QuantSeeker

Weekly Research Recap

QuantSeeker — Tue, 28 Apr 2026 21:26:05 GMT

This week’s Tuesday Roundup features the most valuable investing insights I came across over the last seven days, spanning new academic research, market analysis, high-quality blog posts, and thoughtful online discussions. Links are included throughout for further reading.

Commodities

Call the Zookeeper: A Unified Framework for Commodity Risk Premiums (Fan, Li, Qiao, and Zhang)

Past research has documented many types of risk premiums in commodity markets. This paper tests 81 predictors spanning commodity, macro, and sentiment data, then builds a dynamic long-short factor using 37 commodity-specific characteristics in a forecast model to predict next-month returns and rank commodities. The best-performing long/short portfolio delivered an annualized return of 5.8% with a Sharpe ratio of 0.64. Key takeaway: Combining signals tends to outperform single-factor commodity investing.

Crypto

Cross-Sectional Dispersion and the State Dependence of Cryptocurrency Momentum (Makgolo and Zhang)

Many investors believe that momentum crashes when volatility spikes, but in crypto, the better warning sign may be return dispersion. This paper finds that the dispersion of returns across coins predicts momentum breakdowns better than Bitcoin volatility. In extreme dispersion regimes, momentum weakened sharply. A dispersion-scaled strategy increased Sharpe from 0.63 to 0.80 and cut max drawdown from -42.5% to -17.1%. Key takeaway: For crypto momentum, watch coin dispersion, not just Bitcoin volatility.

Size-Momentum Puzzle in Cryptocurrencies (Xu and Wu)

Momentum in crypto markets depends on size. The authors show that small coins mean-revert sharply, with recent losers rebounding strongly, while large coins display momentum. Using 2018 to 2025 data, the smallest crypto quintile showed a 4.9% weekly reversal spread, while the largest delivered +1.0% weekly momentum, rising further over longer holding periods. Key takeaway: One momentum model does not fit all of crypto; small coins tend to reverse, while large coins trend.

Equities

Short-Term Reversal Persists Globally-If Properly Measured (Stosik and Zaremba)

Standard short-term reversals have weakened considerably over the years. This paper argues that the bigger issue is measurement. Recent losers often rebound once returns are judged relative to industry peers rather than raw price moves. Across 64 countries, the standard reversal signal earned just +0.05% per month, while the industry-adjusted version returned +0.53% monthly with a Sharpe of 0.74. Key takeaway: Short-term mean reversion still exists, but within industries.

Integrating Geopolitical Risk Into Low Volatility Factor Construction (Kallali, Binh, Roseneberg, Tilly, and Sekine)

Low-volatility strategies typically rely only on price data, but this paper finds geopolitical signals can improve them using firm-level news sentiment and regime indicators. The baseline model returned 10.4% annually with a Sharpe of 0.70, while the best geopolitical version delivered 12.8% with similar volatility and a 0.84 Sharpe; a more aggressive version reached 14.5% with a 0.91 Sharpe. Key takeaway: Defensive investing may improve when conditioned on geopolitical regimes.

A Comparative Analysis of Overnight vs. Daytime Static and Momentum Strategies Across Sector ETFs (Salotra, Katikireddy, Anumolu, and Pinsky)

Overnight vs. intraday returns for sector ETFs differ substantially. Across 10 U.S. sector ETFs (1999–2025), overnight returns were stronger than daytime returns, while long overnight plus intraday reversal often performed best. The top strategy turned $100 into over $31,000 in XLK before costs, though much of the edge faded after transaction costs. Key takeaway: Sector ETFs show distinct overnight and intraday return patterns, but monetizing them in practice is challenging.

Save The Date: Analyst/Investor Days as a Trading Signal (Cabrera, Kolokolova, and Zhang)

Analyst/Investor Days may be a stronger signal than most investors realize. Across 1,000+ U.S. firms, announcing an Investor Day led to abnormal returns and a run-up into the event. A long-short strategy buying announcing firms and shorting matched peers earned a Sharpe ratio of 1.06. A machine-learning timing version improved this to a Sharpe of 1.22. Key takeaway: Corporate communication events offer scalable event-driven alpha.

FX

Cross-sectional currency momentum and order flow (Sakemoto and Suda)

Cross-sectional FX momentum has struggled recently, but this paper shows that combining momentum with short-term FX swap order flows can restore returns. Currencies with strong recent performance and positive swap buying pressure earned 3.8% annually (Sharpe 0.57) versus -1.0% for standard momentum. In high-volatility periods, returns rose to 8–10%. Key takeaway: FX trends are strongest when confirmed by hedging flows.

Machine Learning and Large Language Models

ChatGPT as a Time Capsule: The Limits of Price Discovery (Lehner and Lopez-Lira)

Markets may be slower to price public narratives than many assume. This study uses older ChatGPT model snapshots to rank 7,000 U.S. stocks based only on what was knowable at the time. Firms with stronger outlooks earned 1.22% higher next-month returns per standard deviation, while a top-minus-bottom spread reached 2.55% monthly in-sample. The scores also predicted stronger sales growth and analyst upgrades. Key takeaway: LLMs may help process public information that investors aggregate only gradually.

Cross-Stock Predictability via LLM-Augmented Semantic Networks (Huang, Fan, Hu, and Ye)

This paper uncovers alpha from convergence trades between economically linked firms. Using 10-K filings, the authors build stock networks, then use an LLM to classify real firm relationships and remove noisy links. When linked stocks diverge, the strategy bets on mean reversion. A long-short S&P 500 strategy improved Sharpe from 0.74 to 0.82 and cut max drawdown from -10.5% to -7.9%. Key takeaway: LLMs can be used to identify relative-value opportunities across connected firms.

Prediction Markets

Beating the Earnings Game: Why Do Prediction Markets Outperform Professional Analysts? (Rabetti, Shao, and Zhang)

Prediction markets may be coming for Wall Street analysts. Studying 469 Polymarket contracts, this paper finds crowd-implied probabilities beat analyst consensus in forecasting earnings surprises: 78.5% vs 43.7% hit rate. The edge appears linked to continuous updating, diverse participants, and fewer institutional forecast biases. Key takeaway: Decentralized forecasts may become a serious new information signal for investors.

Prediction Market Accuracy: Crowd Wisdom or Informed Minority? (Gomez Cram, Guo, Jensen, and Kung)

Prediction markets are usually sold as collective intelligence in action. This paper suggests the opposite: Prices are made accurate by a small group of informed traders. Using complete Polymarket trading records, the authors estimate that roughly 3% of accounts account for most price discovery, react fastest to news, and earn persistent profits. The crowd supplies liquidity and losses. Key takeaway: What looks like crowd wisdom in prediction markets may simply reflect the decisions of a small informed minority.

Quant Finance

A Comprehensive Review of Statistical Methods in Quantitative Finance: From Classical Inference to Machine Learning Frontiers (Rahaman)

This is a very comprehensive review of quant finance topics, spanning topics such as probability theory, econometrics, time-series modeling, portfolio construction, derivatives pricing, tail-risk methods, Monte Carlo simulation, and machine learning.

Blogs

When Big Gets Small: Trading the Lower Tier of Large Caps and Upper Mid Caps (Quantpedia)

Backtests Lie: Building a Stress-Test Framework for Trading Signals (Vertox)

Podcasts

Sam Hartzmark: The Dividend Mistake Most Investors Still Make (Meb Faber)

How to Trade Futures | Rob Carver (Capital Horizons)

“Concentrated Strategies Will Do Extremely Well” - Sean Emory on Outperforming the Index (Odds on Open)

The $17M+ Prop Firm Roundtable - How The World’s BEST Traders Really Win (Words of Rizdom)

Social Media & Industry Research

The Skip-Month Mystery: What Last Month’s Returns Are Really Telling You (Alpha Architect)

The Inflation Diversification Problem (Man Group)

The MOST Important Thing: Unlocking Investment Wisdom with Antti Ilmanen (AQR)

Last Week’s Most Popular Links

Five-Factor Market-Neutral Strategy Across Korean and U.S. Equity Markets: Structural Alpha Without Regime Filters (Quinn)

Spot-Based Basis and Basis Momentum in Commodity Futures Markets (Luo and Xue)

Survival of the Fittest: A Three-Factor Model in the Currency Market (Liu, Wang, and Zhao)

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Weekly Research Recap

QuantSeeker — Tue, 21 Apr 2026 16:26:27 GMT

This week’s Tuesday Roundup brings together the most useful investing ideas I found recently, from fresh academic studies and market research to thoughtful blog posts and smart discussions online. Links are included throughout so you can explore further.

Commodities

Spot-Based Basis and Basis Momentum in Commodity Futures Markets (Luo and Xue)

A smarter way to trade commodity futures may be to look beyond the futures curve itself. Using 41 Chinese commodities, basis and basis-momentum signals built from actual spot prices and futures prices beat traditional basis/carry measures. The strongest spread implied a return of +1.73% per month with a Sharpe of roughly 1.40. Key takeaway: Spot-market information may contain carry signals that are missed by futures-only models.

Equities

Five-Factor Market-Neutral Strategy Across Korean and U.S. Equity Markets: Structural Alpha Without Regime Filters (Quinn)

A market-neutral strategy spanning Korean and U.S. equities reports unusually strong performance: A Sharpe ratio of 3.64, a maximum drawdown of just -0.3%, and an 12.8% annual alpha after controlling for six standard factors. It combines five low-correlation signals from distinct return sources. Key takeaway: Diversifying across multiple niche inefficiencies usually matters more than chasing a few crowded factors.

Commodity Price Risk and the Cross-Section of Equity Returns (Dong and Palazzi)

U.S. stocks whose returns deviate from levels implied by market + commodity risk exposures tend to reverse next month. Underpriced minus overpriced stocks earned 1.86% monthly, equal-weighted, with a Sharpe of 1.31, the strongest in smaller, less-followed names. Key takeaway: Commodity-linked mispricing may be a niche source of alpha.

Momentum Returns and the Role of Liquidity Improvements (Bro)

Momentum may not be a standalone anomaly at all. This paper argues that much of classic stock momentum reflects improving liquidity: Past winners become easier to trade, while losers become harder. A long-short liquidity-improvement factor returned 0.65% per month (roughly 0.69 annualized Sharpe) and rendered momentum alpha insignificant in factor tests. Key takeaway: Some momentum profits may stem from gradual repricing to changing liquidity conditions.

Factor Investing

Factor Investing in Emerging Markets: From Multi-Factor Industry Portfolios to a Market-Neutral Alpha Strategy (Zhang, Dong, He, and Zheng)

This paper tests factor investing in emerging markets using strict out-of-sample splits. A beta-hedged EM strategy built from six classic signals (size, value, quality, momentum, low vol, yield) delivered a gross Sharpe of 0.83, 0.67 net after costs, 4.16% annual alpha, and just -5.2% max drawdown (2019 to 2024). Key takeaway: In emerging markets, diversified simple factors can still deliver strong risk-adjusted returns.

FX

Survival of the Fittest: A Three-Factor Model in the Currency Market (Liu, Wang, and Zhao)

The crowded “currency factor zoo” may boil down to three core drivers: Dollar (DOL), Carry (CAR), and Output Gap (GAP). Using Bayesian model selection on 1991 to 2024 FX data, this trio ranked highest versus popular alternatives and best explained rival factors. In the authors’ recursive portfolio test, it posted a 1-year out-of-sample annualized Sharpe of 2.99. Key takeaway: In FX, parsimonious macro factor models may beat complexity.

