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 · 43 shown
Financial news can influence investment decisions long after publication. Timely news may reach an investor before prices fully adjust, whereas older news may already be reflected in prices.
PAPER REPORTS · LLM (GPT-4.1 mini) Market Data + Filtered News, 2023–2025 average of annual tests, net of 10 bp per dollar traded: -… · LLM Market Data + News + News Age, 2023–2025, net of 10 bp: 16.73% / 13.26% / 17.17% annualized at 0 / 1 / 4 week…
This study examines the relationship between the dispersion in analysts' target prices and stock returns for firms listed on the KOSPI and KOSDAQ markets.
PAPER REPORTS · High-minus-Low TP_DISP(T) tercile, next-month excess return: 0.595 (t=1.77). Quarterly formation, Jan 2006-Mar 2026.
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…
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%
Which inputs drive a language model's financial forecast? We construct synthetic earnings announcements that independently reverse 20-day momentum, non-informational commentary tone, and whether fixed actual EPS beats a manipulated consensus.
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.
This study aims to analyze the effect of media exposure on stock returns of clothing and luxury goods companies listed on the Indonesia Stock Exchange for the 2020–2024 period, and to examine the role of firm size as a moderating variable with profitability…
Abstract Time series forecasting models play a crucial role as decision support tools in various real-world domains.
PAPER REPORTS · Multi-stock swing trading (17 stocks, equal budget, 3-day confirmation), uptrend Jul 1–Dec 31 2021: average gain 7.64%… · Multi-stock swing trading, downtrend Jan 1–Sep 20 2022: average gain -0.34% across Max Shares 1/5/10 (-7.16%, 3.61%,…
Abstract This study examines the predictive relationship between sentiment extracted from Google News headlines and the log spreads of cointegrated or highly correlated stock pairs, aiming to enhance pairs trading profitability.
PAPER REPORTS · MA_V, Full-Predicted gn_a: return 301.52%, Sortino 1.029, Sharpe 0.693, Max DD 0.016, 181 trades (68-day test period,… · MA_V, Full-Predicted rand_a (random sentiment): return 314.36%, Sortino 0.446, Sharpe 0.686, Max DD 0.021, 325 trades…
We study whether model diversity survives selection into trading. In synthetic markets with a fixed mixture of three language-model families, news presentation changes their representation among submitted orders.
Machine-learning signals built from financial text treat what institutions say, and what the media repeat, as evidence about value. But whoever shapes a narrative may be trading against it.
PAPER REPORTS · Linear model, raw voices + theory features: out-of-sample cross-sectional IC 0.171±0.018 (simulated main market, 5… · Gradient boosting, raw voices + theory features: IC 0.118±0.019 (simulated main market, 5 seeds; costs not stated)
In this work, we study portfolio optimization under the stochastic discount factor (SDF) framework by learning market state representations that capture the underlying risk structures of financial data.
PAPER REPORTS · Sharpe ratio 1.062 (out-of-sample rolling 1-year test windows within June 2011–June 2020, 7-day rebalancing;… · Sortino ratio 1.352; Calmar ratio 0.924 (same setting; costs not stated)
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…
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.
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…
This study integrates the Black-Litterman (BL) model with deep learning techniques to construct a stock portfolio that incorporates investor sentiment.
PAPER REPORTS · BERT_BL, 2024-07-11 to 2024-12-31 (117 trading days), zero transaction costs: annualized return 106.58%, Sharpe 2.64,… · BERT_BL, same period, net of 0.03% commission + 0.1% sell stamp duty: annualized return 51.87%, Sharpe 1.55, Sortino…
In recent years, when traditional portfolio optimization (PO) models deal with highly volatile and irrational market environments, they often face problems of deviation in return prediction and lag in risk response.
PAPER REPORTS · Annualized return 0.196 (out-of-sample test set within 2018-2023, daily rebalancing, no trading costs) · Annualized volatility 0.187; Sharpe ratio 1.048 with risk-free rate assumed zero (no trading costs)
Abstract This paper examines the effect of US-China tensions on stock market volatility across developed and emerging economies using a newly developed US-China Tension (UCT) index.
This study constructs a climate risk attention indicator for Chinese funds by applying Word2Vec-based text analysis to annual fund reports.
PAPER REPORTS · Carhart four-factor alpha regression coefficients (2013-2023, annual fund-year panel, fund and year fixed effects):… · Authors' summary claim: "an annualized alpha of approximately 2.7% associated with acute risk focus" (conclusion…
Abstract Sentiment indicators are widely used in digital asset markets, but their economic meaning remains ambiguous.
