QuantSeeker

Shorting Leveraged ETFs: A Free Lunch or Just an Expensive One?

QuantSeeker — Sun, 26 Jul 2026 21:12:47 GMT

Leveraged ETFs are known to suffer from volatility decay, particularly in turbulent markets. That suggests an intuitive trade: Short leveraged ETFs while hedging their market exposure. With directional exposure largely neutralized, the portfolio should be positioned to harvest the decay.

This week, I examine recent research on this trade and several related implementations, extend the evidence through July 2026, and test what survives key implementation frictions. Most versions become considerably less attractive, but one appears more robust.

Weekly Research Recap

QuantSeeker — Tue, 21 Jul 2026 23:06:43 GMT

Each Tuesday, I share the most interesting market and investing insights I came across during the past seven days, including new research papers, blogs, and podcasts. Links are included throughout for readers who want to explore the ideas in more detail.

Crypto

Demand-Side Fee Flows and Return Predictability on Ethereum (Dunnes and Eckberg)

Ethereum may have a fundamental valuation signal after all. This paper builds a valuation metric from Ethereum’s protocol fees relative to token supply and finds that periods of unusually strong fee activity predict higher ETH returns over the next 1–2 months. The effect appears only after the 2024 Dencun upgrade, with out-of-sample R² reaching 14.4% at a 45-day horizon. Key takeaway: Protocol fundamentals seem to contain predictive information for crypto returns.

The “Actual Retail Price” of Crypto Trades (Rösch, Shohfi, Stanco, and Walz)

Retail crypto trading may be far more expensive than most investors realize. This paper executed nearly 500 real trades across Coinbase, Kraken, Crypto.com, and Robinhood, finding all-in trading costs of 253 to 834 bps on major crypto exchanges, while Robinhood and crypto ETFs often provided much cheaper execution. Key takeaway: For retail crypto investors, choosing the right platform can save more than picking the right coin.

Equities

War Risk and Time-Varying Return Predictability of the Dividend-Price Ratio (Yu, Xiao, Zhou)

The dividend-price ratio predicts future U.S. stock returns primarily during periods of active war. Outside high-war periods, it adds little forecasting value. The authors argue that war speeds the correction of market mispricing, making valuation signals more informative. Key takeaway: Even classic valuation signals aren’t universally useful, their predictive power depends on the market regime.

When Do Anomalies Deserve Capital? Macro-Regime Memory and Dynamic Cross-Sectional Factor Allocation (Taheri Hosseinkhani)

Macro timing is one of investing’s oldest ideas. This paper applies it in a different way. Instead of timing the market, it dynamically allocates capital across factor families like value, momentum, quality, and liquidity using macro regimes. Across 212 equity signals, the approach achieves an out-of-sample Sharpe ratio of 1.51. Key takeaway: In factor investing, the timing of factor allocation can matter as much as the factors themselves.

On Volatility-managed Portfolios: The Role of Conditional Factor Correlations (Barroso, Maio, and Wang)

Only 3 of 9 individual equity factors deliver robust gains from volatility scaling. However, volatility scaling remains effective for diversified factor portfolios. It’s not just about timing volatility, it automatically reduces exposure when factor correlations rise, and the paper shows much of its edge comes from this mechanism. Key takeaway: In multi-factor investing, volatility scaling helps manage the impact of rising factor correlations, not just volatility itself.

Fixed Income

Understanding Corporate Bond Excess Returns (Cui, Lu, and Song)

The excess returns on corporate bonds aren’t as credit-driven as many investors assume. This paper matches every corporate bond with a synthetic Treasury having identical cash flows, isolating returns unique to credit markets. It finds that roughly 41% of the excess return measured over T-bills is actually compensation for interest-rate risk. Key takeaway: Separate duration from corporate-bond-specific risk before evaluating corporate bond strategies.

FX

The Shape of Carry: Construction, Leverage, and Crash Risk Across Currency and Bond Markets (Gouws)

Carry delivers a modest 0.5 Sharpe across currencies and bonds but comes with severe negative skew and crash risk. Proper portfolio construction makes the premium more investable: Diversify carry sources and size positions by risk. Key takeaway: Proper risk management is key to harvesting the carry premium.

Machine Learning and Large Language Models

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

This paper enhances traditional momentum by predicting the entire return distribution rather than a single outcome, then ranking stocks by their expected returns. Across 45 countries, this improved the average annualized Sharpe ratio from 0.98 for traditional momentum to 2.49. Key takeaway: Rather than relying only on the most likely outcome, use the entire predicted probability distribution when constructing momentum portfolios.

Portfolio Construction

When Simplicity Beats Optimization: Evidence from Factor Timing, Volatility Management, and the 1/N Benchmark (Feng)

Investors often assume sophisticated optimization beats simple diversification. This paper suggests otherwise. Across nine equity factors (1976–2025), recursive mean-variance optimization and dynamic factor timing generally failed to outperform a simple equal-weighted (1/N) portfolio out of sample. Key takeaway: Simple diversification remains remarkably difficult to beat once estimation risk is taken into account.

Risk Management

A Brief History of Financial Risk (Hartzmark and Solomon)

For most of market history, investors didn’t think about risk the way we do today. This paper shows that before Markowitz, risk wasn’t volatility, it was largely the chance of losing money over the long run, while stocks were often viewed as speculation rather than investments. Modern portfolio theory fundamentally changed how investors think about risk. Key takeaway: Today's definition of risk is relatively recent, and it fundamentally changed investing.

Blogs

Beyond Diversification: Factor Risk in Sovereign Portfolios (CFA Institute)

Can AI Do Financial Research? (Quantpedia)

Podcasts

~100% Returns in 2025, No Losing Year Since 2008: Erik Smolinski on Edge for Retail Trader Edge (Odds on Open)

The Creator of Claude Code on The Hottest Piece of Software in the World (Odd Lots)

Why Trend Following Is Changing Faster Than Ever ft. Yoav Git (Top Traders Unplugged)

Social Media & Industry Research

The Intramonth Momentum Cycle (Alpha Architect)

Two Accounting Anomalies: One May Be Risk, the Other Is Mispricing (Alpha Architect)

A More Fragile World (Citadel)

Last Week’s Most Popular Links

Winners Glide, Losers Stumble: A Behavioral Reversal Tilt to Momentum (Wen and Feng)

Betting on the Leverage Effect (Doshi, Jacobs, and Sert)

Do AIs Make Good Traders, and Do They Make Good Traders Better? (Bell, Haghani, and White)

Disclaimer: This newsletter is for informational and educational purposes only and should not be construed as investment advice. The author does not endorse or recommend any specific securities or investments. While information is gathered from sources believed to be reliable, there is no guarantee of its accuracy, completeness, or correctness.

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Weekly Research Recap

QuantSeeker — Tue, 14 Jul 2026 20:52:38 GMT

Welcome to this week’s recap, 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.

Equities

Betting on the Leverage Effect (Doshi, Jacobs, and Sert)

Stocks with the strongest negative return-volatility correlation (”leverage effect”) earn significantly higher future returns, and the signal outperforms traditional skewness measures. The premium comes almost entirely from increases in volatility, not volatility declines. Key takeaway: Measure asymmetric risk through return-volatility dynamics, not skewness alone.

Winners Glide, Losers Stumble: A Behavioral Reversal Tilt to Momentum (Wen and Feng)

Most momentum research tries to improve the winner side. This paper argues that the real opportunity lies on the losing side. Among past losers, recent rebounds often fade, so overweighting those names on the short side improves momentum and reduces momentum crash risk. Key takeaway: Momentum has more to gain from better short selection than from better long selection.

Redeeming Value (Penman and Reggiani)

Everyone says value investing is about buying low P/B stocks. This paper argues that it’s too simplistic. Book-to-price only becomes informative when combined with earnings. Together, they help reveal the market’s implied expected return, or the growth expectations embedded in today’s price. Key takeaway: Value investing isn’t about buying “cheap” stocks. It’s about identifying mispriced growth expectations.

