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
Option-implied factors are largely, but not entirely, spanned by the equity factor zoo. We construct 137 option-implied characteristics for optionable U.S.
PAPER REPORTS · bakshiKurt long-short factor (value-weighted decile, S&P 500 constituents, 30-day maturity): -11.23% annualized mean,… · ivs_convexity long-short factor: -14.78% annualized mean, vol 17.94%, Sharpe -0.82, MDD -96.94%, skew -3.59, t = -2.57;…
OUR BACKTEST · Sharpe -0.19 · Return -21.5% · Max DD -44.4%
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 a model-agnostic framework for noise reduction in high-dimensional time series that explicitly targets optimal recovery of a low-dimensional latent dynamic component contaminated by observational white noise.
PAPER REPORTS · Macro panel one-step-ahead forecast (not a trading strategy): average MSE 23.47 (standard error 10.87) for VAR(2) on…
OUR BACKTEST · Sharpe -0.50 · Return -21.3% · Max DD -34.9%
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%
Forecasters often score the same units per date against one standardized realized outcome. We show that every standardized forecast splits exactly into a component aligned with this common target and a component uncorrelated with it.
PAPER REPORTS · Three-way equal-weight selection, LLM equity panel, 157 weekly test dates Mar 2023–Jun 2026: relative-score risk 1.079,… · Three-way equal-weight selection, ETF panel, 196 monthly test dates (2005–2026 sample): relative-score risk 1.076,…
OUR BACKTEST · Sharpe 0.14 · Return +27.2% · Max DD -59.8%
In this paper, we study the diversification properties of convex combinations of iid random variables with infinitely divisible distributions.
OUR BACKTEST · Sharpe 0.45 · Return +46.7% · Max DD -40.4%
Abstract. We study equilibrium feedback strategies for a family of dynamic mean-variance problems with competition among a large group of agents.
OUR BACKTEST · Sharpe 1.68 · Return +3.0% · Max DD -0.3%
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%
In a statistical factor model, principal components (or eigenvectors) of a sample covariance matrix serve as estimates of {\it principal directions}, the true drivers of co-movement of a collection of observed variables.
OUR BACKTEST · Sharpe 0.52 · Return +30.7% · Max DD -31.0%
Abstract We develop a continuous-time framework in which the risk associated with leverage is parameterized by a dimensionless drawdown-risk index relative to an arbitrary reference asset.
PAPER REPORTS · μ_{0|w} = 0.145 with σ_{0|w} = 0.274 and s_{0|w} = 0.665 for the scaled growth-optimal Kelly strategy k_{0|e1}w* on the… · μ_{e1|w} = 0.096 with σ_{e1|w} = 0.223 and s_{e1|w} = 0.543 for w = e1 + k_{0|e1}w*_{e1} (index-relative…
OUR BACKTEST · Sharpe 0.67 · Return +59.2% · Max DD -35.9%
We study libraries of cross-sectional signals: at each date, a forecast vector over $d$ assets intended to predict the next period's cross-sectional return.
OUR BACKTEST · Sharpe 0.11 · Return +11.3% · Max DD -39.9%
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%
Hybrid Deep Learning for equity index forecasting is limited by three problems: propagation of OHLCV noise into derived technical indicators (TIs), channel-indiscriminate multi-scale decomposition that conflates heterogeneous frequency signatures, and static…
PAPER REPORTS · WaVeFuse directional strategy, 365-day 2023 out-of-sample, 10bp per-trade cost: mean CAGR 24.6%, mean Sharpe 3.69, mean… · Per-index directional (2023, 10bp): KOSPI CAGR 34.4%, Sharpe 4.73 [95% CI 4.37-5.09], max DD 1.9%; GDAXI 13.3%, Sharpe…
OUR BACKTEST · Sharpe 0.03 · Return +2.7% · Max DD -41.1%
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%
An ensemble of roughly 3,000 signals over 20 assets was reported to have approximately 90% correlation with the leading component of the asset-space return structure.
OUR BACKTEST · Sharpe -0.16 · Return -29.7% · Max DD -97.0%
Abstract Counterintuitively, the S&P 500 Index rose between January 3, 2022, and December 29, 2023, while exchange-traded funds (ETFs) seeking to deliver $$2\times $$ and $$3\times $$ daily returns of the index delivered substantially negative returns.
OUR BACKTEST · Sharpe 0.05 · Return +0.3% · Max DD -3.2%
This paper introduces FinAnalyzeNet, a novel hybrid deep learning framework designed to address the complex challenges of financial volatility prediction in dynamic market environments.
