arXiv:2606. 24575v1 Announce Type: new Abstract: Modern finance relies heavily on complex machine learning models to find patterns in the stock market.
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:2607. 15414v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as powerful tools for processing the heterogeneous information environments of modern financial markets.
arXiv:2608. 11683v1 Announce Type: new Abstract: AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow.
arXiv:2607. 12455v1 Announce Type: new Abstract: Quantitative strategy optimization remains largely manual, requiring domain experts to identify weak signals, tune risk-control rules, and repeatedly validate iterative revisions.
Quantitative strategy optimization remains largely manual, requiring domain experts to identify weak signals, tune risk-control rules, and repeatedly validate iterative revisions. Large language models can accelerate this process, but directly relying on them to rewrite trading strategies often introduces hallucinated edits, strategy drift, and backtest overfitting.
arXiv:2606. 12843v2 Announce Type: replace Abstract: We present an interpretable machine learning pipeline to decompose cross-sectional equity return predictability into auditable factor contributions.
arXiv:2605.28853v2 Announce Type: replace-cross Abstract: Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction cos...
arXiv:2607. 12248v1 Announce Type: cross Abstract: Large pretrained time-series models such as TimesFM are attractive for financial forecasting, but raw directional accuracy is a misleading scoreboard in equity markets.
AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow. Existing benchmarks mainly target financial data extraction, a narrow slice that current models have largely saturated, while reference-based metrics and generic LLM-as-a-judge scoring fall short on the open-ended, long-form answers that real analyst queries demand.
The paper examines whether benign overfitting—where highly overparameterized models still predict well—occurs in equity return prediction. It finds a double‑descent risk curve for ridgeless models and shows that while ridge regularization slightly improves performance, the advantage vanishes at high parameter‑to‑observation ratios. Ultimately, both models fail to beat a simple historical average, indicating that standard equity predictors lack genuine forecasting power even with flexible machine learning methods.
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.