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 · 19 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%
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%
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%
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%
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%
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%
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%
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 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%
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%
We develop Entropic Value-at-Risk (EVaR) parity for tempered stable returns. EVaR-based inverse risk parity (IRP) and equal risk contribution (ERC) portfolios are constructed using multivariate normal tempered stable models and independent component analysis…
PAPER REPORTS · SECTOR EVaR-ERC ICA+NTS: cumulative return 853.79%, Sharpe 0.588, max drawdown 48.56%, turnover 6.93% (out-of-sample… · SECTOR EVaR-ERC ICA+CTS: cumulative return 821.40%, Sharpe 0.579, MDD 48.39%, turnover 9.49% (gross)
OUR BACKTEST · Sharpe 0.37 · Return +14.3% · Max DD -22.0%
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%
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%
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%
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 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%
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%