PAPER REPORTS · Sharpe ratio 1.42 for the volatility-based trading strategy (test period 2021-2023 implied; no transaction-cost… · Directional accuracy 84.7% (sd 1.2%) on volatility direction, test period 2021-2023
OUR BACKTEST · Sharpe 0.64 · Return +21.0% · Max DD -18.7%
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%
Signal correlation and PnL correlation are correlations over different index sets - across assets at each date versus across dates for scalar payoffs - and practitioners often treat the first as a proxy for the second.
OUR BACKTEST · Sharpe -0.83 · Return -17.6% · Max DD -18.1%
Abstract This research seeks to delve deeper into the financial significance of the eigenvector associated with the second-largest eigenvalue of the correlation matrix of returns.
OUR BACKTEST · Sharpe -0.48 · Return -35.9% · Max DD -49.7%
Robust portfolio rules that reconstruct confidence sets after learning need not preserve the evaluator obtained by prior-by-prior Bayesian transport.
OUR BACKTEST · Sharpe 0.60 · Return +10.8% · Max DD -7.6%
Let \(P\) be a fixed physical law and let \(Q\) be an equivalent martingale measure selected from the martingale-measure set associated with a chosen numeraire.
OUR BACKTEST · Sharpe 0.54 · Return +50.1% · Max DD -37.6%
MASTER's inter-stock multi-head attention -- the module responsible for modeling cross-sectional stock relationships -- accounts for 42.5% of model parameters and 25% of predictive value.
PAPER REPORTS · Long-short portfolio Sharpe at N~3,500 (full A-share, test 2019-2020, 4 seeds, frictionless): PureLSTM 5.6 +/- 0.5;… · IC / Rank IC as the primary reported performance: CSI300 Original 0.058 +/- 0.007 / 0.066 +/- 0.003 (5 seeds); CSI800…
OUR BACKTEST · Sharpe -0.58 · Return -50.8% · Max DD -63.3%
For more than four decades, the 60/40 stock/bond portfolio has served as a benchmark for delivering reasonable returns without excessive risk. More recently, a 50/30/20 stock/bond/alternative portfolio has been suggested.
PAPER REPORTS · Markowitz: 11.6% annualized return, 9.0% annualized volatility, Sharpe 1.08, max DD 18.1%, mean DD 3.0%, turnover… · Simple Markowitz: 10.4% return, 9.3% volatility, Sharpe 0.91, max DD 15.8%, turnover 278.9%, same period and costs
OUR BACKTEST · Sharpe 0.43 · Return +18.9% · Max DD -16.4%
We introduce ALM-GARCH, an asymmetric long-memory GARCH model in which positive and negative innovations enter conditional variance with different injection amplitudes and kernel offsets.
OUR BACKTEST · Sharpe 0.19 · Return +7.4% · Max DD -20.9%
Investment performance is commonly presented either as a conventional cumulative-return chart, which fixes a historical starting date and traces performance forward, or as a trailing-return table, which fixes the current endpoint but reports only a small set…
OUR BACKTEST · Sharpe 0.43 · Return +53.4% · Max DD -59.1%
Frequent market instability and the lack of rigorously validated forecasting frameworks pose significant challenges for predicting market stress in the Dhaka Stock Exchange (DSE).
PAPER REPORTS · Random Forest crash-gated risk-off strategy, pooled equal-weighted, 2019-2022 test period: total return 67.26% (15.01%… · Annualized volatility 16.39% (strategy) vs. 18.11% (buy-and-hold); maximum drawdown -32.21% vs.
OUR BACKTEST · Sharpe 0.47 · Return +25.2% · Max DD -21.6%
Clean energy equities play a pivotal role in sustainable finance and the global energy transition, yet their performance remains highly sensitive to global fossil energy price fluctuations and climate policy uncertainty.
OUR BACKTEST · Sharpe 0.07 · Return +5.2% · Max DD -40.7%
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 paper examines whether the risk-adjusted performance of Environmental, Social, and Governance (ESG)-focused Exchange Traded Funds (ETFs) reflects distinct investment behavior or is primarily influenced by benchmark exposure, geography, and sector…
OUR BACKTEST · Sharpe 0.56 · Return +53.4% · Max DD -37.6%
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 investigate arbitrage in a discrete-time financial market model where, in addition to finitely many dynamically traded assets, there are also static options to choose from.
