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 · 54 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%
Modern portfolio management increasingly demands a balance between traditional risk-adjusted returns and strict Environmental, Social, and Governance (ESG) mandates.
PAPER REPORTS · Europe persona, out-of-sample 2015–mid-2016: Sharpe drop −0.144 (±0.335) vs Λ=[1,0,0,0] baseline; ESG score gains… · Asia persona, out-of-sample 2015–mid-2016: Sharpe drop −0.045 (±0.068); ESG gains +13.12%/+28.75%/+46.60%.
This paper examines asymmetric volatility spillovers and dynamic connectedness among BRICS exchange rates, the US Dollar Index (USDX), the Japanese Yen (JPY), Brent crude oil, and the Geopolitical Risk Index (GPRD), employing a Time-Varying Parameter Vector…
PAPER REPORTS · Minimum Variance Portfolio risk-reduction effectiveness (RE = 1 - var(rp)/var(ri)), Sept 2014 - Oct 2024, no… · Minimum Correlation Portfolio RE, same sample, no costs stated: RUB 0.98, BRL 0.96, ZAR 0.95, JPY 0.86, USDX 0.76,…
Recent advances in LLM agents enable a new paradigm for asset pricing, which we call Agentic Empirical Asset Pricing (AEAP): systems that autonomously conduct the scientific discovery process itself. We define AEAP and identify its core building blocks.
PAPER REPORTS · SEADS mean per-factor OOS Sharpe 0.25 on Panel A (JKP) and 0.16 on Panel B (CRSP/Compustat), OOS windows 2020\u20132025… · SEADS productivity 14.0 (Panel A) / 13.8 (Panel B) admissions out of a 300-candidate budget
We study whether nuclear and energy-adjacent equity options exhibit a harvestable variance risk premium. Using CRSP and OptionMetrics data for 2000-2024, we construct a systematic cash-secured short-put strategy on a curated universe of nuclear-related firms.
PAPER REPORTS · EW put unconditional, 2000-2024 (300 months): 18.7% annualized return, 2.4% annualized volatility, Sharpe 7.81, MaxDD… · CAP-10 unconditional: 18.6% return, 2.4% vol, Sharpe 7.79, MaxDD 0.0%
Foundation models promise accurate forecasts with little or no task-specific training, but whether they can replace models designed specifically for electricity price forecasting remains unclear.
PAPER REPORTS · Total BESS arbitrage profit over 2021-2025 (1,826 days, 1 MWh battery, 25 EUR round-trip operating cost deducted per… · Total profit Poland: unlimited-bid best 106,685 EUR (TabPFN-3) vs Oracle 123,439 EUR, 86.4% of perfect foresight; Spain…
Stablecoins, typically pegged to fiat currencies, cannot achieve true stability because they inherit fluctuations in the underlying unit of account.
PAPER REPORTS · USD evaluation, USD risk space: annualized return 0.117, annualized volatility 0.211, Sharpe 0.554, max drawdown… · USD evaluation, MLV risk space: annualized return 0.156, annualized volatility 0.223, Sharpe 0.699, max drawdown…
Small-cap-inclusive equity universes contain recently listed and intermittently traded securities, so enforcing a common look-back discards a substantial fraction of the available information.
PAPER REPORTS · Annualized five-session volatility 11.17%, January 2000 - December 2025, net of all modeled execution costs… · Sharpe 0.814, same period, net of execution costs
Quantitative trading is moving from isolated predictive models toward agentic workflows that combine reasoning, tool use, memory, and feedback.
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%
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%
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%
PAPER REPORTS · Optimized ESG portfolio: average conditional volatility 1.29% (2020) declining to 1.18% (2022) and 1.23% (2023-2024),… · Optimized ESG portfolio breach rates: 0.3526 volatility violations and 0.4261 drawdown violations on average 2020-2024…
PAPER REPORTS · US single-country equity portfolios, static risk-minimizing FX exposure (lambda=0), 1990s-2023, in-sample, no… · US single-country equity portfolios, unhedged (full exposure), same period: Sharpe 32.13% (CAD), 45.92% (INR), 31.62%…
PAPER REPORTS · Baseline HRP: annualised Sharpe 0.741, total return +417%, max DD -85%, 76 monthly rebalances 2020-02 to 2026-05,… · HRP-family variants span Sharpe 0.701-0.749 over the same window and cost assumption (best HRP_Dynamic_94 0.749,…
PAPER REPORTS · GMV portfolio realised variance (own model AL-MGARCH): 1.261 at h=1, 1.334 at h=5, 1.402 at h=22, vs MGARCH 1.382 /… · MV portfolio realised variance (own model AL-MGARCH): 2.241 at h=1, 2.352 at h=5, 2.437 at h=22, vs MGARCH 2.452 /…
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%
PAPER REPORTS · Monthly PCI-based mean-variance portfolio (1871:02-2023:12), leverage/risk-aversion setting 6: return 2.3331,… · Monthly PCI-based portfolio, setting 8: return 2.9252, volatility 0.0260, Sharpe 16.6522 vs RV benchmark 0.8883; no…
This work complements our previous paper, which studies borrower-side strategies in decentralized lending markets, by focusing on lender-side capital allocation.
