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 · 138 shown
LLM agents that act on live Web data must combine heterogeneous evidence, remember what they have done and improve from outcomes without weakening their own safeguards.
PAPER REPORTS · Live paper portfolio, Apr 8 to Sep 10, 2026 (102 trading days): - Return +5.0% vs SPY +12.4%. · Live paper portfolio, June 22 to July 29, 2026: -0.3%, vs SMH -24.6% over the same window.
We develop an arithmetic multi-factor stochastic volatility model for commodity futures and options.
PAPER REPORTS · V3 vanilla option portfolio one-day hedge, 2013-2024 (2,809 dates), in-sample, no transaction costs stated: hedging… · VC3 calendar spread option portfolio one-day hedge with 2 hedge options, 2013-2024 (2,806 dates), in-sample:…
We propose a regime-aware tactical asset allocation (TAA) framework integrating market regime detection with Nested Clustered Optimization (NCO) for multi-asset portfolio construction.
PAPER REPORTS · Regime-NCO (proposed), OOS Jan 2021 to Aug 2025, monthly, 10bp proportional costs, rf 0.479%/month: cumulative return… · Regime-NCO, same period and settings: Sharpe -0.9522, Sortino -1.2271.
Estimating extreme risk in reinforcement learning becomes increasingly difficult as the confidence level increases, since only a small number of observations contribute to empirical estimates of Conditional Value-at-Risk (CVaR).
PAPER REPORTS · Simulated Merton jump-diffusion, K=140 call, 63 daily steps; transaction costs not mentioned. · Same setting, EVT-trained policy hedging error on 3,000 paths conditional on unhedged payoff > 0.999 quantile: mean…
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%
PAPER REPORTS · Recommended allocation (20% equities + 100 tons gold), historical simulation Nov 2002–Oct 2022, rolling 1-year returns:… · Same recommended allocation, rolling annualized 5-year returns, Nov 2002–Oct 2022: CZK average 3.48%, volatility 2.79%,…
Abstract Evaluating systemic risk transmission across financial and commodity markets is vital for maintaining macroeconomic stability in hydrocarbon-dependent economies.
Bitcoin's extreme price volatility poses significant challenges for investors and traders in making informed investment decisions. Conventional technical analysis approaches are often subjective and unable to capture complex market dynamics accurately.
PAPER REPORTS · Trailing-stop strategy, threshold 0.37, test period 1 Jan 2025-1 Mar 2026: net profit +8,491 USD on 10,000 USD (total… · No-trailing-stop strategy, threshold 0.35, same test period: net profit +8,978 USD (total return +89.78%), win rate…
Automated markets increasingly host autonomous, learning trading agents and are powered by formulaic automated market makers (AMMs).
PAPER REPORTS · Rolling 7-day annualized Sharpe ratio of net strategy returns ranges roughly between -20 and +20 for every agent…
Portfolio optimization is constrained by linear assumptions and limited integration of multi-modal information.
PAPER REPORTS · SBCA 2-asset (CAT, GILD), test 2021–2022, net of 0.25% one-way commission: final portfolio value 1.4392, annual return… · SBCA 4-asset (CAT, GILD, GS, KO), test 2021–2022, net of 0.25% one-way commission: final portfolio value 1.3564, annual…
OUR BACKTEST · Sharpe 2.62 · Return +54.1% · Max DD -10.1%
Tokenized equities trade while listed markets are closed, so their marks could improve off-hours measurement along layered exposures that link Bitcoin, crypto-treasury stocks, and leveraged ETFs.
PAPER REPORTS · Expanding-window out-of-sample forecasts of the opening gap (BTC-and-age spec plus token, July 2025-June 2026, no… · July 2026 holdout with coefficients frozen on June 5, 2026 (forecast, not a trading strategy, no costs): sign hit rate…
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%
The most widely used investment risk measures - variance, standard deviation, and tracking error - treat upside deviations as equally undesirable as downside losses.
OUR BACKTEST · Sharpe 0.71 · Return +64.4% · Max DD -36.4%
We examine climate variables in a pipeline for dynamic Value-at-Risk (VaR) of the SRI-KEHATI ESG index and the IHSG (Jakarta Composite Index).
PAPER REPORTS · Main QRNN backtest, test N=300 (Nov 2023-Dec 2025), VaR violation rates and Kupiec p: SRI-KEHATI tau=0.01 0.33% (1… · Main QRNN DM statistics on pinball loss (positive = QRNN better), all p<0.001. Vs HS-VaR: SRK 4.31 / 6.43 / 7.23 and…
This study examines whether Shannon entropy regularization can mitigate the concentration associated with classical minimum-variance portfolio optimization while preserving risk-adjusted performance.
