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

说明与用途

对股票市场进行12种机器学习模型的测试表明，没有显著优于随机的优势；更高的分数与更差的表现相关，而波动率模型略有优势。

问题

Determine whether machine‑learning models can predict next‑month stock performance better than random.

结果

None of the 12 ML model families achieved significant predictive skill; higher scores correlated with worse performance in the sealed year. Volatility prediction models performed modestly better.

工作流

• 1. Build a score (0‑100) from five measures for ~100 US stocks and Nifty 50.
• 2. Train each model on historical data using walk‑forward testing.
• 3. Lock the last 12 months as a sealed final year; freeze rules before opening.
• 4. Compare model rankings against random rankings.
• 5. Count every configuration and correct for multiple testing.
• 6. Evaluate performance via correlation and t‑tests.
• 7. Publish all results and formulas publicly.

证据

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.

来源

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

来源

Only the past. Every model was trained on earlier data and judged on later data it had never seen (walk-forward testing).

来源

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.

来源

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.

来源