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
We investigate arbitrage in a discrete-time financial market model where, in addition to finitely many dynamically traded assets, there are also static options to choose from.
OUR BACKTEST · Sharpe 0.35 · Return +21.4% · Max DD -34.1%
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%
Hedge ratios, factor models and diversified portfolios all rest on an estimate of which firms move together.
PAPER REPORTS · Variance-harvest attribution (selling variance at VIX-squared against the paper's 12m equal-weighted realized leg, July… · Risk by REC quartile over the same 329 months: probability of loss 0.29, 0.21, 0.26, 0.15; mean loss given loss…
OUR BACKTEST · Sharpe 0.73 · Return +68.2% · Max DD -36.5%
Abstract Market timing models aim to anticipate short-term market movements according to a given source of information. Such information could be extracted from an analysis of history or a forecast of the future.
PAPER REPORTS · S&P500 timing, 2018: index -6.7% annualized; Strat1-L 3.8%, Strat1-LS 14.3%, Strat2-L 0.8%, Strat2-LS 8.2% (no… · S&P500 timing, 2023: index 21.6%; Strat1-L 21.9%, Strat1-LS 22.1%, Strat2-L 25.5%, Strat2-LS 29.4% (no transaction…
OUR BACKTEST · Sharpe 0.61 · Return +49.0% · Max DD -31.2%
Against the background of increasing volatility and complex risk factors in global markets, options and futures have become important instruments for risk hedging and uncertainty management.
PAPER REPORTS · Futures hedged portfolio, 2019-2024: annualized return 7.95%, annualized volatility 10.28%, hedging efficiency 46.87%,… · Option hedged portfolio, 2019-2024: annualized return 8.31%, annualized volatility 8.76%, hedging efficiency 54.73%,…
OUR BACKTEST · Sharpe 0.74 · Return +83.0% · Max DD -47.8%
We develop a geometric theory of arbitrage-free implied variance surface dynamics.
PAPER REPORTS · Out-of-sample RMSE(delta a2) improvement of full (beta,eta,psi) model over SSR-only: 17-21% at 3M-6M (215.7 vs 272.8 at… · Out-of-sample RMSE(delta a1) improvement of adding eta: 1-4% versus SSR-only at 1M-6M, essentially flat at 12M
OUR BACKTEST · Sharpe -0.84 · Return -0.1% · Max DD -0.1%
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%
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%
Fractional Brownian motion (fBm) exhibits attractive features for financial modeling, including long-range dependence, path roughness, and anomalous diffusion.
OUR BACKTEST · Sharpe 0.23 · Return +8.6% · Max DD -6.9%
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%
This note studies the conditional-density equation and its pathwise transformation in local stochastic rough volatility models, with rough Heston (rHeston) as the main explicit example.
OUR BACKTEST · Sharpe -0.45 · Return -4.2% · Max DD -7.5%