Machine Learning and Large Language Models

Machine Learning and Technical Analysis in International Market (Chin and Lin)

Across 25 equity markets, machine learning outperformed OLS when using 107 technical price/volume signals. Global long-short monthly returns reached 1.15% vs 0.58%, with ML also adding value among large firms and in down markets. Key takeaway: Technical indicators may still contain alpha, and flexible models seem better at extracting it.

Option Return Predictability via Large Language Models (Liu, Zhou, Cheng, Zou, and Wang)

GPT-5 is tested as an autonomous generator of option-return factors in U.S. and Chinese markets using only option characteristics. In a post-Sept 2024 out-of-sample test, many generated factors beat benchmarks and delivered significant positive alpha. Many reported Sharpe ratios were strong, based on backtests of delta-hedged portfolios. Key takeaway: LLMs may become valuable tools for idea generation in niche, high-dimensional markets.

Timing Currency Factors with Machine Learning (Torok)

Machine learning may help time FX factors, but gains are concentrated in specific cases. This paper tests 264 signals across currencies, options, equities, and macro data to time carry and USD factors. Most individual predictors added little, but Developed Market USD timing improved Sharpe by 0.4 to 0.5 in 2012–2024 out-of-sample tests. Key takeaway: In FX, ML may be most useful for selective risk timing rather than broad forecasting.

Latency Alpha: Real-Time LLM-Based Semantic Extraction of EIA Petroleum Data for Forecasting and Trading Crude Oil Futures (Pape)

This paper tests an LLM system that reads the weekly EIA petroleum report in real time, converts report surprises into trading signals, and trades WTI futures. In a 2022 to 2023 backtest (88 releases), it reports a Sharpe of 1.82, a Sortino ratio of 2.89, 31.2% cumulative return, and a 68.5% hit rate. Key takeaway: In event-driven markets, faster semantic interpretation of public data may create temporary alpha opportunities.

Interpretable Systematic Risk around the Clock (He)

The author uses U.S. equity market data plus an open-source LLM to identify what drives major market jumps. Macroeconomic jump risk earned the strongest premium: 3.65% annually with a Sharpe of 0.78 (vs market 0.53). A real-time strategy rotating into the best-priced jump-risk theme each year reached a Sharpe of 0.95 out of sample. Key takeaway: Not all volatility is equal; macro shocks appear to earn the richest compensation.

Portfolio Construction

Measuring Strategy-Decay Risk: Minimum Regime Performance and the Durability of Systematic Investing (Alexander and Fabozzi)

Many quant strategies look strong in backtests but weaken across regimes. A new paper proposes Minimum Regime Performance (MRP): The worst Sharpe observed across historical environments. Among the tested equity factors, only Quality kept a positive MRP, while Size and Investment looked fragile. Key takeaway: Judge the durability of strategies across regimes, not just average Sharpe.

Dynamic Allocation with Macro Factor-Mimicking Portfolios (Molinaro and Chaudron)

Inflation may be more tradable than investors think. This paper ranks stocks by inflation beta, builds high- and low-inflation-beta portfolios, then uses macro forecasts to rotate between them. The best strategy delivered 21.4% annual return with a Sharpe of 1.09. Key takeaway: Forecasting macro regimes may add value when expressed through targeted inflation-sensitive equity exposures.

Blogs

Inflation as a trading signal (Macrosynergy)

Pairs Trading in the Metals Complex: A Reality Check (Quantseeker)

From Risk Premia to Constraints: How Markets Really Clear (CFA Institute)

Annual performance update- year 12 (Rob Carver)

Exploiting Mean-Reversion in Decentralized Prediction Markets: Evidence from Polymarket Binary Contracts (Quantpedia)

Podcasts

“It’s the Dumbest Market in the World” - Quant Trader Scott Phillips on Edge in Crypto (Odds on Open)

Markets Look Calm… But Are They? ft. Rob Carver (Top Traders Unplugged)

$1,900 Small Cap Trader Hit $2.9 Mil in Profits · Eduardo Briceño (Chat with Traders)

Social Media & Industry Research

Quantitative Trading Is Going To Eat All Markets (SystematicLongShort)

How should you size your trend trades? (Concretum Research)

Mastering Claude Code in 30 Minutes (via Movez)

Can you trust your intraday database? (Concretum Research)

Last Week’s Most Popular Links

Optimal Buy-and-Hold Asset Allocation: A Multi-Horizon Drawdown-Constrained Approach (Wang and Wang)

Can AI Do Financial Research? LLM-Guided Hypothesis Discovery in Asset Pricing (Liu, Liu, Liu, and Mei)

Don’t Mix What Should Be Separated: Why Combining Value and Momentum Signals Destroys Alpha (Morales)

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Pairs Trading in the Metals Complex: A Reality Check

QuantSeeker — Mon, 20 Apr 2026 18:18:05 GMT

A recent paper reports attractive Sharpe ratios from a simple mean-reversion strategy in precious metals ETF spreads. But do those results survive real-world parameter uncertainty? I replicate the study, test it under walk-forward parameter selection, and show why a simple ensemble approach may be more robust.

Weekly Research Recap

QuantSeeker — Tue, 14 Apr 2026 19:34:47 GMT

Welcome to this week’s Tuesday roundup, a collection of practical investing insights drawn from new academic papers, market research, blogs, and sharp discussions across the web, with links provided throughout.

Asset Allocation

Optimal Buy-and-Hold Asset Allocation: A Multi-Horizon Drawdown-Constrained Approach (Wang and Wang)

Using ETF data from 1996 to 2026, the paper finds that drawdown-constrained optimization often favors unconventional portfolios: About 40% growth equities, 40% gold, and 20% Bitcoin in its 5-, 10-, and 20-year tests. Reported Sharpe ratios range from 0.75 to 1.20, well above benchmark portfolios. Key takeaway: Focusing on drawdowns can lead to very different portfolios than traditional allocation rules.

Bond Hedge Effectiveness (Lachana)

Using data from 2007 to 2023 in the U.S., the study shows that Treasuries are not a constant equity hedge. Protection improves when investor mood turns fearful, as a pessimistic media tone is linked to more negative stock–bond comovement. A signal-driven strategy modestly beats a static 50/50 stock-bond mix on Sharpe ratio, though performance depends on the inflation backdrop. Key takeaway: Stock–bond hedging varies with sentiment and macro conditions.

Equities

Formal Equity Valuation: Overview and Limits (Ohlson and Rueangsuwan)

The authors argue that valuation formulas have real-world limits, especially sensitivity to growth and discount-rate assumptions, but still add value. DCF, dividend, and residual-income models can mislead when used mechanically, yet remain useful frameworks for thinking about earnings, growth, risk, and payout policy. Best practice is to pair them with forward EPS and realistic multiples. Key takeaway: Use formal valuation models as guides, while recognizing their limitations.

Is Return seasonality Due to Risk or Mispricing? Evidence from Anomaly seasonality (Wang)

Using 125 anomaly portfolios, the paper argues that seasonal return patterns in anomaly portfolios largely originate from stock-level seasonality rather than genuine seasonal factor premia. A stock-based seasonality factor cuts alpha on seasonality strategies from 1.45% to 0.27% per month, while anomaly-based seasonality factors only reduce it to 0.86–1.23%. Key takeaway: Seasonal alpha is strongest at the individual-stock level, not in factor portfolios.

Don’t Mix What Should Be Separated: Why Combining Value and Momentum Signals Destroys Alpha (Morales)

Using a 2000 to 2026 backtest of the top 1,000 U.S. stocks, the paper finds that merging value and momentum into one composite rank dilutes the diversification benefit of their negative correlation. Running them as separate strategies produced a higher Sharpe, lower volatility, and a smaller max drawdown. At matched risk, the separate-strategy approach outperformed by 52 bps annually. Key takeaway: Combine factors at the portfolio level rather than merging signals into one rank.

Machine Learning and Large Language Models

Can AI Do Financial Research? LLM-Guided Hypothesis Discovery in Asset Pricing (Liu, Liu, Liu, and Mei)

Using U.S. equity data from 1963 to 2024, the study finds that an LLM can generate understandable return signals rather than only summarize prior work. It produced 280 accounting-based ideas; 159 passed an initial predictive screen, 38 remained after redundancy checks, and roughly 6 to 9 survived the toughest validation hurdles. Key takeaway: AI can expand idea generation, but rigorous testing determines what truly adds value.

Large Language Models and Stock Investing: Is the Human Factor Required? (Crisostomo and Mykhalyuk)

Using a 10-month live IBEX-35 test, the paper finds that LLM-generated stock rankings can outperform the benchmark, especially when prompts are structured and outputs are reviewed by humans. Naive prompts delivered just 0.35% monthly excess return (insignificant), structured prompts 2.24%, and supervised step-by-step workflows 3.04% with IR 0.68. Reasoning and data errors remained frequent. Key takeaway: LLMs can add value, but perform best under human supervision.

Options

Call option pressure and option return predictability: A U-shaped nonlinearity (Cai)

Using China’s SSE 50 ETF options (2019–2026), the paper finds that a net call-positioning measure predicts next-day option returns in a U-shape: Very low readings align with reversals, very high readings with continuation, while moderate readings add little signal. An ETF timing strategy reports a Sharpe of 0.97, rising to 2.43 with additional filtering. Key takeaway: Extreme call-positioning signals contain more information than normal flow.

Options Volume as Noise: Evidence from Three Decades of Earnings Announcements (Taheri Hosseinkhani)

Studying U.S. earnings events, the paper finds that the option-to-stock volume ratio predicts earnings-window returns, but the relationship changes over time. High O/S stocks underperformed by 39 bps around earnings and 118 bps over the next 60 days in full-sample portfolio sorts, while the O/S coefficient flipped sign in 2020–2024 regressions. The put/call ratio also predicted earnings surprises. Key takeaway: The predictive power of options volume depends on the market regime.

Prediction Markets

Trend Quality and Predictability in Prediction Markets: Evidence from Minute-Level Kalshi Data (Greene)

Using 4.9 million minute-level Kalshi observations across 29,590 contracts (Jan–Mar 2026), the paper shows that a simple linear trend-regression signal, trend slope divided by residual noise over the final 30-to-12 minutes before expiry, contains information. Highest-quality trends continued 70.8% vs. 51.2% for the weakest group. Key takeaway: Smooth late price trends often signal short-term continuation.

Volatility

The Role of Price-Volatility Cojumps in Volatility Forecasting (Liao)

Using 5-minute S&P 500 and VIX data, the paper finds that simultaneous price-volatility jumps contain information missed by standard jump measures. Downside cojumps predict higher future volatility, while upside cojumps signal calmer markets. Adding these variables to HAR models significantly improves out-of-sample volatility and VaR forecasts. Key takeaway: Joint price-volatility shocks matter more than price jumps alone.

Blogs

Does “Optimal” Portfolio Construction Actually Pay Off? (Quantseeker)

To Trend or Not To Trend? (Wrong question) (Robot Wealth)

Systematic Tactical Allocation in Emerging Markets vs. U.S.: A Momentum-Based Approach (Quantpedia)

Top 10 Most Read Q1 Enterprising Investor Blogs (CFA Institute)

Book Review: Financial Data Science (CFA Institute)

Podcasts

Now Is the Best Time to Become a Junior Analyst - Ex-Citadel and D. E. Shaw PM Brett Caughran (Odds on Open)

The ICT Trader Who Made $936K In 25 Days - Dhesi (Words of Rizdom)

Samir Varma - Classify Risk Don’t Chase Alpha! (The Algorithmic Advantage)

The Hedge Fund that Polls Regular People Where Markets Are Going? (RCM Alternatives)

Social Media & Industry Research

A Positive Stock-Bond Correlation Is a Terrible Reason to Add More Equity Risk to Your Portfolio (AQR)

Factor MAX: A New Signal for Predicting Factor Returns (Alpha Architect)

The Many Facets of Stock Momentum: Distinguishing Factor and Stock Components (Alpha Architect)

Being Front-Run On DEXes (SystematicLongShort)

Last Week’s Most Popular Links

Intraday Stylized Facts and the Shape of Volatility Build-Up in ICE Brent Crude Oil Futures (Haugom, Ewald, Chen, and Smith-Meyer)

The 52-Week High Momentum Strategy: Evidence in Chinese Stock Market (Lan, Truong, and Zhang)

Dividend Flows and the Foreign Exchange Rate (Zheng)

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Does "Optimal" Portfolio Construction Actually Pay Off?