FX

Rethinking currency factors: The case for mean-variance optimisation (Fan, Kearney, Li, and Liu)

Instead of equal-weighting positions in carry, value, and momentum portfolios, this paper shows that optimizing constituent weights with Bayesian mean-variance optimization boosts the carry factor’s out-of-sample Sharpe ratio from 0.69 to 0.76 and the value factor’s from 0.19 to 0.50. The edge comes from better portfolio construction, not better forecasting. Key takeaway: In factor investing, portfolio construction can be as important as the signal itself.

Skewness Risk Premia and the Cross Section of Currency Returns (Li, Sarno, and Zinna)

Currency investors are potentially overlooking skewness risk premia. Currencies with the highest skewness risk premia subsequently earn the highest excess returns. The signal outperforms other option-based measures and helps explain returns beyond the classic carry trade. Key takeaway: Skewness risk is a distinct, priced source of currency risk.

Machine Learning and Large Language Models

Do AIs Make Good Traders, and Do They Make Good Traders Better? (Bell, Haghani, and White)

Instead of racing to build better market forecasts with AI, this paper suggests the bigger edge lies elsewhere. Claude and ChatGPT matched elite macro traders surprisingly well at inferring market direction from macro news. Their biggest weakness wasn’t prediction; it was position sizing. When researchers explicitly improved the AI’s risk-sizing framework, performance improved without better forecasts. Key takeaway: Correct sizing often matters more than signal.

More Than Words: Visual Sentiment in Social Media Videos and Stock Returns (Jiang, Jin, and Ma)

Using facial expressions from finance influencers’ Douyin (TikTok) videos, the authors build a visual sentiment index that predicts next-day market returns, even after controlling for what the influencers actually say. The effect is strongest during trading hours and in retail-driven stocks. Key takeaway: Nonverbal cues can contain information that text alone misses.

Options

Here Today, Gone Today: First Evidence on European Zero-Day Options (Wilkens)

This paper offers a systematic study of European 0DTE options on the EURO STOXX 50 and DAX and finds that put-call parity holds almost perfectly at mid quotes. However, once you cross the bid-ask spread, round-trip execution costs consume 10 to 13% of an ATM straddle on average and up to 40–70% in the final 30 minutes before expiry. Key takeaway: In 0DTE trading, execution quality can matter more than pricing efficiency.

Textual Risk Option Momentum (Chen, Vasquez, and Yang)

Option momentum spreads across firms with similar risk disclosures. Options on companies whose textual risk peers had strong past delta-hedged returns earn higher future delta-hedged returns, even after controlling for own-option momentum and other peer networks. Key takeaway: Risk similarity creates hidden links through which option-market information diffuses.

Portfolio Construction

An Automated Black-Litterman Framework for Global Fixed Income (Carver, Harris, and Lin)

Traditional bond allocation struggles because expected returns are noisy. This paper combines factor forecasts with an automated Black-Litterman framework that adjusts confidence based on each model's predictive power. Across 13 global bond indices, Sharpe improves from 0.31 to 0.45 with only a modest increase in drawdown. Key takeaway: Portfolio construction isn't just about better forecasts; it's about knowing how much to trust them.

When and Why Naïve Diversification Works: A Simple Diagnostic Strategy (Feng, Huang, Wang, and Zhang)

Equal weighting often beats optimization out of sample, not because optimization is wrong, but because estimation error is costly. This paper argues that equal weighting becomes nearly optimal when the forecast-error covariance structure is close to a specific eigenvector condition. As the data move away from that condition, optimized weights regain their edge. Key takeaway: Before optimizing, ask whether your data justify it.

Blogs

Bitcoin’s Fading Diversification Benefit (Quantseeker)

Economic surprises and global asset class returns (Macrosynergy)

Quantitative Value (Concretum Group)

Podcasts

Has Trend Following Changed Forever? ft. Alan Dunne (Top Traders Unplugged)

From Skid Row Showers to Short Selling Master · David Capablanca (Chat with Traders)

Social Media & Industry Research

After the Reset: Time to Focus on Fundamentals (Citadel)

Bonds Seem to Not Diversify Anymore. Now What? (Alpha Architect)

The Toolkit Expands (Citadel)

Last Week’s Most Popular Links

Does Momentum Time Risk Rather Than Returns? (Zakamulin)

Is Trend Still Your Friend?: A Microstructural Account of the Demise of Short-Term Trend-Following (Kurth, Eisler, Rej, Bouchaud)

Liquidity Premium and Investment Horizons (Aldridge)

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Bitcoin's Fading Diversification Benefit

QuantSeeker — Mon, 13 Jul 2026 23:01:52 GMT

Bitcoin has long been viewed as a portfolio diversifier, an asset that moves independently of stocks and cushions equity drawdowns. However, things have changed. Its correlation with equities has increased; its market beta has risen from essentially zero to roughly one; and the Sharpe ratio benefit of adding a 10% Bitcoin allocation to a portfolio has fallen sharply, although the magnitude depends on the underlying asset mix.

The Changing Correlation and Market Beta

The rolling 3-month correlation between BTC and SPY daily returns tells the basic story. From 2013–2019, it oscillated between roughly -0.3 and +0.3 with no clear trend, an asset trading on its own idiosyncratic cycle, largely detached from equities. Since 2020, the whole distribution has shifted up, spending most of its time between 0.3 and 0.7.

The full-sample correlation between Bitcoin and the S&P 500 is modest (0.16), but it obscures a pronounced regime change. Before 2020, the correlation was essentially zero (-0.02); since 2020, it has averaged 0.37. Investors relying on the full-sample estimate would therefore materially understate Bitcoin's current relationship with equities.

The shift is even clearer when looking at market beta. Before 2020, Bitcoin's market beta was -0.12, implying essentially no systematic equity exposure. Since 2020, it has increased to 1.07, meaning a dollar invested in Bitcoin now carries roughly the same market risk as a dollar invested in the S&P 500. That's not a subtle change. Bitcoin went from behaving largely as an unrelated asset to behaving much more like an equity position.

What might explain this shift? The timing is consistent with a growing literature on Bitcoin’s financialization. An IMF working paper (Che et al., 2023) identifies a common “crypto factor” whose correlation with global equities rose sharply after 2020 as institutional participation increased and the marginal crypto and equity investor became increasingly similar. Conlon and McGee (2020) showed that Bitcoin failed to act as a safe haven during the March 2020 market crash, while Nguyen (2022) found that S&P 500 returns began exerting a stronger influence on Bitcoin returns during periods of high uncertainty.

The rolling correlation also exhibits a second upward shift in early 2024, coinciding with the launch of US spot Bitcoin ETFs on January 11. Around that event, the 30-day rolling correlation increased from roughly 0.02 immediately before the launch to around 0.28 immediately afterwards. While this evidence is descriptive rather than causal, it is consistent with the hypothesis that spot ETFs further integrated Bitcoin into mainstream investment portfolios, as discussed in Hong et al. (2025).

Downside Risk: Where the Real Damage Shows Up

Weekly Research Recap

QuantSeeker — Tue, 07 Jul 2026 19:20:53 GMT

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

Equities

Lost in the Multiverse: Methodological Uncertainty in Studying Global Equity Returns (Cakici, Fieberg, Neszveda, Piljak, and Zaremba)

Most country-level investing “anomalies” may be less robust than they appear. This paper shows that signals like momentum, beta, and idiosyncratic risk often fail to remain robust across alternative research designs. The most reliable predictors were market size, dividend yield, sovereign risk, and short-term momentum. Key takeaway: Always ask whether an investing edge survives different implementations.

Does Momentum Time Risk Rather Than Returns? (Zakamulin)

This paper challenges the standard view of time-series equity momentum. In the U.S. equity market, its edge comes primarily from identifying future volatility rather than predicting future returns. Long-only momentum improves risk-adjusted performance largely by avoiding high-volatility regimes, not by forecasting higher returns. Key takeaway: For equity momentum, risk timing matters more than return timing.

Liquidity Premium and Investment Horizons (Aldridge)

Not all trading volume is equally informative. Signed order flow is shown to predict the cross-section of next month’s stock returns, while raw volume adds far less information. Key takeaway: Net buying and selling carry more information than raw trading volume.

