For some reason, I feel like the past 10-15 articles have been of much higher quality than the ones I used to write, from a conceptual perspective. Having covered many of the fundamentals principles needed to understand the stock market, let us explore a bit more shady territory. And I believe it’s in these kinds of writings where we can keep developing our edge over other investors. Most of them call it a day once they know the basics, but it’s not in the basics where overperformance lies, or at least for what I perceive.

I’ve been exposing myself to a decent amount of financial literature lately and noticed a peculiar overlapping between almost all of them. Authors and investors of all kinds vividly claim how utterly ludicrous it is to think forecasts are worth something.

“It is absurd to think the general public can ever make money out of market forecasts” Graham

The stock market is an astonishingly complex and adaptive system. The number of variables, actors, disciplines, ideas, concepts, etc, in play is infinite. Moreover, given that at the end of the day it’s individual decisions what construct prices, there’s also history and learning embedded into it, making it dynamic and set to change over time.

We’ll go back to the very first article where we defined what a stock actually is:

“A stock, also known as equity, is a security that represents the ownership of a fraction of the issuing corporation” (..) “As it’s inherent nature of being a productive asset, it’s intrinsic value will theoretically be worth the present value of the future cash flows the stock will produce”

So we arrive to this interesting spot where it’s humanly impossible to realize reliable forecasts, but our returns are subject to what will happen in the future. I’ll approach the issue in two separate manners.

In Terry’s shareholder letter I found a crystal clear way to deal with this inconvenience. I had not yet encountered someone putting this into words. Perhaps it’s the ‘obviousness’ of the concept that has allowed it to remain hidden in plain sight.

“The desire of people to rely on forecasting despite its obvious drawbacks is illustrated by an anecdote from the Nobel laureate and retired Stanford University economist Kenneth Arrow. Arrow did a tour of duty as a weather forecaster for the USA Air Force during World War Two. Ordered to evaluate mathematical models for predicting the weather one month ahead, he found that they were worthless. Informed of that, his superior sent back another order: ‘The Commanding General is well aware that the forecasts are no good. However, he needs them for planning purposes.”

This is fascinating, so accurately aimed to the heart of the problem I could not help myself but to share it here. It’s impossible to know the future, but that does not unable us from trying to prepare for what can come.

Yes, we are back to this. Upon the reads, there’s a common pattern that almost always arises, even if it’s not explicitly stated. It has also been a somewhat recurring topic in this newsletter, but the fact of me going back to it should only highlight the importance I find in the idea.

The future’s uncertainty is no new thing and there’s a spectacular saying which I’m paraphrasing:

“The older the problem, the more antique its solution”

In the 17th century, life insurance started being a thing. Life insurance policies provided a lump sum payment to the beneficiaries of the policyholder upon their death. However, to become long-term sustainable businesses, they had to somewhat price their premiums in a way that they did not offset the income received by policyholders. This invariably led to trying infer the probability people had of dying at all ages, so they could price them in a way that generated profit.

In 1693, Edmond Halley published a paper called “An Estimate of the Degree of Mortality of Mankind”. In it, he utilizes statistical methods to calculate life expectancy based on mortality tables.

The latter basically attempted to provide insights into what was the probability of people dying at a particular age. Upon all of this, Edmond then estimated the premium for annuities in the life insurance business.

For interested (short, interesting, but kind of unintelligible): Paper

In conclusion, to make a profit and become durable companies, life insurance businesses dealt with the uncertainty of the future by applying statistical models. This doesn’t mean one will always make the right call, but it does mean that, as decisions average and weight themselves up, if done under actual positive expected values, investor should tend towards profitable investing.