Many interested in IOI are scientists, engineers and mathematicians. We think this is because IOI valuation and investment methodology is based on the same principles and techniques that underlie scientific study: hypothesis creation and testing, estimation, observation, and reliance on provable facts.
We recently ran across an infographic in the scientific journal Nature that, while it is meant to provide guidance to reduce bias in scientific studies, is perfectly applicable to the field of investing and making good investment decisions.
Here are our translations of each of the above “Cognitive Fallacies” and “Debiasing Techniques” in an investing context
Cognitive Fallacies in Investing
Hypothesis Myopia: Analysts and investors usually get an impression for whether they like a company or not before they start the valuation process. When they start the assessment process, they proceed to collect information that supports their initial impression rather than looking for data that refutes it (I have seen this and experienced it firsthand in my years of professional analysis). This process often reinforces what I call the “pirate map method of valuation” – selecting a single point-estimate of value for a company. When you are sure that the value of a company is X, it’s psychologically easy to focus on all the evidence that points to a value of X and away from all the evidence that points to the value of Y.
Texas Sharpshooter: This is the heart of the investing method known as “Technical Analysis.” Years of academic studies and practitioner anecdotes point to technical analysis being as helpful to people making investment decisions as your local paper’s morning horoscope. Nonetheless, because our human brains are exquisite pattern-recognition engines that love symmetry and are very able to find “patterns” even when none exist, technical analysis persists.
Asymmetric Attention: This is especially common during earnings season. “Revenues were bad, but we expected them to be,” writes the analyst, then spends time analyzing the profitability dynamics in gory detail.
Just-So Storytelling: This is phenomenally common thanks to the fact that people love listening to stories. Without a strong framework for assessing value (like the one we train people to use in the IOI 100-Series courses), investors are likely to weave notable anecdotes into a coherent but nonetheless fictional stories about the companies in which they are invested. This tendency may help investors feel better about themselves, but it is destructive when it comes to consistently investing more skillfully.
Devil’s Advocacy: The best-case / worst-case framework that IOI teaches allows for you the investor to take the position of Devil’s Advocate in every investment analysis you carry out. After thinking about what each of the drivers would look like under the best-case scenario and the worst-case one, you are freed up to observe what results are actually generated by the company. No matter what the results turn out to be, you can still be right. And being right about the Devil’s Advocate position makes it much easier to close a position when that’s necessary. “It looks like this company’s valuation drivers are coming in closer to my worst-case assumptions. I guess it’s time to close this investment and move on.”
Pre-Commitment: This is, in essence, what a strict focus on IOI’s key valuation drivers does for you. Before you start looking at a company, you know that the only things you are going to pay attention to are the most material aspects of
- Revenue growth
- Investment Level and Efficacy
Team of Rivals: Using best- and worst-case scenarios as mentioned above works well for this. In addition, IOI is in the process of building an active, robust community of like-minded investors who speak the same language. This community can also serve as a Team of Rivals!
Blind Data Analysis: One of my favorite things to do is to plug a random number into the share count field of the IOI model . This allows me to think carefully about operational scenarios in isolation and consider their likelihood in terms of what I know about the business. Many analysts iterate between making a change to their valuation model, checking the stock price, then re-tweaking their model in order to fit the model to the share price.