the potential risks. It helps give great innovative ideas momentum. Most of the economic crashes through the past 40 years can be attributed to insufficient scenario planning, which has choked off momentum of innovation.
One example provided by Kleiner (2003), was the dot-com bubble of 2000. They noted that one scenario modeled, but not widely implemented was a scenario called ‘gruel’, where it would require companies to build some cash reserves, be more frugal, and focus on short-term revenue streams. These scenarios were widely ignored, and companies continuing to us standard forecasting in thinking the boom in technology would continue on its upward trajectory. We saw the same lack of scenario planning in the 2008 Financial Crisis, as noted by the Us House of Representatives review of the economic meltdown (2009), noting: “in addition to measuring risk using a standard VaR approach, develop scenarios for crises and test capital adequacy under those scenarios.”
Scenarios also provide a source of adoption, and not just risk protection. As contemplated in the Bank of International Settlements 2021 paper (Feyen et al, 2021) about the digital transformation in banking, they lay out that new efficacies in the space could open up competition allowing more companies to compete in the financial services industry. Similarly the use of Moore’s Law in the Crypto currency boom has caused the technology to exponentially grow, but with potentially lack of proper downside scenario planning to protect the innovation from crashing like the dot-com crisis in 2000.
Innovation Winter
Many don’t recall what the economic and innovation environment was like after 2000. When Google had its IPO in 2004, the reaction was generally lack luster. This was after they were by far the largest search engine and a staple in everyone’s lives. But being burnt by the dot-com crash couldn’t see the exponential value in the company. Other tech companies like Amazon, Apple and Netflix shared similar views from 200-2006. Of course, since then these companies have become some of the most valuable companies in the world. Again, markets had a relied back on standard forecasting, just pulling forward the recent trajectory and direction.
What this illustrates is that we tend to weight our scenario planning to the current direction. When things are looking good, we imagine more positive scenarios. When things are looking bad, we imagine the additional excesses of pain there is to be felt. In reality the opposite should be true. When things are going well, one should protect their downside by focusing on risks, and when these are in despair, one should find the path where things can turn around.
I think what is hard when we’re in inflection periods, not knowing if we’ve started a new period of decline and rise. And humans are particular poor at forecasting theses inflection periods (Sherden, 1999). We tend to only recognize them well after they’ve turned.
I think a key method to leverage scenarios for maximum social impact of change is to help us be more pragmatist. To think less in terms of lottery tickets and more in sustainability. Having proper scenario planning helps facilitate this mentality, because you have both the push of optimism, and the pull of risk management working to keep you even. Without one or the other, and only following traditional regression scenarios its too hard to get caught up in the moment.
References:
Chermack, T., Coons, L. (2015). Scenario planning: Pierre Wack’s hidden messages. Futures. Volume 73, ISSN 0016-3287. https://doi.org/10.1016/j.futures.2015.08.012
CFI. (2022). What is Value at Risk (VaR)? Corporate Finance Institute. https://corporatefinanceinstitute.com/resources/risk-management/value-at-risk-var/
Feyen, E. Frost, J., Gambacorta, L., Natarajan, H. and Saal, M. (2021). Fintech and the digital transformation of financial services: implications for market structure and public policy. Bank of International Settlement: Monetary and Economic Department. https://www.bis.org/publ/bppdf/bispap117.pdf
Kleiner, A. (2003). The Man Who Saw the Future. Booz & Company. https://www.strategy-business.com/article/8220
Moody’s. (nd). Economic Scenarios. Moody’s Analytics. https://www.economy.com/products/alternative-scenarios/standard-scenarios
Sherden, W. (1999). The Fortune Sellers: The Big Business of Buying and Selling Predictions.
Wiley Press. ISBN-10 047135844.
USDOS. (nd). The 1973 Arab-Israeli War. Department of State: Office of the Historian. https://history.state.gov/milestones/1969-1976/arab-israeli-war-1973
https://www.wired.com/2009/02/wp-quant/
US House. (2009. The Risks Of Financial Modeling: Var And The Economic Meltdown. House Of Representatives, Subcommittee On Investigations And Oversight, Committee On Science And Technology, Washington, DC.
https://www.govinfo.gov/content/pkg/CHRG-111hhrg51925/pdf/CHRG-111hhrg51925.pdf
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