Measuring Mount Everest: A Lesson For Investors From Behavioural Science
Nearly 100 years before Sir Edmund Hillary and Tenzing Norgay stepped foot on the Summit of Mount Everest, the Himalayan peak was causing a different type of problem. Exactly how high was the mountain that was known up until the 1850s as Peak XV? In the absence of some of the technology we take for granted today – GPS, altimeters - it posed a logistical and mathematical conundrum. By 1854, the task had involved 100s of man hours and taken nearly 100 years of geological surveying to obtain enough reference points. The actual work of measuring the mountain’s height began with Surveyor General of India, Andrew Waugh.
To measure Everest, Waugh’s surveyors used a method called triangulation. Observers examined the peak from several points. Knowing the distance from the points to the mountain, they were then able to measure the angle from Everest’s peak to their observation points. Given the distance and the angle, trigonometry could reveal the mountain’s height relative to the observer.
When Waugh’s team finally completed their observations, measurements and calculations, they observed that Everest was 29,000 feet tall. Exactly 29,000 feet. Despite this, Waugh instinctively knew that the behavioural biases that we all suffer from would come into play. He rightly assumed that his audience would believe that such a round number could only be the result of an estimate. So instead, he added some extra and announced that the mountain was 29,002 feet high. This number seemed more precise than the actual measurement and thus was accepted as the official height of Everest until 1955 (the current height is believed to be 29,029 feet).
The same need for false precision is also evident in Fund Management. Forecasts of stock price movements of 28.65% seem to have more precision than ‘about 30%’. This can lead to overconfidence in one’s own forecast and overconfidence can lead to an underappreciation of risks and when an investment thesis begins to unravel.
We believe that successful investors should understand their biases and limitations. Insights of behavioural science highlight the importance of actively correcting for biases. For example, checklists can improve decision-making under uncertainty in different industries. Our proprietary checklist is at the heart of our process.
We also take a probabilistic approach. By taking a probabilistic approach, we can explore all possible outcomes, fully incorporate tail risk in our forecasts and avoid anchoring. By considering ranges in forecasts, valuations and risk management, we believe that we can tilt the odds of success in our favor, systematically and consistently. The ranges of outcomes generated by our analysis provide us with indications of where we are comfortable in buying, holding or selling companies.
Like mountaineers, investment managers want to minimise the costs of getting things wrong. And like those who attempt to conquer Everest, it isn’t necessary to know the exact measurement of the peak. It is more important to manage the risks appropriately and wait for the right conditions in which to take decisive action.