PAPER REPORTS · core-inspired strategy: 5.5% APY, $100k budget, Morpho USDC markets on Ethereum, Jan 1 2026 - Apr 1 2026, daily… · prime-inspired strategy: 3.3% APY, $100k budget, same period and cost assumption
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%
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%
KellyBoost is a single multi-output XGBoost model whose softmax output is the portfolio: with y the vector of per-asset holding-period returns, the training loss is - log(1 + w y), the negative log growth rate, so the fitted model is the growth-optimal…
PAPER REPORTS · KellyBoost (searched, gross of costs), 2013-01 to 2026-07, 163 monthly decisions: mean log growth 0.47 (x100 per 20-day… · KellyBoost hand-built feature pipeline, same period: logG 0.39, annualized return 5.7%, vol 22.9%, Sharpe 0.28, max DD…
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%
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%
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 develop simulation-based policy iteration for continuous-time portfolio choice with predictable returns and convex constraints. Each outer step re-evaluates a fixed-latent OL-BPTT adjoint after deployment and solves the constrained update.
PAPER REPORTS · Tilted Monte Carlo log-certainty-equivalent gap to the reference on the three-factor, fifty-asset constrained…
We develop a scalable adjoint-to-control framework for continuous-time portfolio choice under smooth pointwise constraints.
OUR BACKTEST · Sharpe 0.19 · Return +24.7% · Max DD -66.8%
We study optimal investment for insurers managing participating (profit-sharing) contracts under probability distortion and probability benchmark (aspiration) constraints.
Reinforcement learning has gained increasing attention as a data-driven approach for stock trading. However, learning a policy that is both profitable and stable remains challenging due to non-stationary market behaviour and noisy reward signals.
PAPER REPORTS · DJI (test 1 Jan 2024 - 31 Mar 2025, 0.1% transaction fee both sides): annual return 21.785%±1.42, cumulative return… · FTSE (same period and costs): annual return 19.164%±1.36, cumulative return 24.596%±1.79, Sharpe 1.124±0.08, max…
The authors present a rigorous empirical evaluation of three distinct optimization paradigms for institutional factor portfolio construction: an entropy-based photonic quantum annealer (Dirac-3, Quantum Computing Inc.), a commercial mixed-integer programming…
PAPER REPORTS · Dirac-3, best overall configuration (beta1=0, beta2=1): Sharpe 0.760, Sortino 0.841, Calmar 0.567, MDD -3.47%, CVaR5%… · Dirac-3 (beta1=0, beta2=0.5): Sharpe 0.721, Calmar 0.538, MDD -2.63% (lowest in both sweeps), CVaR5% -1.107%, annual…
OUR BACKTEST · Sharpe 0.25 · Return +11.4% · Max DD -18.7%
Using neural networks for stock return prediction typically requires choices about depth and hidden-layer width that are difficult to connect to financial interpretation.
PAPER REPORTS · HNN (marginal): pooled out-of-sample R2 0.509% (vs zero forecast), 1987-2016 · HNN (marginal): gross equal-weighted decile long-short spread 3.95% per month, 1987-2016
Against the background of increasing volatility and complex risk factors in global markets, options and futures have become important instruments for risk hedging and uncertainty management.
PAPER REPORTS · Futures hedged portfolio, 2019-2024: annualized return 7.95%, annualized volatility 10.28%, hedging efficiency 46.87%,… · Option hedged portfolio, 2019-2024: annualized return 8.31%, annualized volatility 8.76%, hedging efficiency 54.73%,…
OUR BACKTEST · Sharpe 0.74 · Return +83.0% · Max DD -47.8%
Shariah-compliant equity screening provides a transparent setting in which institutional rules determine who may own a stock.
PAPER REPORTS · SC Malaysia inclusions, 295 continuously listed liquidity-qualified events, Nov 2013-Nov 2025: matched… · 410 continuously listed inclusions (no turnover floor): +0.896pp [0,10] (p_date=0.127; p_wild=0.134) and +1.458pp…
Large language models can extract richer signals from financial news than fixed sentiment lexicons, and recent work has explored feeding such signals into portfolio construction.
PAPER REPORTS · Sharpe 2.33, annualized return 95.9%, cumulative net 100.1%, max drawdown -18.3% — pure beta, GPT-4o mini, Student-t,… · Sharpe 2.44, annualized 100.0%, net 106.9%, max DD -18.3% — same configuration at 50 bps (2025)
This paper investigates whether textual tone derived from large language models (LLMs) can predict future stock returns. Using Korean news articles, we employ five LLMs to extract textual tones: BERT (KrFinBERT), DistilBERT, RoBERTa, ELECTRA, and Llama3.
