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High-frequency lead-lag: Euro Stoxx 50 trades lead the DAX future midpoint on 99% of days, by 100 microseconds. Direction accuracy averages 51.8% and correlates 0.67 with lead-lag strength.
S&P 500 pairs, 2000-2022: multi-view spectral clustering posts Sharpe 0.830 after 5bp. Price-distance pairs fall to 0.338, and the paper's 4.6% drawdown is missing from its table.
Pairwise Granger on 64 futures at 5% and five lags expects 1,008 false links. Trade the PCMCI parent graph; the 1.51 Sharpe stays on the similarity graph.
US equity cross-section: the 21-day over 200-day moving average ratio earns 9.05% annual alpha and kills momentum's. All of it is the long leg, and the short leg pays nothing.
Crypto directional-change trading with a meta-learned threshold: adaptive Sharpe 1.34 vs 0.59 static, p=0.008. Real gain, but the 446% return and the omitted Bitcoin bar do not survive a recount.
Crypto COVOL split into good and bad gives the RCI, and a 125/100/75 tilt claims Sharpe 0.5648 vs 0.3971. The thresholds need hindsight, and Table 6's columns describe two different return series.
Kalshi trend quality (TQR) sorts pre-close contracts into a 19.6pp continuation gap, then dies out of sample: midquotes cut it to 1.9pp, AUC 0.448. A stand-down gauge, not alpha.
Bloomberg's intra-bar high/low timestamps cut test NLL to 0.4075 from 0.4311, but only 0.0013 is timing. Intra-bar timing is a volatility feature, not alpha, and the 72.3% is clock overlap.
Piecewise-linear regime detection on the Swiss SLI reports in-sample R-squared 0.954 at 15 breakpoints. A driftless random walk scores 0.966, and the knee sits on an optimizer failure.
Bitcoin vol clustering: a 3-state Markov chain shows persistence above 0.9 -- High state delivers ~9% moves in 48h, but thresholds are look-ahead by construction.
Stock-bond correlation flipped from +0.22 to -0.24 around 2000 across G7 markets. Real rates most stable driver. US portfolios need 80% stocks post-2000 to match 50/50 pre-2000 performance.
Add a competitor's inventory as a second state variable in Avellaneda-Stoikov: a matrix ODE closed form beats PPO (500M epochs, 99% paired-t). The fill you miss shifts their next quote and degrades yours.