Teach Your Kid How to Invest by Putting Them on the Path to A Multimillion Dollar Retirement Nestegg
(This one will take a while to explain, but give me a chance to see if I can eventually convince you of the truth of the headline.)
So, let me take you back a few weeks. I’m sitting in my “thinking chair” in my office, pondering a couple of questions –
Why is it seemingly impossible for money managers / hedge funds / other very smart individuals to outperform the S&P 500 over long periods of time?
and
Even more puzzling, why do the vast majority of those money managers underperform the S&P 500, even before accounting for fees?
I mean, think about how weird that is.
People have spent (and are still spending) billions of dollars per year on very smart money managers who do nothing but study companies and stocks, and yet not a single one of them can reliably do better than a low-cost index fund when performance is measured over a long period of time.
Maybe Markets Are Efficient? (But I sure don’t think so)
The efficient market hypothesis / modern portfolio theory tells us, among other things, that:
- Random walks: Stock returns are random and follow no distinguishable patterns.
- Markets know best: The market’s price estimate at any time is the best estimate of the actual value of that stock.
- No one can reliably outperform: Following on the bullet above, no individual person or entity can reliably outperform the return of the stock market, on a risk-adjusted basis, than the market except via random chance – so don’t bother trying.
- Diversification is a “free lunch” to minimize risk: Since you cannot reliably pick stocks better than the market, your best bet is to diversify. Buy the biggest basket of stocks possible to minimize idiosyncratic (firm-specific) risk so that you bear only system (market-wide) risk (i.e., no individual company’s failure will doom your portfolio, due to your free lunch / diversification).
Though I acknowledge people way smarter than me have won Nobel Prizes for the theories underlying these and other related topics, I call B.S. on every single one of the bullets above.
But let me explain.
Stocks Most Definitely Do NOT Follow A “Random Walk”
Modern portfolio theory says stock returns are random and are normally distributed about a mean.
That’s just not true.
But don’t believe me – check out the data.
The graph below shows the distribution of total returns for a sample of stocks for a one-year period (end of 2024 thru end of 25).
And right about now, you’re probably thinking, “Hmm, Brad, that sure looks a lot like a normal distribution to me. Maybe lay off the day-drinking?”
But hold on. It’s largely normally distributed, yes … but with a very long right-tail.
And that’s a product of one fact – namely, the left tail (downside) for any stock is capped at a -100% return (i.e., a total loss) … but the right tail (upside) for a stock is unlimited. And we see that clearly by observing that multiple companies had returns in excess of 100% for the sample period.
Returns have positive skew – that’s not a normal distribution!
And now you’re probably thinking, “Uhh, Brad, I’m starting to get the sense that you are a ‘making mountains out of molehills’ type of dude. What’s the big deal? There’s still a huge normal distribution there.”
Ahh, maybe you’re right.
But before we dismiss this as a small problem / rounding error type of thing, take a look at this next graph.
This is the same graph above, except that it shows total returns from end of 2018 to end of 2025 – so a 7-year period, rather than a 1-year period.
Hey, can I ask you a question – does that look normally distributed to you?
Because it sure doesn’t to me.
As long as I’m asking you questions, let me throw another one out there.
What about this chart – would you describe it as normally distributed?
This is again the same graph above, except that it shows total returns from end of 2013 to end of 2025 – so a 12-year period.
And our sacred random, normally distributed returns – the “random walk” that is at the absolute bedrock of Modern Portfolio Theory – have completely and utterly vanished.
Especially concerning – even alarming – the most common total return over that 12-year period is -100% – a literal “lose everything” wipe-out! How come efficient markets couldn’t recognize the literal HUNDREDS of companies whose stocks were virtually worthless?
Houston, we have a problem!
Markets Do Know Best … But Their “Best” Isn’t Very Good
Let’s now look at the next bedrock principle of the Efficient Markets Hypothesis – i.e., the market’s price estimate at any time is the best estimate of the actual value of that stock.
And I don’t know if it’s my finance professor’s voice in the back of my head, or the multiple sets of tire tracks across my chest from when I’ve gotten scorched trying to pick stocks in the past … but I largely accept this premise as true.
But let me make a slight, but very important, differentiation (which we’ll revisit below):
No human being or entity can reliably make a better estimate of the aggregate value of all publicly traded stocks than the market’s estimate.
But wait a minute.
The market’s estimates of aggregate market value might be the best estimates, sure, yep, I agree.
BUT! How good are those estimates? I mean, I’m the best basketball player in my family – but that doesn’t mean I’m any good at basketball.
As it turns out, the market’s estimates are not so good!
The “20/20 Hindsight” Method for How We Can Measure the Market’s Accuracy in Forecasting Future Stock Prices
But before we start grading the market’s predictions, let’s think about how we could measure the accuracy of its predictions.
Here’s how I propose we measure it:
- For starters, let’s just assume that today’s price for any given stock is correct.
- After all, modern financial theory states that the most recent closing price reflects all of the publicly available information about the stock.
- And, because today’s closing price reflects more information than any previous day, if any day’s price is most accurate then it’s gotta be today’s price, right?
- Next, we know the cash dividends that the company has paid and the buybacks that the company has made (which increase the ownership in the underlying company accruing to the share of stock).
- So, in a sense, we can do a “backward-looking DCF model” to determine what the stock price “should have been” on any historical date. After all, we know:
- The terminal value to use in the DCF (i.e., today’s “correct” price); and
- the amount and timing of cash dividends in the past.
