Weather or more precisely the scientific study of the atmosphere – meteorology is an interdisciplinary field that seems to study anything and everything that has to do with the atmosphere. One thing that meteorologist study is the forecasting of tropical storms and hurricanes. Now, the key word “forecasting” brings us to our topic – the pay-off method and the weather. Weather forecasting in the modern sense means the collection of quantitative data about the current state of the atmosphere and the application of scientific methods and technology to predict how the atmosphere will evolve.

This actually does ring a bell, indeed if we look at the previous sentence and just replace the words “weather” and “atmosphere” with the word “markets” we get the following:

“Market(s) forecasting in the modern sense means the collection of quantitative data about the current state of the markets for a and the application of scientific methods and technology to predict how the markets will evolve.”

Now isn’t that kind of interesting… and surely enough the same kind of methods are being used for both, for weather forecasting and forecasting the (financial) markets.There are elaborate mathematical models for the forecasting of weather and the financial markets, run by supercomputers and data and inputs are collected 24/7 and automatically fed into these systems. There is a difference though: atmosphere is not a man made system, but the financial markets are.

Already during my lifetime the reliability of weather forecasts has become vastly better, these days the weather forecasts seem to be mostly right, the inaccuracy having to do with more on the timing of events (when it starts raining) rather than with the events (rain) taking place at all or not. But there are still sometimes days, when rain was forecast, but the skies shed nothing.

We can all have our own opinion about the reliability of the market forecasting (economic) models. My gut is that they are not as good in prediction as the meteorological models. This may be due to the fact that the weather is more predictable than the markets (as it is a natural phenomenon and cannot be affected by humans on the short term). I had a crack at that discussion in an editorial posted here in January – surely a slightly polemical editorial, but with the best of intentions (that pave the way…).

Well enough about that… what else is there, of interest?

I did find something “about the weather” that actually got me a bit excited, and that was finding out that the U.S. National Hurricane Center (NHC) of the National Oceanic and Atmospheric Administration (NOAA)  National Weather Service (NWS) uses what they call “forecast cones” to represent the probable track of the center of a tropical cyclone. [You just gotta love the acronyms]

These forecast cones are obviously of little use if they are presented only as numbers and especially of no use if they are presented without the context – what this means is that the good way to present the data is to visualize it, that is to “draw” the cones and preferably on a map background. Visualized they immediately tell us who should start thinking about finding their way to the basement.

Forecast cone of the tropical storm Dean Aug. 2007 from www.nhc.noaa.gov

The forecast cones are obviously the output from the forecasting (computer) systems that house the complex atmospheric models and they are updated as new information arrives. To be able to understand the accuracy of these predictions the NHS tells us that “Based on forecasts over the previous 5 years, the entire track of the tropical cyclone can be expected to remain within the cone roughly 60-70% of the time.” Link to some more info about the cone from NHC site.

I don’t know about you, but to me the cone looks similar to the “accumulation of the net present value scenarios” graph used in visualizing the intermediate results in connection with the pay-off method. We can also call this the” project NPV cone” – or “the cone of uncertainty for the investment project”. Even the “logic” of the two looks (and should look) the same: the further into the future we go, the wider apart the sides of the cone (minimum and the maximum possible outcomes) are from each other – the width of the cone grows the further into the unknown (further away in time) we try to forecast.

 Graphical presentation of the cumulative net present value, the NPV cone of a project

Now why did I get excited?

The storm forecast cone is easy to intuitively understand and it is being used to report relevant important information by a US government agency to millions of people on-line 24/7. The fact that the cone is an obviously “approved” visualization technique of the output from a mathematical computer driven atmosphere model (that most of us including me would not be able to understand at any detail without considerable study) that allows all of us to understand the output in seconds is just wonderful.

To me this is a corroboration of my thoughts about the benefits of visualizing the accumulation of the net present value (NPV) of a project in the way that the pay-off method likes to do it. It enhances the understandability of the possible “path” of the potential investment, while “honestly” showing the forecasting inaccuracy and that it grows the further into the future we try to forecast.

The moral of the editorial: Use the forecasted NPV cone visually to enhance understandability of the future of the investment and to support decision-making.

Mikael Collan

In the most recent issue ( Jan. 30, 2012) of Time Magazine, Robert Johnson, the executive director of the Institute for New Economic Thinking in New York City, writes about the (crumbled) credibility of economists and economic models. He brings up not only the mistrust and discontent of many students of economics, but also points out the overall demise of the authority of economists everywhere in society.

Johnson brings up four important issues that should be fixed in order to remedy the situation. First, he points out, “economists should resist overstating what they actually know” – referring to the philosopher John Dewey, who called it the quest for certainty in 1929 – in the midst of the great depression, and said that: “Quest for certainty is a dangerous temptress”.

• This is exactly in line with what the pay-off method tries to do, show uncertainty to the decision-maker in a way that the “false sense of certainty” is not conveyed. Showing the pay-off distributions and not only the single number expected or real option values is key in this effort. Also the cumulative net present value graphs of the different scenarios are a tell-tale part in showing how the NPV is accrued over time and what is the distribution of the pay-back time in present value terms.

Second, he observes, economists should recognize the shortcomings of high-powered mathematical models (which are not substitutes for vigilant observation).  Johnson also cites Kenneth Arrow who said: “The math takes a life of its own because the mathematics pushed toward a tendency to prove theories of mathematical, rather than scientific, interest”. Also Frank Knight observed that radical uncertainty, prevalent in times of crisis, means that expectations cannot be anchored as they have nothing to latch to.

•  Again spot on for the pay-off method: as there is no benchmark “mean” to which to “revert” to many models fail, and the information that is available must come from managers. This is something that the pay-off method can live with – unlike some other methods. Trying to be “infinitely precise” in the world where our ability to forecast with excellent accuracy even until tomorrow is compromised (as it is, or anyone want to bet on the weather tomorrow?) we are bound to go wrong, especially in uncertain times. For real options times are often uncertain…

• Interestingly, just an issue before the latest one, of the same Time Magazine, a story told about French quants and them being greatly overrepresented in the investment banks of the world, with a quarter or more of quants being French and about how financial mathematics has fallen out of grace in the Paris top universities. Obviously the financial shocks and the Black Swans as Nicholas Nassim Taleb would call them are real and give the models a bad name – not only give them a bad name, but actually prove that they do not work in turbulent times.

Third, Johnson discusses about introducing the context back to economics (modelling). Indeed if the mathematical models used to drive economic (and financial investment) decision-making are not based on the real world context then they are based on something that is really not of relevance. Objective, Cartesian, approach to economic models may work in theory, but if they work in practice is anybody’s guess.

• Context is in the heart of the pay-off method; it is partially because of the will and the need to include context in a simple way the model was built. Assuming  things and believing that “the market is like the assumptions say” is just plain ignorant. By using the know-how and expertise of the managers about the markets and the firm that owns the real option we can get just that, context, in their estimates for cash-flow scenarios.

Fourth, he argues economists must acknowledge the relationship between politics and economics.

All in all a very welcome viewpoint article with some, not so often voiced commentary on the dire state of economics. For someone who does not believe in the infallibility of mathematics and humans as estimators of the future I would gladly see more of the same…

Mikael Collan (posted from onboard Finnish State Railways train going from Lappeenranta to Salo)

Info about the articles mentioned:

Economists: A Profession at Sea – How to keep economists from missing the next financial crisis by Robert Johnson, Time Jan. 19. 2012 LINK

Making French Bread: Why are France’s Math Graduates in Such Demand? Time, Monday. Jan. 23, 2012 LINK