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 · 90 shown
Portfolio optimization techniques often use historical data to estimate parameters for Markowitz-type or ML models. Effective allocation requires a way to align the available data with conditions expected over the investment horizon.
PAPER REPORTS · Markowitz, lambda=0 (historical mean only, no future information in the view): net Sharpe 0.019 (gross 0.050), net MDD… · LSTM mean of 100 seeds, lambda=0: net Sharpe -0.021 (gross 0.037), net MDD -24.7%, final wealth 0.978.
Formulaic alpha factor mining searches for symbolic expressions that transform market data into predictive signals. Recent LLM-based evolutionary factor mining methods use large language models as semantic variation operators.
PAPER REPORTS · CSI 500, test 2022-05-01 to 2026-04-30, top-20 combined signal, Qlib TopkDropout (top 50, drop 5, daily) net of open… · S&P 500, test 2022-05-01 to 2026-04-30, same backtest and cost settings: annualized return 27.00%, information ratio…
Worst-case risk evaluation is central to finance and operations research, yet existing analytical results remain fragmented and largely confined to ambiguity sets with specific structures.
OUR BACKTEST · Sharpe 0.67 · Return +44.6% · Max DD -28.9%
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.
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%
The value of a forecast depends on its accuracy, but also on its actionability. In trading, an investor must translate return forecasts into positions while accounting for trading costs and the lingering impact of earlier trades.
OUR BACKTEST · Sharpe 0.07 · Return +8.8% · Max DD -55.7%
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…
Classical execution models assume linear impact, diffusive prices, and an exogenous horizon. We study execution under concave power-law impact and Hurst-scaled price fluctuations, and make two contributions.
OUR BACKTEST · Sharpe -0.29 · Return -9.4% · Max DD -22.3%
We develop a framework to identify and quantify arbitrage across logically related prediction-market contracts.
PAPER REPORTS · Accumulated take, settled windows, 23 Jun-3 Aug 2026, net of spreads and taker fees at displayed depth, held to… · First take, settled windows, same period and costs: guaranteed return 2.7% ($1.10M on $40.6M); at resolution 3.2%…
Temporal point processes are used both to measure market mechanisms, for example how fast prices absorb news and how much of the reaction is the market responding to itself, and to predict the next event.
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 entropy regularization for the discrete-time dynamic mean--variance portfolio problem with no-shorting constraints.
PAPER REPORTS · Rolling Fama-French 10 industries, 72 non-overlapping six-month out-of-sample episodes, first beginning Jan 1990 and… · Simulated data (lognormal mixture, 10,000 test paths, no costs), deterministic mode policy: - tau=10^-4: terminal mean…
The rapid growth of crypto markets is increasing investment opportunities, but also volatility risks. There is a need to automate portfolio construction to rapidly adapt investment decisions to rapidly changing markets.
PAPER REPORTS · Crew B (30-day rolling Sharpe max), out-of-sample per Table 6: expected return 10%, volatility 10%, Sharpe 0.72,… · Crew B (30-day window), out-of-sample per the window-sensitivity table: expected return -20.58%, volatility 39.88%,…
Expectiles are the only law-invariant risk measures that are both coherent and elicitable. Unlike Conditional Value-at-Risk (CVaR), however, they do not admit a Rockafellar--Uryasev representation that admits tractable Wasserstein reformulations.
PAPER REPORTS · Proposed model, (rho, lambda)=(0.005, 0.10), FTSE 90 stocks, 3,341 OOS days 2012-2026, gross of transaction costs: ann. · Proposed model, (0.001, 0.10), same sample, gross: ann. return 20.62%, ann. vol 20.52%, Sharpe 1.005, MaxDD 44.41%,…
We develop a framework for an investor who trades until she either reaches a financial goal or an exogenous deadline arrives. Analogous to utility functions over wealth, we measure satisfaction with the timing of reaching a goal by a discount function.
OUR BACKTEST · Sharpe 0.63 · Return +44.3% · Max DD -26.3%
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%
In this paper, we present robust portfolio selection models by incorporating a reward and penalty mechanism into portfolio management.
OUR BACKTEST · Sharpe 0.97 · Return +77.6% · Max DD -27.8%
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.
Taiwan’s central role in global semiconductor manufacturing exposes Taiwan-related ETFs to technology concentration, geopolitical uncertainty, and supply-chain disruptions, resulting in return distributions characterized by heavy tails, volatility clustering,…
OUR BACKTEST · Sharpe 0.37 · Return +28.9% · Max DD -34.3%
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%
Abstract The fact that the solution of the Markowitz model is numerically unstable is recognized as one of the principal reasons for its poor out-of-sample results when applied in practice.
PAPER REPORTS · Out-of-sample Sharpe ratio of the μp=β (δ-stable) portfolio, daily data 2000–2020, rolling window, net of 10 bp ×… · Same setup, DAX: 0.0099 (w=120), 0.013 (w=180), 0.0206 (w=240), 0.0312 (w=480).
OUR BACKTEST · Sharpe 0.66 · Return +49.0% · Max DD -31.1%
In private wealth management, a manager delegating to artificial intelligence (AI) acts as the client's agent and the system's principal.
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).
We study finite-horizon portfolio optimization with proportional transaction costs and trading opportunities arriving at the jump times of a Cox process.
OUR BACKTEST · Sharpe 0.52 · Return +0.0% · Max DD -0.0%
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…
Decision-focused learning (DFL) trains predictors through downstream objectives, but a different loss need not provide an independent parameter-update direction.
