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 study long-term investment in an Ornstein-Uhlenbeck diffusion model with exponential utility. We solve the finite-horizon problem explicitly and show that information delay qualitatively alters long-horizon behavior.
OUR BACKTEST · Sharpe -0.06 · Return -0.6% · Max DD -3.9%
To extend conventional model-level feature importance to economically meaningful portfolio outcomes and decisions, we propose a local first-order attribution-propagation framework that maps allocation-function feature exposures to local sensitivities of…
PAPER REPORTS · XGB baseline, OOS 2006-2024: net Sharpe 0.6175 (annualised, 5bp per unit turnover); gross Sharpe 1.0207; net mean 2.55%… · FFN baseline, OOS 2006-2024: net Sharpe 0.5750 (5bp); gross Sharpe 1.3051; net mean 2.65% p.a.; net FF5 alpha 1.12%…
OUR BACKTEST · Sharpe -0.82 · Return -24.2% · Max DD -31.5%
We investigate a continuous-time financial market where the asset price exhibits weak (sublinear) mean reversion and has a nonzero drift.
OUR BACKTEST · Sharpe 0.09 · Return +0.0% · Max DD -0.1%
This paper examines whether a cointegration-based pairs trading strategy between PepsiCo and The Coca-Cola Company is statistically robust and economically exploitable.
PAPER REPORTS · In-sample 2018–2023, θ*=(2.15, 0.50): annualised net Sharpe 0.67, net P&L $16,941 on $100,000, 9 round-trips. · In-sample 2018–2023 excluding the COVID window: annualised net Sharpe 0.28, net P&L $5,759, 8 trades (0.20% costs).
OUR BACKTEST · Sharpe 0.11 · Return +0.2% · Max DD -1.4%
The equal-weighted portfolio is a passive, rule-based strategy that has historically been difficult to outperform, delivering higher returns than the capitalization-weighted "market" benchmark across many markets and periods.
PAPER REPORTS · Mean-reverting frictional λ*,mr, 1995–2024 monthly, net of 15bp proportional costs: - cumulative net return 44.92 -… · Trending frictional λ*,trend, 1995–2024, net of 15bp costs: - cumulative net return 31.41 - return 0.13, volatility…
OUR BACKTEST · Sharpe 0.24 · Return +0.0% · Max DD -0.0%
Predicting financial asset returns remains one of the most difficult challenges in empirical finance, driven by the low signal-to-noise ratio and the semi-strong form of market efficiency.
PAPER REPORTS · Sector LSTM (main 3-layer, k=10, 1995-2024): mean daily long-short return 0.100% before costs, Newey-West t = 7.81,… · Sector LSTM appendix figures (after 2bp per half turn): mean daily return 0.053%, t-statistic 4.106, annualized return…
OUR BACKTEST · Sharpe -0.03 · Return -9.6% · Max DD -140.3%
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%
We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positive Binance Spot paper policies after assumed costs.
PAPER REPORTS · Frozen mandatory-daily selector: -6.72% compounded, 2026-07-01 to 07-19, 19 cycles, 3 wins/16 losses, 31 bps… · Frozen mandatory-daily selector cost stress, same period: -4.74% at 20 bps, -6.72% at 31 bps, -10.21% at 51 bps…
OUR BACKTEST · Sharpe 0.00 · Return +0.0% · Max DD 0.0%
Building on the identity that expected regret equals the covariance between costs and decisions, this paper develops the complete derivative theory of the covariance regret functional.
OUR BACKTEST · Sharpe 0.57 · Return +131.1% · Max DD -35.8%