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
We propose a market-making game played among $N \ge 2$ strategic market makers and a single non-strategic representative trader.
Indonesia's Islamic capital market has grown rapidly, with PT Bank Syariah Indonesia Tbk (BSI) emerging as one of its actively traded Sharia-compliant issuers.
Autoregressive foundation models offer state-of-the-art capabilities to simulate the market by generating the message flow of limit order books (LOBs).
Logistic stock-price dynamics have been proposed as a way of representing assets whose growth is bounded by competition, saturation or finite market capacity, but the derivations available in the literature retain a logistic drift while invoking a…
The electricity system in Great Britain (GB) allows participants to deliberately produce or consume a different volume to their traded position and has incentivised this practice with a single imbalance price since 2015.
The rapid advancement of generative artificial intelligence has catalyzed a paradigm shift in quantitative finance, transitioning from traditional point forecasting toward comprehensive financial time-series generation (FTSG) across extrapolation, imputation,…
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…
Realistic market impact is essential for generative limit order book (LOB) models to serve as world models for trading agents. If a simulator misprices this mechanism, agents learn policies that are optimal in the simulator but wrong in the real market.
PAPER REPORTS · DCC-ePOET-1, out-of-sample global MVP on 331 S&P 500 stocks: Var 0.445, Annual-SD 10.840, mean Sharpe ratio 2.281,… · DCC-ePOET-2 (latent factors only, no observable factors), same setup: Var 0.464, Annual-SD 11.049, mean Sharpe ratio…
Value-at-Risk (VaR) is a widely adopted quantile-based risk measure in practice, yet incorporating VaR objectives into Markov decision processes (VaR MDPs) remains a longstanding challenge.
PAPER REPORTS · Simulated alpha-VaR of total execution revenue, dynamic vs. optimal static strategy (Regime 2, Almgren-Chriss…
Although advances in artificial intelligence (AI) and machine learning (ML) have transformed financial risk assessment, many early-warning frameworks for systematic risk still rely on isolated macro-financial indicators with limited integration of established…
Pergerakan harga emas yang dipengaruhi oleh volatilitas pasar dan ketidakpastian ekonomi memerlukan metode peramalan serta analisis risiko yang akurat.
PAPER REPORTS · ARIMA(1,1,1) price forecast: MAPE 9.15%, MAE USD 396.14, RMSE USD 454.31, directional accuracy 100%. · MC GBM Geopolitik scenario: MAPE 10.37%, MAE USD 426.43, RMSE USD 465.29, directional accuracy 100%.
OUR BACKTEST · Sharpe 0.01 · Return +0.6% · Max DD -35.1%
The Oracle Approximating Shrinkage (OAS) estimator provides a closed-form, data-driven shrinkage intensity for regularizing sample covariance matrices toward a scaled identity target, but its derivation assumes Gaussian data.
PAPER REPORTS · OAS-Extended GMV portfolio, p=50 US large caps, Jan 2010–Dec 2023, 155 monthly-stepped windows: mean annualized… · OAS-Extended vs OAS realized-variance improvement: t=3.83, p<0.001; bootstrap 95% CI for mean variance reduction [2.0,…
OUR BACKTEST · Sharpe 0.53 · Return +40.1% · Max DD -29.9%
In its traditional form, the smart order router only cares about price when deciding where to send a trade; send the order to the exchange currently showing the tightest spread.
PAPER REPORTS · Mean effective execution cost 6.561 bp (latency-aware router) vs 6.548 bp (price-only baseline). · Hit rate vs baseline: 2.2% of 14,400 held-out rows (latency-aware cheaper than price-only).
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%
We develop a data-driven approach to dynamic portfolio selection that combines vine-copula simulation with reinforcement learning.
PAPER REPORTS · NN-vine-RL ensemble, 22 complete monthly holding periods within 31 Jul 2024–30 Jun 2026 decisions: CAGR 34.13%, net of… · Sharpe ratio 2.309 (zero annual risk-free rate), same sample and costs.
