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
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%
This article investigates the risk exposure of eight Central and Eastern European markets using monthly data.
Abstract This study develops a robust framework for modeling dynamic volatility, asymmetry, and tail dependence in financial returns, focusing on the daily returns of Natural Resource Index () and the Oil and Gas Index ().
OUR BACKTEST · Sharpe 0.47 · Return +42.5% · Max DD -27.9%
Abstract Value-at-Risk (VaR), the most widely used measure of market risk, is typically evaluated through backtesting of point forecasts. Such procedures, however, say little about the uncertainty of the estimated quantile.
We establish the consistency and asymptotic normality of a two-step estimator of conditional expectiles in the context of conditional scale models.
Similar to banks, DeFi protocols expose depositors to operational risk (USD 9.45 billion across 1,075 events since 2020). Unlike banks, they are not required to hold capital against it. A protocol may maintain a buffer voluntarily.
We introduce ISCOS, a cross-entropy importance-sampling calibration method for rare credit-portfolio losses. We derive Gaussian and Gaussian--inverse-Gamma proposals and analyse the propagation of finite-COS approximation errors to the fitted parameters.
Abstract The emergence of cryptocurrencies has presented investors with novel portfolio diversification opportunities.
PAPER REPORTS · Unconstrained mean-CVaR with crypto: mean monthly return 2.63%, mean monthly CVaR 1.15%, mean monthly risk-return ratio… · Unconstrained without crypto: mean monthly return 0.46%, CVaR 0.42%, risk-return ratio 1.09%
Abstract Precious metals historically have been adopted as an effective hedging instrument by investors due to their price dynamics shaped in line with economic and financial risks.
Abstract High-dimensional multivariate normal (MVN) integration is a computational bottleneck in many statistical applications, particularly in finance and econometrics.
Understanding the propagation of extreme events is important in many economic and environmental applications, yet most econometric methods for causal inference focus on average effects rather than tail behavior.
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.
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%
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
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 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%
This study analyzes the microstructural mechanisms through which the rapidly expanding single-stock leveraged ETFs in the Korean capital market impede the price discovery function and amplify endogenous volatility.
Cryptocurrency exchange-traded products (ETPs) listed on European exchanges provide a regulated environment for studying intraday market anomalies.
PAPER REPORTS · AUC-ROC up to 0.823 for one-bar-ahead DPOT prediction (LR, cumulative features, VIRBTC.ST), out-of-sample last 20% of… · AUC-ROC 0.821 for DPOT, LR, cumulative, VBTC.XE; 0.812 session-based
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%
Cross-correlations between financial signals are neither scale-free nor amplitude-independent: they vary with the time scale over which they are measured and with the magnitude of the fluctuations that dominate the average.
PAPER REPORTS · Synthetic minimum-risk MMFC: 10-period 99% VaR 6.923... (stated as 5.923, sd 0.607) and 97.5% ES 6.048 (sd 0.594),… · In-sample empirical MMFC: lowest average monthly drawdown and lowest 10-day 97.5% ES among the five portfolios at every…
We study seven major crypto-perpetual liquidation cascades (2022-2025), and in the largest of them we can watch the mechanism directly.
How deep and how long should the drawdowns of a systematic trading strategy run, given its Sharpe ratio and the statistical structure of its returns? Building on the drawdown framework of Rej, Seager and Bouchaud (2017), we develop the answer in three steps.
The expansion of the cyber insurance market remains exposed to the threat of accumulation events that could simultaneously affect a large number of policyholders.