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 · 43 shown
Worst-case risk evaluation is central to finance and operations research, yet existing analytical results remain fragmented and largely confined to ambiguity sets with specific structures.
OUR BACKTEST · Sharpe 0.67 · Return +44.6% · Max DD -28.9%
This paper studies continuous-time portfolio selection under Range Value-at-Risk (RVaR), with both portfolio performance and risk evaluated in terms of logarithmic returns.
OUR BACKTEST · Sharpe 0.18 · Return +15.4% · Max DD -38.2%
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%
Classical execution models assume linear impact, diffusive prices, and an exogenous horizon. We study execution under concave power-law impact and Hurst-scaled price fluctuations, and make two contributions.
OUR BACKTEST · Sharpe -0.29 · Return -9.4% · Max DD -22.3%
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%
This paper studies decision functionals under ambiguity when pure-risk evaluation is determined by a general, possibly non-affine statistic.
OUR BACKTEST · Sharpe 0.54 · Return +30.9% · Max DD -25.2%
Regime-switching multi-factor models are widely used in quantitative finance, but filtering does not by itself determine when an agent should act on inferred regime changes.
OUR BACKTEST · Sharpe 0.63 · Return +57.2% · Max DD -14.2%
In stochastic control problems involving optimal portfolio choice, one often seeks an optimizer of some local criterion (dynamic programming value function) generated by some global criterion (e.g., expected utility).
OUR BACKTEST · Sharpe 0.80 · Return +34.1% · Max DD -15.2%
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 represent (weighted-)selection-elliptical distributions as an affine combination of the $q$ selection variables plus an elliptical term whose direction alone is independent.
OUR BACKTEST · Sharpe 0.75 · Return +27.1% · Max DD -21.2%
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%
Risk evaluation under distributional ambiguity is central to decision making in finance, economics, and operations research. Wasserstein balls provide a natural way to describe uncertainty around a reference distribution.
OUR BACKTEST · Sharpe 0.58 · Return +43.5% · Max DD -29.9%
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%
We study when to buy a share that will later be sold optimally, when the price follows a geometric multi-skew Brownian motion whose skew levels model support and resistance.
OUR BACKTEST · Sharpe 0.21 · Return +40.7% · Max DD -78.0%
A cross-sectional signal is a forecast vector over $d$ assets at each date; demeaned and normalized, it is a point on a sphere. Its city is the direction of its time-averaged vector in a common target-aligned frame, a compressed summary.
OUR BACKTEST · Sharpe 0.68 · Return +75.9% · Max DD -48.2%
Return predictability in financial markets is self-erasing: once an anomaly is discovered and capital flows toward it, its excess return decays.
PAPER REPORTS · Detector-conditioned strategy (Monte Carlo, synthetic data only): per-period Sharpe 0.224 at β=0.4, window n=100,… · Same strategy, window sweep (synthetic): peak per-period Sharpe 0.217 near n=100 at β=0.4.
OUR BACKTEST · Sharpe 0.33 · Return +13.5% · Max DD -10.2%
Abstract We investigate statistical tests for multivariate realized volatility models which are estimated using realized covariance matrices computed from intraperiod (high-frequency) financial data.
OUR BACKTEST · Sharpe 0.54 · Return +24.3% · Max DD -22.3%
We study finite-horizon portfolio optimization with proportional transaction costs and trading opportunities arriving at the jump times of a Cox process.
OUR BACKTEST · Sharpe 0.52 · Return +0.0% · Max DD -0.0%
We solve the perpetual liquidation problem for a geometric multi-skew Brownian motion carrying local-time pushes upward at a support level and downward at a resistance level, a model of technical analysis that is Markov in the price alone.
OUR BACKTEST · Sharpe -0.21 · Return -3.5% · Max DD -10.3%
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%
This article is concerned with the asymptotic shape of quantile surfaces, defined as the set of quantiles at a given level $α$ generated by a controlled one-dimensional distribution.
OUR BACKTEST · Sharpe 0.53 · Return +55.1% · Max DD -40.5%
In this paper, we study the diversification properties of convex combinations of iid random variables with infinitely divisible distributions.
OUR BACKTEST · Sharpe 0.45 · Return +46.7% · Max DD -40.4%
Abstract. We study equilibrium feedback strategies for a family of dynamic mean-variance problems with competition among a large group of agents.
OUR BACKTEST · Sharpe 1.68 · Return +3.0% · Max DD -0.3%
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%
We study exact-cardinality, equally weighted minimum-variance portfolio selection under a one-factor covariance model supplied in factor form. In the nonnegative homoskedastic regime, selecting the K smallest loadings is optimal.
