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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UNVERDICTED 10representative citing papers
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.
Machine learning regression models assess the impact of US tariffs on the Australian stock market index around the April 2025 implementation date.
citing papers explorer
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Algometrics: Forecasting Under Algorithmic Feedback
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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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.
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ReSGA: A Large Tail Risk Model for Learning Value-at-Risk and Expected Shortfall
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.
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Plan Before You Trade: Inference-Time Optimization for RL Trading Agents
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.
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Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
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Generating Input Distributions for Explaining Portfolio Optimization Pipelines
A gradient-based sample generation framework is proposed to identify macroeconomic conditions inducing specific behaviors in portfolio optimization pipelines.
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Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations
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.
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Benchmarking Deep Time Series Models for Equity Portfolios
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.
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Machine Learning-Based Bitcoin Trading Under Transaction Costs: Evidence From Walk-Forward Forecasting
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.