Options

Hearing and Seeing the Fed: Multimodal Signals Beyond Text in Intraday Option Markets (Filippou, Zhao, Zhou, and Zhou)

Markets don’t just react to what the Fed says; they react to how it’s said. Jerome Powell’s tone of voice and facial expressions are shown to contain incremental information beyond the transcript. Those multimodal signals improved intraday return forecasts and generated profitable 0DTE option trades even after full bid-ask spreads. Key takeaway: How the Fed communicates matters alongside what it communicates.

Portfolio Construction

Bond Portfolio Optimization (Ben Slimane, Cherief, Roncalli, and Xu)

Most portfolio optimization assumes historical risk is informative about future risk. The authors argue this assumption breaks down for individual bonds. As bonds age, duration falls, and credit risk evolves, making historical covariance matrices progressively less informative. Key takeaway: Fixed income requires dynamic risk models, not equity models with different inputs.

Hidden Factors in Portfolio Risk Models: A Finite-Sample Approach to Residual PCA (Kolm and Ritter)

The paper argues that residual PCA is a sensible way to search for missing risk factors, but the resulting factor should only be used if it is strong enough to be distinguished from estimation noise and its estimated loading direction can be reliably recovered. Key takeaway: Validate residual PCA factors before incorporating them into portfolio construction.

Prediction Markets

Settlement Manipulation in Prediction Markets (Dai, Jia, and Yu)

Prediction markets are often praised for improving price discovery. This paper argues that Polymarket's 5-minute Bitcoin prediction market can do the opposite. The authors show that it creates incentives to manipulate the underlying into settlement, degrading price discovery and transferring wealth from retail traders to manipulators. Key takeaway: Ultra-short asset-price prediction markets can create incentives that distort the underlying market rather than improve price discovery.

Trend Following

Is Trend Still Your Friend?: A Microstructural Account of the Demise of Short-Term Trend-Following (Kurth, Eisler, Rej, Bouchaud)

Why did short-term trend following stop working after 2009? This paper argues that the culprit wasn't overcrowding or capacity constraints. Instead, fast trend largely disappeared in small-tick futures contracts, while remaining surprisingly resilient in large-tick contracts, consistent with HFT-driven changes in market microstructure. Key takeaway: The viability of fast trend following depends on contract microstructure, not just the signal itself.

Blogs

Volatility Indicators for Predicting S&P 500 Drawdowns (Quantseeker)

Jumping back in the pool(ing): testing pooling by asset class and portfolio weight distance (Rob Carver)

Silicon vs. Satoshi: Tactical Asset Rotation Between NASDAQ-100 and Bitcoin (Quantpedia)

Podcasts

Ben Wellington – Complex Feature Engineering at Two Sigma (Flirting with Models)

The Bridge Ep. 11: Expensive Isn't a Bubble (iCapital, featuring Cliff Asness)

Ex-Two Sigma Quant: Why You Should Bet Against Conviction (Odds on Open)

Social Media & Industry Research

Learning to Replicate Expert Judgment in Financial Tasks (Mira Murati, Thinking Machines)

When Fear Spikes, Should You Buy? (Victor Haghani, Elm Wealth)

From Energy to Metals: Why to Still Diversify Into Commodities (Goldman Sachs)

The Disappearing Overnight Drift (Boyarchenko, Larsen, and Whelan; New York Fed)

Cruel Summer for Fixed Income (Citadel)

Last Week’s Most Popular Links

Skewness Managed Portfolios (Gong, Lynch, and Ogden)

Oil–VIX States as Conditional Signals for Cross-Asset Allocation (Aylsworth, Poechhacker, Schwaiger, and Werbach)

Systematic FX trading with regression learning and transaction cost analysis (Macrosynergy)

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Volatility Indicators for Predicting S&P 500 Drawdowns

QuantSeeker — Mon, 06 Jul 2026 21:50:41 GMT

In a recent post, Which Macro Indicators Actually Predict Market Drawdowns?, I discussed and tested new research on forecasting S&P 500 drawdowns using macro-financial indicators.

In this post, I revisit that topic and test whether two volatility indicators that we have previously shown to be useful for forecasting realized volatility and timing VX futures also help predict future S&P 500 drawdowns. The findings suggest there is additional information embedded in option markets that can help predict near-term market risk.

Finally, I apply the framework to today's market and discuss the current drawdown outlook.

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Weekly Research Recap

QuantSeeker — Tue, 30 Jun 2026 19:29:34 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.

Asset Allocation

Oil–VIX States as Conditional Signals for Cross-Asset Allocation (Aylsworth, Poechhacker, Schwaiger, and Werbach)

Oil prices alone are an incomplete allocation signal. This paper argues that it's the combination of oil and market volatility (VIX) that matters. High oil with low volatility resembles a late-cycle expansion, while high oil with high volatility signals stress. Key takeaway: The same oil price can imply very different portfolio risks.

Crypto

The Party’s Over: How the Bitcoin ETF Killed Crypto’s Cool Factor (Krause)

This paper argues that Bitcoin ETFs didn't just institutionalize crypto; they changed its character. After the ETF launch, Bitcoin's volatility and equity beta declined, while Dogecoin's Sharpe ratio fell from 0.30 to 0.01 and Google search interest dropped 63%. Key takeaway: As Bitcoin becomes institutionalized, the retail speculation that once fueled crypto booms is likely to become less dominant.

Equities

Returns to “Do-Nothing” Portfolios (Bessembinder)

The last decade rewarded concentration in mega-cap stocks. However, history tells a different story. From 1971 to 2025, concentrated portfolios generally underperformed broader portfolios, while randomly selected narrow portfolios lagged the index more often than they beat it. Key takeaway: Recent mega-cap dominance is the exception, not the rule.

The CAPE that Cried Wolf (Palazzo)

CAPE may not have failed; its denominator changed. The author argues that accounting changes have depressed reported earnings, making traditional CAPE overstate valuations. Adjusting for these distortions restores much of its long-run predictive power. Key takeaway: Before dismissing CAPE, consider whether the accounting changed.

Main Street in Wall Street (Ghezzi)

Wall Street may know more about Main Street than we think. Filtering out high-frequency market noise, stock prices can be used to recover investors’ real-time expectations of economic activity, often before traditional macro data catches up. Key takeaway: Markets are a leading macro indicator, not just a pricing mechanism.

Skewness Managed Portfolios (Gong, Lynch, and Ogden)

Many classic factor premiums are driven by just a handful of extreme winners. The authors show that tilting anomaly portfolios toward stocks with higher expected skewness on the long side and lower expected skewness on the short side improves returns by 5.5% annually across 18 anomalies while also boosting Sharpe ratios. Key takeaway: Expected skewness appears to add value beyond traditional factor signals.

AI Premium (Borri, Liu, and Tsyvinski)

AI adoption may now be priced as a systematic risk factor. Using 380 trillion AI tokens across 400+ LLMs, the authors construct an AI factor from realized AI consumption. Firms with the highest AI exposure subsequently outperformed the least exposed by 64 bps per week, even after standard factor adjustments. Key takeaway: Markets seem to price AI exposure as a distinct source of risk.

Machine Learning and Large Language Models

What NLP Sentiment Can’t Hear -How Vocal Delivery Improves Earnings-Call NLP Sentiment (Pope)

Adding vocal delivery to NLP sentiment improves return predictability. The strongest effect is on the downside: Negative language delivered with vocal strain underperformed similar negative language delivered with greater control by about 1% over the next 20 trading days. Key takeaway: Voice appears to make text-based sentiment more informative, not by replacing NLP, but by helping investors interpret it.

Portfolio Optimization

Adapting the Black-Litterman Framework to Fixed-Income Portfolios (Tandle)

The author adapts the Black–Litterman framework to fixed-income investing by combining market-implied returns with macroeconomic views. While the model improved returns in some risk regimes, it generally failed to beat a simple equal-weight portfolio on a risk-adjusted basis and required much higher turnover. Key takeaway: Sophisticated optimization isn't automatically superior to simple diversification.

