Research
A continuously updated feed of research papers that pass our automated relevance screening for systematic trading — plus every paper we have published a review of, whatever it scored. 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.
15,697 papers screened · 250 on the radar · 27 shown
Frequent market instability and the lack of rigorously validated forecasting frameworks pose significant challenges for predicting market stress in the Dhaka Stock Exchange (DSE).
PAPER REPORTS · Random Forest crash-gated risk-off strategy, pooled equal-weighted, 2019-2022 test period: total return 67.26% (15.01%… · Annualized volatility 16.39% (strategy) vs. 18.11% (buy-and-hold); maximum drawdown -32.21% vs.
OUR BACKTEST · Sharpe 0.47 · Return +25.2% · Max DD -21.6%
This paper examines whether the risk-adjusted performance of Environmental, Social, and Governance (ESG)-focused Exchange Traded Funds (ETFs) reflects distinct investment behavior or is primarily influenced by benchmark exposure, geography, and sector…
OUR BACKTEST · Sharpe 0.56 · Return +53.4% · 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%
Portfolio risk assessment ordinarily relies on reliable estimates of cross-asset return covariances, which are difficult to obtain in short, high-dimensional panels.
PAPER REPORTS · In-sample standardized variance percentile of the news-only allocation: 0.69%-1.33% across four prespecified capped… · Standardized in-sample variance 0.357, 8.3% below the equal-risk (inverse-volatility) benchmark and 35.6% above the…
OUR BACKTEST · Sharpe 0.47 · Return +44.2% · Max DD -29.9%
PAPER REPORTS · Minimum Variance Portfolio (MVP), 2019-2025 daily, no transaction costs stated: mean daily return 0.0005519, daily… · Minimum Correlation Portfolio (MCP): mean daily return 0.0007516, std dev 0.0014739, Sharpe (std dev) 0.5099, Sharpe…
OUR BACKTEST · Sharpe 0.21 · Return +11.2% · Max DD -24.8%
Abstract A key puzzle in finance is why algorithmic traders with advanced neural models sometimes fail to beat simple traditional strategies, while in other cases they clearly outperform them.
PAPER REPORTS · Cluster 0 (most efficient), test 2025-2026, net of 0.1% one-way costs: DDPG annualised return 34.72%, cumulative… · Cluster 0 best classical: HRP Sharpe 1.591, Calmar 2.3562, max drawdown -13.07%, annualised return 30.80%
OUR BACKTEST · Sharpe 0.78 · Return +25.3% · Max DD -11.7%
Deep hedging is a data-driven approach to learn hedging strategies. It relies on synthetic price paths generator, as real market data is often limited for training.
OUR BACKTEST · Sharpe 1.78 · Return +5.9% · Max DD -0.6%
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 present in this article a non-parametric value-at-risk (VaR+CVaR) algorithm that remains accurate for an arbitrarily large number of underlying positions. The algorithm solves the two inherent problems of VaR estimation.
PAPER REPORTS · Median 99% daily VaR breach rate 1.0 +/- 0.1% across 9 parameter settings, 500 random portfolios, no transaction costs… · Per-configuration medians (no added delay): 0.97%, 1.06%, 1.09% (1260-day window, 14/30/45-day vol); 0.90%, 0.99%,…
OUR BACKTEST · Sharpe 1.58 · Return +17.6% · Max DD -3.4%
We develop parametric Entropic Value-at-Risk (EVaR) portfolio optimization for tempered stable Lévy returns.
PAPER REPORTS · ICA+NTS minimum-EVaR (EVaR_95): gross annualized Sharpe 0.616, CAGR 8.60%, annualized vol 15.28%, cumulative return… · ICA+NTS minimum-EVaR net Sharpe: 0.608 at 5bp, 0.599 at 10bp, 0.573 at 25bp; net cumulative return 630.95% at 25bp
OUR BACKTEST · Sharpe 0.24 · Return +20.5% · Max DD -37.6%
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%
Against the background of increasing volatility and complex risk factors in global markets, options and futures have become important instruments for risk hedging and uncertainty management.