We propose a structured continuous-time stochastic volatility model for one-day-ahead integrated-variance forecasting.
PAPER REPORTS · SPX out-of-sample (second half of 2004–2022, raw RV proxy): QLIKE 0.0709, MSE_var 2.6538, MAE_var 0.4285 · Best-loss counts across 17 indices: QLIKE 15/17, MSE_var 12/17, MAE_var 17/17; retained in 90% MCS on 15/17 (QLIKE),…
We study dynamic portfolio decarbonization under incomplete information about firms' carbon intensities and the underlying transition state.
Expected Shortfall (ES) measures the average size of tail losses. Its sequential backtests typically also require a Value-at-Risk (VaR) forecast.
A conditional normalizing flow can produce extreme scenarios even when its point forecasts appear reasonable. We compare two fitted models for daily Treasury yield changes at eleven maturities reconstructed from Federal Reserve curve parameters.
PAPER REPORTS · Stabilized flow, development windows (5×60 dates, 2002–2026; forecast metrics only, no trading strategy or costs):… · Stabilized flow, subsequent window April 13–July 8, 2026 (frozen checkpoints): scenario-mean MSE 0.3351, 90% coverage…
OUR BACKTEST · Sharpe 0.03 · Return +0.2% · Max DD -5.6%
We present a statically and dynamically arbitrage-free European option market model with smooth interpolation between option prices, based on the SANOS framework, and prove that its dynamics admit a unique strong solution.
Market making under realistic order-flow dynamics is challenging due to the clustered, self-exciting nature of order arrivals, which are naturally modeled by non-Markovian Hawkes processes.
PAPER REPORTS · HSN mean cumulative episodic reward, simulated (no market data), multi-asset cross-excitation cs=0.7: 0.0548±.0007 over… · HSN, simulated power-law kernel ε=0.15: 0.0216±.0004 mean cumulative reward, +32.5% vs the best baseline (p<0.001).
A path indistinguishable from its own reverse carries no arrow of time, so time irreversibility is a necessary condition for a directed mechanism to be readable from trajectories alone.
Conditional volatility-based option pricing research generally emphasizes improving volatility inputs, but has not yet fully explained the relative contribution of each parameter to variation in simulated option-pricing responses for Indonesian index stocks.
Correlation matrices combine nonlinear positive-definiteness constraints with a simultaneous row-column permutation symmetry. Their generation therefore involves two distinct problems.
OUR BACKTEST · Sharpe 0.00 · Return +0.0% · Max DD 0.0%
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).
A realized tail-risk conditional autoregressive expectile (RTCARE) model is proposed for joint forecasting of value-at-risk (VaR) and expected shortfall (ES).
PAPER REPORTS · RTCARE out-of-sample mean FZ0 loss (IBM, 3513 rolling one-day forecasts within 2005-2024): 1.100 (5%), 1.379 (2.5%),… · RTCARE NO INT out-of-sample mean FZ0 loss: 1.097 (5%), 1.372 (2.5%), 1.769 (1%), 2.057 (0.5%)
OUR BACKTEST · Sharpe 1.82 · Return +9.2% · Max DD -6.2%
When a trader executes a large order, the relevant question is not \emph{what will the price be?} but \emph{what would the price have been had I not traded?} Market impact is thus a causal quantity, yet classical models (Almgren--Chriss, propagators) regress…
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.
We develop a posteriori primal-dual bounds for numerical approximations of European option prices in the Uncertain Volatility Model.
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…
Financial LLM agents are often evaluated by comparing their end-to-end returns with those of a baseline and testing the paired difference against zero.
PAPER REPORTS · Full agent (A0) vs frozen-rule baseline, 723 earnings events, 2020-2024: gross deployment value +15.2 bps/event… · Same comparison net of a pre-stated 40 bps cost per nonzero event position: -1.2 bps/event (95% CI [-59.8, +54.3]),…
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%
The complex and nonlinear behaviors of financial markets make it exceedingly challenging for average investors to evaluate the performance of a specific asset or make meaningful predictions about its future behavior.