OUR BACKTEST · Sharpe 0.35 · Return +21.4% · Max DD -34.1%
Portfolio risk assessment ordinarily relies on reliable estimates of cross-asset return covariances, which are difficult to obtain in short, high-dimensional panels.
PAPER REPORTS · In-sample standardized variance percentile of the news-only allocation: 0.69%-1.33% across four prespecified capped… · Standardized in-sample variance 0.357, 8.3% below the equal-risk (inverse-volatility) benchmark and 35.6% above the…
OUR BACKTEST · Sharpe 0.47 · Return +44.2% · Max DD -29.9%
Large language models (LLMs) are increasingly used to discover trading strategies, and much of the resulting literature shares a methodological weakness: many candidate strategies are generated, the best is reported, and neither look-ahead bias nor the…
PAPER REPORTS · Best gpt-4.1 discovery (E3, RSI x volume, 453-stock universe): design Sharpe 1.69 (2017-2021), evaluation Sharpe 0.18… · Best claude-sonnet-5 discovery: design Sharpe 0.44 (2017-2021), evaluation Sharpe -0.33 / -29% (2022-2025), DSR 0.18,…
OUR BACKTEST · Sharpe -0.10 · Return -10.8% · Max DD -47.8%
Algorithmic trading now represents a market exceeding $20 billion, where even marginal gains in signal robustness can translate into economically significant returns.
PAPER REPORTS · Hybrid ensemble, OOS 2025 (252 trading days), 2.2bp per trade per leg: total return 51.26%, Sharpe 2.44, Sortino 5.35,… · Hybrid ensemble, OOS 2025: annualised CAPM alpha 0.423 (p = 0.011), beta 0.048, Probabilistic Sharpe Ratio 0.960
OUR BACKTEST · Sharpe 0.75 · Return +696.5% · Max DD -365.1%
We model equity markets using geometric Brownian particles entering and exiting at rank-dependent intensities.
PAPER REPORTS · p=0 (equally weighted) diversity-weighted portfolio capitalization growth: +3.4% per year above the front speed,… · p=1 (market) portfolio: -0.5% per year relative to the front, 1975-2024, frictionless
OUR BACKTEST · Sharpe 0.63 · Return +66.6% · Max DD -37.1%
Lead-lag relationships are widely used in financial time series, and many clustering algorithms based on them have been developed. The traditional DTW-KMedoids algorithm performs well both on the synthetic dataset and the real financial dataset.
PAPER REPORTS · Sharpe 0.866, annual return 6.21%, annual volatility 7.17%, max drawdown -63.908, hit rate 0.520, profit-loss ratio… · Sharpe 0.808 / 0.790 (KShape mod / med), lead strategy, 679 assets, same period; drawdowns -67.604 / -69.418
OUR BACKTEST · Sharpe 0.39 · Return +27.5% · Max DD -39.3%
We show that the key optimization results of the classical Markowitz portfolio selection theory, originally formulated for variance as the risk measure, remain available in explicit closed form under a broader class of strictly convex quadratic risk measures.
OUR BACKTEST · Sharpe 0.50 · Return +143.8% · Max DD -75.0%
In this paper we propose a new formulation of the Bayesian Filter as used in the discrete-time Markov-Switching-Multifractal (MSM) model of volatility based on existing permutation symmetry within the likelihood structure.
OUR BACKTEST · Sharpe 0.90 · Return +54.5% · Max DD -20.2%
Large language models (LLMs) are increasingly used in investment decision-making, yet prior work shows that they exhibit systematic, model-specific investment preferences.
PAPER REPORTS · Weekly rebalanced equal-weight long-only top-100 portfolio from Qwen3-8B scores, 427 S&P 500 stocks, 29 signal weeks /… · Realized Sharpe of the top-100 portfolios is shown only graphically (Figure 5c, axis range roughly 2–4); no point…
OUR BACKTEST · Sharpe 0.52 · Return +57.3% · Max DD -40.6%
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%
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%
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%
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%
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%
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%
PAPER REPORTS · Minimum Variance Portfolio (MVP), 2019-2025 daily, no transaction costs stated: mean daily return 0.0005519, daily… · Minimum Correlation Portfolio (MCP): mean daily return 0.0007516, std dev 0.0014739, Sharpe (std dev) 0.5099, Sharpe…
OUR BACKTEST · Sharpe 0.21 · Return +11.2% · Max DD -24.8%
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%
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%