What can we learn from Back Country Risk Management?
Managing the risk of a trading position can look very abstract at times. There are lots of policies and processes to follow. There are lots of numbers that measure all sort of the sensitivities which gives an illusion of control to an outsider. In many cases you have the luxury of being wrong!
When you are in back-country and trying to cross an avalanche train, risks are very real. There is very little data that can help you make judgment. There are number of parameters that quantifiable but there are lots more that are not. In the back country there are more unknowns that known. There is also the issue of external pressure which can significantly impact your judgment. In the back-country you need to constantly make split second decisions with very little information.
Risk Management in the mountains might look different from trading, but I would argue there is a lot that trading professionals can learn from Back Country risk management.
Layers of Information
Information is about influencing decision making and without it information is just a data point. One of the great problems with financial risk management are proliferation of the disjointed static reports and dashboard. They generally fail to tell a story or influence an action.
One of the best examples of layered actionable information is from Canadian Avalanche Society. The purpose of this information is to help Back-country Skiers and Ice Climbers to assess avalanche risk before they head out into the backcountry.
There are few features about this info-graphic I really like.
- Highlevel View of the Current State: This is the most useful information and most immediate relevance. This information can result very quick go/no go decision but it also gives choice in terms of where the problems are and how bad the problem is!
- Forward Looking Information Providing the forward looking information gives users who are in committing multiday trips can plan their risk management strategy. This is very much like taking on trading position in liquid asset.
- Useful Details Explaining the Rating Risk is defined as = Probability*Consequence. You will be surprised how many people don’t understand this simple concept. This is generally essential to have a rough estimate of both parameter even if the numerical estimate is not possible.
There is also all the details that we can drill down in plain English…. but that is not the starting point.
Generally speaking, risk info-graphics is a good starting point of the risk analysis, it kick starts further rationalization in a structured way. Every step of the way gets you closer to a decision. As with Back Country Risk Assessment risk informed decision is a combination of data and soft signals. As much as we would like to think we don’t always use analytic to make decisions.
Good risk info-graphic goes a long way to helping us to ask the right questions.
Combining Data and Judgment
This has been one of my biggest pre-occupations as a risk manager for the past few years. How do we create unified decision framework that combines all the analytic with intuition and experience? This is specially very relevant for decisions that are strategic in nature vs. tactical and small decision. For example when in physical trading we bid on long term assets such as refineries and pipelines. There are few lessons we can learn from how we assess the risk of avalanche.
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Break the risk into its principle components There are number of conditions and factors that create an environment where the possibility of avalanche will increase. This could be anything from Elevation, Sun, Temperature Gradient, and Recent Snow Activity, and Train…. Discipline of breaking avalanche likelihood into principle can help us better digest the top event. As you see this is not exactly a science… but breaking the event into its priors help us to develop better mental framework for risk assessment. At the end of the day decision making in the back-country is a snap judgement, based on all sort of cognitive trigger and associations. I would argue this is not dissimilar to trading decisions, albeit, in the trading world we have better post analysis to support the original decision.
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BBN Deision Tree One method that I became familiar as my days of being an Nuclear Safety Engineer is Beysian Belief Network as framework to assess the risk of an accident. This method works perfectly as a Trading/Risk Management framework. I will have a more detailed post on the details, but it is suffice to say that BBN is, in my view, is the best way to transform any uncertainty into a risk based decision framework. It is also a way to systematically collect and analyze information about risk/reward opportunities. As with everything in a poor-data-environment-analytic, the journey is more important than the final number….
Simple Rules to Follow
Few years ago I was at a Glacier travel and Crevasse Rescue workshop. The discussion was around what would you do if in the course of your travels someone falls into a crevasse. You manage to rescue them but, as a group do you decide to continue? or trace back you route and exit if you could? This is a dilemma from a purely statistical perspective falling into Crevasse every-time is independent so you might be tempted to say continue and hope you are luckier next time. In real life however the fundamental question you need to ask is why you fell into Crevasse in the same place? Are you as smart as you think you are?
I have to admit this is where financial risk manager specially in the technical/quantitative trading framework have a leg up! The general rule is that you add to a winning trade and you reduce loosing trades. In some cases we don’t have the luxury of scaling into and out of risk but in some cases we can structure our deals to replicate scaling in and out (e.g. negotiating to buy and option to an asset vs. buying an liquid asset or expand into a market by buying small % stake in a company or real asset)
Simple rules are very powerful decision making tools. They allow for efficient decision making. At the end of the day cost of not making a decision could be much higher than a sub-optimal decision… There is an excellent book which lots of anecdote on how simple decision can help. I have to admit I am 1/2 way through the book but it is very interesting so far. I have added a cover if you are interested to buy this book.
