Algometrics proves that deployment risk cannot be identified from passive historical data alone, that model rankings can invert under crowding, and that randomized actions can identify short-horizon linear feedback.
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The Review of Financial Studies 33(5):2223–2273, DOI 10.1093/rfs/hhaa009
10 Pith papers cite this work. Polarity classification is still indexing.
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A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.
ReSGA, a large autoencoder, outperforms prior methods on joint VaR-ES forecasting for US equities and converts the edge into economic gains via a size-enhanced momentum strategy, with gains attributed to data complexity.
FPILOT optimizes pre-trained RL trading policies at inference time using forecasted price trajectories to improve portfolio allocations and risk-adjusted returns on the DJ30 benchmark.
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
A gradient-based sample generation framework is proposed to identify macroeconomic conditions inducing specific behaviors in portfolio optimization pipelines.
FPQC-SAC adds a bounded parameterized quantum circuit to SAC to constrain representations in low-SNR financial environments, reporting 66.89% higher cumulative returns than standard SAC on real portfolio tasks.
Benchmark of 15 time-series architectures on equity portfolios finds no model dominates, with TransEnc-8 at 0.352 rank-1 acceptability and all promoted models showing negative net Sharpe at 20 bps costs under constraints.
Cost-aware execution filters enable selected machine learning strategies, particularly long-only XGBoost, to achieve over 65% annualized returns and Sharpe ratios above 1 in hourly BTC trading despite 10bp costs.
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Tests for Independence of High-Dimensional Nonstationary Time Series
A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.