Thread Rating:
  • 0 Vote(s) - 0 Average
  • 1
  • 2
  • 3
  • 4
  • 5
7 Tools for Skeptical Thinking
11-06-2019, 01:53 PM.
Post: #1
7 Tools for Skeptical Thinking
Source: http://musingzebra.com/7-tools-for-skeptical-thinking/

In tennis, winners in amateur level competitions are often those that make the least number of unforced errors. The same can be said with investing. The number of mistakes you make (and the losses on each of them) determines your long-term return more than how much you make when you win.  


There are two reasons why we lose money in the market. One is the analytical method used. But many successful investors have shown that there’s more than one way to make money. Ray Dalio uses a top-down approach on macroeconomic factors while Warren Buffett focuses on a firm’s economics, for example. Therefore, trying to find ‘the best’ method is a waste of time. The second reason is the application of the method itself. Or in other words, the faultiness in thought process when applying any analytical method. 

Here are 7 tools you can apply to your thought process to reduce unforced errors regardless of the analytical method you use.

Spin more than one hypothesis
Carl Sagan puts it this way:
“If there’s something to be explained, think of all the different ways in which it could be explained. Then think of tests by which you might systematically disprove each of the alternatives. What survives, the hypothesis that resists disproof in this Darwinian selection among multiple working hypotheses, has a much better chance of being the right answer than if you had simply run with the first ideas that caught your fancy.”

Finding more than one explanation helps avoid satisficing. Never accept an explanation that makes the ‘most sense’. What make sense can be totally wrong. Besides, what makes sense doesn’t matter. Why? Because if something makes sense to you, chances are it makes sense to others as well. Which means the opportunity has been exploited and priced in.

We make assumptions due to the lack of information, uncertainty, opportunity cost and so on. That is fine. But it is key to understand your assumptions are just assumptions or hypotheses that are yet to be verified. Don’t get overly attached to a hypothesis just because it’s yours. Instead of finding information to support your hypothesis, you want to kill it as fast as possible. The hypothesis that is the hardest to kill is most likely the right answer. Falsification, not confirmation, is the most effective way to get to the ‘truth’.

Quantify
1. Absolute scale – What’s the denominator
  • “A company reported sales of a product line has increased by 100%, growing from $10 million to $20 million.” What’s that on an absolute scale? For a company that does a billion in total sales annually, that product line only makes up 2% of total sales, up from 1%.

  • “Company earned $1 billion over the past year.” Based on how much of incremental invested capital? Terrific on $5 billion capital; dismal on $50 billion capital.
    This favourable currency trend should increase the quarterly profit of this company. What is the probable foreign exchange gain compared to the total profit? 1%?
2. Probability – Remove fuzzy thinking
  • Express degree of belief in numeric probability i.e “there is a 75% chance that this company can increase its profit…” instead of “I’m fairly confident this company can increase its profit….” The word fairly (or likely, possibly, probably) is vague and uninformative.

  • You believe a company has a 70% probability of capturing x% of the market share in 3 years. Assign a probability to all of its competitors, then sum it up. Does it exceed 100%? If yes, then you’re overestimating the company’s prospect.

  • An investment thesis has 6 variables that need to work out to be profitable. If there’s a 90% confidence that all of them will happen, the success rate is 53% (0.9^6). At 80% confidence, the success rate is 26% (0.8^6). Less variable is more.
3. Bayes rules – Belief updating
A company reported two consecutive lower quarterly earnings due to a challenging environment, is that a canary in the coal mine for more bad news or just a temporary setback? 

To apply Bayes rules, you need 3 things:
  1. Prior probability. The initial estimate that the business’s earning power is impaired before the bad news. Let’s say you estimate there is a 15% probability.

  2. Next, we need the probability that the hypothesis is true—2 consecutive lower quarterly earnings is indeed a sign that the earning power is impaired. Based on your understanding of the company, you think it is unlikely. But at the same time, the fact that the company has never reported any lower earning prior to this event is a cause of concern. You place the probability for the hypothesis to be true at 50%.

  3. Last, the probability that the hypothesis is false. If the earning power remains intact, what could explain those lower earnings? It could be a shift in product mix to lower margin items, lower volume sold due to supply constraints and so on. Let’s say you put this at 28% after considering all alternative outcomes.
Now, you can establish a posterior probability—how likely the earning power is impaired given there are two consecutive lower earnings? The probability has increased from the initial 15% to 24%. 24% will become the prior probability to update your belief when another evidence shows up.

[Image: y8ay9J0LK4soNraEodpcgH80rUUzmnp_opLGQgqf...HOgOQXDSsf]

4. Expected return
The expected return is the probable payoff of all possible outcomes. 
Formula:
(Probability of losing x magnitude of losing) + (probability of gain x magnitude of gain) = Expected return
  • The market carries a 70% chance of going up by 1%; 30% chance of going down by 10%. The expected return is negative.

  • An investment has a 99% chance of making 100x return and a 1% chance of total ruin. It has a positive expected return but an unwise investment.

  • The valuation shows a 50% upside based on the current price. What is the probability that this (bull) case scenario will happen? And what’s the % downside for the bear case and the probability of it happening? Use the formula.
Law of small numbers
A fallacy of generalizing small sample size that leads to extrapolation and false conclusion. A few examples:
  • My uncle doubled his investment in 6 months. He is better than the majority of investors.

  • My neighbour has smoked for 50 years and looks healthy. Smoking is harmless.

  • This company has increased its profit for the past 2 quarters. It will perform well in the coming quarter.

  • Company has maintained a top line 15% growth rate for the past 5 years, it should continue to do so in the next 5 years.
Invert
Invert a statement, question or assumption to examine its logic.
  • Oil price increase will result in higher profit. Does lower oil price reduce profit? Or lower profit increases oil price?

  • How can I increase my investment return? What reduces my return so I can stop doing them?

  • How can I improve my relationship? What is the biggest relationship killer so I can avoid it?

  • Is this a buy? How can I kill this idea fast?
Incentives
Events considered as irrational or unexpected can be understood or anticipated from an incentive perspective.
  • A company selling its products at a loss. Capturing market share to achieve scale economies and network effect.

  • Most fund managers failed to outperform the market. Career risk is more important than investment risk.

  • An investor rationalises his decisions to hold onto a losing position. Internalised position and associate cutting lost as being wrong.
And then what? – 2nd & higher order effect
  • Increase in demand should increase the selling price and profit. And then what? Sellers will increase supply capacity to meet demand. And then what? Price and profit would fall back due to the competition.

  • I can make money by taking advantage of the market’s foolishness. And then what? I’ll make money from these losers. And then what? Losers gradually exit the market. And then what?Leaving behind only winners. Conclusion: Market ain’t foolish all the time.

  • The valuation shows this stock has a 100% upside. And then what? I should ride on this. And then what? But there’s nothing about this stock that I know that the market doesn’t know about. And then what? I have to recheck my assumptions.
Base rate – Outside view
Ask “has this happen somewhere before?” using the law of large numbers.
  • This stock can achieve CAGR of 15% for the next 5 years. What is the base rate growth rate of the competitors and the industry? 10%? What explains the gap?

  • I am taking up this once-in-a-lifetime initial public offering (IPO). What is the base rate return for all historical IPOs?

  • The company decided to enter an adjacent market which increases valuation uncertainty. Has other firm done something similar before? In those cases, did the incumbents retaliate? What challenges did the entrant encounter?

Find Reply


Forum Jump:


Users browsing this thread: 1 Guest(s)
Valuebuddies.com | Return to Top | Lite (Archive) Mode | RSS Syndication | CONTACT US: nas......@valuebuddies.com |