Turning Stock Selection Into a “Big Data” Problem

When Data Becomes Understanding

There are days when the market feels almost human to me—full of moods and contradictions, hopes and disappointments, little flashes of brilliance followed by long, brooding silences. For years, I treated it that way, as if understanding stocks required not just analysis but intuition, some private sense of what a company felt like beneath the surface of its numbers. And maybe that’s true to a degree. But over time, I realized something quieter, something steadier: the market isn’t a mystery to be intuited. It’s a story told in data.

My son doesn’t always see it that way. When we talk about investing, he gravitates toward the excitement—the narratives, the potential, the futuristic promises of technologies just beginning to awaken. And I remember being like that, measuring opportunity by the spark it lit in me. But sparks fade quickly. Data endures.

I told him recently that selecting stocks is, in many ways, a big data problem—one made of rolling windows, drawdowns, moving averages, trend lines, earnings rhythms, economic tides. At first, he gave me that look of his, the one that mixes hesitation with curiosity. I could almost hear the unspoken question: Doesn’t that make investing cold? Mechanical? Lifeless?

But to me, data isn’t lifeless. It’s a record of life—every rise, every fall, every breath the market has taken. When you look at enough numbers long enough, you begin to see patterns that feel almost poetic in their repetition. Cycles that echo one another. Behaviors that reveal themselves in the quiet margins of charts and spreadsheets.

The way I analyze stocks now wasn’t built overnight. It grew slowly from years of watching companies stumble and recover, expand and contract. I began to notice that those fluctuations weren’t random—they were patterned. They carried histories. They told the truth about a company more honestly than any press release ever could.

Turning stock selection into a data problem didn’t strip away the humanity for me—it clarified it. I began to see volatility as temperament, drawdown as resilience, rolling-year strength as consistency of character. Data didn’t erase the soul of the market; it helped me finally see it.

But the real gift of treating investing this way is the calm it brings. The market can be loud, overwhelming, emotional. It’s easy to get swept into fear or excitement, to make decisions rooted in impulse rather than intention. When you approach it as a data problem, the noise quiets. You step back. You measure. You breathe. Decisions become less about reaction and more about recognition—seeing what has already been revealed.

I told my son that even Bitcoin, the asset he loves to track, can be understood through data. The way it dances around its 200-day moving average, the way momentum shifts, the way its cycles echo their own history. He nodded then, because he’s felt that rhythm too—even if he didn’t have words for it yet.

The beauty of big data isn’t its complexity. It’s its honesty. It shows what happened—not what we wish happened. And in that honesty, we learn to trust ourselves more deeply.

So yes, stock selection is a big data problem. But it is also a kind of meditation. The patient gathering of truth. The slow uncovering of structure. The realization that the future may always be uncertain, but the patterns that brought us here are anything but.

In the end, data doesn’t replace intuition—it strengthens it.
And when the two finally meet, investing begins to feel less like guessing and more like understanding.
A quiet knowing.
A steadying presence in a world that often moves too fast.