We tested 12 machine-learning models on the stock market. None of them worked, and we published everything.

Quantooox — Mon, 26 Oct 2026 11:15:33 +0000

Most finance content shows you what worked. We built a free site, quantooo.com, to do the opposite: test ideas properly, in public, and publish the result whether it is good or bad.

Here is what we found, and how we tested it.

The setup

The site gives about 100 large US stocks (and the Nifty 50 in India) a score from 0 to 100, built from five plain measures: trend, stability, company fundamentals from official filings, the market backdrop, and the tone of the news. Every step of every score is shown on the page, with its source and date.

The question was simple: do stocks with a higher score do better over the next month than stocks with a lower one?

How we tested it

Only the past. Every model was trained on earlier data and judged on later data it had never seen (walk-forward testing).
A sealed final year. The last 12 months were locked away. We froze the rules first, then opened that year once.
Random referees. Every result was compared with random rankings. If a coin flip does as well, the model isn't smart.
Counting every try. Try 100 ideas and one will look brilliant by luck, so every configuration we tried is counted and the results are corrected for it.

What we found

The score: in development (2020–2025) its link with next-month results was +0.0085, statistically indistinguishable from random (t = 0.46). On the sealed final year it was −0.0585 (t = −2.47): higher-scored stocks actually did worse.

Twelve model families went through the same test: simple rules, ridge regression, LightGBM, neural networks, kNN, LSTM, GRU, a regime-switching model and more. None showed a significant edge. As a check on the pipeline, a model trained on shuffled labels scored about zero, so the test isn't leaking information.

India, same method, with company results used only from the minute they were published: +0.009 (t = 0.21), no skill either.

The one thing that helped, a little

Predicting direction failed. Predicting turbulence did a bit better. A volatility model for the S&P 500 had lower forecast error than the standard HAR model on 2025–26 (QLIKE 0.816 vs 1.047, p = 0.047). In an exploratory follow-up, the decades-old GARCH model did best of all.

That matches the research paper the site is built on, Where Machine Learning Helps: machine learning is far more useful for measuring risk than for picking winners.

Why publish a failure?

Because a result you only hear about when it works tells you nothing. Every night the scores are saved before the market opens, fingerprinted, and timestamped on the Bitcoin blockchain, so nobody, including us, can quietly edit them later. Mistakes go into a public corrections log.

We are also running hypothetical paper portfolios for one year from 19 October 2026, with rules registered before the first trade. We'll report the result either way. (Hypothetical, not a claim of returns; they could easily lose.)

Check our work

The leaderboard of all 12 models: https://quantooo.com/models
The backtest, controls and the sealed year: https://quantooo.com/research
Every formula: https://quantooo.com/method
The track record nobody can edit: https://quantooo.com/track-record

It's free, with no ads, no fees and no broker links. If you see a flaw in the method, we'd genuinely like to hear it in the comments.

Written by Quantooo Lab, the makers of quantooo.com. Educational, not investment advice.