QuantSeeker — Sun, 12 Apr 2026 21:14:07 GMT

Most investors spend their time deciding what to own. Fewer think deeply about how to weigh those holdings. Yet portfolio construction often determines diversification, drawdowns, turnover, and long-run returns just as much as asset selection and signal generation.

Many theoretical portfolio construction methods seem elegant, but few survive real market conditions.

In this post, I test six popular portfolio construction approaches across two very different universes, a multi-asset ETF portfolio and a diversified commodity futures basket, to see which methods actually held up out of sample, after trading costs.

Weekly Research Recap

QuantSeeker — Tue, 07 Apr 2026 18:25:15 GMT

Here’s this week’s Tuesday roundup, a selection of the most practical investing ideas from recent academic studies, industry research, blogs, and insightful discussions across social platforms, with links included throughout.

Commodities

Intraday Stylized Facts and the Shape of Volatility Build-Up in ICE Brent Crude Oil Futures (Haugom, Ewald, Chen, and Smith-Meyer)

Using tick-level Brent futures data (2006–2025), the paper documents a range of intraday regularities: 1-minute returns display short-term reversals (first-lag autocorrelation of about −0.1), while volatility is persistent and follows a pronounced daily cycle. Volatility builds up unevenly over the day, rising more rapidly after market openings and information releases, and does so more strongly and rapidly in near-dated contracts. Key takeaway: Intraday volatility in Brent is predictable, making execution timing, dynamic risk control, and time-aware volatility modeling essential.

Crypto

Cryptocurrencies: Asset Classification, Trading and Portfolio Management (Gottwald, Sun, Chan-Lau, and Mitra)

Cryptocurrencies exhibit commodity-like behavior, including pronounced fat tails and frequent extreme moves. Momentum strategies are strongly regime-dependent, delivering gains mainly during periods of market stress and elevated volatility. Combining crypto with commodities improves portfolio efficiency and Sharpe ratios relative to commodities alone. Key takeaway: Crypto behaves like a speculative, commodity-like risk factor best used as a diversifier rather than a standalone allocation.

Equities

The 52-Week High Momentum Strategy: Evidence in Chinese Stock Market (Lan, Truong, and Zhang)

The 52-week high strategy emerges as a robust momentum signal in Chinese equities while traditional momentum is negative and industry momentum is weak. Its predictive power remains after controls and dominates in cross-sectional tests, but weakens beyond a 12-month holding period. Key takeaway: Price proximity to the 52-week high is a behaviorally driven momentum signal that seems to hold up in Chinese equities.

Geopolitical Risk and Equity Returns: Evidence From Global Markets (Coqueret and Zhou)

Geopolitical risk is priced immediately in equities: A one-unit shock lowers market returns by about 1% on impact, with stronger effects in emerging markets. At the firm level, exposures vary widely, with many stocks reacting immediately while others, particularly in emerging economies, adjust with a lag. Key takeaway: While geopolitical risk is an important risk factor, its effect in equities is primarily contemporaneous, with limited predictive power.

Fixed Income

The Co-Pricing Factor Zoo (Dickerson, Julliard, and Mueller)

Sparse models fail to explain corporate bond returns, particularly when pricing bonds and equities jointly. Risk premia are driven by a broad, dense set of factors, not a few dominant ones. After accounting for duration risk, bond-specific factors add little beyond equity and macro factors, while combining many signals delivers strong performance (Sharpe of 1.5 to 1.8). Key takeaway: Once duration risk is handled, equity and macro factors price corporate bond returns, but you need a broad set of them.

FX

Dividend Flows and the Foreign Exchange Rate (Zheng)

Dividend payments, though known in advance, generate predictable FX pressure: Currencies with large payouts depreciate by 4.7 bps within 2 days and 6.5 bps within about a week. A short-term strategy earns 30 bps/month in alpha, unexplained by standard FX factors. The effect is driven by benchmark investors’ dividend repatriation and is stronger when intermediary constraints bind. Key takeaway: Even predictable, mechanical flows can move currencies and create small but consistent trading opportunities.

Machine Learning and Large Language Models

Do LLMs Make Markets More Efficient? (Lu, Xu, and Vulicevic)

LLM access speeds up how quickly markets react to news. When LLMs are available, next-day return predictability from news sentiment falls by about 46 to 61%, with no reversal. During outages, predictability, and the profits of a simple news-sentiment strategy, roughly double to triple. Key takeaway: LLMs accelerate price discovery, but outages create brief, exploitable delays.

Multiple Testing

The False Discovery Rate in Finance: Identification Failure and Search-Adjusted Estimation (Lopez de Prado and Fabozzi)

Published factor results are typically the most favorable outcomes from many tested specifications, not single-trial estimates. The paper shows this search-and-selection process breaks standard inference and leads to systematically underestimated false discovery rates (FDRs). Under realistic assumptions, search-adjusted FDR can exceed 70 to 80%, even when nominal significance appears strong. Key takeaway: Most reported factors likely reflect selection bias; robust inference requires out-of-sample validation or explicit modeling of the search process.

Options

Time variation of size premium in the options market (Nguyen)

Delta-hedged options on large firms outperform small-firm options by 1.0 to 1.4% per month, but this spread disappears in recessions and down markets. The effect is strongest in expansions, consistent with demand-based pricing: Options on small, lottery-like firms become overpriced when investor demand is high, while flight-to-quality reduces this mispricing in downturns. Key takeaway: Time-varying demand for lottery-like options, not risk, drives the inverse size effect in options.

Prediction Markets

From Iran to Taylor Swift: Informed Trading in Prediction Markets (Mitts and Ofir)

Prediction markets appear efficient, but a small subset of traders consistently outperforms by trading just before major events. Accounts flagged for unusual timing, size, and concentration win about 70% of the time and capture roughly $143M in profits, far above random outcomes, suggesting informational advantages rather than skill alone. Key takeaway: Prediction markets aggregate information but also reward those with private information.

Blogs

How imputation helps statistical learning for macro trading signals (Macrosynergy)

VIX vs. Policy Uncertainty: What They Signal for Risk (CFA Institute)

One Year Later: Is ChatGPT Finally Worth Using for Quantitative Analysis? (Quantpedia)

Uncertainty (Quantitativo)

GitHub

Financial Datasets MCP Server

Podcasts

Faheem Osman – Commodity QIS: An Under-Appreciated Source of Systematic Returns? (Flirting with Models)

“Positions Can Be LESS Risky at Higher Prices” - Derek Pilecki on Finding Edge in Financials (Odds on Open)

Private Equity’s Low Volatility Isn’t Real (Meb Faber Show)

The ICT Trader Who Made $2 Million After SWITCHING To Orderflow - Yush (Words of Rizdom)

Social Media & Industry Research

A Practical Approach to Weighting Signals (Factset Insights)

Mean Reversion in Play: Carry is BACK?! (Alpha Architect)

Winning the Long Game with RAFI (Research Affiliates)

Last Week’s Most Popular Links

Rethinking Trend Following: Optimal Regime-Dependent Allocation (Zakamulin)

Momentum and Reversal on the Short-Term Horizon: Evidence from Commodity Markets (Ding, Kang, Yu, and Zhao)

Bimodality Everywhere: International Evidence of Deep Momentum (Han and Qin)

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Weekly Research Recap

QuantSeeker — Tue, 31 Mar 2026 22:33:37 GMT

This week’s Tuesday roundup brings together the most actionable investing ideas from recent academic work, industry research, blogs, and high-quality discussions across social media, with links throughout.

Commodities

Momentum and Reversal on the Short-Term Horizon: Evidence from Commodity Markets (Ding, Kang, Yu, and Zhao)

Using CFTC position data, the paper shows that short-horizon returns are driven by two offsetting mechanisms: Speculators’ net trading predicts reversals, while the return component orthogonal to trading flows exhibits continuation. The latter generates about 12 bps per week (about 6.2% annualized) and, when used as a sorting signal, improves intermediate-term momentum returns from 5.2% to 9.9% annually. Key takeaway: Decomposing returns into trading-flow and orthogonal components improves momentum signals.

Equities

Prediction reconditioned: Revisiting relevance (Polakow, Flint, Turro, and van Rooyen)

Relevance-Based Prediction (RBP) focuses on observations similar to current market conditions, but this selectivity often backfires. In predicting S&P 500 returns, the paper finds that OLS achieves higher explanatory power and lower forecast error. Simulations show why: Conditioning on “relevant” data reduces bias but increases variance due to smaller samples. Key takeaway: In noisy financial markets, using more data can outperform using “better” data.

Measuring Equity Factor Uncertainty (Huovinen)

The paper introduces a Factor Uncertainty Index (FUI) based on a broad set of equity factors. FUI predicts future factor returns, with explanatory power rising to 30% at a 12-month horizon and out-of-sample R² around 10 to 13%. Using FUI for risk scaling improves performance, lifting Sharpe from 1.25 to 1.53. Key takeaway: Factor uncertainty predicts returns and provides a simple, effective signal for dynamic risk scaling.

FX

One Leg at a Time: The Hidden Structure of Carry Trade Profitability (Panayotov)

Carry trades look noisy because exchange-rate swings drown out the underlying signal. By stripping out this noise, the paper shows that carry profits arrive in distinct regimes and come from only one side of the trade at a time. Timing which leg to hold using policy-rate dynamics roughly doubles Sharpe from 0.45 to 0.87. Key takeaway: Carry is a regime-dependent strategy and performance improves when you trade it selectively.

Machine Learning and Large Language Models

Bimodality Everywhere: International Evidence of Deep Momentum (Han and Qin)

Momentum returns are inherently unstable: Past winners and losers both face a meaningful risk of sharp reversals. A machine learning “Deep Momentum” approach that models the full return distribution (via predicted probabilities) delivers 41% annual returns with a Sharpe of about 2.5, versus 21% and 1.0 for standard momentum, with consistent global outperformance. Key takeaway: Modeling return distributions, not just averages, improves momentum investing.

Reviving Anomalies (Beckmeyer, Berg, Wiedemann, and Wortmann)

Most anomalies fail after costs, but the paper shows they become profitable when conditioned on ML-based expected returns and transaction costs. By filtering each anomaly to retain only high-conviction stocks, the number of implementable anomalies rises from 10 to 100. A simple 1/N portfolio delivers 12 to 16% annual returns with Sharpe ratios up to 1.0. Key takeaway: Alpha comes from selectively implementing existing factors, keeping only trades with strong expected returns after costs.

Options

The Changing Information Content of Options Markets: Evidence from 1996–2024 (Taheri Hosseinkhani)

Option markets still contain valuable information, but not all signals survive structural change. The implied volatility gap between calls and puts delivers 25 to 30 bps/week alpha with strong significance, while volume-based signals weaken and even reverse after 2020 as retail trading distorts flows, remaining informative mainly in hard-to-short stocks. Key takeaway: In option markets, price-based signals are more robust, while volume-based signals become unreliable when the composition of traders shifts.

