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 · 104 shown
In January 2021, retail investors coordinating on Reddit’s r/WallStreetBets forced large price increases in heavily shorted stocks such as GameStop and AMC, imposing substantial losses on several hedge funds.
PAPER REPORTS · Reddit-favoured (top-5 WSB mentions) cohort: mean three-month holding-period return 13.00%, median 20.00%, win rate… · Institutionally accumulated (top-5 net 13F increase) cohort: mean three-month holding-period return 6.78%, median…
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.
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%
Weak-form efficiency implies symmetry restrictions on the Markov chain governing movement across the cryptocurrency capitalisation distribution.
PAPER REPORTS · Equal-weighted bottom-decile, survivors only, market-adjusted, gross: 27.49%/wk (t=3.69) frontier 2013:04-2017:11;… · Bottom decile with delisting at -100%, gross, market-adjusted: 20.41%/wk (t=2.84) frontier; 6.91%/wk (t=8.06)…
Bitcoin's extreme price volatility poses significant challenges for investors and traders in making informed investment decisions. Conventional technical analysis approaches are often subjective and unable to capture complex market dynamics accurately.
PAPER REPORTS · Trailing-stop strategy, threshold 0.37, test period 1 Jan 2025-1 Mar 2026: net profit +8,491 USD on 10,000 USD (total… · No-trailing-stop strategy, threshold 0.35, same test period: net profit +8,978 USD (total return +89.78%), win rate…
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%
Backtests of intraday strategies can drift away from what the live system actually does; in our case, the drift never showed in summary statistics.
Corporate financial disclosures often unfold over time, with announcements, prepared remarks, and discussion spanning multiple high-frequency return intervals.
Stock price forecasting is considered difficult due to the volatility of financial markets. A reliable stock price forecasting model is critical for making sound investing decisions while managing financial risks and portfolios.
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%
We examine climate variables in a pipeline for dynamic Value-at-Risk (VaR) of the SRI-KEHATI ESG index and the IHSG (Jakarta Composite Index).
PAPER REPORTS · Main QRNN backtest, test N=300 (Nov 2023-Dec 2025), VaR violation rates and Kupiec p: SRI-KEHATI tau=0.01 0.33% (1… · Main QRNN DM statistics on pinball loss (positive = QRNN better), all p<0.001. Vs HS-VaR: SRK 4.31 / 6.43 / 7.23 and…
How can we identify stable patterns to accurately predict stock price movements amidst severe market noise? Predicting stock price movements is a crucial problem in financial data mining, and has attracted significant attention from researchers and financial…
PAPER REPORTS · COVID-19 crisis (test 2020-01 to 2020-03), threshold long-short, 6 bps cost on sells, zero risk-free rate: S&P500 ASR… · AI rally (test 2024-01 to 2024-06), same protocol: S&P500 ASR 2.842, RMDD 6.8%; CSI300 (long-only) ASR 1.653, RMDD…
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%,…
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%
As option markets grow and AI advances, agentic systems for option trading are gaining increasing attention.
PAPER REPORTS · Total return 18.32% over 2025-03-03 to 2025-08-29 (six-month out-of-sample), 10 underlyings; fills at bid-ask midpoint… · Annualized Sharpe ratio 1.60 (same period and costs)
Reality may establish that an outcome occurred without identifying which evolving procedure produced it or why. This distinction matters in production ML systems whose code, configuration, and artifacts change while external feedback accumulates.
PAPER REPORTS · Full year 2025-10-01 to 2026-09-30 (241 daily obs), net of recorded actual settlement costs: total return +11.1391% vs… · Full year multi-factor (liquidity/momentum/volatility proxies) annualized intercept 13.52%, HAC(3) p=0.226
Shallow models are special cases of deep models, and deep models theoretically have the potential to outperform the shallow ones. However, the existing empirical asset pricing literature provides strong benchmarks for shallow models.
PAPER REPORTS · Deep ResNet+ (depths 11-20, spec-averaged): annualized VW decile long-short SR 2.07, monthly mean 3.48%, SD 5.82%, Feb… · Deep ResNet+ FF5 alpha 3.35% per month (significant at 1%, Newey-West 12 lags), Feb 1987-Dec 2023, gross of costs
Formulaic alpha discovery seeks symbolic expressions that predict cross-sectional asset returns.
