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 · 9 shown
We examine the conditional volatility dynamics of the USD 1Yx10Y forward swap rate using GARCH(1,1), GJR-GARCH(1,1), and a two-regime Markov-switching GARCH (MSGARCH) model.
PAPER REPORTS · 95% one-day-ahead interval coverage, 1500 test days (34.36% of sample): GJR 94.00%, GARCH 93.80%, MSGARCH 93.8% · 1300 test days: GJR 93.62%, GARCH 93.23%, MSGARCH 93.154%
Modeling the dynamics of option implied volatility surface (IVS) is crucial for pricing, hedging, and risk-managing option portfolios.
Decentralized exchanges record trading and liquidity provision on public blockchains, but empirical analysis requires interpreting these records and linking them to execution metadata.
Abstract This research seeks to delve deeper into the financial significance of the eigenvector associated with the second-largest eigenvalue of the correlation matrix of returns.
OUR BACKTEST · Sharpe -0.48 · Return -35.9% · Max DD -49.7%
In financial markets, a sequential policy that reacts systematically to price movements may become predictable to other market participants.
PAPER REPORTS · Exposure-matched within-stock timing alpha, principal 10-minute Qwen3.5 price text: -45.7 bps per stock-day, 95% CI… · Chart-only 10m: -29.9 bps, CI [-33.7,-26.2], 14,937 stock-days; multimodal 10m: -48.9 bps, CI [-52.7,-45.0], 14,438…
In this paper we propose a new formulation of the Bayesian Filter as used in the discrete-time Markov-Switching-Multifractal (MSM) model of volatility based on existing permutation symmetry within the likelihood structure.
OUR BACKTEST · Sharpe 0.90 · Return +54.5% · Max DD -20.2%
Costly LLM features matter only if calibration lets them affect the forecast. We document a failure of this link in a next-day risk study of two broad-market funds. Full-history scoring preceded the 2022 calibration.
PAPER REPORTS · Prespecified LLM importance feature: zero improvement, 95% interval [0,0], on all four endpoints (SPY/VIX binary and… · Signed LLM repair, SPY/VIX continuous variance: improvement -0.007452, 95% interval [-0.015888, -0.001066], Bonferroni…
Human capital is a central organizational input, but standard financial data reveal little about firm-specific disruptions to workforce availability, cost, skills, and continuity.
OUR BACKTEST · Sharpe 0.56 · Return +50.1% · Max DD -40.8%
This paper investigates whether textual tone derived from large language models (LLMs) can predict future stock returns. Using Korean news articles, we employ five LLMs to extract textual tones: BERT (KrFinBERT), DistilBERT, RoBERTa, ELECTRA, and Llama3.
PAPER REPORTS · BERT (KrFinBERT) equal-weighted quintile High-Low: 0.242% per day, t=3.772 (5.324% per month), May 2023-May 2024, gross… · BERT High-Low Fama-French 3-factor alpha 0.232% per day (t=3.582); 5-factor alpha 0.232% per day (t=3.566), same…