Portfolio Allocation

Geopolitical Risk and Asset Pricing Across Market Regimes (Abou Rjaily)

Geopolitical risk is priced unevenly across assets and regimes. In a two-state framework, equities exhibit significant negative exposure, while bonds and currencies respond more to regional and bilateral shocks than to domestic risk. Safe-haven assets such as JPY and CHF benefit during stress, and responses vary widely across asset classes and market conditions. Key takeaway: Geopolitical risk is not a single factor; its impact depends on regime, geography, and asset class.

Sports Betting

The ‘Sleeping Giant’: A Behavioral Anomaly in Prediction Markets (Vaze)

Playoff betting markets systematically overestimate the win probability of recent “dynasty” teams when they are underdogs, by about 10 percentage points. This creates a profitable strategy, with 16% gross returns (63% win rate), while comparable non-dynasty underdogs show no bias. Key takeaway: Even liquid markets can misprice assets when investors overweight recent success.

Trend Following

Rethinking Trend Following: Optimal Regime-Dependent Allocation (Zakamulin)

Trend-following performance depends critically on how exposure is allocated across regimes. While most research focuses on refining signals, the paper shows that optimizing regime-dependent position sizing alone delivers large gains. A Sharpe-optimal rule lifts Sharpe from 0.41–0.57 to 0.56–0.73 in U.S. equities, from 0.05 to 0.30 internationally, and from 0.21 to 0.51 in diversified portfolios. Key takeaway: Optimizing momentum exposures across regimes is a powerful and underexplored source of improvement.

Blogs

What Drives the Commodity Skewness Premium? (Quantseeker)

Breaking the Rules of Intraday Trading (Concretum Group)

Brave New Backtest (Robot Wealth)

War and Interest Rates (John H. Cochrane)

Why Alternatives Still Command High Fees (CFA Institute)

Podcasts

How Billionaire Hedge Fund Managers Are Using Generative AI to Invest (Odds on Open)

David Bush - Build a High-Performance Quant Crypto Portfolio Without Blowing Yourself Up! (The Algorithmic Advantage)

Sports Betting and Prediction Markets—Through a Trading Lens · Stefan Stadie (Chat with Traders)

Social Media & Industry Research

If You Can’t Beat It, Stack It: How Portable Alpha Overlays May Enhance Equity Returns (Quantica Capital)

I Did Not Predict What is Going on in Privates (Cliff Asness)

Unlocking Hidden Patterns: How Daily Returns Predict Future Stock Performance (Alpha Architect)

Last Week’s Most Popular Links

Understanding the Convolutional Neural Network’s Stock Return Predictability (Guan, Li, and Si)

Empirical Asset Pricing via Learning-to-Rank (Lin, Su, and Zhu)

Expected Returns with Trends and Cycles (Hillenbrand and McCarthy)

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What Drives the Commodity Skewness Premium?

QuantSeeker — Mon, 30 Mar 2026 15:30:55 GMT

Over the past week, I have continued to explore skewness strategies in commodities. In a previous post, I showed that a time-series skewness strategy delivers a meaningful Sharpe ratio. A natural next question is whether this performance is broad-based across the commodity universe or concentrated in a smaller set of sectors or contracts. I examine and test this below.

Weekly Research Recap

QuantSeeker — Tue, 24 Mar 2026 14:11:51 GMT

Welcome to this week’s Tuesday roundup, featuring a curated mix of the most relevant investing ideas from the past week, sourced from academic papers, industry research, blog content, and sharp insights shared across social media, with links throughout.

Equities

Retail limit orders (Anand, Samadi, Sokobin, and Venkataraman)

Limit orders outperform marketable orders on an all-in basis: The implementation shortfall is 8 to 9 bps lower, even after accounting for a 16 bps opportunity cost embedded in the measure. High fill rates (65%) help contain execution costs, and this advantage is even more pronounced in smaller stocks. Key takeaway: Patient, liquidity-supplying limit orders consistently reduce trading costs for retail investors.

Expected Returns with Trends and Cycles (Hillenbrand and McCarthy)

Valuation ratios (like price-dividend) seem weak as return predictors because they blend two effects: Discount-rate variation and shifting cash-flow dynamics. The latter introduces noise and dilutes the signal in standard regressions. Once you separate these components, a clear return signal emerges: Out-of-sample R² is around 9% at one year and over 20% at five years, while raw ratios underperform a simple historical average. Key takeaway: Valuation ratios contain return signals, but they only become visible after stripping out cash-flow noise.

One Hundred Years in the U.S. Stock Markets (Bessembinder)

From 1926 to 2025, U.S. equities delivered 10% annual returns and created $91T in wealth, but outcomes are extremely skewed. The typical stock lost money, and only 41% beat T-bills. Net wealth creation is entirely driven by a small minority; about 3.7% of firms account for all gains, with concentration rising sharply over time. Key takeaway: Equity investing is a power-law game; missing a few big winners can derail long-term performance.

Dinner Table Alphas (Cao, Chen, Cohen, and Zhao)

Fund managers linked to senior executive spouses consistently outperform. The edge is highly concentrated: In spouse-linked industries, stocks they buy outperform those bought by managers with non-executive spouses by 4% per quarter (with sold stocks underperforming similarly). These trades also anticipate earnings surprises and corporate events. Key takeaway: Alpha isn’t just skill, it’s who you’re connected to.

FX

Geopolitical Risk in Currency Markets (Melone and Stathopoulos)

Sorting currencies by their sensitivity to a text-based geopolitical threats index, constructed from the share of news articles covering war, terrorism, and global tensions, yields a clear return spread. A long–short strategy (high minus low exposure) earns 3.3% annually (Sharpe about 0.39) with a 2.8% alpha beyond standard risk factors. Key takeaway: Geopolitical risk is a distinct priced factor in FX; high-exposure currencies earn a premium.

Machine Learning and Large Language Models

Empirical Asset Pricing via Learning-to-Rank (Lin, Su, and Zhu)

Learning-to-rank produces superior portfolios by directly optimizing stock rankings rather than estimating expected returns. It delivers roughly 1.5 to 2.3% monthly excess returns with Sharpe ratios up to 1.2, versus 0.35 to 0.7 for standard return models. The gains come from more accurate identification of top and bottom stocks and better hedging. Key takeaway: Optimizing relative rankings is a more powerful way to build long–short strategies.

Understanding the Convolutional Neural Network’s Stock Return Predictability (Guan, Li, and Si)

A convolutional neural network trained on 5-day price charts delivers strong cross-sectional predictability: A long–short decile strategy earns 54.5% annually with a Sharpe of 3.74 (2020–2024), rising above 5 over longer samples (before costs). Performance is driven primarily by the structure of OHLC bars; removing them roughly halves Sharpe, while adding features such as 52-week levels degrades results. Key takeaway: Simple price patterns, not added complexity, contain powerful, exploitable short-term signals.

Agentic Artificial Intelligence in Finance: A Comprehensive Survey (Aldridge et al.)

This literature review synthesizes research on agentic AI in finance, showing a shift from rule-based systems to autonomous, goal-driven agents that plan, learn, and coordinate. Multi-agent models can deliver strong risk-adjusted performance, with Sharpe ratios above 2 and relatively small drawdowns. Key takeaway: Autonomous systems can improve performance, but they also amplify risk.

Large Language Models and Stock Investing: Is the Human Factor Required? (Crisostomo and Mykhalyuk)

LLMs can generate stock recommendations, but only under strict guidance. Simple prompts produce weak results, while imposing a multi-factor framework (valuation, growth, quality, momentum, macro, sentiment with weights) improves performance. Adding human review lifts returns to 3.0% monthly alpha, and incorporating regulatory filings pushes performance toward 4%. Key takeaway: LLMs can assist in stock selection, but only when constrained by a disciplined factor model and human oversight.

Portfolio Construction

Complex Modern Portfolio Theory (Hellum, Jensen, Kelly, and Malamud)

Modern Portfolio Theory (MPT) breaks down when the number of assets (N) approaches the number of observations (T), but the paper shows that performance recovers once N ≫ T. In this high-dimensional regime, Sharpe ratios increase and can surpass those in standard settings. The gains come from implicit regularization in the covariance matrix, not better return estimates. Key takeaway: MPT can benefit from very large asset universes, provided the number of assets greatly exceeds the available number of observations.

Blogs

From Hype to Reality: Building a Hybrid Transformer-MVO Pipeline (Jonathan Kinlay)

AI Will Create Millions of Quants (RobotWealth)

More of the Disease, Faster (What happens when you ask an LLM to find you an edge) (RobotWealth)

Unlocking relative value across asset classes (Macrosynergy)

Timing Value vs. Growth: Evidence from 100 Years of Small Value–Large Growth Spread (Quantpedia)

Podcasts

How the World’s Largest Oil Derivatives Trading Firm Is Navigating the Iran War (Odds on Open)

The Hidden Cracks in Systematic Strategies No One Talks About ft. Nick Baltas (Top Traders Unplugged)

The Christina Qi Episode (The Alternative Data Podcast)

Social Media & Industry Research

The Return of the King: Trend Following Is Back – But Will It Last? (Alpha Architect)

A Trend Following Deep Dive: The Optimal Market Mix for a Trend Follower (Man Group)

Last Week’s Most Popular Links

Beyond Prompting: An Autonomous Framework for Systematic Factor Investing via Agentic AI (Huang and Fan)

An Index of Commodity Futures Returns Since 1871 (Janardanan, Qiao, and Rouwenhorst)

Save The Date: Analyst/Investor Days as a Trading Signal (Cabrera, Kolokolova, and Zhang)

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Weekly Research Recap

QuantSeeker — Tue, 17 Mar 2026 23:54:41 GMT

Welcome to this week’s Tuesday roundup, your curated selection of the most actionable investing ideas from the past week, spanning academic research, industry reports, blog posts, and insightful discussions across social media, with links throughout.

Commodities

An Index of Commodity Futures Returns Since 1871 (Janardanan, Qiao, and Rouwenhorst)

Using a uniquely hand-collected dataset of 230 commodity futures contracts dating back to 1871, the paper shows that a diversified futures index has generated approximately excess returns of 5.5% with a volatility of 14% (Sharpe ratio of about 0.38), comparable to equities. It outperforms stocks 43% of the time and beats inflation by more than 6% annually. Key takeaway: Commodities deliver equity-like premia with low co-movement to stocks, supporting their role as a diversifier.

Equities

Save The Date: Analyst/Investor Days as a Trading Signal (Cabrera, Kolokolova, and Zhang)

Using 1,000 U.S. Analyst/Investor Days, the paper shows that stocks rise about 3.7% from the announcement to the event, with significantly larger (7.6%) run-ups when firms actively “hype” the event, often followed by reversals. A long–short strategy (long hosts, short peers) earns 0.08 to 0.13% daily alpha (Sharpe of about 1.1 to 1.2), remaining significant after costs. Key takeaway: Trading the pre-event run-up to Investor Days is a possible event-driven strategy.

Price Impact in Closing Auctions, Opening Auctions, and Continuous Markets: A Benchmark for Cost of Trading on Anomalies (Goyal, Jegadeesh, and Wu)

Using U.S. equity data, the paper shows trading costs depend heavily on execution venue: Closing auctions have a lower price impact than continuous trading, while opening auctions are the least liquid. Executing in lower-cost venues reduces anomaly strategy costs to about 9–21 bps annually (ex-microcaps). Key takeaway: Execution matters, using lower-impact venues like closing auctions can meaningfully improve net returns.

FX

Bias-Corrected Feature Selection for Short-Horizon FX Trading: Evidence from Liquid Currency Pairs (Jukl and Lansky)

Across 14 liquid FX pairs, controlling feature-selection bias reveals a small but exploitable edge: Next-day strategies generate roughly 15 to 30% annual returns, Sharpe ratios above 1 (portfolio >2), and 55–60% win rates, even after costs. However, performance decays rapidly, and at longer horizons, Sharpe turns negative. Key takeaway: Any edge in FX is extremely short-lived; robust performance depends more on proper feature selection than on model sophistication.