PAPER REPORTS · ERMV (λ=0.01), full sample (in-sample Σ), 2015–Jul 2026, daily simple returns, no transaction costs: annualized mean… · ERMV (λ=0.01), full sample, no costs: Sharpe 0.2966 at 4.5% rf, 0.5140 at 2% rf, 0.6879 at 0% rf.
This paper studies semi-static variance-optimal hedging of variance swaps in the rough Heston model under a risk-neutral measure.
PAPER REPORTS · In-model (simulated, risk-neutral, frictionless) hedging error reduction of the paper's semi-static hedge vs purely… · Same setting with m=12 options: 97.13% (H=0.1), 99.32% (H=0.3), 99.96% (H=0.5).
This paper studies exit timing for SPX short-straddle portfolios using a two-stage supervised and reinforcement-learning framework.
PAPER REPORTS · Supervised MLP exit policy, OOS 2024-2026 (T≈2.42y), stress-budget compounding sizing, bid entry/ask exit plus… · AC+MLP, 7 seeds, OOS 2024-2026, same costs: cumulative return 37.54% ± 0.92%, ARC 14.05% ± 0.32%, ASD 7.97% ± 0.14%,…
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%
This article studies a retirement planning problem from a new perspective in which a retiree delegates an initial lump sum to a professional fund manager.
Deep hedging learns trading policies from historical or simulated market trajectories, yet under nonstationarity these training paths may not represent future market conditions.
PAPER REPORTS · Nonstationary Heston (simulated), European call, CVaR_0.95 of hedging loss, no transaction costs: WRAP-W far-future… · Nonstationary Heston (simulated) with proportional costs c=0.005: WRAP-U far-future CVaR 8.349±0.189 vs ERM-U…
Entropic risk optimisation is a general framework for pricing and hedging financial derivatives in incomplete markets that can be used to decompose P&L into market and model risk contributions.
The enhanced index tracking (EIT) portfolio selection problem aims to construct a portfolio that is expected to outperform a benchmark index.
PAPER REPORTS · M-ER, known mean/covariance, short selling allowed, OOS Jan 2020-Dec 2024, monthly rebalancing, transaction costs not… · M-TSV, known mean/covariance, short selling allowed, same period, costs not stated: mean return 0.048939, SD 0.150532,…
OUR BACKTEST · Sharpe 0.70 · Return +62.4% · Max DD -32.5%
We study the anatomy of commodity risk by distinguishing micro, market-level, and economy-wide sources.
Abstract This paper addresses the issue of time inconsistency in recursive stochastic control problems, in which the forward state process evolves under the influence of an additional recursive utility system.
We develop a spouse-protected tontine in which a first death changes the household state but generates no pool transfer.
PAPER REPORTS · Out-of-sample learned policy, payment-seeking (λ=0.3): expected cumulative real payments 1860.4 (thousand AUD over 35… · Out-of-sample learned policy, intermediate (λ=1): expected cumulative real payments 1704.8, CVaR_0.95 terminal…
We introduce a deep kernel hedging framework that combines the flexibility of deep learning with the structural inductive bias of kernel methods.
PAPER REPORTS · S&P500 out-of-sample (Jan 1 2023 to May 31 2026), ATM European call, quadratic hedging with order-3 signature features,… · S&P500 out-of-sample (Jan 1 2023 to May 31 2026), ATM European call, CVaR hedging, frictionless.
OUR BACKTEST · Sharpe 2.30 · Return +22.0% · Max DD -2.8%
We present an end-to-end deep learning framework for systematic options trading that directly embeds hedging behavior through explicit control of portfolio-level risk exposures.
PAPER REPORTS · DP (L1), α=10^2: annualized Sharpe 2.339, E[Return] 0.436, Vol 0.186, MDD 0.210, Sortino 3.583, Calmar 2.078, hit rate… · ENP (L2), α=10^1: Sharpe 2.260, E[Return] 0.409, MDD 0.236, hit rate 0.638. Zero costs.
The internal-models approach of the Fundamental Review of the Trading Book (FRTB-IMA) determines market-risk capital at the aggregate level of a bank's approved trading desks, but it does not say how that charge should be attributed to individual trades.
We study simple predictable processes whose coefficients are represented by neural networks. On finite measure spaces, we establish density results for neural networks in Orlicz spaces.
European electricity trading in the EU operates as a constrained multi-layer system in which legal design, exchange microstructure, and network physics are executed jointly across forward, day-ahead, intraday, and balancing horizons.
Volatility forecasts play a central role in financial risk management because their overall level and day-to-day movements affect downstream decisions. Most studies compare forecasting models while keeping the training loss fixed.
