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 · 248 shown
Option prices are prices of insurance, so the risk-neutral probabilities they imply overstate physical crash risk. A power utility pricing kernel undoes the premium.
KellyBoost is a single multi-output XGBoost model whose softmax output is the portfolio: with y the vector of per-asset holding-period returns, the training loss is - log(1 + w y), the negative log growth rate, so the fitted model is the growth-optimal…
PAPER REPORTS · KellyBoost (searched, gross of costs), 2013-01 to 2026-07, 163 monthly decisions: mean log growth 0.47 (x100 per 20-day… · KellyBoost hand-built feature pipeline, same period: logG 0.39, annualized return 5.7%, vol 22.9%, Sharpe 0.28, max DD…
Implied volatility surfaces summarise the option market and are central to many financial applications.
PAPER REPORTS · Surface point-forecast RMSE aggregated over 30 horizons: 0.01262 vs persistence 0.01343, +6.09% gain; MAE gain +3.45%;… · h=1: RMSE 0.00619 vs persistence 0.00535 (-15.72%); MAE -34.49%
OUR BACKTEST · Sharpe -0.00 · Return -1.0% · Max DD -329.1%
W-shaped smiles appear in near-expiry options around binary events such as earnings, and have been associated with bimodal risk-neutral densities. The three-parameter eSSVI slice cannot produce them.
We study discrete-time asset pricing with bid-ask spreads and model uncertainty. The family of probability measures enters the no-arbitrage condition through the union of its supports.
At 15-minute horizons, directional mean reversion is far stronger and more pervasive in cryptocurrency markets than in US equities: scored under one matched, strictly out-of-sample protocol, 90% of 183 Binance pairs carry significant directional reversal…
PAPER REPORTS · Gross edge per trade peaks near 1.3 bp of notional (BTC/ETH, 15m, 2025-01 to 2026-02) against a 5 bp cheapest maker… · Directional accuracy rises monotonically with confidence threshold, clearing 56% on the most confident bars (BTC/ETH);…
Basket options are difficult to value under correlated lognormal dynamics because weighted sums and differences of lognormal variables have no tractable distribution.
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%
The Marketron model of \cite{HalperinItkin2025Mark} and its option pricing extension in \cite{HalperinItkinMarketron2} suffer from structural non-identifiability: an eighteen-parameter space traps solvers in suboptimal local minima and renders economic…
Hedge ratios, factor models and diversified portfolios all rest on an estimate of which firms move together.
PAPER REPORTS · Variance-harvest attribution (selling variance at VIX-squared against the paper's 12m equal-weighted realized leg, July… · Risk by REC quartile over the same 329 months: probability of loss 0.29, 0.21, 0.26, 0.15; mean loss given loss…
OUR BACKTEST · Sharpe 0.73 · Return +68.2% · Max DD -36.5%
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%
Costly LLM features matter only if calibration lets them affect the forecast. We document a failure of this link in a next-day risk study of two broad-market funds. Full-history scoring preceded the 2022 calibration.
PAPER REPORTS · Prespecified LLM importance feature: zero improvement, 95% interval [0,0], on all four endpoints (SPY/VIX binary and… · Signed LLM repair, SPY/VIX continuous variance: improvement -0.007452, 95% interval [-0.015888, -0.001066], Bonferroni…
Abstract Market timing models aim to anticipate short-term market movements according to a given source of information. Such information could be extracted from an analysis of history or a forecast of the future.
PAPER REPORTS · S&P500 timing, 2018: index -6.7% annualized; Strat1-L 3.8%, Strat1-LS 14.3%, Strat2-L 0.8%, Strat2-LS 8.2% (no… · S&P500 timing, 2023: index 21.6%; Strat1-L 21.9%, Strat1-LS 22.1%, Strat2-L 25.5%, Strat2-LS 29.4% (no transaction…
OUR BACKTEST · Sharpe 0.61 · Return +49.0% · Max DD -31.2%
Automated market makers (AMMs) are a cornerstone of decentralised finance (DeFi). Constant product markets with concentrated liquidity, such as UniswapV3, are now a well-established design.
PAPER REPORTS · PPO_narrow, risk-neutral, sigma=0.01, g=2: mean PnL 49.91 +/- 0.38 USDC, 5% CVaR 9.15 +/- 0.65, over 1000 evaluation… · PPO, risk-neutral, sigma=0.01, g=2: mean PnL 42.31 +/- 0.97 USDC, 5% CVaR 6.08 +/- 0.97
We introduce Deep-MKV-TS, a path-dependent McKean-Vlasov framework for financial scenario generation. The stochastic dynamics are chosen by matching selected path and volatility features of generated scenarios to those observed in the data.
