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 · 146 shown
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%
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%
This study evaluates the reliability and investment consequences of scenarios from a specified time-series generative adversarial network (TimeGAN) in Rachev-ratio portfolio allocation.
PAPER REPORTS · Historical RRP: 3.48% annualized net return, volatility 9.42%, Sharpe 0.26, realized RR 0.96, MDD 39.59%, daily 5% CVaR… · Hybrid eta=1/2, M=756: 2.80% return, volatility 8.98%, Sharpe 0.19, MDD 41.50%, turnover 420.65% (5-seed average; same…
OUR BACKTEST · Sharpe 0.48 · Return +27.1% · Max DD -26.9%
OUR BACKTEST · Sharpe 0.60 · Return +35.6% · Max DD -23.8%
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%
OUR BACKTEST · Sharpe 0.92 · Return +94.9% · Max DD -29.7%
Accurate prediction of stock price movements is of substantial importance to investors, portfolio managers, and financial regulators, and although machine learning models are increasingly deployed in quantitative finance, the limited transparency of many such…
PAPER REPORTS · XGBoost long/flat, hold-out period, as-is configuration: annualized return −11.59%, Sharpe −0.241 at 10 bps one-way… · XGBoost long/short, hold-out period, as-is configuration: annualized return −34.29%, Sharpe −1.014 at 10 bps one-way…
OUR BACKTEST · Sharpe 0.09 · Return +2.4% · Max DD -14.9%
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%
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%
Language models that read financial news are usually trained on text from the same years used to test them, so a backtest can reward hindsight.
PAPER REPORTS · Monthly, price+text combination across all readers and models, 63-day shocks, test 2022–2024, net of 10bp per dollar… · Same monthly buffered portfolio: net return 12.4%/yr, volatility 10.4%, max drawdown 8%, turnover 60% per month,…
OUR BACKTEST · Sharpe -0.73 · Return -19.9% · Max DD -34.5%
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%
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%
We study long-term investment in an Ornstein-Uhlenbeck diffusion model with exponential utility. We solve the finite-horizon problem explicitly and show that information delay qualitatively alters long-horizon behavior.
OUR BACKTEST · Sharpe -0.06 · Return -0.6% · Max DD -3.9%
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%
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%
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%
In stochastic control problems involving optimal portfolio choice, one often seeks an optimizer of some local criterion (dynamic programming value function) generated by some global criterion (e.g., expected utility).
OUR BACKTEST · Sharpe 0.80 · Return +34.1% · Max DD -15.2%
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%
To extend conventional model-level feature importance to economically meaningful portfolio outcomes and decisions, we propose a local first-order attribution-propagation framework that maps allocation-function feature exposures to local sensitivities of…
PAPER REPORTS · XGB baseline, OOS 2006-2024: net Sharpe 0.6175 (annualised, 5bp per unit turnover); gross Sharpe 1.0207; net mean 2.55%… · FFN baseline, OOS 2006-2024: net Sharpe 0.5750 (5bp); gross Sharpe 1.3051; net mean 2.65% p.a.; net FF5 alpha 1.12%…
OUR BACKTEST · Sharpe -0.82 · Return -24.2% · Max DD -31.5%
Formulaic alpha mining seeks to discover executable expressions that capture predictive market signals in a vast, noisy search space.
PAPER REPORTS · AlphaRLM-8B CSI 300: annualized excess return 12.14% (0.1214±0.0011), MDD -6.15%, IC 0.0256, RankIC 0.0424, ICIR… · AlphaRLM-8B CSI 1000 (CSI 300-trained factor pool): annualized excess return 17.45%, MDD -11.98%, IC 0.0302, RankIC…
OUR BACKTEST · Sharpe 0.83 · Return +105.8% · Max DD -50.8%
Portfolio decisions are often made under uncertainty about expected returns, and investors revise their beliefs as new information arrives.
OUR BACKTEST · Sharpe 1.07 · Return +152.8% · Max DD -57.4%
PAPER REPORTS · AIM_PU_CVaR (real data, daily, 2020-01-01 to present, T=1471 days): Sharpe ratio 0.7718; annualization not stated; no… · AIM_PU_CVaR: total reward 1.5048, mean daily return 0.0010, total CVaR-like cost 0.0194, 23 crisis days (>5% daily…
OUR BACKTEST · Sharpe 0.98 · Return +61.8% · Max DD -22.0%
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%
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%
We represent (weighted-)selection-elliptical distributions as an affine combination of the $q$ selection variables plus an elliptical term whose direction alone is independent.