Blogs

Buying the Dip Isn’t Free (Quantseeker)

Rolling, rolling, rolling.... updating statistical estimates yes or no (Rob Carver)

One of These Things (Is Not Like the Others). Or is it? Pooling rule p&l estimates across instruments. (Rob Carver)

Systematic FX trading with regression learning and transaction cost analysis (Macrosynergy)

Guardrails Make the Researcher: What an AI Agent Got Right (And Wrong) Replicating Nine Equity Anomalies (Quantpedia)

Podcasts

Peter Hecht – Portable Alpha: Solving the Funding Problem of Alternatives (Flirting with Models)

Ex-Citadel Quant on Trading the Most Asymmetric Market - Neel Somani (Odds on Open)

Social Media & Industry Research

Beta-arbitrage returns and stock market bubbles (Ptuomov)

Reads you may have missed (Man Group)

Catching Angels Before They Fall (Man Group)

Credit’s Systematic Shift (Acadian Asset Management)

Last Week’s Most Popular Links

Boundaries of Time Series Momentum (Suominen and Hjalmarsson)

Beyond Growth Rates: Macro Trends and Price Cycles (Favero, Melone, Myers, and Tamoni)

To Cluster Or Not To Cluster That is the Question... (Rob Carver)

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Buying the Dip Isn't Free

QuantSeeker — Thu, 25 Jun 2026 22:03:26 GMT

Buying the dip is one of the oldest rules in investing.

Markets sell off, prices become cheaper, and the instinctive reaction is obvious: This is the opportunity to buy.

The logic seems compelling. If you liked the market a month ago, shouldn’t you like it even more now that it’s cheaper?

Not necessarily.

A market decline can mean one of two things. Prices may have temporarily overreacted and will eventually recover, or they may be reflecting a genuine deterioration in fundamentals or macro conditions. In real time, you don’t know which.

Even if the decline eventually proves temporary, another question remains: Does buying the dip actually improve your portfolio?

The answer is less obvious than most investors assume.

Buying after a sell-off isn’t free. Every purchase must be financed by selling another asset or by holding cash in reserve. If the money comes from bonds, your portfolio becomes progressively more equity-heavy as markets fall. If it comes from cash, that cash carries an opportunity cost while waiting for the next decline.

The question, then, isn’t whether buying at lower prices feels intuitive. It’s whether the additional equity exposure created during market declines is rewarded with sufficiently higher returns to justify the extra risk.

Weekly Research Recap

QuantSeeker — Tue, 23 Jun 2026 23:39:21 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. Links are included throughout so you can explore further.

Equities

High-end IPO prices (Mueller and Verwijmeren)

This paper finds a surprisingly strong signal in IPOs priced exactly at the top of the range. Across 9,152 U.S. IPOs, high-end priced deals generated significantly higher first-day returns than IPOs priced just below or above that level. Key takeaway: The most underpriced IPOs may not be those that exceed expectations, but those that stop exactly at them.

Short-term Reversal in Chinese A-Shares: Statistical Predictability, Economic Tradability, and Multiple Testing (Fang and Wang)

Short-term reversal in Chinese A-shares appears real. A strategy that buys recent losers and sells recent winners earned 1.72% per month and remained significant after controlling for common risk factors. Yet the investment case weakens considerably once transaction costs, short-sale constraints, and multiple testing are considered. Key takeaway: Statistical predictability does not necessarily translate into investable alpha.

Explaining Two Prominent Accounting Pricing Anomalies: The Accrual Anomaly and the Post-Earnings-Announcement Drift (Penman and Zhu)

Two of accounting’s most famous anomalies may have very different origins. This paper argues that the accrual anomaly largely reflects rational pricing of risk, while post-earnings-announcement drift (PEAD) looks more like genuine mispricing. Key takeaway: Investors should be careful not to treat every anomaly as evidence of market inefficiency.

Which Portfolios? The Construction Dependence of Factor Model Performance (Shin)

Which factor model is best? This paper suggests the answer depends as much on the test portfolios as on the model itself. Using thousands of randomly generated portfolios, the author finds that rankings shift materially with portfolio construction, weighting, and rebalancing choices. Key takeaway: Many debates about the “best” factor model may really be debates about test-asset design.

Macro

Beyond Growth Rates: Macro Trends and Price Cycles (Favero, Melone, Myers, and Tamoni)

Stocks and consumption growth barely move together. Yet this paper finds that stock prices remain anchored to consumption over the long run. When prices stray too far from that anchor, future returns become more predictable. Key takeaway: Consumption contains more information about expected returns than investors realize.

Momentum

Boundaries of Time Series Momentum (Suominen and Hjalmarsson)

Time-series momentum is one of the most persistent effects in finance. Using nearly 100 years of U.S. data and evidence from 20 international markets, the authors find that trend-following works best when valuations are in a normal range. Near extreme levels of CAPE, dividend yield, or the yield curve slope, trends are more likely to reverse, and momentum breaks down. Key takeaway: Valuation extremes are where trend-following strategies are most vulnerable.

Volatility

Volatility Disagreement in the Options Market (Bali, Kelly, and Moerke)

Investors often focus on volatility itself. This paper suggests disagreement about future volatility may matter more. Using a machine-learning measure of volatility disagreement, the authors find that high-disagreement stocks subsequently exhibit much lower delta-hedged straddle returns. Key takeaway: Volatility disagreement appears linked to option overpricing.

Blogs

Testing an AI-Assisted Research Workflow for Multi-Asset Pullback Strategy Discovery (Quantpedia)

To Cluster Or Not To Cluster That is the Question... (Rob Carver)

Breaking Badly: finding the structural breaks in parameter estimates (Rob Carver)

Recession Risk Through a Real-Economy Lens (CFA Institute)

Podcasts

LTCM Co-founder Victor Hagani: “Taking Risk Is Always a Negative.” (Odds on Open)

What Separates Market Wizards From All Other Traders (TraderLion)

Why Most Trend Following Improvements Should Fail ft. Rob Carver (Top Traders Unplugged)

Social Media & Industry Research

Systematic Strategies & Quant Trading (HedgeNordic)

Chasing Trends or Chasing Performance? (Quantica Capital)

Last Week’s Most Popular Links

Regime-Aware Asset Allocation with Dual-Regime Signals and Regime-Dependent Asset Selection (Luo and Mulvey)

A new decomposition approach to modeling financial returns: Conditioning sign on magnitude (Brou and Luger)

Order flow and cryptocurrency returns (Anastasopoulos, Gradojevic, Liu, Maynard, and Tsiakas)

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Weekly Research Recap

QuantSeeker — Tue, 16 Jun 2026 18:39:25 GMT

Asset Allocation

Regime-Aware Asset Allocation with Dual-Regime Signals and Regime-Dependent Asset Selection (Luo and Mulvey)

The authors combine global and asset-specific regime forecasts to dynamically rotate across equities, bonds, REITs, commodities, gold, and cash. Their dual-regime allocation framework achieved an out-of-sample Sharpe ratio of 1.43 from 2000 to 2025. Key takeaway: The edge may come less from forecasting market direction and more from identifying the assets most likely to thrive in each market regime.

Crypto

Order flow and cryptocurrency returns (Anastasopoulos, Gradojevic, Liu, Maynard, and Tsiakas)

A measure of global cryptocurrency order flow, the balance of buying and selling pressure across exchanges and currencies, strongly predicts future returns. Weekly order-flow portfolios achieved a Sharpe ratio of 1.9, while machine learning models with daily-rebalanced long-short portfolios achieved Sharpe ratios above 3.5. Key takeaway: In crypto, order flow appears to be a stronger predictor of future returns than fundamentals.

Equities

In good and in bad times? The Relation between anomaly returns and market states (Muller and Preissler)

Across 133 equity anomalies in 56 markets, abnormal returns were 70% higher during unfavorable market states, with roughly 75% of the performance gain coming from the short side. Key takeaway: Anomaly returns appear strongest when market conditions are poor, consistent with mispricing and limits-to-arbitrage playing a major role in their persistence.

A new decomposition approach to modeling financial returns: Conditioning sign on magnitude (Brou and Luger)

Rather than predicting returns in the standard way, this paper suggests decomposing returns into direction and magnitude. By forecasting the probability of gains and losses separately from the magnitude of market moves, the authors uncover nonlinear patterns that standard predictive regressions miss, thereby improving out-of-sample market timing. Key takeaway: Volatility contains information not only about risk but also about the direction of future returns.