PAPER REPORTS · Futures hedged portfolio, 2019-2024: annualized return 7.95%, annualized volatility 10.28%, hedging efficiency 46.87%,… · Option hedged portfolio, 2019-2024: annualized return 8.31%, annualized volatility 8.76%, hedging efficiency 54.73%,…
OUR BACKTEST · Sharpe 0.74 · Return +83.0% · Max DD -47.8%
The Triadic Stress Index (TSI) takes a network index whose four factors were first observed in soil microbiome co-occurrence networks and applies it, without alteration, to the correlation network of financial assets.
PAPER REPORTS · Out-of-sample F1@p90 = 0.447 (TSI with memory, 2016-2026, 897 windows, OFR 23-window crisis list; no transaction costs… · F1 gap vs Absorption Ratio = 0.273 (0.447 vs 0.174) out of sample 2016-2026, block-bootstrap 95% CI [0.095, 0.392],…
OUR BACKTEST · Sharpe 0.87 · Return +229.7% · Max DD -46.7%
In this paper we investigate the information content of the lower part of the spectrum of financial correlation matrices, as a source of information on market synchronization.
OUR BACKTEST · CAPITAL EXHAUSTED · FAILED SANITY CHECK
We develop a certified, scalable approximation for high-dimensional Wasserstein distributionally robust portfolio optimization. For expected-utility maximization under order-one Wasserstein ambiguity, standard duality yields a semi-infinite convex program.
PAPER REPORTS · ε=10^-2 (best DRO policy), 476-asset monthly rebalanced, 2021-2025, no transaction costs: cumulative return 3.64,… · ε=10^-4, 2021-2025, no costs: CR 4.77, σ 0.45, SR 0.93, MDD 0.38, Calmar 1.12
OUR BACKTEST · Sharpe 0.63 · Return +106.7% · Max DD -45.0%
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%
Risk-aware Q-learning (RaQL) provides a model-free, two-timescale estimator for dynamic risk objectives, but its finite-budget behavior remains fragile: fixed inner-loop hyperparameters can produce unstable value estimates, persistent Bellman residuals, and…
PAPER REPORTS · Scheme 6 out-of-sample (918 daily obs, chronological test set, after 5bp turnover costs, mean over 20 seeds): Sharpe… · Scheme 0 fixed-parameter baseline out-of-sample (same test set, after 5bp costs, 20 seeds): Sharpe 0.5628 (sd 0.2281),…
OUR BACKTEST · Sharpe 0.34 · Return +16.6% · Max DD -10.4%
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%
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%
Automated market makers (AMMs) are typically interpreted and evaluated as decentralized exchanges.
PAPER REPORTS · VBIAX, monthly TE, Jan 2, 2014 – Jun 30, 2026: G3M Pareto-dominates (higher CAGR and lower TE) for gamma in [2.73%,… · EQL NAV, economic mandate, Jun 19, 2018 – May 29, 2026: G3M dominates for gamma in [3.22%, 7.09%]
OUR BACKTEST · Sharpe 0.71 · Return +133.0% · Max DD -42.0%
Agentic AI is gaining acceptance in asset management, but governance has not kept pace: 88% of surveyed finance professionals report no operational governance framework for agentic AI despite universal awareness of its deployment, and only 24 of 75 large U.S.
OUR BACKTEST · Sharpe -1.47 · Return -45.1% · Max DD -49.7%
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%
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%
This paper studies conditional allocation between a growth/technology ETF basket, denoted by $G$, and a defensive income/value-oriented ETF basket, denoted by $D$.
PAPER REPORTS · Selected smooth-score policy, 2017-06-28 to 2026-05-15, 10bp cost: 19.24% CAGR, 19.29% vol, Sharpe 1.01, Sortino 1.22,… · Selected policy vs 50/50 G/D: annual excess 1.78%, tracking error 3.74%, info ratio 0.48, max DD improvement 1.95%
OUR BACKTEST · Sharpe 0.92 · Return +111.2% · Max DD -31.6%