PAPER REPORTS · CSI300, test 2023-2025, long-only top-20% daily cohorts held 20 days, net of 5bp buy / 15bp sell, mean of 5 runs: IC… · CSI500, test 2023-2025, same protocol and costs: IC 0.0444, ICIR 0.2855, RankIC 0.0763, RankICIR 0.4578, annualized…
Factor mining seeks to discover signals from financial data that predict future asset returns and guide portfolio construction. Automated factor mining now spans genetic programming, reinforcement learning, generative models, and large language model agents.
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%
Financial LLM agents are often evaluated by comparing their end-to-end returns with those of a baseline and testing the paired difference against zero.
PAPER REPORTS · Full agent (A0) vs frozen-rule baseline, 723 earnings events, 2020-2024: gross deployment value +15.2 bps/event… · Same comparison net of a pre-stated 40 bps cost per nonzero event position: -1.2 bps/event (95% CI [-59.8, +54.3]),…
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%
The complex and nonlinear behaviors of financial markets make it exceedingly challenging for average investors to evaluate the performance of a specific asset or make meaningful predictions about its future behavior.
PAPER REPORTS · Directional hit rate (MDA) of the LSTM forecaster on a random 80/20 holdout validation set (27,433 samples, 159 stocks,… · Diebold–Mariano statistics vs baselines on the same validation set: 3.42 (p = 0.001) vs persistence and 2.15 (p =…
Deep reinforcement-learning policies for order execution can vary substantially across training seeds, so apparent architectural gains may reflect favourable training realisations rather than reproducible properties of the architecture.
PAPER REPORTS · Mean implementation shortfall, out-of-sample Jan 16–20 2023, frictionless replay with no commissions or market impact,… · Per-run CVaR-95 under the same setup: DDQL 110.8, MoE K=8 122.2, DDQL-cap8 104.1 bps.
Large language models are moving from producing trading signals to writing the code that executes them.
Forecasting stock index prices remains a challenging task because financial markets are volatile, non-stationary, and influenced by several economic and behavioral factors.This study evaluates 12 supervised machine learning models against a Naïve Persistence…
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).
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%
In this paper we study whether a trading signal can accumulate enough statistical evidence for reliable deployment before its economic value decays.
PAPER REPORTS · Funding-rate lifetime-score signal, top quintile (109 events): 100/109 events with positive next-seven-day…
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%
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%
News may reveal systematic risk, but whether its context enhances the construction of systematic risk factors is still unclear.
PAPER REPORTS · Spherical ST + sparse-soft c-TF-IDF + multi-horizon IPCA, L=60: annualized OOS excess-return Sharpe 1.0334, Sep 2020 to… · Same raw spherical portfolio: annualized volatility 81.68%, maximum drawdown 62.23%; not risk-managed, not…
Self-evolving agents aim to turn research feedback into reusable skills, tools, and research rules. Whether these accumulated capabilities continue to improve later research requires controlled evaluation.
PAPER REPORTS · Mean final portfolio IC, development 2025-01-01 to 2025-12-31 (in-sample, per-bar cross-sectional Pearson vs next… · Mean final portfolio IC, OOS 2026-01-01 to 2026-06-30, with development-period weights frozen (predictive IC only; no…
Monetary-policy announcements and central-bank communications play a central role in foreign exchange markets, yet their qualitative, unstructured form makes their forecasting value difficult to quantify.
PAPER REPORTS · Directional hit rate, out-of-sample rolling windows, 2024-06-13 to 2026-06-12, no transaction costs considered: 66.08%… · Directional AUC 0.744 at 1h, 0.729 at 3h, 0.707 at 8h, 0.634 at 24h. Balanced accuracy 0.721, 0.698, 0.705, 0.626.
Abstract Binary direction classifiers can appear stable on one chronological split while offering little predictive or economic value out of sample.
PAPER REPORTS · Full AND-gate (LightGBM + trend + Hurst), 3 expanding walk-forward folds, Mar-May 2025, 9 configurations: precision… · Full AND-gate unlevered next-bar accepted-signal mean return: -0.16 bps at 0 cost, -10.16 bps at 10 bp round-trip cost,…
Financial time series evolve across multiple temporal resolutions, challenging forecasting systems to incorporate newly available information without repeatedly recomputing unchanged representations.