Machine Learning and Large Language Models

E-TRENDS: Enhanced LSTM Trend Forecasting for Equities (Buchanan and Benhamou)

An LSTM model forecasting changes in equity trends improves predictive accuracy and trading performance. Across 30 S&P 500 stocks, it increases directional accuracy from 56% to 62% and lifts out-of-sample Sharpe from 0.85 to 1.10, outperforming linear and tree-based models. Gains are robust across most stocks and across different market regimes. Key takeaway: Predicting changes in trends with nonlinear methods can improve the robustness and risk-adjusted performance of trend-following strategies.

Beyond Prompting: An Autonomous Framework for Systematic Factor Investing via Agentic AI (Huang and Fan)

The paper proposes an agentic AI framework that replaces manual factor research with an iterative loop that proposes, tests, and refines trading signals under strict out-of-sample validation. Individual factors deliver statistically significant alphas with Sharpe ratios typically above 1, while combining them yields about 60% annual returns with a Sharpe of about 3.1 and drawdowns around 11%. Key takeaway: Integrating a disciplined, self-improving research loop into the investment process can systematically generate robust and scalable alpha.

DatedGPT: Preventing Lookahead Bias in Large Language Models with Time-Aware Pretraining (Yan, Tang, Gao, Jiang, and Lu)

Many LLMs suffer from look-ahead bias as they may “know the future” because training data includes events after the prediction date. This paper introduces DATEDGPT, a series of models trained separately for each year (2013–2024) using only data available up to that point. The models remain competitive, and tests show a clear performance drop on post-cutoff data, indicating no future leakage. Key takeaway: Strictly controlling the model’s information set is essential to mitigate look-ahead bias in financial LLMs.

Macro

Stagflation Risk and Financial Markets: A Real-Time Composite Index (Bonaparte)

The paper builds a real-time stagflation index combining macro conditions and geopolitical sentiment. Higher stagflation significantly lowers contemporaneous equity returns and raises volatility. However, it has little predictive power for future returns, while strongly and persistently forecasting higher volatility over the next 1 to 6 months. Key takeaway: Stagflation risk is primarily priced through volatility, making the index more useful for risk management than return prediction.

Performance Evaluation

The Sharpe Stability Ratio: Temporal Consistency of Risk-Adjusted Performance (Bajo Traver and Rodriguez Dominguez)

The paper argues that the classic Sharpe ratio hides an important dimension: How returns are earned over time. It introduces the Sharpe Stability Ratio (SSR), which penalizes strategies whose performance comes in bursts rather than steadily. Using simulations and hedge fund indices, it shows that strategies with similar Sharpe ratios can differ sharply in consistency. Key takeaway: True skill shows up as consistency; favor strategies with stable risk-adjusted returns, not just high averages.

Blogs

War and Oil (John H. Cochrane)

Skewness as a Time-Series Signal in Commodity Futures (QuantSeeker)

Transformer Models for Alpha Generation: A Practical Guide (Jonathan Kinlay)

How to write a tweet that gets over 300k views; and why diversification is probably good (Rob Carver)

Anomaly-Based Trading Strategies in the Real Estate Sector. Can the Market Be Beaten? (Quantpedia)

Podcasts

Annie Duke on Thinking in Bets - And Why Winners Can Be Wrong (Odds On Open)

The World’s BEST Trader Reveals the 10 Commandments To Profitable Live Trading (Words of Rizdom)

Managing Uncertainty: How a Major UK Pension CIO Thinks About Managing Billions (Capital Horizons)

Social Media & Industry Research

Why Value, Quality, and Momentum Belong Together (Research Affiliates)

An Interview with Jordan Brooks: Multi-Asset Strategies and Asset Allocation (AQR)

Unlocking Hidden Value: How Corporate Language Reveals the Future of Intangible Investment (Alpha Architect)

Last Week’s Most Popular Links

Quantitative Strategies For Momentum And Trend Reversal: Integrating Macroeconomic Factors, Advanced Signal Processing, And Regime Awareness (Charlotte Sim)

Systematic Reversal and Industry Momentum (Gao, Li, Yuan, and Zhou)

Generative AI for Finance: A New Framework (Chai, Jiang, Meng, You, and Zhou)

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Skewness as a Time-Series Signal in Commodity Futures

QuantSeeker — Mon, 16 Mar 2026 21:47:45 GMT

Over the past week, I’ve spent some time working on skewness strategies in the commodity space. In a previous post, I demonstrated that sorting commodity futures by realized skewness, going long the most negatively skewed contracts and shorting the most positively skewed, yields meaningful Sharpe ratios after accounting for costs.

A natural follow-up question is whether the time-series dimension of skewness also predicts returns. Instead of comparing which contracts are most negatively skewed relative to each other at a given point in time, we can ask if a contract is currently more negatively skewed than its own history. These are related, but different questions, and the answer has direct implications for how to construct and combine skewness strategies in practice.

Weekly Research Recap

QuantSeeker — Tue, 10 Mar 2026 15:54:28 GMT

Welcome to this week’s Tuesday roundup, where I highlight the most actionable investing ideas from the past week. The selection spans academic papers, industry research, blog posts, and insightful social-media discussions, with links provided throughout.

Equities

Monetary Policy Surprises and the Cross Sectional Stock Return Predictability in -Volume Sorted Portfolios (Wang)

Previous research shows that stocks experiencing abnormal trading volume tend to outperform. This paper finds that Fed forward-guidance surprises (changes in expected future policy rates) predict the high-volume return premium. Hawkish guidance significantly reduces future high-minus-low volume spreads. Key takeaway: Expectations about future interest-rate paths affect cross-sectional stock returns, volume-based long–short signals work best when markets anticipate looser policy.

The Granular Origin of Tail Dispersion Risk (Andersen, Ding, and Todorov)

Using high-frequency data on S&P 500 stocks, the paper shows that the source of tail risk matters for the price of risk. Stocks exposed to systematic tail shocks earn lower returns, while exposure to firm-specific tail shocks is associated with higher returns. Combining both signals yields a zero-cost portfolio with improved Sharpe ratios.
Key takeaway: The source of tail risk matters; both systematic and idiosyncratic tail shocks command risk premia, but with opposite signs.

Quantitative Strategies For Momentum And Trend Reversal: Integrating Macroeconomic Factors, Advanced Signal Processing, And Regime Awareness (Charlotte Sim)

The paper reviews momentum and reversal strategies and illustrates them with simple backtests. Using U.S. data from 2014 to 2024, price momentum earns about 9.2% annually with a Sharpe of 0.74, while a macro-based signal delivers 7.8% with a Sharpe of 0.56. Combining the two reduces volatility and yields a Sharpe of 0.70. Key takeaway: Macro signals can potentially diversify traditional price momentum strategies.

Systematic Reversal and Industry Momentum (Gao, Li, Yuan, and Zhou)

Previous research shows that industry portfolios exhibit short-term momentum. Using U.S. equity data, this paper shows that momentum profits are partly suppressed by exposure to a short-term reversal factor. Industry momentum has a Sharpe of about 0.56, but dynamically hedging reversal exposure raises the Sharpe to 1.11 and increases alpha. Key takeaway: Removing reversal exposure strengthens industry momentum strategies.

Machine Learning and Large Language Models

Generative AI for Finance: A New Framework (Chai, Jiang, Meng, You, and Zhou)

The paper applies a transformer model (RPBERT) that treats stocks as sequences ordered by characteristics, allowing it to learn cross-firm relationships. In U.S. equities, it achieves 17.9% out-of-sample R² and a long–short portfolio earns 2.5% per month with Sharpe ratios of 2.9–3.5 (before costs), outperforming standard ML and factor models. Key takeaway: Modeling cross-firm interactions with transformers can improve return prediction and risk-adjusted performance.

Algorithms for Asset Allocators: Review (Taysom, Firoozye, and Treleaven)

The paper surveys how machine learning and AI can assist large asset allocators. Rather than producing new alpha models, it maps practical use cases: Portfolio optimization, regime detection, manager selection, and AI-driven document analysis. The biggest barriers are sparse data, long investment cycles, and governance constraints. Key takeaway: For large allocators, AI’s immediate value lies in improving research and operational workflows rather than replacing human asset allocation decisions.

Market Microstructure

The Price Impact of Nothing: Rejected Orders as Predictors of Future Returns (Albers, Cucuringu, Howison, and Shestopaloff)

Using granular data from the Hyperliquid BTC–USD perpetual market, the paper shows that rejected “post-only” orders, orders that never enter the order book, predict short-term price movements. Rejected orders correctly forecast the direction of the next price change about 72% of the time. These orders are not random errors but likely reflect traders attempting to position ahead of expected price moves.
Key takeaway: Even non-executed order attempts can reveal traders’ information and predict short-term price movements.

Portfolio Construction

From Black Box to Explainable Portfolio Optimization: Tracing Allocations to Views and Constraints (Landais, Perchet, Soupe, Carvalho)

The paper opens the “black box” of portfolio construction by decomposing the final multi-asset allocation into intuitive components: A portfolio replicating the long-term benchmark, tilts from tactical views, expected manager alpha net of fees, and adjustments caused by portfolio constraints. The approach works under both classical and robust optimization. Key takeaway: Every portfolio weight can be traced back to the initial investment views, alpha assumptions, and constraints.

Dynamic Tracking Error and the Total Portfolio Approach (Alankar, Maymin, Maymin, Scholes, Zhang)

Using U.S. equity and bond data, the paper shows that institutional portfolio frameworks such as Strategic Asset Allocation and the Total Portfolio Approach mainly differ in how tightly tracking error is constrained. Across constraints from 0.5% to 5%, Sharpe ratios are statistically indistinguishable, while the volatility of tracking error differs by roughly 12×. Key takeaway: The key governance choice is not the portfolio framework itself, but how much tracking-error flexibility investors allow.

Prediction Markets

A Microstructure Perspective on Prediction Markets (Palumbo)

Using trade-level data from Kalshi’s NFL prediction markets, the paper shows that liquidity providers systematically accumulate directional exposure rather than offsetting inventory as traditional market makers do. Over the season, they earned about $29 million in aggregate, though profits were highly volatile week to week. Key takeaway: Liquidity provision in prediction markets appears very different from traditional markets.

Regime Models

Multivariate Time Series Classification With Online Expert Advice: An Application to Market Regimes (Guibert and Cuervo-Paloma)

Using global equity data and 567 macro and financial features, the paper develops a supervised system to detect upcoming equity stress regimes by combining logistic regression, random forest, XGBoost, and a neural network in an ensemble. The approach significantly improves prediction accuracy, while inflation-related indicators emerge as the most important predictors. Key takeaway: Cross-asset macro signals, especially inflation surprises, help anticipate future equity stress regimes.

Retirement Planning

The 4-Percent Rule Was Never Failproof: On the Folly of Fixed Rate Withdrawals (McQuarrie)

Using U.S. mutual fund data, the paper shows that the classic 4% retirement withdrawal rule is problematic. For retirees starting in the 1960s, portfolios often ran out of money prematurely. Small changes, such as 33 bps lower returns, slightly higher volatility, or unfavorable return sequencing, can flip success into failure.
Key takeaway: Fixed withdrawal rules are fragile; retirement spending should adapt to market conditions.