Factor themes and the principal-component structure of factor momentum (Kim and Ryu)

Decomposing factor momentum into within- and across-theme components, the authors find that most of the variation in factor momentum comes from shifts between themes such as Value, Quality, Momentum, and Low Risk. The across-theme component is also more robust to trading costs and closely linked to the common drivers of factor returns. Key takeaway: Factor momentum may be driven more by rotation between factor themes than by selection within individual factors.

Measures of Accounting Quality and the Cross-Section of Stock Returns (Wang)

Many classic accounting-quality signals have lost their predictive power. This paper finds the edge has migrated elsewhere: Beneish’s M-score and a machine-learning Accounting Quality Score continue to predict future returns, while traditional accrual measures largely fail. Key takeaway: The accounting-quality premium is probably better measured today by misstatement-detection signals than by traditional accrual measures.

When Does Insider Trading Work? Evidence from Weekly Timing and Investor Attention (Ma, Cheng, Ding, Fu, and Zhou)

Insider purchases cluster on Fridays, while sales cluster on Mondays. Using 2.6 million insider trades, this paper finds that Friday purchases and Monday sales are the most informative insider signals, especially when investor attention is high. Key takeaway: Insider-trading signals depend on timing as much as information.

Futures

Does the Nikkei Quanto Spread Contain a Monetizable Risk Premium? (Tan)

This paper shows that a naive implementation of the so-called Nikkei quanto premium is unlikely to survive transaction costs. However, the author identifies a small but persistent edge linked to the market’s tendency to extrapolate recent stock–currency covariance and suggests alternative implementations. Key takeaway: The alpha appears to come from mispriced covariance.

Machine Learning and Large Language Models

International corporate bond returns: Uncovering predictability using machine learning (Li, Lu, Qi, and Zhou)

This paper applies machine learning to 24,000 corporate bonds across 86 countries and finds meaningful return predictability in both U.S. and international markets. The most important signals differ by region: Momentum dominates in the U.S., while downside risk and illiquidity matter more abroad. Long-short portfolios achieved Sharpe ratios of roughly 1 to 1.6. Key takeaway: The drivers of corporate bond returns vary across regions, suggesting that successful bond signals may need to be market-specific.

Technical indicators and the cross-section of corporate bond returns in a machine learning era (Chin, Guo, Lin, and Mei)

Technical indicators derived from prices, yields, and trading volume are highly informative for forecasting cross-sectional corporate bond returns. Surprisingly, machine learning adds little beyond simple linear models. Key takeaway: In corporate bonds, market-based signals appear to contain more predictive information than traditional bond characteristics, and complexity doesn’t necessarily beat simplicity.

Portfolio Construction

Selection versus diversification in noisy alpha environments (Goto and Yamada)

Investors are sometimes told to discard signals that fail stringent statistical tests. This paper finds that doing so can hurt portfolio performance. Portfolios built from a broader set of signals outperform portfolios restricted to only strongly statistically significant predictors. Key takeaway: For portfolio construction, diversification across signals can outweigh strict significance-based signal selection.

Blogs

Is There Alpha in the COT Report? (QuantSeeker)

FIFA* World Cup (*Fitting and Forecasting Actual data) Portfolio Optimisation competition with real returns (Rob Carver)

Enhancing bond index returns with systematic FX hedging (Macrosynergy)

A Hidden Trade Around SpaceX IPO? (Concretum Group)

Shock Accounting (John H. Cochrane)

Podcasts

When Trend Following Meets Equities ft. Eric Crittenden & Andrew Beer (Top Traders Unplugged)

The 3 Timeless Rules Shared by 100 Years of Market Wizards · Jack Schwager & George Coyle (Chat With Traders)

Neeraj Khemlani and Matt Ankrum, CFA: The Hunt for 100-Bagger Stocks (Enterprising Investor)

Social Media & Industry Research

The Factor Zoo Has Hundreds of Animals — But Only a Handful of Species (Alpha Architect)

Dividend Timing and Global Dividend Premium (Alpha Architect)

The whirlwind is upon us (Acadian Asset Management)

Something Has Got to Give (Eventually) (Man Group)

Last Week’s Most Popular Links

Rejoicing, Regret and Stock Returns – US and International Evidence (So and Zhang)

Price Path Continuity and the Cross-Section of Cryptocurrency Returns (Kim)

Crowded Anomalies over the Business Cycle (Jung)

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Is There Alpha in the COT Report?

QuantSeeker — Fri, 12 Jun 2026 22:40:18 GMT

Every Friday at 3:30 PM Eastern, the CFTC publishes the Commitments of Traders report, a public record of how hedge funds, commercial hedgers, and other market participants are positioned in US futures markets. Traders have used this data for decades.

The obvious question is: Does it actually predict returns?

I discuss recent research on this topic and test a commodity signal that appears to predict returns. Importantly, I examine what happens when you account for publication timing and implement the strategy in a way that is actually tradable.

Weekly Research Recap

QuantSeeker — Tue, 09 Jun 2026 20:33:43 GMT

Each Tuesday, I share the most interesting market and investing insights I came across during the week, including new research papers, blogs, and podcasts. Links are included throughout for readers who want to explore the ideas in more detail.

Crypto

Price Path Continuity and the Cross-Section of Cryptocurrency Returns (Kim)

Crypto momentum is more nuanced than it appears. Cryptocurrencies whose gains are built through many small, persistent price increases significantly outperform those driven by a few large jumps. The effect is strongest outside the largest coins. Key takeaway: Smooth trends typically contain information that investors incorporate more slowly than explosive price jumps do, leading to greater predictability of returns.

The Election Anomaly in Bitcoin Returns (Shanaev, Maksikov, and Vasenin)

Investors often debate whether Bitcoin is digital gold. This paper points to a different role: A hedge against political uncertainty. Across 60 major elections in G20 democracies since 2010, Bitcoin gained roughly 2–3% on election days on average, regardless of which party won. Key takeaway: Bitcoin’s value may partly stem from its role as a politically neutral asset when uncertainty spikes.

Crypto has Fundamentals: A Seven-Factor Model for Digital Asset Returns (Babayev and Aliyev)

A common belief is that crypto is disconnected from fundamentals. This paper suggests otherwise. Across 90 cryptocurrencies, the strongest return predictor wasn’t size or momentum; it was on-chain quality, a measure of actual network usage. The authors also find short-term reversal, with recent losers outperforming recent winners. Key takeaway: Blockchain fundamentals seem to contain information about future returns that markets do not fully price.

Equities

Rejoicing, Regret and Stock Returns – US and International Evidence (So and Zhang)

This paper introduces a signal based on a stock’s volatility-adjusted performance relative to industry peers. Stocks that recently underperformed their peers outperformed recent industry winners by over 16% annually in the U.S. The signal resembles an industry-relative short-term reversal strategy but remains predictive after controlling for traditional reversal measures. Key takeaway: Recent industry laggards typically offer better opportunities than recent leaders.

The Power of Position: Display Salience in Specialized ETFs (Park)

Stocks displayed in a thematic ETF's top-10 holdings attract more retail attention, see larger increases in retail ownership, and outperform other constituents by about 1.6% around ETF launch. Most of the gain later reverses. Key takeaway: Visibility can create temporary price pressure, even when fundamentals haven't changed.

Option-Implied Market Risk Premium and Asset Pricing (Kang, Chen, and Gan)

For decades, investors have questioned whether higher-beta stocks actually earn higher returns. This paper suggests the answer depends on the market’s expected risk premium. Using a forward-looking measure derived from S&P 500 index options, the authors find that the classic beta-return relationship reappears when investors demand unusually high compensation for market risk. Key takeaway: Beta matters most when aggregate risk aversion is high.

Crowded Anomalies over the Business Cycle (Jung)

Many anomaly strategies look similar on average, yet behave very differently across the business cycle. Using a new measure called excess centrality, the author identifies which anomalies are most exposed to macroeconomic conditions. Key takeaway: Anomaly returns seem to depend as much on macroeconomic regimes as on the signals themselves.

