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Trade prices of about 1000 New York Stock Exchange-listed stocks are studiedat one-minute time resolution over the continuous five year period 2018--2022.For each stock, in dollar-volume-weighted transaction time, the discrepancyfrom a Brownian-motion martingale is measured on timescales of minutes toseveral days. The result is well fit by a power-law shot-noise (or Gaussian)process with Hurst exponent 0.465, that is, slightly mean-reverting. As acheck, we execute an arbitrage strategy on simulated Hurst-exponent data, and acomparable strategy in backtesting on the actual data, obtaining similarresults (annualized returns $\sim 60$\% if zero transaction costs). Nextexamining the cross-correlation structure of the $\sim 1000$ stocks, we findthat, counterintuitively, correlations increase with time lag in the rangestudied. We show that this behavior that can be quantitatively explained if themean-reverting Hurst component of each stock is uncorrelated, i.e., does notshare that stock's overall correlation with other stocks. Overall, we find that$\approx 45$\% of a stock's 1-hour returns variance is explained by itsparticular correlations to other stocks, but that most of this is simplyexplained by the movement of all stocks together. Unexpectedly, the fraction ofvariance explained is greatest when price volatility is high, for exampleduring COVID-19 year 2020. An arbitrage strategy with cross-correlations doessignificantly better than without (annualized returns $\sim 100$\% if zerotransaction costs). Measured correlations from any single year in 2018--2022are about equally good in predicting all the other years, indicating that anoverall correlation structure is persistent over the whole period.

NYSE Price Correlations Are Abitrageable Over Hours and Predictable Over Years