Whether we get a *steady* ~10% per year, or these highly *fluctuating* returns, the end result is the same.
We'll be left with the same ~$17M at the end of 30 years.
7/
Also, the *order* doesn't matter.
The "really bad" year, the 2 "pretty bad" years, and the 27 "pretty good" years could be arranged in any order -- and we'll still be left with exactly the same ~$17M after 30 years.
8/
This "indifference to order" is a very nice feature.
It means we don't have to worry about predicting which years will be good and which will be bad.
As long as we stay the course and leave the $1M untouched, we'll do pretty well -- if our geometric average is decent.
9/
But leaving money *untouched* means neither *adding* to it nor *removing* from it.
Most of us don't invest like that.
As we earn, most of us save and *add* to our investments over time.
And in retirement, most of us *remove* from our investments to meet expenses.
10/
Here's the deal:
When we add and remove money like this, the *order* of up years and down years is no longer irrelevant.
It suddenly becomes *super* important.
11/
Let me show you an example.
Say we're just getting started with the "earning, saving, and investing" phase of our journey.
During this phase, we start with $0.
But every year for the next 30 years, we save $100K and add it to our investments.
12/
If we get a *steady* 10% return during these 30 years, we'll be left with ~$16M, as this simulation shows:
13/
What if we get "front loaded" returns? Good years first, bad years later.
For example, first 27 "pretty good" years. Then, 2 "pretty bad" years. Finally, 1 "really bad" year.
Now, we'll only be left with $10M. Not ~$16M.
See? The *order* makes a huge difference.
14/
What if it's the reverse -- "back loaded" returns? Bad years first, good years later.
Now, it turns out, we'll end up with almost 6 times as much money -- ~$57M!
15/
During 30 years of earning, saving, and investing, a few bad years are inevitable.
It's far better to be hit by these years *early*, rather than *late*.
Why? Because, early on, we only have small amounts of capital. At this stage, a bad year or two can't do big damage.
16/
So, to be hit by a big crash just as we're getting started may *seem* like an unlucky thing.
But it may in fact be a blessing in disguise.
The important thing is to stay calm and continue to invest intelligently.
To *not* get discouraged and abandon the pursuit.
17/
"Path dependence" is basically this idea -- that "front loaded" vs "back loaded" returns can result in starkly different financial outcomes.
In our example, the difference was a net worth of ~$10M vs ~$57M.
18/
Unfortunately, most financial projections I've seen don't consider this super important effect.
They blithely assume a *steady* 7% or 10% return and completely ignore path dependence. This can lead us to make dangerous financial decisions.
19/
Therefore, when we make financial projections, it's a good idea to simulate different scenarios to work out the impact of path dependence.
We can hope for good paths (eg, steady or back loaded returns).
But we must also be prepared for bad paths (eg, front loaded returns).
20/
As you may have guessed, what's a *good* path when we're *adding* money is a *bad* path when we're *removing* money.
During retirement, for example, we should hope for *front loaded* returns. Bad years early on can wipe our retirement portfolio and make us run out of money.
21/
For example, suppose we want to withdraw $100K per year from our portfolio to meet our expenses during retirement.
Following the "4% Rule", let's say we start with a $2.5M portfolio.
We'll probably be OK if we get front loaded returns:
22/
But if we get back loaded returns (bad years early on during retirement), there's a real danger of running out of money -- as the simulation below shows.
For more on retirement planning, the 4% Rule, and such:
Path dependence also plays a big role when companies do share buybacks.
For example, suppose we own stock in a company that regularly buys back a lot of shares.
Say we buy the stock at time t_buy, and sell it at time t_sell.
24/
Of course, at t_sell, we should hope for the stock to trade at a high multiple of earnings.
But *between* t_buy and t_sell, we'd be better off if the stock price *languished* -- allowing the company to buy back more shares on the cheap. Another example of path dependence.
25/
As Buffett explained in his 2011 letter:
(He used IBM as an example to illustrate this, which was perhaps unfortunate as the investment didn't work out for Berkshire. But the principle still holds.)
26/
An extreme example of path dependence can occur when investors take too much risk with leverage, margin loans, shorting stocks, etc.
Even if they're right about the underlying business, the *path* followed by its stock price can wipe them out.
27/
The title of a chapter in @Gautam__Baid's book, The Joys of Compounding, says it all:
To finish first, you must first finish.
And if we take reckless risks, path dependence can get us thrown out of the game.
28/
If you're still with me, kudos!
Paraphrasing Robert Frost, you took the path less traveled -- all the way to the end of this thread.
And that, to me, makes all the difference.
Thank you very much. Stay safe. Enjoy your weekend!
/End
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In this thread, I'll help you understand the basics of Binomial Thinking.
The future is always uncertain. There are many different ways it can unfold -- some more likely than others. Binomial thinking helps us embrace this view.
2/
The S&P 500 index is at ~3768 today.
Suppose we want to predict where it will be 10 years from now.
Historically, we know that this index has returned ~10% per year.
If we simply extrapolate this, we get an estimate of ~9773 for the index 10 years from now:
3/
What we just did is called a "point estimate" -- a prediction about the future that's a single number (9773).
But of course, we know the future is uncertain. It's impossible to predict it so precisely.
So, there's a sense of *false precision* in point estimates like this.
In this thread, we'll cover *State Quantities* and *Flow Quantities*.
This mental model can help us analyze many different entities -- from complex engineering systems to publicly traded companies.
2/
A "State Quantity" is something that's associated with a *specific point in time*.
For example, the number of followers a Twitter account has is a state quantity. This number can go up and down over time. But at any one *specific* time point, it's fixed.
3/
Similarly, the amount of cash a company has is a state quantity.
This cash balance can also rise and fall over time.
But pick any one *specific* time point -- and the company can only have one *specific* cash balance at that time point.
In this thread, I'll help you understand Warren Buffett's famous quote: it's better to buy a wonderful business at a fair price than a fair business at a wonderful price.
2/
This quote has 2 parts:
Part 1. Business Quality. To understand this, we need a way to distinguish *wonderful* businesses from just fair/mediocre ones.
Part 2. Price. We should know when we're getting a business for a wonderful price, and when we're paying too much for it.
3/
And the quote itself says that Part 1 (Business Quality) is way more important than Part 2 (Price).
So let's dive into Part 1. What makes a business *wonderful*?