Blogs

NLP and Yield Curve Prediction From Central Bank Minutes (CFA Institute)

Trading Equity Volatility with a Bond Market Signal (QuantSeeker)

2-Year Notes Momentum: Extracting Term Structure Anomalies from FOMC Cycles (Quantpedia)

Reinforcement Learning for Portfolio Optimization: From Theory to Implementation (Jonathan Kinlay)

Macro trading signals with regression-based machine learning (Macrosynergy)

Podcasts

When Narratives Change Faster Than Markets ft. Alan Dunne (Top Traders Unplugged)

James Choi: Portfolio Theory in a Spreadsheet (Rational Reminder)

Social Media & Industry Research

Capacity Discipline (Thomas Babbedge, GreshamQuant)

Managing Investment Risk During Geopolitical Shocks (Man Group)

The Best Defensive Strategies: Two Centuries of Evidence (Alpha Architect)

How To Reason About A Messy Future (SystematicLongShort)

Last Week’s Most Popular Links

Price-Path Convexity and Short-Horizon Return Predictability (Gulen and Woeppel)

More Bets, Better Bets (Quantitativo)

Idiosyncratic volatility (Feldman, Kang, and Zhao)

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Trading Equity Volatility with a Bond Market Signal

QuantSeeker — Thu, 05 Mar 2026 18:58:11 GMT

I have discussed and tested strategies for trading volatility products in several previous posts. In this article, I explore a volatility trading strategy based on forecasting the VIX using information from both equity and bond volatility. The resulting signal can be used to trade either VIX futures or S&P 500 futures, and including bond volatility in the forecast significantly improves performance.

Weekly Research Recap

QuantSeeker — Tue, 03 Mar 2026 14:31:37 GMT

It’s Tuesday, and time for this week’s curated roundup of the most actionable investing insights from the last seven days, spanning academic research, industry reports, blogs, and social media, with links throughout.

Crypto

From Network Fundamentals to Macro-Financial Integration: The Evolving Predictability of Bitcoin Returns (Palazzi, Junior, and Klotzle)

Research shows that Bitcoin has become more correlated and financially integrated with equities. This paper documents that the return predictability of Bitcoin expanded from mainly blockchain signals to include stronger macro-financial drivers after 2019, with spillovers from traditional markets intensifying during stress episodes. Key takeaway: Bitcoin is not a reliable safe haven; on-chain valuation metrics consistently matter, but macro risk dominates when it matters most.

Decentralized Finance (DeFi): A Review and Research Agenda (Momtaz)

This comprehensive survey paper on DeFi reviews nearly 90 academic studies. For example, it shows that tokens can help launch and coordinate platforms, but power in trading, mining, and governance often becomes concentrated again. Key takeaway: DeFi changes how finance is organized, but it does not remove risk or incentives; it simply shifts who bears them.

Equities

Price-Path Convexity and Short-Horizon Return Predictability (Gulen and Woeppel)

Price-path convexity, the curvature of recent prices, strongly and negatively predicts short-horizon returns. At the aggregate level, a 1σ rise in convexity lowers next-month returns by 0.40%. Cross-sectionally, a low-minus-high convexity strategy earns 0.84% per month. The effect persists after factor controls and is linked to return overextrapolation. Key takeaway: When price paths become too convex, short-term returns tend to disappoint, suggesting an opportunity to fade overextrapolated moves.

Tail Risk Around FOMC Announcements (Jacobs, Ke, and Pan)

Using 7-day option-implied moments measured two days before pre-scheduled FOMC meetings, the authors show that left-tail risk is priced around announcements. More negative abnormal skewness and higher kurtosis predict stronger post-FOMC excess returns for up to four days. The effect is larger during expansionary monetary shocks and high-uncertainty regimes. Key takeaway: When crash risk rises into Fed meetings, investors demand and earn a temporary tail-risk premium afterward.

Idiosyncratic volatility (Feldman, Kang, and Zhao)

Conventional idiosyncratic volatility (IV), residual variance from factor models, misclassifies systematic zero-beta risk as diversifiable noise. In Fama–French 100 portfolios, true IV averages 13.6% annually versus 15–17% using the standard measure; the true/misspecified ratio averages 77–78% in typical scenarios. Key takeaway: Much of the “IV puzzle” may be measurement error; rethink IV-based strategies using a correctly defined risk decomposition.

Assessing Factors and the CAPM in 2026 (Welch)

Surveying more than 250 top U.S. finance academics, the paper finds a 5% expected equity premium. Of 20 factors, only momentum, profitability, value, and market beta receive positive future return forecasts; most others cluster at zero. Roughly one-third still back the CAPM. Key takeaway: Academics largely reject the idea that most factor premia compensate for risk; only beta and value retain a risk label, while momentum and profitability are seen as behavioral.

Voice Beyond Words: Evidence That Managerial Tone Predicts Returns When Text Does Not (Pope)

Even when the sentiment of earnings transcripts is neutral, this paper shows executive vocal delivery still predicts returns in Russell 3000 earnings calls. Sorting CEO/CFO Q&A speech into voice-based quintiles yields 40–70 bps excess returns over 10–30 days, with a +0.60% top–bottom spread over 20 days. Text alone loses power under neutrality. Key takeaway: When words and text are neutral, tone still predicts returns; investors should mine managerial voice, not just transcripts.

Hedge Funds

Partisan Hedge Funds (Chen, Huang, Sun, and Teo)

Hedge funds that load up on stocks tied to the sitting president’s economic agenda trail those that avoid them by 4.44% per year after risk adjustment. Politically aligned managers hold more of these stocks, talk more positively about them, and perform even worse after polarization shocks like mass shootings or major protests. The effect is strongest among highly partisan managers. Key takeaway: Letting politics guide stock selection can meaningfully hurt returns, even for professional investors.

Options & Volatility

A Parsimonious and Interpretable Factor Model of the Implied Volatility Surface (Wan, Wei, and Zhu)

This paper develops a five-factor model of the implied volatility surface, estimated from 7.2 million SPX options. It sharply reduces in-sample implied volatility fitting errors relative to standard polynomial benchmarks and delivers the lowest out-of-sample surface forecast errors. The extracted factors also outperform the VIX in predicting realized volatility. Key takeaway: A small, economically interpretable set of IV factors captures tail risk and improves both surface fitting and volatility forecasting.

Prediction Markets

Who Profits from Prediction Markets? Execution, not Information (Della Vedova)

Analyzing millions of Polymarket trades, the paper shows that predicting outcomes is not what drives profits. Retail traders are accurate in direction 51.3% of the time, yet lose 2.0% on invested capital on average ($79M in total), while high-frequency bots hover at chance (49.9%) but earn +0.56%, totaling $134M. A roughly 2.5-cent per-contract execution advantage explains the difference. Key takeaway: Speed, pricing, and liquidity provision, not superior forecasts, separate winners from losers, on average.

Blogs

Estimating the Term Structure of Corporate Bond Risk Premia (New York Fed)

More Bets, Better Bets (Quantitativo)

State-Space Models for Market Microstructure: Can Mamba Replace Transformers in High-Frequency Finance? (Jonathan Kinlay)

Geopolitical Risk and Portfolio Oversight (CFA Institute)

Systematic Allocation in International Equity Regimes (Quantpedia)

Three Levers That Drive VC Returns (CFA Institute)

The Winter of our Pairs Trading Discontent: Problems, limitations, frustrations (Robot Wealth)

Podcasts

Richard Craib - Crowd-Sourced Alpha with Numerai (Flirting with Models)

Alpha Comes From a Differentiated View - Ex-Point72 Prop Research Head Kirk McKeown on Edge in 2026 (Odds on Open)

3 AI Stock Winners & 3 Write-Offs - Prof. Damodaran (Meb Faber)

Social Media & Industry Research

The Concentration Game: Understanding Portfolio Effects of U.S. Equity Market Concentration (D.E. Shaw)

Should Trend Follow Carry: Lessons from Bonds, Gold, and 2022 (Research Affiliates)

Exploiting Myopia: The Returns to Long-Term Investing (Alpha Architect)

How To Be A World-Class Agentic Engineer (SystematicLongShort)

Last Week’s Most Popular Links

Enhancing Asset Allocation and Portfolio Rebalancing Through Dynamic Theme Detection (Rubio)

Large Language Models and Generative Factor Discovery in Crypto Markets (Sun, Wang, and Zhang)

Expectation Bias and Short-term Momentum (Gao, Ma, and Yuan)

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Weekly Research Recap

QuantSeeker — Tue, 24 Feb 2026 08:44:15 GMT

Welcome to this week’s briefing, your curated roundup of the most actionable investing insights from the past seven days, drawing from academic research, industry reports, blogs, and social media, with links to everything.

Crypto

Volatility Transmission to Bitcoin: The Role of VIX Term Structure and Crypto Options Markets (Luo, Tsai, and Yen)

Bitcoin prices react instantly to volatility shocks from both crypto and equity markets. A 1-point rise in VIX cuts same-day Bitcoin returns by 0.68%, dwarfing Bitcoin’s own IV impact (-0.25%). The VIX term-structure slope dominates: Its coefficient (-0.77) is over twice the level effect. After Jan-2024 ETF approval, Bitcoin’s sensitivity to its own IV collapses, but VIX influence persists. Key takeaway: Watch the VIX curve, not crypto IV, to manage Bitcoin risk.

Equities

Interest Rates and Equity Valuations (Gormsen and Lazarus)

The authors decompose real rates into expected growth (survey forecasts), risk (VIX²), and a discounting residual, the portion left after regressing rates on growth and risk. Only this residual moves equity valuations, explaining 80% of cross-country valuation changes since 1990; in the U.S., just 35% of the rate decline passed through to stocks. Key takeaway: Don’t necessarily equate lower rates with higher stocks, only the discounting component truly matters.

Short Selling Around News in International Stock Markets (Gorbenko)

Short sellers across 38 countries reliably predict negative returns, but trading on negative news adds incremental alpha in only 6 markets. Outside the U.S., the evidence suggests most short-seller edge seems to arise from anticipating bad news via private information rather than superior public news processing. Key takeaway: Follow shorts as a signal of hidden information, news itself is rarely the alpha.

FX

Assessing cross-currency predictability in forex markets: Insights from limit order book data (Petrova, Vilhelmsson, and Norden)

Using limit order book data on five FX pairs, the authors test PCA, supervised PCA, LASSO, and random forests at 1-minute to 1-hour horizons. All models underperform a historical-average benchmark. Cross-currency features add little, and only order flow shows brief 1-minute predictability, pointing to short-lived microstructure effects, not durable alpha. Key takeaway: Any edge in modern FX order-book data is tiny and fleeting, don’t expect persistent, tradeable predictability.

Machine Learning & Large Language Models

Enhancing Asset Allocation and Portfolio Rebalancing Through Dynamic Theme Detection (Rubio)

Using NLP-driven product “themes” instead of static GICS sectors, the paper builds a dynamic thematic equity portfolio. In backtests, the strategy delivers 180.8% cumulative return vs 132.7% for SPY, albeit with higher volatility and deeper drawdowns (Sharpe is 0.97 vs. SPY 1.02). Key takeaway: AI-based theme classification can potentially uncover return drivers that traditional sectors miss.

Expectation Bias and Short-term Momentum (Gao, Ma, and Yuan)

Using ML to extract predictable analyst forecast errors (expectation bias), the paper finds a strong return signal: A long–short portfolio earns about 0.92% per month (11% annually) with a Sharpe ratio of 0.67. Expectation bias explains why 1-month momentum appears mainly in high-turnover stocks. Key takeaway: Short-term stock momentum comes from investors’ slow updating of their beliefs following news.

Large Language Models and Generative Factor Discovery in Crypto Markets (Sun, Wang, and Zhang)

The authors use GPT-5.2 to auto-generate crypto factors on BitMEX, producing alpha: The top long–short signal earns 658% annualized (Sharpe 4.13) in-sample and 808% (Sharpe 4.63) out of sample. A dynamic multi-factor portfolio returns 373% (Sharpe 3.21). Key takeaway: LLMs can uncover market-neutral crypto alpha, but strict controls on overfitting are needed.

Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization (Sood, Papasotiriou, Vaiciulis, and Balch)

Using S&P 500 sector data, a deep reinforcement learning (DRL) allocator consistently beats mean–variance optimization: 12.1% vs 6.5% annual return and Sharpe 1.17 vs 0.68, with similar max drawdowns (-33%). Key takeaway: Optimizing directly for risk-adjusted returns via DRL can materially outperform classical MVO in real multi-asset allocation.

Portfolio Construction

The paper introduces a portfolio that overweights assets most statistically different from peers. In sector rotation, it beats equal-weighting by 28 to 121 bps/year with similar volatility and drawdowns (Sharpe 0.65–0.71); asset-allocation tests add 25–35 bps/year. Gains come from momentum capture and priced tail risk. Key takeaway: Letting dissimilarity in assets’ return distributions drive weights naturally loads the portfolio on trends and tail premia.

Blogs

Moneyball: Finding Undervalued Pairs Using Unconventional Metrics (Robot Wealth)

Crisis Alpha with Positive Carry and -0.5 Correlation to SPY (QuantSeeker)

Combining Calendar Strategies into the Trading Portfolio (Quantpedia)

Why Static Portfolios Fail When Risk Regimes Change (CFA Institute)

Time Series Foundation Models for Financial Markets: Kronos and the Rise of Pre-Trained Market Models (Jonathan Kinlay)

Podcasts

Peak Bubble? Why Markets Feel Different in 2026 ft. Mark Rzepczynski & Alan Dunne (Top Traders Unplugged)

What Druckenmiller Style Investing Gets Wrong - Alfonso Pecatiello on Edge in Macro Trading (Odds on Open)

Hendrik Bessembinder: Constant Leverage & Measuring Investor Outcomes (Rational Reminder)

Social Media & Industry Research

Making portfolio optimisation understandable for humans (Raul Leote de Carvalho)

Why Bitcoin Is Not the New Gold (Campbell Harvey)

Last Week’s Most Popular Links

Improving Performance with Fast Alphas; A Tactical Overlay for Intraday Trend Trading (Zarattini and Pagani)

Systematic Trend-Following with Adaptive Portfolio Construction: Enhancing Risk-Adjusted Alpha in Cryptocurrency Markets (Bui and Nguyen)

Trend Following in Strategic Asset Allocation: A Long-Horizon Analysis and Retail-Oriented Implementation (Galletta)

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Crisis Alpha with Positive Carry and -0.5 Correlation to SPY

QuantSeeker — Thu, 19 Feb 2026 22:28:58 GMT

This week, I test a crisis strategy that delivers roughly 7 to 8% annual returns with about –0.5 correlation to the S&P 500, while making money in every major equity drawdown since 2007. Using 18+ years of ETF data, I discuss how this simple long/short structure can materially cut portfolio drawdowns, without relying on explicit market timing or options.

Weekly Research Recap

QuantSeeker — Tue, 17 Feb 2026 16:07:10 GMT

Here’s your latest weekly briefing featuring the most actionable investing insights from the past seven days, spanning academic research, industry reports, blogs, and social media, complete with direct links throughout.

Asset Allocation

Trend Following in Strategic Asset Allocation: A Long-Horizon Analysis and Retail-Oriented Implementation (Galletta)

Using monthly data from 1979 to 2025, simple equity trend filters (10-month MA or 12–1 momentum) preserve equity-like returns (about 10% CAGR) while slashing maximum drawdowns from −54% to about −20% and lifting Sharpe from 0.72 to 0.93. Key takeaway: Trend following should be viewed more as dynamic risk control rather than alpha, materially reducing crash risk while preserving equity-like long-run returns.

Crypto

Systematic Trend-Following with Adaptive Portfolio Construction: Enhancing Risk-Adjusted Alpha in Cryptocurrency Markets (Bui and Nguyen)

AdaptiveTrend combines 6-hour trend signals with volatility-scaled trailing stops, monthly Sharpe-filtered asset selection, and a 70/30 long–short tilt. Across 150+ crypto pairs, it delivers 40.5% annualized return, Sharpe 2.41, and a −12.7% max drawdown, significantly beating TSMOM and buy-and-hold. Dynamic trailing stops contribute roughly +0.7 Sharpe. Key takeaway: In crypto, intermediate-frequency trends plus adaptive risk and portfolio construction can turn raw momentum into robust performance.

Equities

Risk Appetite and (Mis)Pricing (Guo, Li, Li, and Li)

Using a risk appetite index, the paper shows that the CAPM works only when aggregate risk aversion is high. In those periods, the security market line (SML) is strongly upward sloping (226 bps per unit of beta per month) with an insignificant intercept. When risk aversion is low, the SML flips negative (slope of −77 bps) and high-minus-low beta alphas reach −153 bps/month. Key takeaway: The beta anomaly is state-dependent, and risk appetite governs whether beta earns a premium or reflects mispricing.

Expected Investment Growth and REIT Returns (Liang, Eshraghi, and Wang)

Expected future investment growth predicts returns across U.S. REITs. A long–short portfolio (long high-growth, short low-growth) earns about 0.51% per month (6% annually) and remains significant after standard factor controls. High-growth REITs systematically increase leverage and their cash-flow risk, explaining the risk premium. Key takeaway: Forward-looking growth expectations are a priced signal in REITs that investors can potentially harness.

Multiples for Valuation: Go High, Go Low, Ignore the Middle (Estrada)

Multiples predict 10-year returns mainly when valuations are extreme. Using U.S. data from 1871 to 2025, dividend, earnings, and CAPE yields show much higher in-sample correlations and out-of-sample forecast accuracy in the top/bottom quartiles (CAPE yield correlation up to 0.70), while predictive power largely fades in the middle range.
Key takeaway: Long-horizon valuation signals matter most at extremes.

Value vs. Growth: What Drives the Value Premium? (Chen, Huang, and Jiang)

About half of the value and growth stocks are newly classified each year. The New Value–New Growth spread earns 0.33% per month versus 0.15% for incumbents, with a higher alpha, Sharpe, and a much fatter right tail, driven mainly by the persistent underperformance of new growth stocks. Key takeaway: A disproportionate share of the value premium comes from recent style migrants, especially newly promoted growth stocks.

Factor Models

Covariance Implied Risk Factors (Kaebi)

Standard PCA distorts latent factors when assets exhibit heterogeneous idiosyncratic variances, overweighting high-variance assets. Heteroskedastic PCA fixes this by iteratively correcting the diagonal of the covariance matrix, lifting out-of-sample Sharpe ratios by roughly 50 to 150%. Key takeaway: Improve factor estimation by correcting heteroskedasticity before expanding the factor set.

Machine Learning and Large Language Models

High-Frequency Trading in the Chinese Stock Market: Predictability, Profitability, and Regulation (Wang, Wang, and Zhou)

Ultra-high-frequency returns in China are strongly predictable: Machine-learning models achieve 15.5% out-of-sample R² at 5-second horizons and about 60% directional accuracy, stronger than comparable U.S. results. Predictability spikes near daily price limits and is driven mainly by order-flow imbalance and price-limit proximity. Key takeaway: Millisecond alpha exists, but it’s fragile and weakens once arbitrage constraints ease.

Deep Learning for Electricity Price Forecasting: A Review of Day-Ahead, Intraday, and Balancing Electricity Markets (Yu et al.)

This is a survey paper discussing how electricity price forecasting is shifting toward deep learning with probabilistic outputs. Day-ahead models increasingly use Transformers and GNNs, while intraday research is moving toward orderbook-based signals. Key takeaway: Forecasting performance in electricity markets depends as much on market-specific model design as on the choice of architecture.

Options

The Impact of Early Option Exercise on Ex-Dividend Stock Returns (Sperling and Schlie)

Early exercise of in-the-money calls before ex-dividend dates triggers mechanical selling on the ex-day. Across 270k U.S. dividend events, a 1-σ rise in exercise activity lowers ex-day returns by 6.4 bps, while top-decile events underperform by 12 to 16 bps. The effect strengthens with high dividend yields and weakens when options are liquid. Key takeaway: Early option exercise amplifies ex-dividend day selling pressure.

Trading

Improving Performance with Fast Alphas; A Tactical Overlay for Intraday Trend Trading (Zarattini and Pagani)

Fast intraday signals can look exceptional in frictionless backtests (a 5-minute reversal delivers 32% gross CAGR with Sharpe >2) but turn unprofitable after transaction costs. Used instead as an execution overlay on a slower intraday trend strategy, the same signal adds 200 bps per year and lifts Sharpe from 0.87 to 0.99 by improving entry and exit timing. Key takeaway: Fast signals that fail standalone after costs can still add meaningful value through smarter execution.

Blogs

Big Picture Asset Pricing (John H. Cochrane)

Seeing Through the Shutdown’s Missing Inflation Data (New York Fed)

Volatility Clustering Across Asset Classes: GARCH and EGARCH Analysis with Python (2015–2026) (Jonathan Kinlay)

1951 (John H. Cochrane)

Podcasts

Quality of Earnings and Dilution Risk with Ryan Telford (Planet MicroCap)

You Can’t Eat Risk-Adjusted Returns | AQR’s Pete Hecht on Portable Alpha’s Capital Efficient Edge (Excess Returns)

“I think of everything as a bet” - Ex-SIG Quant Trader Andrew Courtney (Odds on Open)

The Cost-Benefit of Being Trendy ft. Andrew Beer & Tom Wrobel (Top Traders Unplugged)

Social Media & Industry Research

A Trend Following Deep Dive: AlphaTrend and Agentic Research Workflows (Man Group)

Why Bonds Still Belong: Rethinking Fixed Income in Modern Portfolios (Return Stacked, Corey Hoffstein)

A Visual Appreciation For Black-Scholes Delta (Kris Abdelmessih)

Last Week’s Most Popular Links

The Unpriced Risk in Momentum Strategies (Gao and Yuan)

QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining (Han et al.)

Pairs Trading with Time-Series Deep Learning Models (Yilmaz and Sefer)

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Weekly Research Recap

QuantSeeker — Tue, 10 Feb 2026 18:33:25 GMT

Here’s your fresh weekly roundup of the most actionable investing insights from the last seven days, across academia, industry research, blogs, and social media, packed with direct links.

Equities

The Unpriced Risk in Momentum Strategies (Gao and Yuan)

Standard momentum is dominated by unpriced, time-varying factor risk. The authors address this issue by cross-sectionally regressing each stock’s 12-month momentum signal on Fama-French factor characteristics and ranking stocks on the residual. This “specific” momentum earns 1.14% per month with a Sharpe ratio of 1.05, versus 0.61 for raw momentum. Key takeaway: Momentum alpha lives in the factor-orthogonal residual.

Fiscal Uncertainty and Time-Varying Expected Market Returns (Yu, Xiao, Zhou, and Zhou)

Fiscal policy uncertainty (FPU) strongly predicts equity risk premia. Using U.S. data (1985–2024), the authors show FPU delivers out-of-sample R² of 1 to 4% across 1 to 12 months and surges to 17% at 12 months during high-FPU regimes. Economic gains appear only in these high-uncertainty states. Key takeaway: Fiscal uncertainty acts as a regime signal, with expected returns rising sharply when FPU spikes.

The decay of cay (Dauber and Lawrenz)

The consumption–wealth ratio (cay) has largely lost its predictive power: Today it shows near-zero quarterly R² and only 58 bp higher next-quarter excess returns per 1σ move, versus 220 bp historically. The decline reflects a breakdown in cointegration as asset wealth decoupled from aggregate consumption. A cay built on the top 10% households works better, but also fades. Key takeaway: Wealthy households’ consumption predicts returns better than aggregate consumption, but even this effect is shrinking.

