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 · 20 shown
Abstract This study develops a robust framework for modeling dynamic volatility, asymmetry, and tail dependence in financial returns, focusing on the daily returns of Natural Resource Index () and the Oil and Gas Index ().
OUR BACKTEST · Sharpe 0.47 · Return +42.5% · Max DD -27.9%
Lead-lag relationships are widely used in financial time series, and many clustering algorithms based on them have been developed. The traditional DTW-KMedoids algorithm performs well both on the synthetic dataset and the real financial dataset.
PAPER REPORTS · Sharpe 0.866, annual return 6.21%, annual volatility 7.17%, max drawdown -63.908, hit rate 0.520, profit-loss ratio… · Sharpe 0.808 / 0.790 (KShape mod / med), lead strategy, 679 assets, same period; drawdowns -67.604 / -69.418
OUR BACKTEST · Sharpe 0.39 · Return +27.5% · Max DD -39.3%
Implied volatility surfaces summarise the option market and are central to many financial applications.
PAPER REPORTS · Surface point-forecast RMSE aggregated over 30 horizons: 0.01262 vs persistence 0.01343, +6.09% gain; MAE gain +3.45%;… · h=1: RMSE 0.00619 vs persistence 0.00535 (-15.72%); MAE -34.49%
OUR BACKTEST · Sharpe -0.00 · Return -1.0% · Max DD -329.1%
Deep hedging is a data-driven approach to learn hedging strategies. It relies on synthetic price paths generator, as real market data is often limited for training.
OUR BACKTEST · Sharpe 1.78 · Return +5.9% · Max DD -0.6%
We study continuous-time dynamic portfolio optimization under a Conditional Value-at-Risk (CVaR) constraint on the investor's terminal loss.
PAPER REPORTS · Complete market, binding c = -0.94, T = 1 simulated: E[W_T] = 1.0242, CVaR_0.95(-W_T) = -0.9406, average exposure… · Complete market, nonbinding c = -0.86, T = 1 simulated: E[W_T] = 1.0322, CVaR_0.95(-W_T) = -0.8671, average exposure…
OUR BACKTEST · Sharpe 0.75 · Return +55.2% · Max DD -26.8%
We develop a scalable adjoint-to-control framework for continuous-time portfolio choice under smooth pointwise constraints.
OUR BACKTEST · Sharpe 0.19 · Return +24.7% · Max DD -66.8%
Diffusion generative models have rapidly emerged as powerful tools for modeling complex financial data.
OUR BACKTEST · Sharpe 0.53 · Return +79.3% · Max DD -50.5%
This article presents with DYSANOS the first generative market model for smooth SANOS option surfaces for all strikes and expiries which are free of static arbitrage.
OUR BACKTEST · Sharpe -0.19 · Return -0.0% · Max DD -0.1%
OUR BACKTEST · Sharpe 0.48 · Return +15.8% · Max DD -20.6%
The enormous growth in datasets, both in number and size, has prompted investors to adapt to new ways for assimilating information.
OUR BACKTEST · Sharpe 0.11 · Return +31.6% · Max DD -96.3%
Control policies optimized in simulation can perform poorly in the real system when the parameters $x$ of the simulator are estimated from limited data but the resulting parameter uncertainty is not represented inside the simulation.
PAPER REPORTS · In-simulator spectral risk (x100, warm-up H=T), BS-VOL: RLM-trained policy 10.15 on RLM paths and 10.33 on SLM paths;… · In-simulator spectral risk (x100, warm-up H=T), HESTON-CORR: RLM policy 19.87 (SLM paths) / 19.93 (RLM paths) vs SLM…
OUR BACKTEST · Sharpe -0.18 · Return -66.9% · Max DD -81.1%
Automated market makers (AMMs) are typically interpreted and evaluated as decentralized exchanges.