PAPER REPORTS · Frozen ES drawdown-risk decision, 123 held-out sessions (January-June 2026): Deep-MKV-TS average exposure 1.99 +/- 0.06… · Conditional-forecast CRPS (x1000, lower better) on the 123-session chronological held-out test, four-seed mean +/- sd:…
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%
Abstract We study the distributional and tail-risk properties of Bitcoin and the major cryptocurrencies using daily returns from June 2014 to May 2026.
This study evaluates the return performance and risk characteristics of selected companies suitable for mutual-fund and equity investment analysis over the period 2021–22 to 2025–26.
We introduce a reinforcement learning framework for market making in a limit order book.
PAPER REPORTS · Normalized cash flow (20), noise market: LN mean 6.03, sd 2.81 (M=2); mean 4.66, sd 1.41 (M=20); 10,000 test episodes,… · Normalized cash flow, noise+tactical market: LN mean 9.11, sd 2.20 (M=2); mean 5.68, sd 1.03 (M=20)
Asset-pricing models typically condition on a fixed information set. This paper endogenises the market's conditioning architecture by allowing portfolios to choose representations whose induced exposures affect prices.
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 simulation-based policy iteration for continuous-time portfolio choice with predictable returns and convex constraints. Each outer step re-evaluates a fixed-latent OL-BPTT adjoint after deployment and solves the constrained update.
PAPER REPORTS · Tilted Monte Carlo log-certainty-equivalent gap to the reference on the three-factor, fifty-asset constrained…
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%
We measure volatility roughness across asset classes using a common data infrastructure and pipeline. Our data covers 3,926 United States equities, 34 CME futures roots, rates, FX, and commodities, and options on 44 underlyings over 2010-2025.
We propose a novel valuation framework for contingent convertible (CoCo) bonds based on the issuing bank's Common Equity Tier 1 (CET1) ratio, which is widely acknowledged as an indicator of a bank's solvency.
PAPER REPORTS · Pricing RMSE 5.04% (LYG, CoCo prices 01/04/2021–12/29/2023, in-sample calibration, no transaction cost assumption… · Pricing RMSE 7.95% (LYG, 11/23/2009–12/30/2011) vs best benchmark RMSE 11.32% in Wilkens and Bethke (2014)
We develop a scalable adjoint-to-control framework for continuous-time portfolio choice under smooth pointwise constraints.
OUR BACKTEST · Sharpe 0.19 · Return +24.7% · Max DD -66.8%
We study optimal investment for insurers managing participating (profit-sharing) contracts under probability distortion and probability benchmark (aspiration) constraints.
Reinforcement learning has gained increasing attention as a data-driven approach for stock trading. However, learning a policy that is both profitable and stable remains challenging due to non-stationary market behaviour and noisy reward signals.
PAPER REPORTS · DJI (test 1 Jan 2024 - 31 Mar 2025, 0.1% transaction fee both sides): annual return 21.785%±1.42, cumulative return… · FTSE (same period and costs): annual return 19.164%±1.36, cumulative return 24.596%±1.79, Sharpe 1.124±0.08, max…
Investors interpret social disclosures from a risk perspective, yet relevant information can reach them through channels that differ sharply in regulatory enforcement and materiality: SEC filings, sustainability reports, or financial reports.
Introduction In the context of global climate governance, corporate environmental performance is becoming critical for market competitiveness.
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%
Two old market sayings hold that news is already priced in by the time it is published, and that the rumor is bought while the news is sold. Both place the price move associated with a piece of news before and at publication rather than after it.
PAPER REPORTS · Fade small-cap launch/partnership news (short after positive, buy after negative; enter close of day +5, exit close of… · Short any covered small cap (sentiment-ignoring benchmark, 260,472 events, same windows, 2023-2026): 15.9% annualized,…
OUR BACKTEST · Sharpe -0.23 · Return -18.9% · Max DD -51.9%
The authors present a rigorous empirical evaluation of three distinct optimization paradigms for institutional factor portfolio construction: an entropy-based photonic quantum annealer (Dirac-3, Quantum Computing Inc.), a commercial mixed-integer programming…
PAPER REPORTS · Dirac-3, best overall configuration (beta1=0, beta2=1): Sharpe 0.760, Sortino 0.841, Calmar 0.567, MDD -3.47%, CVaR5%… · Dirac-3 (beta1=0, beta2=0.5): Sharpe 0.721, Calmar 0.538, MDD -2.63% (lowest in both sweeps), CVaR5% -1.107%, annual…
OUR BACKTEST · Sharpe 0.25 · Return +11.4% · Max DD -18.7%
Using neural networks for stock return prediction typically requires choices about depth and hidden-layer width that are difficult to connect to financial interpretation.