PAPER REPORTS · Neural DFL vs MSE, N=100, K=20, 9 annual test folds 2016–2025: annualized TE 0.0276 vs 0.0331 (−16.5%), one-sided… · Same neural folds, K=20: worst-fold TE 0.0360 vs 0.0564 (−36.3%), max tracking drawdown 0.0306 vs 0.0362 (−15.5%),…
OUR BACKTEST · Sharpe 0.71 · Return +69.7% · Max DD -35.0%
European option smiles determine the risk-neutral marginal laws of an asset, but not their intertemporal coupling, which is decisive for many applications.
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.
In a recent paper, Schmelzer and Hastie argue that Markowitz's Critical Line Algorithm and the LASSO path trace the same curve. Here we use that identity to compute efficient frontiers with a stock LASSO solver, \texttt{lars\_path} from \texttt{scikit-learn}.
OUR BACKTEST · Sharpe 0.00 · Return +0.0% · Max DD 0.0%
Abstract. In this paper, infinite horizon stochastic difference equations and backward stochastic difference equations with fractional noise are studied. The main difficulty comes from the self-dependence of fractional noise on an infinite horizon.
OUR BACKTEST · Sharpe 0.59 · Return +40.5% · Max DD -26.3%
While symbolic regression (SR) has been successfully used in science to discover new equations, its use in financial valuation is hindered by several limitations.
PAPER REPORTS · Out-of-sample (last 20% of quarters) next-quarter price-level MAPE, not trading performance; no portfolio, Sharpe,…
OUR BACKTEST · Sharpe 0.41 · Return +37.0% · Max DD -44.3%
This article is concerned with the asymptotic shape of quantile surfaces, defined as the set of quantiles at a given level $α$ generated by a controlled one-dimensional distribution.
OUR BACKTEST · Sharpe 0.53 · Return +55.1% · Max DD -40.5%
This paper investigates cost-sensitive online window size selection for portfolio management under changing market conditions.
PAPER REPORTS · Hedge, DJIA 2021/03/04-2024/02/25, net of c=0.001 turnover cost: cumulative return 41.6977%, MDD -12.6326%, annualized… · Fixed Share, DJIA 2021/03/04-2024/02/25, net of c=0.001: cumulative return 38.1074%, MDD -12.9282%, annualized Sharpe…
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.
The equal-weighted portfolio is a passive, rule-based strategy that has historically been difficult to outperform, delivering higher returns than the capitalization-weighted "market" benchmark across many markets and periods.
PAPER REPORTS · Mean-reverting frictional λ*,mr, 1995–2024 monthly, net of 15bp proportional costs: - cumulative net return 44.92 -… · Trending frictional λ*,trend, 1995–2024, net of 15bp costs: - cumulative net return 31.41 - return 0.13, volatility…
OUR BACKTEST · Sharpe 0.24 · Return +0.0% · Max DD -0.0%
Stock price forecasting is a long-standing challenge in computational finance, driven by the inherent randomness of markets and complex temporal patterns.
PAPER REPORTS · CSI300 test (2017-01-01 to 2020-08-01), Qlib top-50 long, 20 replacements/day: excess annualized return 0.1809, excess… · CSI500 test (2017-01-01 to 2020-08-01), same strategy: excess annualized return 0.1529, excess information ratio…
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%
Nested clustered optimization allocates within each cluster from the cluster's own covariance block and then across the resulting cluster portfolios.
Abstract We consider a robust asymptotic growth problem under model uncertainty in the presence of stochastic factors.
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.
Variance swaps are a convenient instrument for trading vega and convexity, and a listed contract now trades on Cboe.
This study integrates the Black-Litterman (BL) model with deep learning techniques to construct a stock portfolio that incorporates investor sentiment.
PAPER REPORTS · BERT_BL, 2024-07-11 to 2024-12-31 (117 trading days), zero transaction costs: annualized return 106.58%, Sharpe 2.64,… · BERT_BL, same period, net of 0.03% commission + 0.1% sell stamp duty: annualized return 51.87%, Sharpe 1.55, Sortino…
We study exact-cardinality, equally weighted minimum-variance portfolio selection under a one-factor covariance model supplied in factor form. In the nonnegative homoskedastic regime, selecting the K smallest loadings is optimal.
OUR BACKTEST · Sharpe 0.65 · Return +50.5% · Max DD -24.3%
Early warning of market turbulence is usually treated as a classification problem, yet a usable system must also produce trustworthy probabilities, stay parsimonious and convert forecasts into defensible positions.
PAPER REPORTS · Optimized policies, mean of the eleven metaheuristics on the untouched holdout (25 Apr 2019-21 Feb 2020, 5 bp per unit… · Best optimized method by mean return (CS), 30 runs: balanced accuracy 0.693 +/- 0.026, AUC 0.763 +/- 0.019, return…
OUR BACKTEST · Sharpe 0.89 · Return +22.5% · Max DD -8.6%
We study strategic trading around index reconstitution in a continuous-time, multiasset game with transient cross-asset price impact and heterogeneous beliefs about future index membership.
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
Special Markowitz (SM) regularises returns and covariance jointly, relative to a reference state (mu_ref, Sigma_ref). Each eigendirection of the whitened relative operator carries a spectral reliability potential Phi_k, derived from its estimation quality.
OUR BACKTEST · Sharpe 0.30 · Return +32.3% · Max DD -54.9%
We develop a diffusion-model framework for dynamic implied-volatility surface generation and evaluate its economic usefulness through data-driven hedging. The framework consists of two models.
PAPER REPORTS · AD-Seq-Vol hedging, held-out 1 Jul 2018 - 28 Feb 2023, transaction costs included as half bid-ask spread in the… · AD-Seq-Vol, same period, COVID window (13 Feb - 21 Jul 2020) excluded: mean 0.78, median 0.02, std 8.33, 5% VaR 10.72,…