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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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1122 · Sep 202519922001200920172026
10 results for multi-day

Analyzes multi-day stock returns, showing linear volatility and mean dependence.

problem Linear dependence of volatility and mean in accumulated stock returns.
method Modified Jones-Faddy skew t-distribution analysis.
result Linear dependence of volatility and mean on the number of days of accumulation.

Master thesis applies deep learning to sEMG hand gesture recognition, improving accuracy.

problem Reliability issues in sEMG-based hand gesture recognition due to motion artefacts and variability.
method Used deep learning on Unibo-INAIL dataset, collecting data over 8 sessions of 7 subjects.
result Deep learning architecture yields 81.2% inter-posture test accuracy and 75.9% inter-day test accuracy.

LEMs extend transformer-based architectures for complex execution problems.

problem Handling flexible time boundaries and multiple execution constraints in deep learning.
method Decouples market information processing from execution allocation decisions using TKANs, VSNs, and multi-head attention mechanisms.
result LEMs achieve superior execution performance compared to traditional benchmarks.

Hybrid pipeline detects spike-and-wave discharges in long-term EEG recordings.

problem Manual identification of spike-and-wave discharges in long-term EEG recordings is labour-intensive and error-prone.
method A hybrid pipeline that combines analytical features with a shallow ANN for SWD detection.
result The method correctly detected 384 out of 392 annotated SWD events, achieving high sensitivity and specificity.

Breaks circular dependency in synthetic option pricing with a novel model.

problem Circular dependency in implied volatility limits synthetic data for machine learning and risk analysis.
method Uses a Jump-Hidden Markov Model to generate price paths and a modified Heston process to convert paths into implied volatility.
result Framework generates realistic synthetic American option prices without external calibration.