OUR BACKTEST · Sharpe 0.75 · Return +27.1% · Max DD -21.2%
Wavelet-based financial forecasters typically use the transform only to denoise, or reduce it to a single spectral snapshot at the forecast origin, and the convolution that produces the coefficients is usually bilateral, so it can read past the forecast…
PAPER REPORTS · S&P 500 DSTNet asymmetric risk-off allocation, test 2023-06-01 to 2024-05-31, 10 bps per unit turnover, idealised… · NYSE DSTNet risk-off, same period, 10 bps: Sharpe 1.80, MaxDD 5.65%, CumRet 11.31%, exposure 56.77%, total turnover…
OUR BACKTEST · Sharpe 0.46 · Return +21.1% · Max DD -24.7%
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%
We investigate a continuous-time financial market where the asset price exhibits weak (sublinear) mean reversion and has a nonzero drift.
OUR BACKTEST · Sharpe 0.09 · Return +0.0% · Max DD -0.1%
Finding which variables cause which others in multivariate time series, and at what lags, is central to science and policy, yet existing methods force a choice between flexible confounder adjustment, data-driven lag selection, and inference that controls the…
PAPER REPORTS · ACLE-VAR (the paper's adaptive-lag procedure, no confounders): weighted long-short, SPY dollar-hedged, open-to-close. · ACLE-VAR by strategy (Ann. Return % / Sharpe), 2004–2020, no costs: Naive 4.64/1.15; Top 50% 8.54/1.31; Top 25%…
OUR BACKTEST · Sharpe -0.33 · Return -2.2% · Max DD -4.6%
We study when to buy a share that will later be sold optimally, when the price follows a geometric multi-skew Brownian motion whose skew levels model support and resistance.
OUR BACKTEST · Sharpe 0.21 · Return +40.7% · Max DD -78.0%
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%
We develop a price-based framework for stochastic portfolio theory in which trading strategies are generated from nominal price weights and evaluated relative to a price-weighted benchmark.
PAPER REPORTS · Cap-based p-diversity-weighted portfolios, terminal log relative wealth vs cap-weighted index on U^S (2002–2021,… · Cap-based p-diversity-weighted portfolios, terminal log relative wealth vs cap-weighted index on U^P (2002–2021, no…
OUR BACKTEST · Sharpe 0.38 · Return +39.6% · Max DD -47.5%
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%
A cross-sectional signal is a forecast vector over $d$ assets at each date; demeaned and normalized, it is a point on a sphere. Its city is the direction of its time-averaged vector in a common target-aligned frame, a compressed summary.
OUR BACKTEST · Sharpe 0.68 · Return +75.9% · Max DD -48.2%
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%
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%
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%
Reinforcement learning for trading often struggles to balance upside participation with drawdown control.
PAPER REPORTS · SPY, test 2020–2022, seed 42, 0.1% proportional transaction cost: total return 27.62%, annualized return 8.48%, Sharpe… · SPY, test 2020–2022, mean ± std over seeds {7,11,19,23,42}, 0.1% cost: total return 0.2725 ± 0.0109, Sharpe 0.6219 ±…
OUR BACKTEST · Sharpe 0.89 · Return +42.3% · Max DD -15.6%
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%
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%
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%
We solve the perpetual liquidation problem for a geometric multi-skew Brownian motion carrying local-time pushes upward at a support level and downward at a resistance level, a model of technical analysis that is Markov in the price alone.
OUR BACKTEST · Sharpe -0.21 · Return -3.5% · Max DD -10.3%
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%
OUR BACKTEST · Sharpe -0.03 · Return -1.8% · Max DD -19.0%
This paper examines whether a cointegration-based pairs trading strategy between PepsiCo and The Coca-Cola Company is statistically robust and economically exploitable.
PAPER REPORTS · In-sample 2018–2023, θ*=(2.15, 0.50): annualised net Sharpe 0.67, net P&L $16,941 on $100,000, 9 round-trips. · In-sample 2018–2023 excluding the COVID window: annualised net Sharpe 0.28, net P&L $5,759, 8 trades (0.20% costs).
OUR BACKTEST · Sharpe 0.11 · Return +0.2% · Max DD -1.4%
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%
Predicting stock price movements is highly challenging due to the noisy, nonlinear, and multi-factor nature of financial markets.
OUR BACKTEST · Sharpe 0.70 · Return +20.9% · Max DD -6.5%
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%