The Hidden History Behind the Market’s 10% Return
What a dusty database, a clunky mainframe, and modern AI all have in common.
Through the mid-20th century, investing was more folklore than fact. People swapped stories of booms and busts, but no one knew what the stock market actually returned over time. The data existed, but it was scattered, incomplete, and largely ignored.
Then, in the 1960s, a radical experiment turned decades of stock market history into a single, simple insight: The market’s average return was far more reliable than anyone imagined.
This wasn’t trivia. It was a paradigm shift. For the first time, investors had evidence, not just anecdotes, that the broad market itself was a dependable engine of wealth.
Flying blind
If you ask someone today what the stock market returns over the long run, “around 10% a year” rolls off the tongue. It’s one of the most quoted numbers in finance.
But back then, no one had a clue. Investors and money managers alike were flying blind, relying on hunches, newspaper clippings, and cocktail-party wisdom. The past was fuzzy, and the present just as cloudy.
People knew fortunes were made in stocks and lost just as easily. Stories from The Roaring Twenties, The Crash of 1929, and the post-war booms and busts dominated the conversation, but anecdotes don’t make a reliable compass.
The breakthrough
Everything changed in 1964. Two University of Chicago professors, Lawrence Fisher and James Lorie, did something unprecedented: For two years, they painstakingly collected every transaction of every public company, fed it into one of the earliest computers (think punch cards and refrigerator-sized machines), and asked it to spit out the truth.
The result was the first version of the CRSP database (Center for Research in Security Prices). For the first time, investors could see the market’s record with clarity.
The finding was startling: Since 1926, U.S. stocks had delivered about 10% a year on average.
That single fact transformed investing. Suddenly, you didn’t need a hot tip or mystical stock-picking skills to justify owning equities. The market itself became the evidence.
The foundation of evidence-based investing
CRSP became the cornerstone of evidence-based investing because it broke investors out of what psychologists Daniel Kahneman and Amos Tversky later called the inside view—the habit of relying on stories, predictions, and narratives that feel compelling but rarely reflect reality.
For decades, this is where most investors lived: obsessing over company anecdotes, manager mystique, and whatever recent drama dominated the headlines.
Fisher and Lorie changed that by introducing the outside view: a baseline, a reference class, a way of stepping back and asking a more powerful question: What usually happens in situations like this?
Their database didn’t offer hot takes or clever forecasts; it offered something far more valuable: context. A yardstick. A map of long-term market behavior built on hard data rather than lore.
And with that shift, investors finally had what they’d been missing all along: a lens that helped them understand the past clearly enough to make better decisions about an uncertain future.
I wrote about a similar inflection point in The 4% Rule Revisited, where another deceptively simple discovery reshaped retirement spending. The most powerful financial ideas are often the simplest. They’re hiding in plain sight, waiting for someone to uncover them.
From mainframes to AI
Fast forward to today. Once again, we’re living through another shift just as consequential. Not from dusty stock ledgers to mainframes, but from human judgment to artificial intelligence.
Investing often gets framed as a cage match:
Humans vs. Algorithms.
Emotion vs. Computation.
Instinct vs. Speed.
But the biggest edge won’t come from choosing sides. It will come from combining them. And the best example of this isn’t from Wall Street, but from a 2,500-year-old board game.
Lessons from Go
For decades, Go was considered the Everest of strategy games.
In 2016, an AI system named AlphaGo stunned the world by defeating a human Go champion—a milestone experts thought was at least a decade away. This was headline-making: Go is staggeringly complex with more possible board configurations than atoms in the universe.
But that wasn’t the full story.
A few years later, a top-ranked human beat the best AI systems. How? By playing with a computer as a teammate. It wasn’t human intuition alone that triumphed. It was human intuition amplified by machine analysis.
Investing mirrors this story. Pure quants may stumble in uncharted territory. Pure fundamentalists can fall prey to bias or emotion. But when human intuition meets machine precision, the result is a disciplined process that adapts to an uncertain world.
As I argued in 5 Investing Concepts That Are Counterintuitive, skill isn’t measured by short-term results. Only process stands the test of time.
Why this matters for you
The CRSP breakthrough reshaped how we understood the past, but it didn’t—and couldn’t—predict the future. Every investment decision is made in the face of uncertainty, and the lesson isn’t that we can know what will happen next. It’s that we can improve how we respond.
The market’s 10% return wasn’t a gift from the gods of finance. It was uncovered by humans asking better questions with better tools. That same principle, big insights hiding in plain sight, is also why compounding is so powerful.
Today, AI may be the next powerful tool, but the future will always surprise us. The edge belongs to those who combine insight with discipline, curiosity with evidence, and human judgment with machine precision.
Don’t wait for certainty that will never come. Audit your planning process, tighten your rules, and build a system that keeps you grounded when those around you lose their footing. That’s how you turn an unknowable future into an advantage.
As always, invest often and wisely. Thank you for reading.
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The content provided is for informational and educational purposes only. It does not constitute legal, tax, investment, financial, or other advice. You are welcome to share, quote, or use the content — including for research or machine learning — please credit Cosmo P. DeStefano and link to www.CosmoDeStefano.com.
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Originally published at www.CosmoDeStefano.com