We study the infinite-horizon optimal investment and consumption problem in a general class of continuous financial markets, where uncertainty is driven by a continuous non-decreasing stochastic clock representing accumulated variance.
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%
We study optimal stopping under dynamic risk measures with simultaneous ambiguity in the probability model and the discount rate.
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 This research utilizes explainable artificial intelligence (XAI) techniques to explore the relationship between size-sorted environmental, social, and governance (ESG) portfolio indices (large-cap, mid-cap, and small-cap ESG portfolios) and their…
Purpose: This paper explores the asymmetric and state-dependent dependence relationship of the S&P Green Bond Index with six international markets and assesses whether such dependence patterns are correlated with the risk-reduction benefit associated with…
PAPER REPORTS · Long-only two-asset minimum-variance portfolio, green bonds + S&P 500: GB weight 86.8%, annualized return 1.47%,… · Green bonds + S&P Bitcoin: GB weight 99.3%, annualized return 0.13%, volatility 7.48%, Sharpe 0.017, variance reduction…
Abstract We consider a robust asymptotic growth problem under model uncertainty in the presence of stochastic factors.
This paper develops inference for a Gaussian-nested hypergeometric family of distribution functions.
Abstract We develop a modular forecast-to-construction framework for static option-portfolio design with piecewise-affine contingent claims.
PAPER REPORTS · Out-of-sample predictive-region coverage on 200 purged test origins (20 Jun 2025 – 7 Apr 2026): 200/200 = 1.000000 at… · Deterministic hedge certificate for the single deployed portfolio: Profit(x) ≥ 0 on [4.0, 8.5] (e.g.
An Oracle result is not yet a protocol payout, a redeemable position is not yet collateral in a holder's account, and a redemption event is not a complete measure of economic entitlement.
Prediction-market resolution is often reduced to a terminal outcome and one timestamp.
Retail proprietary-trading firms sell a two-stage product: a paid evaluation that must reach a profit target before breaching a trailing drawdown, then a funded account that must survive a minimum window and a consistency rule before a payout.
PAPER REPORTS · Adversarial implementation of a published practitioner rule set (not the authors' own edge, but the authors' own… · Best in-sample payout rate achievable by the authors' payout-rate optimisation: 4.6% (Lucid Flex) versus a 9.5%…
This paper proposes an investment strategy through resource allocation into Kalshi Crypto Event Contracts in order to effectively hedge exposure to spot asset volatility.
PAPER REPORTS · Bullish 2024 (Mar-Dec 2024 contract), Strategy C with Kalshi fees: +38.69% return vs BTC +36.23%; Sharpe 1.27 (1.30… · Flat Dec 2025 (Nov-Dec 2025 contract), Strategy C: -3.04% (Table 1) vs BTC -3.58%; Sharpe -0.10 with fees (+0.05…
Managing drawdown, the peak-to-trough decline in an investment portfolio's value, is a precondition for long-term survival in practical investment management.
PAPER REPORTS · NASDAQ 2010.01-2025.04, zero transaction costs and zero slippage: CAST annualized Sharpe 0.523, MDD 0.114, Calmar… · CSI300 2010.01-2025.04, frictionless: CAST ASR 0.269, MDD 0.158, CR 0.104, SoR 0.257, FV $1272
Abstract Counterintuitively, the S&P 500 Index rose between January 3, 2022, and December 29, 2023, while exchange-traded funds (ETFs) seeking to deliver $$2\times $$ and $$3\times $$ daily returns of the index delivered substantially negative returns.
OUR BACKTEST · Sharpe 0.05 · Return +0.3% · Max DD -3.2%
In recent years, when traditional portfolio optimization (PO) models deal with highly volatile and irrational market environments, they often face problems of deviation in return prediction and lag in risk response.
PAPER REPORTS · Annualized return 0.196 (out-of-sample test set within 2018-2023, daily rebalancing, no trading costs) · Annualized volatility 0.187; Sharpe ratio 1.048 with risk-free rate assumed zero (no trading costs)
Abstract This paper introduces a Coupled realised real-time EGARCH model with Filtered Historical Simulation (FHS), referred to as the “CouRTES” model, as an extension of the Realised EGARCH-type models for multi-horizon volatility and distributional…
PAPER REPORTS · QLIKE loss (realised measure target, 250 rolling origins, 30 May 2018 - 29 May 2019): lowest in 11/14 horizons within… · sMAPE (total log-return target): best in 9/14 horizons; mean improvement 0.84% vs Coupled GARCH(1,1) and 1.75% vs…
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%
We define the variance swap delta as the sensitivity of the price of variance to a change in underlying price. We use Carr-Madan spanning formulas to analyze this sensitivity when the implied volatility smile curve may depend on the underlying price.