US stock prediction with machine learning: where our model actually works
Our model hits ~54% directional accuracy on US large-caps across ~830 verified calls — a real, sample-backed edge, but a modest one, not a money printer.
I run Trading Agent, and I publish every prediction we make — including the ones that lose — at /predictions. So when I tell you the United States is the one market where our model genuinely earns its keep, I'm not selling you a dream. I'm telling you what ~830 verified predictions actually show, and then I'm going to spend the rest of this article explaining why "actually works" is a much smaller claim than most of the industry wants you to believe.
The number, and what it isn't
On US-listed equities, our model's verified directional accuracy is about 54% across roughly 830 verified predictions. That's our deepest sample and our strongest market by a clear margin. For context, our blended accuracy across all 13 markets we cover is around 46% on ~2,248 verified predictions — so the US genuinely stands apart.
Now let me kill the fantasy before it forms. 54% is not a money printer. It is a modest edge. If you flip a fair coin you get 50%, so we're talking about a few percentage points of signal above noise. That sounds underwhelming, and honestly it should — but a small, real edge applied consistently across many independent decisions is worth something, whereas a big, fake edge applied once is worth nothing. The whole game is telling those two apart. I wrote about why so much of the industry blurs that line in why most AI stock-picking tools are lying, and it's the backbone of how I think about this.
Why the US is where it works
This isn't luck, and it isn't because US stocks are magic. It's market structure.
The US is the deepest and most liquid equity market on earth. That liquidity matters enormously for a model like ours, because the features we lean on — RSI, MACD, moving averages, volume, volatility — are all derived from price and volume behaviour. In a thin, sparsely-traded market, those signals are mostly noise: a couple of large orders can swing the tape and your "momentum signal" is really just one fund rebalancing. In the US, continuous, liquid order flow means a momentum or volatility pattern is far more likely to reflect something structural rather than an accident of low volume.
There's also the training-data angle, which people underrate. The US market has deep, clean, long price history. When you train a machine-learning model, the quality and length of your historical data is doing a lot of the quiet heavy lifting. We had more to learn from here, and it shows. Frankly, our feature set was effectively tuned against US-style data — so it's not a coincidence this is the market where it performs best. We optimised on home turf.
The flip side of the same coin: the US is institution-dominated and fairly efficient. Smart, well-resourced money is competing on every tick, which is precisely why the edge is small. An efficient market doesn't hand out large, durable edges to a solo-built model — if it did, the edge would be arbitraged away. So the 54% isn't despite the market's efficiency; it's the natural ceiling that efficiency imposes. A small real edge is roughly what an honest model should find in a market this competitive. If someone shows you 80% on US large-caps, your first instinct should be suspicion, not envy.
How to actually use a 54% signal
If you take one thing from this, take this: a modest edge is an input, not an instruction.
We publish Bullish / Neutral / Bearish directional reads on large-cap names — and I want to be precise about what those are. They are model output describing a probability lean, not a "buy" or "sell". I'm not going to tell you to buy any specific stock, here or anywhere, because (a) I'm legally not in that business, and (b) it would be intellectually dishonest given what 54% actually means. The right mental model is: this is one signal you might weigh alongside your own research, your risk tolerance, position sizing, fundamentals, and whatever else you bring to a decision. Compounded over many independent decisions, a slight directional tilt can be useful. Bet the farm on any single call and the edge is far too thin to protect you.
I'd also gently point out that mentioning a ticker like AAPL or MSFT in this article is me using them as examples of large-cap market structure — the kind of liquid, well-covered names where our signals are least noisy. It is not me pointing at them. The structural point is the point.
The honest bottom line
The US is where our model has a real, sample-backed, structurally-explainable edge — and it's still only about 54%. I'd rather tell you that plainly than dress it up. If you want to see the full methodology behind how we generate and verify these calls, it's at /methodology, and the running scoreboard of hits and misses is always at /predictions. Read both before you decide what, if anything, our signal is worth to you. That's the deal with radical honesty: I show you the work, and you make up your own mind.
This article is educational content about machine learning and market structure. It is not financial advice, not a recommendation to buy or sell any US-listed or other security, and not directed at any individual's circumstances. Trading Agent is a quantitative research tool operated by WU Capital Limited (New Zealand).