That right there is all we need to put together a DCF model that will calculate exactly what someone in the past who had perfect knowledge about all that would happen between that past date and today would have paid for the stock. For instance, if a stock trades at $100 today and paid $5 of dividends yesterday, we could reverse engineer a DCF model by asking ourselves “How much would someone pay today, assuming (i) the stock is worth $100 in one year and (ii) will pay $5 of cash dividends in one year?”
In other words, we’ve created 20/20 hindsight.
We can then compare the actual historical price to the “price the stock should have been, based on what we know today.”
So Just How Accurate Is the Market In Forecasting Future Stock Prices?
Okay, we’re now ready to test the market’s predictive power by applying the methodology above to answer this question –
If today’s price is correct, and using 20/20 hindsight on what actually happened, what should the stock have cost at any past date to deliver a [10]% return?
Put another way, we know the market is the best price predictor, but is it also an accurate predictor? (Or does everyone just suck at predicting future stock prices?)
Alright, let’s start with one the biggest and most well-known companies in the world at the moment – GOOGL. They’ve been a known commodity for a long time. Surely the market was able to forecast their intrinsic value with reasonable accuracy.
The first chart below shows two lines:
- The blue line shows the company’s actual historical price (i.e., the market price, which, Modern Portfolio Theory tells us, is the best estimate of the market value of any stock at any time).
- The dotted green line shows what the company’s historical price should have been if the market at the past date had possessed perfect information between that past date and today.
And the second chart shows how over- or under-valued the stock was relative to the “20-20 Hindsight” price we calculate today.
- A green bar indicates that the stock was undervalued using 20-20 hindsight.
- A red bar indicates that the stock was overvalued.
So how did the market do?
In a word, YIKES!
At every single past period, the stock was massively mispriced. At certain points it was more than 70% undervalued, and even as recently as 2025 (i.e., last year) it was still almost 20% undervalued.
I’m reading your mind again! “Dude, Brad, come on, nice try here … but you’re blatantly cherry-picking. Sure, sure, maybe the market doesn’t get it right every time – nobody bats 1.000 – but if you stop cherry-picking and show aggregate results for all stocks, we’ll all see how foolish you sound.”
Okay, challenge accepted! Let’s see how the market does across all stocks.
Take a look at the chart below, which shows the distribution of market mispricing each year across around 5,000 stocks, again using the “20/20 Hindsight” methodology above.
For any given year:
- The median mispricing is shown by the blue line.
- The mean mispricing is shown by the yellow line.
- The range of mispricing between the 25th and 75th percentiles is in darker blue.
- And the range of mispricing between the 10th and 25th percentiles, and between the 75th and 90th percentiles is shown in lighter blue.
- Positive values indicate that the market price at the measured time was overvalued compared to the 20/20 hindsight value.
- Negative values indicate that the market price at the measured time was undervalued compared to 20/20 hindsight.
So what does the chart tell us? Quite a few things, actually.
- First, the median market mispricing was remarkably small – it never deviates too far away from the 20/20 hindsight price.
- And that’s pretty darn impressive!
- And also, I think I just heard efficient market enthusiasts and finance professors exclaim a church-style “Hallelujah!” followed by a couple “Amen’s” and a “Preach!”
But before we get all euphoric and in awe of the market, can I just say something?
“YEAH BUT!”
(And there are a lot of yeah but’s, but I’ll just list a few).
- Yeah but … the average market mispricing was HUGE!
- It doesn’t fall below an average of 15% mispricing until a year out from the date of measurement.
- Go back more than two years into the past, and the average mispricing is above (and sometimes FAR above) 30%!
- You know what we call a weatherman with the same track record of average errors in his forecast as the stock market in its mispricing? FIRED!
- That’s bad!
- Yeah but … the average market mispricing was overvaluing stocks, not undervaluing.
- It would be one thing if the market was undervaluing stocks. I mean, for a buy-and-hold investor, that just means that the investor got, on average, a bargain price!
- But that’s not what happened! The average stock bought at market prices 2+ years ago was 30% or more overvalued. For a buy-and-hold investor, the average price you paid was 30%+ more than the dang thing was actually worth! That’s putrid!
- Yeah but… look at how insanely wide that distribution is!
- I mean, sure, you got it generally right if I look at the median.
- But who cares – the distribution was enormous, and that just proves how wildly inaccurate the market’s historical prices were.
- Consider that, four years ago, 15% of all stocks were OVERVALUED by between 36.5% and 65.9%.
- And another 10% were even more overvalued than that!
- (I sure hope you didn’t buy one of those… but I’m 100% certain you did if you bought a total market index fund).
Time for me to predict your thoughts again!
- “Maybe efficient markets aren’t nearly as efficient as I thought in accurately pricing stocks.”
- “Maybe the market isn’t nearly as all-knowing as I’ve been led to believe.”
- “I can’t believe I’m even thinking this, but … with all of this wild mispricing … could it actually be possible … to outperform the market … if I just concentrate on a few areas where I might have an edge? Am I crazy for thinking this? Am I seeing ghosts?”
You’re not seeing ghosts. You’re seeing O-P-P-O-R-T-U-N-I-T-Y!
I’m right there with you.
Let’s explore how we might plausibly be able to do so.
Maybe You Can’t Out-Predict the Market on EVERY Stock… But What About Just A Few Stocks?
So let’s recap. We’ve established that stock prices are not normally distributed random walks, especially as we measure returns over extended periods of time.
[BRAD NOTE TO ANYONE BORED ENOUGH TO HAVE READ THIS FAR – I’m writing this article when I have free time – which, as a husband, the father of two kids, and the primary caretaker of two very needy dogs, I don’t have a ton of. So I have to stop writing now but will continue when time permits. Sorry to leave you with half-finished (or, really 20% finished) thoughts – but I think you’ll find it pretty interesting.]