OUR BACKTEST · Sharpe 0.65 · Return +50.5% · Max DD -24.3%
An ensemble of roughly 3,000 signals over 20 assets was reported to have approximately 90% correlation with the leading component of the asset-space return structure.
OUR BACKTEST · Sharpe -0.16 · Return -29.7% · Max DD -97.0%
Signal correlation and PnL correlation are correlations over different index sets - across assets at each date versus across dates for scalar payoffs - and practitioners often treat the first as a proxy for the second.
OUR BACKTEST · Sharpe -0.83 · Return -17.6% · Max DD -18.1%
Let \(P\) be a fixed physical law and let \(Q\) be an equivalent martingale measure selected from the martingale-measure set associated with a chosen numeraire.
OUR BACKTEST · Sharpe 0.54 · Return +50.1% · Max DD -37.6%
Modern portfolio theory identifies diversification as the primary tool for risk reduction. However, under model uncertainty, this cornerstone may no longer remain optimal.
OUR BACKTEST · Sharpe -0.52 · Return -29.5% · Max DD -24.9%
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%
We show that the key optimization results of the classical Markowitz portfolio selection theory, originally formulated for variance as the risk measure, remain available in explicit closed form under a broader class of strictly convex quadratic risk measures.
OUR BACKTEST · Sharpe 0.50 · Return +143.8% · Max DD -75.0%
We study continuous-time dynamic portfolio optimization under a Conditional Value-at-Risk (CVaR) constraint on the investor's terminal loss.
PAPER REPORTS · Complete market, binding c = -0.94, T = 1 simulated: E[W_T] = 1.0242, CVaR_0.95(-W_T) = -0.9406, average exposure… · Complete market, nonbinding c = -0.86, T = 1 simulated: E[W_T] = 1.0322, CVaR_0.95(-W_T) = -0.8671, average exposure…
OUR BACKTEST · Sharpe 0.75 · Return +55.2% · Max DD -26.8%
We consider the question of the optimal timing of the sale of an asset with stochastic dynamics. Our analysis is based on the method of the distribution builder introduced by Sharpe, Goldstein and Blythe [SGB00] for the purpose of optimal portfolio selection.
OUR BACKTEST · Sharpe 0.59 · Return +60.8% · Max DD -39.2%
Using the local time-space calculus of Peskir (2005) and the method developed in Mijatovic (2010), we derive a new integral representation for the distribution of the first-passage time (FPT) of a diffusion process through a time-dependent barrier.
OUR BACKTEST · Sharpe 0.66 · Return +35.3% · Max DD -18.7%
We study Lambda-quantiles, a generalisation of classical quantiles in which the constant probability level $λ\in [0,1]$ is replaced by a functional parameter $Λ\colon \mathbb{R} \to [0,1]$.
OUR BACKTEST · Sharpe 0.68 · Return +249.4% · Max DD -84.8%
The enormous growth in datasets, both in number and size, has prompted investors to adapt to new ways for assimilating information.
OUR BACKTEST · Sharpe 0.11 · Return +31.6% · Max DD -96.3%
We introduce a framework for preference-robust decision making when preferences over risk are modelled through generalised distortion risk measures. Unlike distributional robustness, our approach addresses ambiguity in the risk functional itself.
OUR BACKTEST · Sharpe 0.27 · Return +30.1% · Max DD -23.4%
This paper builds Path Portfolio Optimization: portfolio theory on a path-first framework in which the signature is the universal coordinate of the price path, and asks whether it survives estimation.
PAPER REPORTS · Cross-area lead-lag portfolio, sign-carrying excitation 1→2 with q=0.85: mean P&L +0.000189, s.e. · Cross-area, sign-carrying excitation 2→1 with q=0.85: mean P&L −0.000227, s.e. 0.000012, t=−18.92, annualized Sharpe…
OUR BACKTEST · Sharpe 0.45 · Return +21.4% · Max DD -19.5%
We develop a unified modeling framework that connects two distinct types of bubbles defined in the literature: the rational bubbles (aka P-bubbles), and the local martingale bubbles (aka Q-bubbles).
OUR BACKTEST · Sharpe 0.47 · Return +220.6% · Max DD -131.6%
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%
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%
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%
The theory of portfolios, and its allied notions and fundamental results concerning growth optimality, the numéraire property, and ``market viability'' -- which rules out the possibility of financing nontrivial future liability streams starting with…
OUR BACKTEST · Sharpe 0.51 · Return +48.5% · Max DD -23.8%