Aggregation Consistency and Return Predictability: Evidence from CAPE Ratios (Ma, Marshall, Nguyen, and Visaltanachoti)

Traditional CAPE ratios implicitly weights firms by earnings, while the market itself is value-weighted. Rebuilding CAPE from the stock level and then value-weighting the components improves long-horizon return forecasts by more than 10%. Key takeaway: Sometimes alpha comes from measuring familiar signals more correctly, not from inventing new ones.

Machine Learning and Large Language Models

Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning (Lebiedz and Slepaczuk)

Adding a Deep Reinforcement Learning execution layer on top of a traditional mean-reversion strategy improves out-of-sample performance in crypto pairs trading. Rather than generating signals, the model learns how to execute them while operating within predefined risk limits. Key takeaway: In systematic trading, execution can be just as important as signal generation.

The Implied Volatility Surface and the Cross-Section of Stock Returns: Evidence from Machine Learning (Ye, Xiong, Yang, Jiao)

Investors often compress option markets into a handful of indicators such as skew, smirk, or volatility spreads. This paper suggests that compression may discard valuable information. Machine-learning models that analyze the entire implied volatility surface generate stronger stock-selection signals than most traditional option metrics. Key takeaway: The predictive information in option markets seems to reside in the full volatility surface rather than in any single option metric.

Macro Economists in the Machine: A Multi-Agent LLM Framework for Commodity-Related ETF Portfolio Construction (Wang, Dai, Ma, and Geng)

Investors often assume macro investing is a forecasting problem. This paper suggests it may be an interpretation problem. Using the same economic data and the same portfolio rules, LLM agents modestly outperformed a traditional macro model by handling conflicting signals more flexibly. Key takeaway: Better decisions can come from combining existing information more intelligently rather than collecting more of it.

Blogs

UFC - Ultimate Fitting Championships (Evaluating and calibrating portfolio optimisation methods with random data) (Rob Carver)

Forecasting statistical estimates when data gets real (Rob Carver)

The crossword puzzle of fitting - why across and then down? (Rob Carver)

Resourcing a Triangulated Stat Arb Operation as a Solo Trader (Robot Wealth)

The Non-Linear Costs of Trading (Concretum Group)

Podcasts

Jack Schwager and George Coyle Interview with Michael Covel on Trend Following Radio (Michael Covel)

Ben Carlson: Investing at All-Time Highs (Rational Reminder)

Social Media & Industry Research

Don’t Look Down: Reflections on Cross-Asset Drawdowns (Man Group)

A pessimistic take on optimistic growth forecasts (Acadian Asset Management)

Reading Between the Lines: Natural Language Processing for Long-Horizon Factor Investing (Research Affiliates)

Last Week’s Most Popular Links

Harvesting Factor Premia Across Regimes: An Anchor-Stabilized Hidden Markov Framework for Multifactor Portfolios (Yang)

Capital Market Assumptions And Strategic Asset Allocation Using Multi Asset Tradable Factors (Sepp, Hansen, and Kastenholz)

Recession Detection Using Real Time GDP Data (Sikand and Zhang)

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Weekly Research Recap

QuantSeeker — Tue, 02 Jun 2026 19:07:46 GMT

Every Tuesday, I highlight the best investing research, market insights, podcasts, and ideas worth your attention, all in one curated weekly digest.

Asset Allocation

Harvesting Factor Premia Across Regimes: An Anchor-Stabilized Hidden Markov Framework for Multifactor Portfolios (Yang)

This paper tests a factor timing model that uses a Hidden Markov Model to identify market regimes and dynamically rotate across equity factors. From 2013 to 2023, the strategy achieved Sharpe ratios above 1.2 and drawdowns below 6%, substantially lower than those of broad equity benchmarks. Key takeaway: Rather than forecasting factor returns directly, investors can improve factor allocation by conditioning on latent market regimes.

Capital Market Assumptions And Strategic Asset Allocation Using Multi Asset Tradable Factors (Sepp, Hansen, and Kastenholz)

Most asset allocation models estimate expected returns on an asset-by-asset basis, creating substantial estimation noise. This paper instead estimates risk premia for a handful of tradable factors and derives expected asset returns from their factor exposures. The result is a more stable efficient frontier and roughly 50% less estimation uncertainty. Key takeaway: Estimating expected returns through a small set of factor risk premia is more robust than estimating them asset by asset.

Equities

The co-pricing factor zoo (Dickerson, Julliard, and Mueller)

After analyzing 18 quadrillion stock and bond factor models, the authors find that many factors appear to be different expressions of the same underlying risks. No small factor set can jointly explain stock and corporate bond returns. The strongest results come from combining information across dozens of factors. Key takeaway: Alpha may come less from discovering new factors and more from identifying the common risks they share.

Excess returns on idiosyncratic profitability: Evidence from a hedge portfolio strategy (Han, Jackson, and Monroe)

Most profitability signals treat earnings as one number. This paper suggests investors should look deeper. Stocks with high idiosyncratic profitability, profits unexplained by market or industry factors, earned significant abnormal returns even after controlling for standard factors. Key takeaway: Investors underreact to firm-specific profitability.

Pervasive Styles (Wang and Yu)

Investors spend enormous effort searching for the next great factor. This paper suggests the bigger edge may come from understanding how investors chase styles. Across 312 stock characteristics, over 84% of style-based signals predict future returns, even when the underlying characteristic itself have little predictive power. Key takeaway: Investor flows into styles are a more pervasive source of return predictability than the characteristics themselves.

Buying the Dip in Retail Trading (Yin and Zou)

Retail investors are often portrayed as momentum chasers. This paper finds the opposite. Using data covering 80% of U.S. retail trading, the authors show that retail investors systematically buy falling stocks, but don’t symmetrically sell rising ones. Key takeaway: Retail investors are helping stabilize markets by absorbing institutional selling pressure during drawdowns.

Passive Flows and the Limits to Arbitrage (Deng and Sammon)

Passive investing may be quietly eroding classic factor profits. Large passive inflows sharply compress returns to accounting-based anomalies by pushing up the stocks arbitrageurs are shorting more than the stocks they’re buying. Key takeaway: The growth of passive investing is creating a new limit to arbitrage by making the short side of anomaly trades increasingly difficult to exploit.

Conditional Equity Factor Risk Premium (Kim)

Most investors treat factor premiums as static. This paper suggests many aren't. Using a Kalman filter to estimate time-varying factor risk premia, the author finds that dynamically adjusting exposure to factors such as Value, Profitability, BAB, and Long-Term Reversal meaningfully improves risk-adjusted returns. Key takeaway: Factor investing may be as much about timing factor premiums as identifying them.

Machine Learning and Large Language Models

Daily Market Return Prediction with Transformer (Han, Huang, Huang, and Zhou)

A Transformer trained only on recent daily market returns generates Sharpe ratios above 1 and outperform simple moving-average signals. The edge comes from uncovering nonlinear patterns hidden in past returns. Key takeaway: Market returns contain more short-term information than traditional linear models can extract.

Regime-Based Portfolio Allocation Using Hidden Markov Models and Reinforcement Learning (Verma, Putri, and Lesupi)

Using a Hidden Markov Model to detect volatility regimes and Reinforcement Learning to allocate across stocks, bonds, and gold, the authors outperform static allocation rules out of sample. Key takeaway: Market regimes can be easier to predict than returns, and that information can improve asset allocation.

Macro

Recession Detection Using Real Time GDP Data (Sikand and Zhang)

Investors obsess over yield curves, payrolls, and dozens of other indicators to identify recessions. This paper suggests a much simpler signal may work. Using only real-time GDP releases available at the time, the authors build recession classifiers that identify all 12 U.S. recessions since 1947 without a single false signal in-sample. Key takeaway: Recession detection is less about finding new indicators and more about extracting the information already embedded in GDP data.

