Watching markets over the past few years has taught me a lot; the conceptual models that I borrow from economics more broadly have proven useful in other contexts.
- Correcting for Cognitive Bias
- Thinking in Terms of Risk/Reward
- Relative Value
- Timing is Everything
- False Positives and the Limitations of Models
People are intuitively bad at probability and signal processing. To establish this premise, I’ll invoke this excerpt from Enlightenment Now by Stephen Pinker:
People are by nature illiterate and innumerate, quantifying the world by “one, two, many” and by rough guesstimates…They underestimate the prevalence of coincidence. They generalize from paltry samples, namely their own experience, and they reason by stereotype, projecting the typical traits of a group onto any individual that belongs to it. They infer causation from correlation. They think holistically, in black and white, and physically, treating abstract networks as concrete stuff. They are not so much intuitive scientists as intuitive lawyers and politicians, marshaling evidence that confirms their convictions while dismissing evidence that contradicts them. They overestimate their own knowledge, understanding, rectitude, competence, and luck.
Watching markets over the past few years has taught me the necessity of decoupling personal experience from analysis. Indeed, personal experience may be leveraged as a signal to warrant further investigation, but never should be invoked as the sole justification behind an important decision.
While studying at LSE in June 2016, I asked Londoners about their preference on Brexit. With evidence that local sentiment was strongly against Brexit, I placed a bet in favor of
STAY at a local betting market. London’s vote tally confirmed my suspicions with greater than 75% of voters voting in opposition to Brexit. Even so, I failed to account for high turnout from rural areas, which happened to outnumber the London vote and instigate England’s eventual exit from the EU. Later the same year, I repeated the same mistake while engaged in the liberal echochamber at the University of Virginia; I bet on Hillary Clinton’s election over Donald Trump on
predictit.org. Through these experiences and many others, I’ve learned how local bias can shape incorrect predictions.
This comes as no surprise. The human brain is simply not wired to think in terms of mathematical probability. For an easy example, consider the infamous Monty Hall problem – I like how it is presented in this clip from the movie 21. Although I’ve never been able to attain an intuitive grasp for this solution, the proof clicks when presented as an application of Bayes Theorem.
Another example that is too often overlooked relates to how conditional predictions compound the probability of being incorrect. If there is a 50% chance that it will rain tomorrow and a 50% chance that the fishermen will decide against fishing in the event of the rain, the probability that the fishermen do not fish is actually 25% (50% * 50% = 25%). Because our brains don’t operate in terms of the laws of probability, we tend to haphazardly make conditional predictions without accounting for this seemingly obvious rule.
Upon reflecting on these common logical fallacies, it is easy to recognize the value of using abstract thought models to realign our thinking so that our decisions reflect all accessible information.
By attempting to account for the probability of positive/negative outcomes occurring, we can make more educated decisions that optimize for risk-adjusted return. How?
First, identify a benchmark. In finance, we called this
β (beta). The objective is to identify outcomes or decisions that yield return above the benchmark without incurring additional risk. The margin by which we achieve return above the benchmark is usually called
Speaking in terms of
β is useful simply for acknowledging the risk associated with different outcomes. A binary pros and cons list isn’t sufficient when some pros are extremely unrealistic while some cons have a higher likelihood of occurring. Although assessing relevant probabilities isn’t always straightforward, an estimate based on the individual’s risk tolerance in context is much better than simply hoping for the best possible outcome, especially in situations when the outcome’s determinism extends beyond the individual’s agency.
Price is a function of what you’re buying as well as
- how much you’re buying
- quality of the thing you’re buying
- psychology of others (and their knowledge of your buying needs); the invisible hand which does manipulate prices!
Identifying relative value requires considering each of these inputs and discerning opportunities in context.
The market can stay irrational longer than you can stay solvent ~ John Maynard Keynes
Identifying cycles is valuable – whether they’re seasonal or based on social constructs like when taxes are due. For an example of a company taking advantage of cyclical regulations, read up on Lehman Brothers, Repo 105.
It doesn’t matter what you call these things in all honesty. The value extracted from grouping these concepts doesn’t come from their label; it is derived from the usefulness of applying these ideas to situations in your every day life.
Before saying anything else, I should mention that terminology does matter – it is extremely useful for communicating ideas among large groups of people. Indeed, it allows us to overcome fundamental group thresholds and coordinate our actions on an increasingly global scale.
At the same time, the problem arises when people become defensive and maybe even elitist about their terminology. In these cases, someone unaware of “alpha” is determined to know nothing about finance, but this quick characterization ignores the generality of “return above a benchmark”. Instead of gatekeeping based on who is aware of the industry vernacular, people should encourage outside contribution even if it doesn’t immediately conform to industry norms.
Admittedly, this is difficult – the reason finance people prefer others who speak their language is because it is easier to vet those that communicate with their terminology. However, it also increases the likelihood of false-positive judgements in which someone who is adept at mimicking the style of speech can easily gain the trust of members of the group. In a world increasingly filled by noise, humans revert to primitive pattern matching which may see subjects like finance attract individuals who are aware of the ease by which the system can be duped.
If you thought it was weird for me to use the word “humans” in this last sentence, this supports my last point regarding tacit inclusion/exclusion based on conformity to vernacular. Regardless, it is helpful to sometimes take a step back, disassociate from humanity and consider modern rituals in the context of evolution at the most primitive level.