Research
Every research paper we have written up, newest paper first. New arrivals are screened automatically for topical fit with systematic trading; the ones that clear it are read, written up, and — where the idea is testable on our data — backtested. 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.
16,915 papers screened · 588 on the radar · 121 shown
This study analyzes the microstructural mechanisms through which the rapidly expanding single-stock leveraged ETFs in the Korean capital market impede the price discovery function and amplify endogenous volatility.
Financial forecasting models are typically developed in full precision, yet production deployment often requires low-precision inference to reduce memory and computational cost. Post-training quantization (PTQ) enables such deployment without retraining.
PAPER REPORTS · FP32 mean daily IC (cross-sectional Spearman, averaged over test dates, 2018-2025 walk-forward): TSMixer 0.490±0.043,… · Baseline mean daily IC over the same folds: pooled HAR 0.408, persistence (trailing 5-day realized vol) 0.315
OUR BACKTEST · Sharpe 0.41 · Return +41.7% · Max DD -37.2%
Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training. This paper asks where such information should enter a neural cross-sectional volatility forecasting model.
PAPER REPORTS · U.S. panel, 30 walk-forward windows Apr 2018 - Oct 2025, mean over 30 seeds: IC 0.5469 +/- 0.0012, ICIR 6.14 +/- 0.06,… · IC advantage over capacity-matched MLP-L: +0.0048 full sample (p<1e-4); +0.0207 top market-vol decile; +0.0322 COVID…
OUR BACKTEST · Sharpe 0.48 · Return +15.8% · Max DD -20.6%
The growth of decentralized finance (DeFi) and sustainability-linked investment markets has been rapid.
Our primary goal is to forecast and empirically examine the evolution of the implied volatility (IV) surface, with particular focus on the dates of scheduled meetings of the Federal Open Market Committee (FOMC).
PAPER REPORTS · h=1 out-of-sample RMSE, calls, ConvLSTM on SVI surface without dummy: 0.085 (sd 0.003) vs random walk 0.092; test year… · h=1 out-of-sample RMSE, puts, ConvLSTM on SVI surface without dummy: 0.077 (sd 0.003) vs random walk 0.084; test year…
Cryptocurrency exchange-traded products (ETPs) listed on European exchanges provide a regulated environment for studying intraday market anomalies.
PAPER REPORTS · AUC-ROC up to 0.823 for one-bar-ahead DPOT prediction (LR, cumulative features, VIRBTC.ST), out-of-sample last 20% of… · AUC-ROC 0.821 for DPOT, LR, cumulative, VBTC.XE; 0.812 session-based
Hawkes-based microstructural foundations for rough volatility, leverage, and rough Heston-type limits were developed by El Euch et al.
This paper develops a unified mathematical theory of implied, local, and learned volatility surfaces.
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%
The impact of web datasets on market prices has suggested the development of new sources of information, such as social media and web portals, indicating the possibility of an emergent phenomenon.
PAPER REPORTS · Out-of-sample one-step-ahead return forecast Mean Error improved ~10% (-0.05112 to -0.04284) with the open-information… · Out-of-sample RMSE improved ~0.01% (1.94575 to 1.94543) and MAE ~0.1% (1.52465 to 1.52311); no trading strategy,…
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 the VIX-derived volatility (VDV) model, a VIX-first framework for joint SPXVIX modeling.
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%
How deep and how long should the drawdowns of a systematic trading strategy run, given its Sharpe ratio and the statistical structure of its returns? Building on the drawdown framework of Rej, Seager and Bouchaud (2017), we develop the answer in three steps.
Recently, it has been proposed to model the microstructure noise in prices by a continuous-time process with continuous sample paths that are rougher than those of a standard Brownian motion.
Implied volatility surface forecasting is essential for option valuation, hedging,and risk management, but remains difficult because future surfaces are stochastic while pricing inputs must satisfy static no-arbitrage shape restrictions.
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%
The Gasoil options market is illiquid, making it difficult to construct its implied volatility surface directly. However, it is closely linked to the highly liquid Brent options market.
The Observable Matrix Dynamics (OMD) approach monitors the time development of complex non-linear systems through the trajectory of a fixed-size distance matrix and its spectrum.
PAPER REPORTS · Early-warning ROC AUC (the paper's own signal, causal trailing 126-day features, label = forward peak-to-trough…
OUR BACKTEST · Sharpe 0.69 · Return +120.4% · Max DD -33.3%