Blogs

A Simpler Way to Rotate Across Sectors (Quantseeker)

When Is a Mispricing Not a Mispricing? (Robot Wealth)

Tech Stock Singularity (John H. Cochrane)

The Sharpe stability ratio of trading strategies (Macrosynergy)

Building Reliable Algo Trading Systems (Concretum Group)

Podcasts

Ex-WorldQuant Head of Data Strategy: Quants “Don’t Care About the Stock Market” (Odds on Open)

The AI Bubble Might Be Exactly What We Need (William Goetzmann Explains) (Meb Faber)

Fmr FI Head Man Group: AHL Made a Billion When Everyone Thought the World Was Ending | Rob Carver (Personable)

Social Media & Industry Research

Trend-Following Filters – Part 10 (Alpha Architect)

The Inflation Wobble (Man Group)

Last Week’s Most Popular Links

A hidden Markov model for statistical arbitrage in international crude oil futures markets (Fanelli, Fontana, and Rotondi)

Factoring in the Low-Volatility Factor (Soebhag, Baltussen, and van Vliet)

Total Portfolio Approach: A Quant Lens (AQR)

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A Simpler Way to Rotate Across Sectors

QuantSeeker — Mon, 01 Jun 2026 23:39:12 GMT

Sector rotation is one of the oldest ideas in investing. The challenge is figuring out which sectors are likely to lead next.

Some approaches rely on business-cycle forecasts: Overweight cyclicals during expansions and defensives during recessions. The problem is that economic regimes are often only obvious in hindsight, and macro indicators tend to lag. By the time investors recognize the regime, much of the market move has already taken place.

Rather than trying to forecast the economy, a different approach is to ask a simpler historical question:

When the market looked like this in the past, what did each sector do next?

No macro forecast or recession call is required.

In this article, I discuss research built around that idea, test it on sector ETFs, and assess whether it adds anything beyond simply holding SPY or an equal-weighted sector portfolio.

Weekly Research Recap

QuantSeeker — Tue, 26 May 2026 17:35:10 GMT

Every Tuesday, I curate a selection of market insights, research papers, podcasts, and investing ideas that stood out during the week, along with links for further reading.

Commodities

A hidden Markov model for statistical arbitrage in international crude oil futures markets (Fanelli, Fontana, and Rotondi)

By combining Brent, WTI, and the newer Shanghai crude oil futures in a hidden Markov regime-switching framework, the authors show that temporary dislocations between global oil benchmarks remain surprisingly tradable. The strongest models delivered roughly 6–8% annualized excess returns after transaction costs, with Sharpe ratios around 1 to 1.3 out of sample. Key takeaway: New markets may create fresh inefficiencies before arbitrage capital fully compresses them.

Crypto

Cryptocurrency Return Forecasting Using Technical Analysis: Out-of-Sample Evidence, Economic Value, and Macro-Conditional Predictability (Magner, Hardy, Lavin, and Sanhueza)

Testing 84 technical signals across the 10 largest cryptocurrencies from 2017 to 2026, this paper finds that trend and momentum signals in BTC, BNB, SOL, and XRP delivered the strongest out-of-sample performance after costs, with surviving strategies showing net Sharpe ratios around 0.8 to 1.1. Predictability strengthens during high BTC volatility, crypto sentiment shifts, and gold inflows. Key takeaway: Alpha in crypto is regime-dependent.

Equities

Factoring in the Low-Volatility Factor (Soebhag, Baltussen, and van Vliet)

Low-volatility investing manages sizeable AUM, yet most asset pricing models still ignore it. This paper argues that the disconnect stems from unrealistic assumptions about frictionless long-short investing. Once trading costs and factor asymmetry are considered, low-vol materially improves standard factor models because its returns survive implementation frictions better than many competing factors. Key takeaway: Low-vol investing becomes even more valuable once real-world frictions are considered.

Continuous Timing Signals for Growth-Defensive Style Allocation: Factor Attribution, Risk Matching, and Out-of-Sample Evidence (Xiong)

Many “growth vs defensive” rotation models rely on rigid market regimes. Instead, this paper adjusts exposure between growth/tech and defensive/value ETFs using continuous signals tied to rates, VIX stress, drawdowns, and growth crowding. From 2017 to 2026, the smooth-allocation model achieved a 1.01 Sharpe ratio with materially smaller drawdowns than 100% growth exposure. Key takeaway: Style timing may work better as a continuous risk-management process than a binary regime switch.

Machine Learning and Large Language Models

Battle of transformers: Adversarial attacks on financial sentiment models (Can Turetken and Leippold)

Financial sentiment models increasingly drive trading, yet this paper shows how fragile they remain. GPT-4o-generated paraphrases fooled FinBERT and FinGPT in 20–54% of cases, cutting accuracy by up to 26 percentage points. The main weakness? Overreliance on trigger words and weak numerical reasoning. Key takeaway: Many financial NLP models still pattern-match language better than they understand finance, making them vulnerable to manipulation.

The Volatility Premium of Machine Learning: Decomposing Signal from Mechanism in Directional Trading Strategies (Gonzalez Maiz Jimenez, Lopez-Herrera, and Reyes-Santiago)

Using 216k+ out-of-sample forecasts across 48 U.S. stocks, this paper shows that, under perfect fills, random signals produce Sharpe ratios nearly identical to those of ML models. But as fills became more realistic, ML adds substantial value, especially during high-volatility regimes associated with stronger investor overreaction. Key takeaway: Execution quality may matter as much as the model itself.

Options

Navigating IV: Options Trading Strategy in Earnings Season (Lu)

Across 5,000+ option trades from 2020 to 2025 on 30 high-volume tech stocks, selling 5–10% OTM strangles around earnings produced strong risk-adjusted performance, while long-volatility strategies were broadly unprofitable. Key takeaway: Systematically harvesting post-earnings IV crush appears more effective than betting on volatility expansion.

Retail Option Imbalance and the Cross-Section of Stock Returns (Liu)

Sorting stocks by retail call-option buying pressure generates a long-short strategy with a Sharpe ratio of 1.76 (before costs) and a daily alpha of roughly 27 bps. The effect peaks after 2 trading days, then gradually reverses. Key takeaway: Retail option flow may create short-lived price overshooting.

Vanilla Options: A Unified Mathematical Framework for Pricing, Hedging, and Volatility Modeling -A Comprehensive Review (Rahaman)

This is a comprehensive review paper, walking through option pricing, Greeks, hedging, volatility modeling, and American option valuation, while also highlighting where theory breaks down in practice through transaction costs, discrete hedging, and volatility dynamics.

Uncovering the asymmetric information content of high-frequency options (Alexiou, Bevilacqua, Hizmeri)

The sign of high-frequency option returns matters. Negative jumps in OTM calls and positive jumps in OTM puts strongly predict future volatility, variance risk premia, and even equity returns. In out-of-sample volatility timing tests, these signals generate economic gains worth up to 206 bps annually. Key takeaway: Measures of downside risk in options contain information that aggregate volatility measures miss.

Blogs

Timing VX Futures with the Front-End VIX Curve (Quantseeker)

Active Dual Momentum GTAA Strategy (Quantpedia)

The Metamorphosis (Robot Wealth)

Warsh’s Challenges: Monetary Policy (John H. Cochrane)

Podcasts

The Only 4-Sharpe Crypto Fund — Leigh Drogen, Starkiller Capital (Odds on Open)

He Studied 100 Years of Bubbles. He Exposed Private Equity’s Volatility Illusion | The Weekly Wrap (Excess Returns)

Patrick Welton’s Journey from Stanford Oncologist to one of Trend Following’s Quiet Legends (RCM Alternatives)

Social Media & Industry Research

When Everyone Trades the Same Factor Playbook (Alpha Architect)

Total Portfolio Approach: A Quant Lens (AQR)

The Hidden Macro Bets in Quant Portfolios (Man Group)

Last Week’s Most Popular Links

Agentic Workflows for Alpha Research (Jonathan Kinlay)

ENSO Signals and Out-of-Sample Predictability in Soft Commodity Futures (Apte)

Dynamic Momentum Trading via Deep Q-Networks: An Intelligent Execution Framework for Portfolio Management (Deng, Xu, Li, Ji, and Xu)

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Timing VX Futures with the Front-End VIX Curve

QuantSeeker — Mon, 25 May 2026 19:07:32 GMT

Last week, I showed that the front end of the VIX term structure is a strong predictor of near-term realized volatility, even when controlling for the VIX level itself and standard HAR persistence effects.

