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 · 12 shown
The value of a forecast depends on its accuracy, but also on its actionability. In trading, an investor must translate return forecasts into positions while accounting for trading costs and the lingering impact of earlier trades.
OUR BACKTEST · Sharpe 0.07 · Return +8.8% · Max DD -55.7%
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%
Portfolio decisions are often made under uncertainty about expected returns, and investors revise their beliefs as new information arrives.
OUR BACKTEST · Sharpe 1.07 · Return +152.8% · Max DD -57.4%
We develop a framework for an investor who trades until she either reaches a financial goal or an exogenous deadline arrives. Analogous to utility functions over wealth, we measure satisfaction with the timing of reaching a goal by a discount function.
OUR BACKTEST · Sharpe 0.63 · Return +44.3% · Max DD -26.3%
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%
Abstract. In this paper, infinite horizon stochastic difference equations and backward stochastic difference equations with fractional noise are studied. The main difficulty comes from the self-dependence of fractional noise on an infinite horizon.
OUR BACKTEST · Sharpe 0.59 · Return +40.5% · Max DD -26.3%
Mitigating \emph{drawdown}, the decline in wealth from its running peak, presents a canonical problem in path-dependent risk control.
PAPER REPORTS · Figure 1 reports higher estimated expected terminal return E[R_pi] for the drawdown-modulated policy than for the… · Maximum drawdown is guaranteed by construction: P(d* <= d_max) = 1 and V(k) >= (1 - d_max)V_max(k) > 0 for all k with…
OUR BACKTEST · Sharpe -1.60 · Return -0.0% · Max DD -0.0%
Abstract We develop a continuous-time framework in which the risk associated with leverage is parameterized by a dimensionless drawdown-risk index relative to an arbitrary reference asset.
PAPER REPORTS · μ_{0|w} = 0.145 with σ_{0|w} = 0.274 and s_{0|w} = 0.665 for the scaled growth-optimal Kelly strategy k_{0|e1}w* on the… · μ_{e1|w} = 0.096 with σ_{e1|w} = 0.223 and s_{e1|w} = 0.543 for w = e1 + k_{0|e1}w*_{e1} (index-relative…
OUR BACKTEST · Sharpe 0.67 · Return +59.2% · Max DD -35.9%
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%
Conformal prediction has traditionally been used to quantify prediction uncertainty.
PAPER REPORTS · DEV 2016-2021 (1,511 days), Config A: 28.45% annualised net log growth, Sharpe 1.336, max drawdown 27.68%, Calmar… · DEV 2016-2021, Config B: 25.84% annualised net log growth, Sharpe 1.386, max drawdown 20.26%, Calmar 1.376, annualised…
OUR BACKTEST · Sharpe 0.41 · Return +45.1% · Max DD -40.4%
Finite multiplicative systems often cease to evolve when a lower continuation threshold is reached,whereas standard growth-optimal benchmarks assume uninterrupted continuation.
OUR BACKTEST · Sharpe 0.68 · Return +16.4% · Max DD -8.3%
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%