Research
A continuously updated feed of research papers that pass our automated relevance screening for systematic trading — plus every paper we have published a review of, whatever it scored. Particular focus on alpha hypotheses that can be formalised and tested. The Radar also covers portfolio construction, market risk and execution where the research is directly relevant to systematic investment processes. Follow new entries by RSS.
15,697 papers screened · 250 on the radar · 13 shown
Recent advances in LLM agents enable a new paradigm for asset pricing, which we call Agentic Empirical Asset Pricing (AEAP): systems that autonomously conduct the scientific discovery process itself. We define AEAP and identify its core building blocks.
PAPER REPORTS · SEADS mean per-factor OOS Sharpe 0.25 on Panel A (JKP) and 0.16 on Panel B (CRSP/Compustat), OOS windows 2020\u20132025… · SEADS productivity 14.0 (Panel A) / 13.8 (Panel B) admissions out of a 300-candidate budget
Market-order flow in financial markets exhibits long-range correlations. This is a widely known stylised fact of financial markets. A popular hypothesis for this stylised fact comes from the Lillo-Mike-Farmer (LMF) order-splitting theory.
Large language models (LLMs) are increasingly used to discover trading strategies, and much of the resulting literature shares a methodological weakness: many candidate strategies are generated, the best is reported, and neither look-ahead bias nor the…
PAPER REPORTS · Best gpt-4.1 discovery (E3, RSI x volume, 453-stock universe): design Sharpe 1.69 (2017-2021), evaluation Sharpe 0.18… · Best claude-sonnet-5 discovery: design Sharpe 0.44 (2017-2021), evaluation Sharpe -0.33 / -29% (2022-2025), DSR 0.18,…
OUR BACKTEST · Sharpe -0.10 · Return -10.8% · Max DD -47.8%
PAPER REPORTS · Baseline HRP: annualised Sharpe 0.741, total return +417%, max DD -85%, 76 monthly rebalances 2020-02 to 2026-05,… · HRP-family variants span Sharpe 0.701-0.749 over the same window and cost assumption (best HRP_Dynamic_94 0.749,…
Backtests of trading strategies are often selected after many parameter trials. A strong historical result can therefore reflect search luck rather than a persistent signal.
PAPER REPORTS · Genuine-edge discrimination AUROC 0.9890 in synthetic ground truth at headline difficulty (n = 2000, T = 1260 daily… · OOS-survival (Sharpe_OOS > 0) AUROC 0.863 and Spearman 0.611 vs realized OOS Sharpe, same synthetic headline cell
Reinforcement learning has gained increasing attention as a data-driven approach for stock trading. However, learning a policy that is both profitable and stable remains challenging due to non-stationary market behaviour and noisy reward signals.
PAPER REPORTS · DJI (test 1 Jan 2024 - 31 Mar 2025, 0.1% transaction fee both sides): annual return 21.785%±1.42, cumulative return… · FTSE (same period and costs): annual return 19.164%±1.36, cumulative return 24.596%±1.79, Sharpe 1.124±0.08, max…
The authors present a rigorous empirical evaluation of three distinct optimization paradigms for institutional factor portfolio construction: an entropy-based photonic quantum annealer (Dirac-3, Quantum Computing Inc.), a commercial mixed-integer programming…
PAPER REPORTS · Dirac-3, best overall configuration (beta1=0, beta2=1): Sharpe 0.760, Sortino 0.841, Calmar 0.567, MDD -3.47%, CVaR5%… · Dirac-3 (beta1=0, beta2=0.5): Sharpe 0.721, Calmar 0.538, MDD -2.63% (lowest in both sweeps), CVaR5% -1.107%, annual…
OUR BACKTEST · Sharpe 0.25 · Return +11.4% · Max DD -18.7%
Using neural networks for stock return prediction typically requires choices about depth and hidden-layer width that are difficult to connect to financial interpretation.
PAPER REPORTS · HNN (marginal): pooled out-of-sample R2 0.509% (vs zero forecast), 1987-2016 · HNN (marginal): gross equal-weighted decile long-short spread 3.95% per month, 1987-2016
Large language models can extract richer signals from financial news than fixed sentiment lexicons, and recent work has explored feeding such signals into portfolio construction.
PAPER REPORTS · Sharpe 2.33, annualized return 95.9%, cumulative net 100.1%, max drawdown -18.3% — pure beta, GPT-4o mini, Student-t,… · Sharpe 2.44, annualized 100.0%, net 106.9%, max DD -18.3% — same configuration at 50 bps (2025)
The paper considers the problem of variable selection for forecasting electricity spot prices.
PAPER REPORTS · BMT hourly rMAE averaged across six areas: 0.501 (2022-2025 out-of-sample, no transaction-cost concept; rMAE < 1 means… · BMT daily baseload rMAE_t averaged across six areas: 0.408 (2022-2025)
Conformal prediction has traditionally been used to quantify prediction uncertainty.
PAPER REPORTS · DEV 2016-2021 (1,511 days), Config A: 28.45% annualised net log growth, Sharpe 1.336, max drawdown 27.68%, Calmar… · DEV 2016-2021, Config B: 25.84% annualised net log growth, Sharpe 1.386, max drawdown 20.26%, Calmar 1.376, annualised…
OUR BACKTEST · Sharpe 0.41 · Return +45.1% · Max DD -40.4%
I revisit the exchange rate disconnect puzzle, first documented by Meese and Rogoff (1983), using generative artificial intelligence (AI) to forecast currency returns based on economic fundamentals.
PAPER REPORTS · Annualized Sharpe ratio 0.594 at 48-month lookback (cross-sectional long top-2 / short bottom-2 of 9 currencies,… · Annualized Sharpe ratios 0.577 / 0.604 / 0.594 / 0.491 / 0.467 for 36 / 42 / 48 / 54 / 60-month lookbacks (2001-2024,…
When do outcome records carry enough signal to support reliable inferences about skill? When they do not, what should evaluators substitute? The framework answering the first question characterizes any decision domain with two parameters: the noise reflected…