The paper introduces μs’ autotune, an automatic tuning strategy for the Lasso that optimizes a penalized Gaussian log‑likelihood over regression coefficients and noise standard deviation. Extensive simulations on regression and VAR models show that autotune is faster and yields better generalization and model selection, especially in low signal‑to‑noise regimes. The method also delivers a new noise‑standard‑deviation estimator, a visual diagnostic for sparsity, and is demonstrated on a real‑world financial dataset, with an accompanying R package available on GitHub.
arXiv:2306. 14851v5 Announce Type: replace-cross Abstract: Given a high-dimensional covariate matrix and a response vector, ridge-regularized sparse linear regression selects a subset of features that explains the relationship between covariates and the response in an interpretable manner.
arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.
arXiv:2609.38263v1 Announce Type: new Abstract: Feature selection in neural networks remains a challenging problem, particularly in the presence of noisy or contaminated data. LassoNet is a recent ap...
arXiv:2606. 10219v1 Announce Type: cross Abstract: AI efficiency at scale is becoming critical in finance as market data volumes surge across equities, ETFs, FX, options, and high-frequency trading streams.
arXiv:2502. 18834v3 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investment strategies.
arXiv:2608. 27076v1 Announce Type: new Abstract: Algorithmic trading now represents a market exceeding $20 billion, where even marginal gains in signal robustness can translate into economically significant returns.
The paper explores tabular deep learning for equity signal generation, training five model classes on daily data from about 300 large‑cap US stocks over eleven years. By using Bayesian optimisation that targets trading performance across three distinct market regimes, the authors achieve regime‑robust hyperparameter selection, yielding out‑of‑sample signal precision above random and a Hybrid ensemble (XGBoost + TabNet) with an annualised return of 51.26% and a Sharpe ratio of 2.44. The study also finds that alternative data adds limited value beyond technical and fundamental features, and that the ensemble’s outperformance is driven by stock selection rather than market exposure.
arXiv:2608. 03259v1 Announce Type: cross Abstract: As time-series foundation models have emerged, the need for benchmarks that can evaluate their forecasting ability in meaningful ways has become increasingly important.
arXiv:2608. 04667v1 Announce Type: cross Abstract: Selective inference (SI) provides statistically valid $p$-values for hypotheses selected by applying an algorithm to the data, correcting for the bias that arises when the same data are used both to select and to test a hypothesis.
arXiv:2510. 11546v3 Announce Type: replace-cross Abstract: High-dimensional regression often suffers from heavy-tailed noise and outliers, which can severely undermine the reliability of least-squares based methods.
arXiv:2609.04239v2 Announce Type: replace Abstract: This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series foundation model (TSFM) tailored to financial...
arXiv:2606. 04576v1 Announce Type: cross Abstract: Learning Value-at-Risk (VaR) and Expected Shortfall (ES) is important for managing financial risks effectively.