PAPER REPORTS · Expanding-window out-of-sample R-squared vs historical-mean benchmark, 2018-2026 sample, no transaction costs… · Out-of-sample directional hit rate of the ridge sentiment model: 49.8% (1d), 51.5% (7d), 48.1% (30d), no cost…
Abstract This study examines the role of different social media sentiment dimensions in explaining stock market volatility in Pakistan.
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%
Costly LLM features matter only if calibration lets them affect the forecast. We document a failure of this link in a next-day risk study of two broad-market funds. Full-history scoring preceded the 2022 calibration.
PAPER REPORTS · Prespecified LLM importance feature: zero improvement, 95% interval [0,0], on all four endpoints (SPY/VIX binary and… · Signed LLM repair, SPY/VIX continuous variance: improvement -0.007452, 95% interval [-0.015888, -0.001066], Bonferroni…
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…
Human capital is a central organizational input, but standard financial data reveal little about firm-specific disruptions to workforce availability, cost, skills, and continuity.
OUR BACKTEST · Sharpe 0.56 · Return +50.1% · Max DD -40.8%
Two old market sayings hold that news is already priced in by the time it is published, and that the rumor is bought while the news is sold. Both place the price move associated with a piece of news before and at publication rather than after it.
PAPER REPORTS · Fade small-cap launch/partnership news (short after positive, buy after negative; enter close of day +5, exit close of… · Short any covered small cap (sentiment-ignoring benchmark, 260,472 events, same windows, 2023-2026): 15.9% annualized,…
OUR BACKTEST · Sharpe -0.23 · Return -18.9% · Max DD -51.9%
This study examines the dynamic effects of monetary policy changes on derivatives pricing behavior, emphasizing applications in financial risk management for industrial commodities.
Financial markets do not evolve uniformly through calendar time. Periods of intense information arrival accelerate market activity, while information-poor periods produce the familiar intraday lull in trading.
This study focuses on developing an AI-supported prototype for multiperspective interest rate forecasting that combines classical econometric models with modern artificial intel-ligence methods.
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…
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)
Foundation models for time series forecasting demonstrate impressive zero-shot generalization but often underperform on specialized domains such as high-frequency finance.
PAPER REPORTS · Mean per-day correlation 0.3730 (GatedLinear+RF, 40 test days per stock, Dec 2024-Jan 2026 sample, transaction costs… · Pooled correlation 0.5972 and cross-day correlation 0.5631 (GatedLinear+RF, same 40-day test window)
OUR BACKTEST · Sharpe -0.42 · Return -0.6% · Max DD -0.8%
This paper documents an applied natural-language-processing framework for measuring the tone of Brazilian Monetary Policy Committee (Copom) statements.
The impact of web datasets on market prices has suggested the development of new sources of information, such as social media and web portals, indicating the possibility of an emergent phenomenon.
PAPER REPORTS · Out-of-sample one-step-ahead return forecast Mean Error improved ~10% (-0.05112 to -0.04284) with the open-information… · Out-of-sample RMSE improved ~0.01% (1.94575 to 1.94543) and MAE ~0.1% (1.52465 to 1.52311); no trading strategy,…
Financial sentiment classifiers are commonly evaluated against human labels, but strong linguistic performance does not necessarily imply economically useful return predictability. This study separates these questions through two experiments.
PAPER REPORTS · One-day mean rank IC, 2019 Benzinga S&P 100 sample: FinBERT 0.0143 (largest), Financial-RoBERTa 0.0141, Naive Bayes… · FinBERT one-day long–short: gross total return 12.96%, annualized Sharpe 1.11, max drawdown -6.37% (2019, gross — no…
OUR BACKTEST · Sharpe -1.16 · Return -61.4% · Max DD -64.2%
Agentic AI is gaining acceptance in asset management, but governance has not kept pace: 88% of surveyed finance professionals report no operational governance framework for agentic AI despite universal awareness of its deployment, and only 24 of 75 large U.S.
OUR BACKTEST · Sharpe -1.47 · Return -45.1% · Max DD -49.7%
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%
Powered by advances in LLMs and autonomous agents, deep research has become one of the most widely adopted agentic products. However, most deep research systems write general-purpose reports, which are inadequate for financial deep research.
PAPER REPORTS · FinanceHarness (Qwen3.6-27B backbone): overall rubric score 32.4%, pre-cutoff 45.7%, post-cutoff 11.8%, bootstrap SE… · FinanceHarness + GRPO (RFT): overall 32.8%, pre-cutoff 46.2%, post-cutoff 12.1% (same backbone and benchmark)