PAPER REPORTS · VBIAX, monthly TE, Jan 2, 2014 – Jun 30, 2026: G3M Pareto-dominates (higher CAGR and lower TE) for gamma in [2.73%,… · EQL NAV, economic mandate, Jun 19, 2018 – May 29, 2026: G3M dominates for gamma in [3.22%, 7.09%]
OUR BACKTEST · Sharpe 0.71 · Return +133.0% · Max DD -42.0%
Agentic AI is gaining acceptance in asset management, but governance has not kept pace: 88% of surveyed finance professionals report no operational governance framework for agentic AI despite universal awareness of its deployment, and only 24 of 75 large U.S.
OUR BACKTEST · Sharpe -1.47 · Return -45.1% · Max DD -49.7%
This paper builds Path Portfolio Optimization: portfolio theory on a path-first framework in which the signature is the universal coordinate of the price path, and asks whether it survives estimation.
PAPER REPORTS · Cross-area lead-lag portfolio, sign-carrying excitation 1→2 with q=0.85: mean P&L +0.000189, s.e. · Cross-area, sign-carrying excitation 2→1 with q=0.85: mean P&L −0.000227, s.e. 0.000012, t=−18.92, annualized Sharpe…
OUR BACKTEST · Sharpe 0.45 · Return +21.4% · Max DD -19.5%
Local-stochastic volatility (LSV) combines vanilla marginals with richer smile dynamics, but calibration requires a slow, noisy and sequential McKean--Vlasov fixed point. We learn a projection-consistent operator for the calibration triple.
PAPER REPORTS · Calibration latency 0.60 ms/surface vs 98.5 ms particle baseline (paired, same hardware, synthetic held-out states) · Vanilla repricing RMSE 58.2 +/- 3.3 bps on 8 held-out surfaces, 2 seeds (spread across seeds, not a confidence…
OUR BACKTEST · Sharpe 0.53 · Return +5.6% · Max DD -2.0%
We propose an arbitrage-aware latent flow-matching framework for unconditional implied volatility surface generation.
OUR BACKTEST · Sharpe -0.06 · Return -1.2% · Max DD -5.1%
We present a unified approach to designing trend-following (TF) systems and classify them into European, American, and Time Series Momentum categories.
PAPER REPORTS · European TF system: annualized Sharpe 0.47, monthly returns net of transaction costs and net of 2%/20%… · American TF system: annualized Sharpe 0.50, net of transaction costs and 2%/20% fees, 31 December 1999 to 30 June 2026…
OUR BACKTEST · Sharpe 0.10 · Return +8.4% · Max DD -30.4%
The distribution of a normal mean-variance mixture depends on the law of its positive mixing variable. We compare six parametric mixing laws with a grid nonparametric maximum likelihood estimator under the same determinant identification constraint.
PAPER REPORTS · Model-based lower-envelope CPT value at the robust optimum: -0.04478 at 5% annual reference return; -0.09245 at 10%… · Empirical holdout CPT value at the robust weights: -0.04867 at 5% reference; -0.10049 at 10% reference (holdout 483…
OUR BACKTEST · Sharpe 0.13 · Return +0.1% · Max DD -0.2%
Heavy-tailed diffusion models replace Gaussian noise by a Gaussian variance mixture: denoising Levy probabilistic models (DLPM) take the mixing variables i.i.d. across coordinates, while Student-t EDM shares one mixing variable per sample.
OUR BACKTEST · Sharpe -0.33 · Return -3.3% · Max DD -5.8%
Gaussian Boson Sampling (GBS) provides a native photonic quantum heuristic for sampling dense subgraphs from adjacency matrices, offering a scalable physical approach to combinatorial graph search problems.
PAPER REPORTS · {"costs": "zero transaction costs and zero price impact assumed", "period": "2020 (trading effectively April 2020… · {"costs": "zero transaction costs and zero price impact assumed", "period": "2020", "sharpe": "2.050 ± 0.190",…
OUR BACKTEST · Sharpe -1.00 · Return -5.8% · Max DD -6.2%