PAPER REPORTS · HNN (marginal): pooled out-of-sample R2 0.509% (vs zero forecast), 1987-2016 · HNN (marginal): gross equal-weighted decile long-short spread 3.95% per month, 1987-2016
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%
This study examines the dynamic effects of monetary policy changes on derivatives pricing behavior, emphasizing applications in financial risk management for industrial commodities.
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%
Generative models of limit orderbook (LOB) data have advanced rapidly, but their evaluation often focuses on stylised facts and selected market statistics.
Limit order book (LOB) simulators are most useful to practitioners when they combine realistic market dynamics, computationally efficient sampling, controllable scenario generation, and the ability to generalize beyond the instruments seen during…
Narrow Uniswap v3 liquidity ranges resemble short dated options, and Panoptic's streaming premium echoes the short maturity concentration of Black-Scholes theta near the strike.
Financial markets do not evolve uniformly through calendar time. Periods of intense information arrival accelerate market activity, while information-poor periods produce the familiar intraday lull in trading.
Cryptocurrency time-series forecasting is a challenging task because market data usually exhibit high noise, strong volatility, non-stationarity, nonlinear dynamics, and long-range dependencies.
PAPER REPORTS · Bitcoin h=96: MSE 0.175 ± 0.009, MAE 0.314 ± 0.008 (5 seeds, most recent 20% of data as test) · Dogecoin h=96: MSE 0.413 ± 0.012, MAE 0.374 ± 0.010
This study focuses on developing an AI-supported prototype for multiperspective interest rate forecasting that combines classical econometric models with modern artificial intel-ligence methods.
We develop a geometric theory of arbitrage-free implied variance surface dynamics.
PAPER REPORTS · Out-of-sample RMSE(delta a2) improvement of full (beta,eta,psi) model over SSR-only: 17-21% at 3M-6M (215.7 vs 272.8 at… · Out-of-sample RMSE(delta a1) improvement of adding eta: 1-4% versus SSR-only at 1M-6M, essentially flat at 12M
OUR BACKTEST · Sharpe -0.84 · Return -0.1% · Max DD -0.1%
Diffusion generative models have rapidly emerged as powerful tools for modeling complex financial data.
OUR BACKTEST · Sharpe 0.53 · Return +79.3% · Max DD -50.5%
This article presents with DYSANOS the first generative market model for smooth SANOS option surfaces for all strikes and expiries which are free of static arbitrage.
OUR BACKTEST · Sharpe -0.19 · Return -0.0% · Max DD -0.1%
As more investors contemplate private markets and contend with limited transparency, sparse disclosures, and infrequent transactions, identifying economically meaningful peer companies for comparison is a fundamental challenge for valuation, due diligence,…
PAPER REPORTS · Out-of-sample log-valuation MAE 1.08, RMSE 1.44, R^2 0.46, MAPE 0.06, MdAPE 0.05 (CatBoost, 20% held-out test set… · Relative improvement over OLS baseline: MAE +8%, RMSE +8%, R^2 +28%, MAPE +14%, MdAPE 0%
Shariah-compliant equity screening provides a transparent setting in which institutional rules determine who may own a stock.
PAPER REPORTS · SC Malaysia inclusions, 295 continuously listed liquidity-qualified events, Nov 2013-Nov 2025: matched… · 410 continuously listed inclusions (no turnover floor): +0.896pp [0,10] (p_date=0.127; p_wild=0.134) and +1.458pp…
Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training. This paper asks where such information should enter a neural cross-sectional volatility forecasting model.
PAPER REPORTS · U.S. panel, 30 walk-forward windows Apr 2018 - Oct 2025, mean over 30 seeds: IC 0.5469 +/- 0.0012, ICIR 6.14 +/- 0.06,… · IC advantage over capacity-matched MLP-L: +0.0048 full sample (p<1e-4); +0.0207 top market-vol decile; +0.0322 COVID…