PAPER REPORTS · BERT (KrFinBERT) equal-weighted quintile High-Low: 0.242% per day, t=3.772 (5.324% per month), May 2023-May 2024, gross… · BERT High-Low Fama-French 3-factor alpha 0.232% per day (t=3.582); 5-factor alpha 0.232% per day (t=3.566), same…
A new class of software systems is transforming investment analysis. Large language model agents assembled into collaborative team structures including analysts, researchers, and risk managers are increasingly deployed across financial markets.
PAPER REPORTS · Author's own prior human practice (not the AI framework): 127.17% return vs 50.67% benchmark within sixteen months,… · Ranked first among 276 comparable peer funds nationally through the 2020 market-stress period (peer average negative);…
We develop a certified, scalable approximation for high-dimensional Wasserstein distributionally robust portfolio optimization. For expected-utility maximization under order-one Wasserstein ambiguity, standard duality yields a semi-infinite convex program.
PAPER REPORTS · ε=10^-2 (best DRO policy), 476-asset monthly rebalanced, 2021-2025, no transaction costs: cumulative return 3.64,… · ε=10^-4, 2021-2025, no costs: CR 4.77, σ 0.45, SR 0.93, MDD 0.38, Calmar 1.12
OUR BACKTEST · Sharpe 0.63 · Return +106.7% · Max DD -45.0%
Current portfolio construction methods are either agnostic to the effects of idiosyncratic shocks (standard factor models) or to the latent data structure driving systematic returns (recent graph-based approaches).
PAPER REPORTS · Contagion Cut (proposed): CAGR 21.0%, Sharpe 1.07, Calmar 0.611, Jan 2019-Mar 2026, 0 bps transaction costs · Contagion Cut: Sharpe 1.04 at 10 bps, 1.01 at 20 bps, 0.929 at 50 bps (CAGR 20.5%, 19.9%, 18.3%), Jan 2019-Mar 2026
OUR BACKTEST · Sharpe 0.75 · Return +143.1% · Max DD -40.3%
Predicting financial asset returns remains one of the most difficult challenges in empirical finance, driven by the low signal-to-noise ratio and the semi-strong form of market efficiency.
PAPER REPORTS · Sector LSTM (main 3-layer, k=10, 1995-2024): mean daily long-short return 0.100% before costs, Newey-West t = 7.81,… · Sector LSTM appendix figures (after 2bp per half turn): mean daily return 0.053%, t-statistic 4.106, annualized return…
OUR BACKTEST · Sharpe -0.03 · Return -9.6% · Max DD -140.3%
Neural and numerical policy solvers can produce feasible controls even when the optimal rule and its binding constraints are unavailable.
The enormous growth in datasets, both in number and size, has prompted investors to adapt to new ways for assimilating information.
OUR BACKTEST · Sharpe 0.11 · Return +31.6% · Max DD -96.3%
The growth of decentralized finance (DeFi) and sustainability-linked investment markets has been rapid.
This paper studies the investment and insurance strategies of defined-contribution (DC) pension plans under the mean-variance framework. We consider a stochastic environment with time-varying interest rates, contributions, and mortality risk.
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…
Automated market makers (AMMs) are typically interpreted and evaluated as decentralized exchanges.
PAPER REPORTS · VBIAX, monthly TE, Jan 2, 2014 – Jun 30, 2026: G3M Pareto-dominates (higher CAGR and lower TE) for gamma in [2.73%,… · EQL NAV, economic mandate, Jun 19, 2018 – May 29, 2026: G3M dominates for gamma in [3.22%, 7.09%]
OUR BACKTEST · Sharpe 0.71 · Return +133.0% · Max DD -42.0%
We ask a representative sample to write prompts seeking spending and investing advice from LLMs, then simulate the lifetime effects of following the advice under realistic asset and labor market conditions.
This paper builds Path Portfolio Optimization: portfolio theory on a path-first framework in which the signature is the universal coordinate of the price path, and asks whether it survives estimation.
PAPER REPORTS · Cross-area lead-lag portfolio, sign-carrying excitation 1→2 with q=0.85: mean P&L +0.000189, s.e. · Cross-area, sign-carrying excitation 2→1 with q=0.85: mean P&L −0.000227, s.e. 0.000012, t=−18.92, annualized Sharpe…
OUR BACKTEST · Sharpe 0.45 · Return +21.4% · Max DD -19.5%
Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs).
PAPER REPORTS · FinSMART (static): cumulative return 264.9%, annualized return 91.5%, Sharpe 1.97, Sortino 2.40, Calmar 4.23, RankIC… · FinSMART (periodically retrained every 6 months): cumulative return 406.2%, annualized return 125.7%, Sharpe 2.41,…
OUR BACKTEST · Sharpe -0.46 · Return -48.2% · Max DD -75.2%
The theory of portfolios, and its allied notions and fundamental results concerning growth optimality, the numéraire property, and ``market viability'' -- which rules out the possibility of financing nontrivial future liability streams starting with…
OUR BACKTEST · Sharpe 0.51 · Return +48.5% · Max DD -23.8%