Factor Models

Factor pricing across asset classes (Dang, Hollstein, and Prokopczuk)

The authors test 77 factors across seven asset classes and construct an integrated eight-factor model (U.S. market; international size, quality, and management; corporate bond carry and equity momentum; currency momentum; and equity index carry). It achieves out-of-sample Sharpe ratios above 0.8, over 50% higher than single-asset models, and when applied to 16,000 mutual funds, just 111 retain positive alpha. Key takeaway: Cross-asset factors explain most mutual fund returns, and most apparent “manager skill.”

Fixed Income

Interest Rate Surprises When the Fed Doesn’t Speak (Miranda-Agrippino and Williams)

Interest-rate expectations adjust just as predictably, and by similar magnitudes, after CPI or payroll releases as after FOMC announcements. At medium horizons, repricing is comparable: One-year rate surprises are about 5–8 bps across Fed and non-Fed events, and past payroll news significantly predicts these moves. Accounting for risk premia does not remove predictability. Key takeaway: “Policy surprises” largely reflect systematic macro-information updates, with Fed communication often serving as another transmission channel.

Macroeconomic Belief Distortions and Expected Returns in Treasury Bonds (Gong, Zhao, and Zhu)

Treasury returns are strongly influenced by systematic macro belief errors beyond what term-structure risk explains. A real-time macro factor captures biased growth and inflation expectations, delivering up to 33% out-of-sample R², with Sharpe ratios around 1.4. Predictability is strongest when disagreement is high. Key takeaway: Systematic errors in macro expectations are a powerful driver of expected bond returns.

Machine Learning and Large Language Models

Pairs Trading with Time-Series Deep Learning Models (Yilmaz and Sefer)

Transformers improve pairs trading by predicting the direction of factor-based residuals jointly across assets, replacing static mean-reversion rules. After transaction costs, iTransformer achieves 34% annualized returns with a Sharpe ratio of 1.94 on S&P 500 (vs 0.57 for classical relative value), and a crypto Sharpe of 2.2. Key takeaway: Cross-asset residual prediction, not simple mean reversion, drives superior, tradable alpha.

Disentangling the sources of cyber risk premia (Marechal and Monnet)

Using NLP on 10-K filings, the authors construct firm-level cyber risk scores orthogonal to standard characteristics. Stocks in the highest cyber-risk quintile outperform the lowest by 0.6% per month (about 7% annually), with a long–short Sharpe ratio of 0.68. Returns are not explained by conventional factors. Key takeaway: Cyber exposure behaves like a priced risk factor and deserves consideration in equity portfolio construction.

Insider Purchase Signals in Microcap Equities: Gradient Boosting Detection of Abnormal Returns (Zhao)

Using XGBoost on SEC Form 4 microcap purchases, the paper predicts the probability of stocks exceeding 10% abnormal return over 30 days following an insider buy. The dominant feature is distance from the 52-week high (36% of signal), followed by recent returns, volatility, liquidity, and insider traits. Buys disclosed after >10% run-ups deliver 6.3% abnormal returns. Key takeaway: In microcaps, insider alpha comes from buying into strength, not buying dips.

QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining (Han et al.)

The paper introduces QuantaAlpha, an LLM-driven alpha discovery framework that treats research as an evolving system rather than one-shot prompt engineering. On CSI 300, it delivers 27.8% annualized excess return with just 8.0% max drawdown, and the learned signals transfer well to CSI 500 and S&P 500 (160% / 137% cumulative excess returns). Key takeaway: Alpha discovery as an evolving system, not one-shot prompts, builds more robust, regime-resilient signals.

Blogs

Point-in-time economics and financial market forecasting (Macrosynergy)

A Simple Intraday Signal That Predicts Next-Day Returns (QuantSeeker)

Pragmatic Asset Allocation Across Market Cycles (Quantpedia)

Three Risks of Relying on the S&P 500 in Retirement Planning (CFA Institute)

Podcasts

Lowest Cash Levels Ever | Kevin Muir on Why It’s Time to Buy Hedges (Excess Returns)

Trading Legend: His Strategy Has Made the MOST Millionaire Traders - StockBee (Words of Rizdom)

Biotech Just Had Its Big Reset - Here’s What Comes Next (Dan Rasmussen, D.A. Wallach & Meb Faber) (Meb Faber Show)

Social Media & Industry Research

The Long Volatility Premium: Short the Market, Get Paid? (Alpha Architect)

Understanding Implied Forwards (Kris Abdelmessih)

Everything Everywhere All at Once: Conglomerates and the Disappearing Diversification Discount (Research Affiliates)

Last Week’s Most Popular Links

Option Factor Momentum (Käfer, Mörke, and Wiest)

A Trend Following Deep Dive: The Optimal Market Mix for a Trend Follower (Man Group)

The Asset Allocation Wisdom of Wall Street (Mamaysky)

A Simple Intraday Signal That Predicts Next-Day Returns

QuantSeeker — Sun, 08 Feb 2026 10:29:38 GMT

In this week's Research Insights, I test a recently proposed intraday signal on SPY. Most trading signals look at closes, opens, or yesterday's move, but what if the real information is in the pattern of today's intraday action? The signal is straightforward to calculate and utilizes only OHLC data. I replicated it on 26 years of SPY data and tested it under realistic transaction costs. Like most intraday signals, it's sensitive to execution costs, but I discuss when and how this type of edge can actually survive.

Weekly Research Recap

QuantSeeker — Tue, 03 Feb 2026 22:02:46 GMT

Time for a fresh recap of the most actionable investing insights from the past seven days, spanning academic papers, industry reports, blogs, and social media, with direct links throughout.

Asset Allocation

The Asset Allocation Wisdom of Wall Street (Mamaysky)

Target-date funds can be closely replicated using simple combinations of broad equity and bond ETFs. These ETF replicas track fund returns almost perfectly and often outperform due to lower fees, even when based on last year’s allocations. Key takeaway: Target-date funds are straightforward asset-allocation products that can be easily replicated with ETFs.

Equities

Can AI Detect Tail Events? Stock Performance Around Tail Events During Different Sentiment Regimes (Jurt, Uhl, and Walliser)

Analyzing one-day drops in S&P 100 stocks using millions of news articles, the authors show that large price declines accompanied by relatively upbeat firm-level sentiment tend to rebound. These stocks earn materially higher next-month abnormal returns, and a sentiment-filtered reversal strategy achieves economically meaningful Sharpe ratios. Key takeaway: Panic-driven selloffs not confirmed by negative news present attractive dip-buying opportunities.

Index rebalancing and stock market composition: Do indexes time the market? (Sammon and Shim)

Value-weighted index funds mechanically rebalance around issuance and buybacks, embedding negative market timing. The authors show these forced trades earn about −4.6% annually, implying a 46–69 bps drag at the fund level, far larger than expense ratios. Key takeaway: Index design is a hidden cost of passive investing.

Noisy Factors? The Retroactive Impact of Methodological Changes on the Fama-French Factors (Akey, Robertson, and Simutin)

Using archived factor vintages since 2005, the paper shows that Fama–French factor returns change materially over time, driven primarily by methodological revisions rather than data updates. This affects factor Sharpe ratios, shifts mutual fund alphas by more than 1% per year, and alters betas for roughly a third of stocks. Key takeaway: Factor vintages matter; robust inference requires fixed-method factors or explicit vintage disclosure.

Labor Links, Comovement, and Predictable Returns (Liu and Wu)

Using job-posting data to identify labor-market peers, the paper shows stock returns comove through labor links that often cut across industries. Markets underreact to information flowing through these networks, creating a lead–lag effect. A labor-peer long–short strategy earns about 0.75 to 0.77% per month (about 9% annually), unexplained by standard factors. Key takeaway: Labor networks offer a potential source of overlooked systematic mispricing.

Price Discovery Within Earnings Calls (Oh)

Breaking earnings calls down minute by minute, the study shows that negative wording affects prices almost immediately, within about a minute. Voice tone by itself has little impact, but a gloomy tone amplifies declines when the message is already bad. Key takeaway: Markets react quickly to substance; tone mainly reinforces negative news.

Fixed Income

Imperfect Inflation Expectations and Treasury Bond Returns (Su)

Using survey forecasts and CPI surprises from 1968 to 2024, the paper shows investors update inflation beliefs sluggishly, leaving bond prices slow to adjust. A one-standard-deviation upward inflation revision predicts roughly 23 bps lower next-year excess returns on 2-year Treasuries, with similar effects across maturities and strong out-of-sample performance. Key takeaway: Systematic underreaction to inflation news is a source of bond return predictability.

Machine Learning and Large Language Models

Compute, Complexity, and the Scaling Laws of Return Predictability (Timmermann and Vulicevic)

Using large families of neural networks, the paper shows that return predictability scales smoothly with computing power but converges to a hard limit conditional on the information set. In firm-level strategies, Sharpe ratios increase with model scale and plateau around 1.4–1.5. A historical 25% compute advantage raised Sharpe by roughly 10%, with gains fading as markets become more sophisticated. Key takeaway: Extra compute generates meaningful alpha until predictability is exhausted by the information set.

Generative AI for Stock Selection (Rasekhschaffe)

Using U.S. equities from 2019 to 2024, the paper shows that LLM-generated features significantly enhance short-horizon stock selection when paired with high-quality retrieval. Ensemble strategies reach Sharpe ratios around 1.6, with low correlation to standard signals and statistically significant alpha. Key takeaway: Generative AI is a powerful complement to, rather than a substitute for, human feature engineering.

Options

Option Factor Momentum (Käfer, Mörke, and Wiest)

Using 28 equity option factors, the study documents powerful time-series and cross-sectional factor momentum. Monthly strategies deliver roughly 6 to 15% annualized returns with Sharpe ratios approaching 3, remaining highly significant after controlling for leading option factor models. Factor momentum fully absorbs single-option momentum effects. Key takeaway: Focus on timing option factors, not individual options.

Risk Management

Correlation: the most common myths in Risk Management practice (Puccetti and Cagliani)

This paper shows that correlation is a fragile and often misleading risk tool. Near-zero correlation can hide strong nonlinear dependence, and moderate correlation can coexist with nearly perfect dependence. Crucially, portfolio tail risk (VaR) does not necessarily increase with correlation. Key takeaway: Manage tail risk using explicit dependence models, not correlation heuristics.

Blogs

Decoding CTA Allocations by Trend Horizon (CFA Institute)

GVZ and Gold Returns (QuantSeeker)

Do S&P500 0DTEs Options Increase Market Volatility? (Quantpedia)

Seasonality in Bitcoin Intraday Trend Trading (Concretum Group)

Podcasts

”Human + AI Beats Both Alone” - Alix Pasquet on Analog Training (Odds on Open)

When Volatility Becomes the Signal ft. Katy Kaminski (Top Traders Unplugged)

Russell Napier: Gold Is Screaming a Warning (But No One’s Listening) (Meb Faber)

Social Media & Industry Research

A Trend Following Deep Dive: The Optimal Market Mix for a Trend Follower (Man Group)

Revaluation Alpha: Why Past Factor Returns May Be Misleading (Alpha Architect)

Using Log Returns And Volatility To Normalize Strike Distances (Kris Abdelmessih)

Last Week’s Most Popular Links

A Causal Regime-Risk-Stability Framework for Global Tactical Asset Allocation (Bailer and Alber)

A Piecewise-Linear Model For Market Regime Identification (Steiner)

Fear & Greed Index as a Predictor for S&P 500: A Machine Learning Approach to Market Sentiment Analysis (Byun)