PAPER REPORTS · Directional hit rate (MDA) of the LSTM forecaster on a random 80/20 holdout validation set (27,433 samples, 159 stocks,… · Diebold–Mariano statistics vs baselines on the same validation set: 3.42 (p = 0.001) vs persistence and 2.15 (p =…
Reinforcement learning (RL) is increasingly used for financial optimal-control problems when complex dynamics make analytical strategies difficult to obtain.
PAPER REPORTS · Proposed method (frozen analytical policy + residual PPO), execution cost halved from 0.0012 to 0.0006, σ_ξ=100, full… · Same method at a_true=0.0012: change 0.00019 [-0.00041,0.00079]; at a_true=0.0018: change 0.00019 [-0.00034,0.00071]…
We propose an interactive robo-advising framework that learns personalized risk preferences from scores provided by clients.
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%
In private wealth management, a manager delegating to artificial intelligence (AI) acts as the client's agent and the system's principal.
Abstract Estimating value-at-risk on time series data with potentially heteroskedastic dynamics is a highly challenging task.
PAPER REPORTS · LSTM VaR estimator, Fama-French 25 BM/OP portfolios, VaR 1%, n=250, test period ending 08.03.2019 (~500 backtest days):… · LSTM VaR estimator, Fama-French 25 portfolios, VaR 1%, n=250, test period ending 28.02.2022 incl.
We study whether model diversity survives selection into trading. In synthetic markets with a fixed mixture of three language-model families, news presentation changes their representation among submitted orders.
When developers change one component of an agent, such as its controller, a learned model or its verifier, they usually judge the change by an aggregate task score.
PAPER REPORTS · ARC-AGENT (full): Sharpe 0.56±0.29 (across-seed s.d.), MaxDD 10.5%, mean per-rebalance turnover 18.1%. · ARC-AGENT Sharpe excess vs static EW/MinVar frontier at matched drawdown: −0.04 (synthetic U3, 8 bps costs).
In this work, we study the pricing of American options under stochastic local volatility (SLV) models extended by including stochastic correlation driven by an additional stochastic process.
In this paper, we extend earlier findings of supra-competitive outcomes in optimal-execution games by identifying a learned punitive mechanism that deters deviations and provides behavioural evidence of collusion.
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…
Bank treasury portfolios must balance yield, liquidity, and interest-rate risk across bonds of different maturities.
PAPER REPORTS · DP mean terminal wealth over 6 months, starting from 1 (simulated DNS-VAR chain, unconditional-mean initial curve):… · DP mean terminal wealth over 12 months: 1.1801 at c=0; 1.1166 at c=0.01.
Machine-learning signals built from financial text treat what institutions say, and what the media repeat, as evidence about value. But whoever shapes a narrative may be trading against it.
PAPER REPORTS · Linear model, raw voices + theory features: out-of-sample cross-sectional IC 0.171±0.018 (simulated main market, 5… · Gradient boosting, raw voices + theory features: IC 0.118±0.019 (simulated main market, 5 seeds; costs not stated)
We develop a non-perturbative framework for stochastic-volatility option pricing organised by the two-particle-irreducible (2PI) effective action and the Dyson-Schwinger gap equations of quantum field theory.
We model market impact as the response to submitted order flow net of counterflow from latent traders, activated when price displacements from the level that would prevail without the order exceed individual thresholds.
In quantitative finance, standard regression losses are misaligned with the economics of return prediction.
PAPER REPORTS · BTC 15-min, CZAR tuned (alpha about 0.054). Test window 2,000 candles (about 21 days); costs, slippage and sizing… · BTC 15-min, CZAR fixed alpha=0.005 to 1: naive annualized Sharpe of 5.62, -0.95, 3.95, 1.15, 4.76 and 5.08…
Abstract The Probability of Informed Trading (PIN) treats informational events as independent across trading days, leaving it silent on how long informational regimes last.