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9 Rules for Superforecasting
16-01-2019, 09:09 AM.
Post: #1
9 Rules for Superforecasting
Source: http://musingzebra.com/9-rules-for-superforecasting/


Philip Tetlock spent 30 years exploring what makes someone a superforecaster (not the ones you see on TV). In his book Superforecasting, he distills his work into several commandments on how we can improve our judgment without any complex algorithm.  

1. Triage: Focus on questions where your hard work is likely to pay off

Tetlock wrote “Don’t waste time either on easy questions (where simple rules of thumb can get you close to the right answer) or on impenetrable questions (where even fancy statistical models can’t beat the dart-throwing chimp). Concentrate on questions in the Goldilocks zone of difficulty, where effort pays off the most.”

In investing, you want to spend a majority of your time on the 2 to 3 most important variables that determine 80% of the outcome. Why only 2 to 3 variables? Because success rate falls exponentially when an investment has many moving parts. If an investment idea requires 6 variables to work out to be profitable, the success rate quickly plummets to 53% (0.9^6) even when you’re confident that each variable carries a 90% likelihood of happening.
You also want to avoid questions that are too hard to answer. Let’s say you are researching an O&G company. You figure that the oil price is an important variable but what’s your probability of predicting it correctly 3 to 5 years out? That’s an impenetrable question where you won’t do much better than flipping a coin. Instead, another important yet solvable variable is to find out the cost structure of the company and its sustainability. Given that most O&G players are a price taker, the most efficient company sitting at the bottom of the variable-cost curve will reap the most benefits regardless of the oil price level.

2. Breaking seemingly intractable problems into tractable sub-problems

George Polya, a Hungarian mathematician, once advice “If you can’t solve a problem, then there is an easier problem you can solve: find it.”

Is this stock a buy? That is a big hairy problem. One way to solve it is to break it into sub-problems. Every time you break down a big problem, you’re asking “What needs to happen for this to be true?”. In this case, what needs to happen to qualify this stock as a buy? Let’s say you break it into 3 sub-problems:

  1. Market valuation

  2. Business characteristics

  3. Portfolio hurdle rate
You can further break each of this sub-problem into more sub-problems as necessary until you can solve it. If you’re looking at the business characteristics, you can break that further into competitive advantage, financial strength, industry dynamic and so on. Once you’re able to solve those sub-problems, you can begin to move your way up by solving the bigger problem right above and that will eventually lead you back to answer “Is this stock a buy?”

3. Strike the right balance between inside and outside views.

The inside view looks at specific circumstances surrounding a situation, while the outside view tied circumstances to an appropriate reference class by asking what happens when others encounter something similar?
It is easy to engross in the specifics surrounding a company, such as the growth story and extrapolate based on what we see. But a good forecast also requires the outside view or base prediction. If a company is forecasted to grow 15% annually over the next 5 years (inside view), while its competitors have a growth rate closer to 9% (outside view), then you need a good reason to explain the difference. More often than not, your revised growth rate will likely fall somewhere in between those numbers. We shouldn’t dismiss the inside view entirely, of course. What’s unique to a company i.e culture, could sometimes turn out to be a good predictor of success. But at the same time, the outside view tames overconfidence and avoid base rate neglect so we don’t miss the forest for the trees. As a rule of thumb, start from the outside view before adjust towards the inside view to avoid anchoring bias and overestimation.

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