PAPER REPORTS · HARN test-split forecast error (not a trading strategy; no costs or P&L), AAPL M15, 2025-03-06 to 2026-08-10: MAE… · HARN test-split forecast error, EURUSD M15, 2025-12-19 to 2026-07-29: MAE 0.000306±0.000002, RMSE 0.000467, sMAPE…
Volatility forecasts play a central role in financial risk management because their overall level and day-to-day movements affect downstream decisions. Most studies compare forecasting models while keeping the training loss fixed.
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%
Financial language models can transform unstructured firm-specific news into structured decision signals, but financial AI research lacks an integrated deployment framework for evaluating whether those signals remain useful in financial decision systems.
PAPER REPORTS · Long-short net Sharpe 2.85 (LLaMA-3, value-weighted quintile, net of 5bp one-way costs, 10% ADV cap, Jun 2024-Jan 2026) · Mean daily net return 0.34%, daily vol 1.89%, max drawdown -12.3%, cumulative long-short return 180% (LLaMA-3, same…
Day-ahead electricity price forecasts support trading and storage decisions, but for battery arbitrage predicting intraday price spreads is more relevant than predicting individual hourly prices.
PAPER REPORTS · Germany, 01.01.2021-31.12.2025, 1 MWh BESS, eta=1, 25 EUR round-trip cost: best Spread THieF total profit 154,265 EUR… · Spain, same period and setup, eta=1: best Spread THieF total profit 81,859 EUR (NARX) vs Oracle 95,336 EUR; relative…
Abstract This research utilizes explainable artificial intelligence (XAI) techniques to explore the relationship between size-sorted environmental, social, and governance (ESG) portfolio indices (large-cap, mid-cap, and small-cap ESG portfolios) and their…
This paper proposes discrete-time approximations to rough continuous-time models of realized variance (RV). The leading rough models can be viewed as autoregressive processes driven by fractional Gaussian noise.
PAPER REPORTS · 1-day-ahead MSE relative to HAR benchmark, average over 10 ETFs, 2010-01-04 to 2024-12-31 rolling 500-day window, no… · 1-day-ahead QLIKE relative to HAR, same sample: log-RAR 0.7942, log-RHAR 0.7925
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.
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%
Public evidence can become sufficient to settle a prediction-market contract before the venue records its first determination, but the relevant boundary depends on the applicable rule version, exact release object, source hierarchy, correction history, and…
This paper proposes an investment strategy through resource allocation into Kalshi Crypto Event Contracts in order to effectively hedge exposure to spot asset volatility.
PAPER REPORTS · Bullish 2024 (Mar-Dec 2024 contract), Strategy C with Kalshi fees: +38.69% return vs BTC +36.23%; Sharpe 1.27 (1.30… · Flat Dec 2025 (Nov-Dec 2025 contract), Strategy C: -3.04% (Table 1) vs BTC -3.58%; Sharpe -0.10 with fees (+0.05…
Price-level limit order book (L2) data reveal aggregate liquidity but not the ordered queue required by price--time priority.
PAPER REPORTS · Passive-at-touch implementation shortfall, 1301.T, 18 held-out July 2025 dates, 1,080 episodes, zero… · Passive-at-touch implementation shortfall, 7911.T, same 18 held-out dates: 0.765 bps [0.547, 0.974] front, 0.926…
Stock price prediction remains challenging due to the non-stationary and noisy nature of financial time series.
PAPER REPORTS · S&P 500 2024 out-of-sample (365 days): total return 29.41%, Sharpe 1.43, Sortino 1.85, max drawdown -9.75%, win rate… · DJIA 2024: total return 21.44%, Sharpe 1.15, Sortino 1.75, max DD -8.94%, win rate 57.7%, 26 trades — vs buy-and-hold…
Forecasting financial markets remains challenging due to nonlinear dynamics, structural changes, volatility clustering, and uncertainty transmission across asset classes.
PAPER REPORTS · No trading strategy, Sharpe ratio, return, alpha or drawdown is reported anywhere in the paper; performance is reported… · Best-model out-of-sample R2 by target (one-step-ahead monthly, price levels, final 20% of sample): VIX 0.999982…