The natural question is whether it translates into better trading.

In a previous post, I discussed a VX futures strategy utilizing bond market implied volatility as a cross-asset predictor of VIX. This week, I replace bond vol with the front-end VIX slope, run both models side by side on the same out-of-sample period, and test what happens when the two signals are combined.

The signals reflect different dimensions of market stress. Bond vol captures macro uncertainty spilling over from bond markets into equity volatility, while the front-end VIX slope reflects concentrated short-term fear embedded in equity options.

Combining them proves to be more effective than using either one alone.

Weekly Research Recap

QuantSeeker — Tue, 19 May 2026 22:30:07 GMT

Every Tuesday, I share the most interesting investing and market insights I found during the week, including new research papers, blog posts, and podcasts. I’ve included links throughout for readers who want to explore the ideas further.

Commodities

ENSO Signals and Out-of-Sample Predictability in Soft Commodity Futures (Apte)

Using NOAA El Niño data alongside price-based features for coffee, cocoa, sugar, cotton, and orange juice futures, a simple Ridge model achieves an out-of-sample Sharpe ratio above 1 after transaction costs. Climate variables alone have little standalone predictive power, but improve risk-adjusted performance by roughly 10% when combined with market signals. Key takeaway: Public climate data may still be only partially reflected in commodity prices.

Crypto

Are Day-of-the-Week Effects in Cryptocurrencies Real? Intraday Evidence from Active and Less Active Cryptocurrencies (Aalipour, Mehdian, and Rezvanian)

Most crypto “calendar anomalies” may be statistical illusions created by daily data aggregation. This paper utilizes hourly data across 12 cryptocurrencies and finds that apparent day-of-week effects are typically driven by just a few isolated intraday hours, rather than persistent daily behavior. Bitcoin’s “Monday effect,” for example, comes largely from only two specific hours. Key takeaway: Many crypto inefficiencies are short-lived microstructure effects rather than durable alpha signals.

What Do Crypto Options Tell Us? Risk Premia Implied by BTC Option Prices (Atanasova, Miao, Segarra, and Willeboordse)

Bitcoin options may contain a genuine risk-premia structure, not just speculative flow. This paper finds that option-implied factors, especially volatility-of-volatility, help predict future BTC excess returns, while crypto variance risk premia remain highly persistent even without strong institutional hedging demand. Key takeaway: Crypto derivatives are evolving into a genuine risk-transfer market with their own priced risk factors.

Arbitrage trading between decentral and central cryptocurrency exchanges (Schwertfeger and Vogt)

This paper analyzes live high-frequency arbitrage between decentralized exchanges (DEXs) and centralized exchanges (CEXs). Four arbitrage bots generate monthly returns between 6% and 33%, but realized profits are heavily constrained by slippage, liquidity limits, and latency. Key takeaway: The edge in crypto arbitrage is increasingly coming from execution infrastructure.

Equities

The decision-usefulness of stock recommendations in analyst reports: Evidence from long-window excess returns (Li)

Using 440k+ analyst reports from China’s A-share market, this paper finds that “Buy” and “Strong Buy” recommendations outperform comparable stocks by roughly 2.7 to 2.9% over the next 240 trading days. But much of the move starts before publication. The real edge may come from analysts identifying improving earnings and undervalued firms before the market fully reprices them. Key takeaway: Sell-side research still contain slow-moving information in less efficient markets.

Do Insider Trading Profits Result from the Superior Processing of Public Information? Evidence from Subtle Peer Firms’ Earnings Announcements (Campbell, Raleigh, and Zhao)

Most investors assume insider trading profits come from private information. This paper suggests another edge: Superior processing of subtle public signals. The authors show insiders buy their own company’s stock after positive earnings surprises from linked firms. CEOs appear especially skilled, and these trades earn annualized alphas of roughly 14 to 28%. Key takeaway: Some insider alpha come from spotting information spillovers before the market does.

Amplifying Long-Horizon Equity Returns: Evidence from Long-Term Equity Anticipation Securities (LEAPS) (Hegde)

Over 30 years of rolling 2-year windows, at-the-money SPY LEAPS produced average returns above 100%, versus roughly 18% for SPY itself, with realized amplification near 5x. But the tradeoff is real: Some LEAPS expired worthless, volatility was extreme, and risk-adjusted performance was not materially better than simply owning SPY. Key takeaway: LEAPS may offer convex exposure to long-run equity growth.

Machine Learning and Large Language Models

Dynamic Momentum Trading via Deep Q-Networks: An Intelligent Execution Framework for Portfolio Management (Deng, Xu, Li, Ji, and Xu)

This paper combines momentum signals, LSTM forecasts, portfolio optimization, and reinforcement learning that dynamically decides when to enter or exit trades. Across U.S. and Chinese equities, the framework improves Sharpe ratios and reduces momentum-crash drawdowns versus static strategies. Key takeaway: Adaptive execution matter as much as the signal itself in momentum investing.

Portfolio Construction

Portfolio Risk Parity with different Risk Aversion Degrees (Piantoni and Lozza)

Using S&P 500 stocks, the authors show that classic risk parity increasingly converges toward 1/N allocation as the number of holdings rises. But a Gini-based version, designed to emphasize downside and tail-risk protection rather than overall volatility, delivers stronger drawdown control and, in several tests, superior risk-adjusted performance during stressed markets. Key takeaway: The real innovation in risk parity may come from explicitly targeting tail risk, not just equalizing volatility.

Volatility

Maturity Alignment Matters: The Predictive Power of the Variance Risk Premium (Plihal and Mampouya)

Using SPY options from 2013 to 2025, the authors show that variance risk premia forecast realized volatility far better when option expiries closely match the prediction horizon. Forecast gains became strongest after SPY options moved to daily expirations. Key takeaway: Volatility signals lose information when the option maturity and forecast window are misaligned.

Blogs

How to Manage an Intraday Trend Trade (Concretum Group)

What the Front End of the VIX Curve Knows (Quantseeker)

How to build macro-aware equity indices (Macrosynergy)

Agentic Workflows for Alpha Research (Jonathan Kinlay)

Podcasts

“Market Crashes Are Good for My Strategy” - One-Man Hedge Fund PM George Livadas (Odds on Open)

When Crisis Alpha Hides in Plain Sight ft. Yoav Git & Rob Croce (Top Traders Unplugged)

He Invested Through Five Bubbles. He Wrote the Book on Them | What We Learned This Week (Excess Returns)

Last Week’s Most Popular Links

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

Dual Momentum Allocation Between Physical Gold and Bitcoin (Digital Gold) (Vojtko and Dujava)

Volatility Scaling in Multi-Asset Portfolios: Evidence from a Systematic Risk-Targeting Strategy (Almeida and Farias)

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What the Front End of the VIX Curve Knows

QuantSeeker — Mon, 18 May 2026 19:12:33 GMT

It has long been known that the shape of the VIX term structure contains information beyond the VIX level itself. When short-dated implied volatility spikes above longer-dated implied volatility, the market may be signaling not only higher uncertainty but also an imminent volatility shock.

Researchers have long used the slope of the VIX curve as a regime indicator and tactical filter for equity exposure. In a previous post, I explored a related idea by using bond market implied volatility as a cross-asset signal for forecasting shifts in the VIX complex.

But a more interesting question is whether the magnitude of the inversion matters. Not just whether the curve has inverted, but how sharply, and whether the very front end of the curve contains information that the conventional short-to-medium slope misses.

Recent research suggests the answer is yes. The magnitude of front-end VIX curve inversion appears to contain incremental information about future realized volatility, even after controlling for the VIX level itself and standard HAR-style models of volatility persistence.

In this post, I replicate the latest research findings on independent data and discuss what they imply for anyone using the VIX term structure as a volatility or risk signal.

Weekly Research Recap

QuantSeeker — Tue, 12 May 2026 20:42:52 GMT

Asset Allocation

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

Simple 6 to 12 month trend signals between EM and U.S. equities help transform a historically weak EM-vs-US spread into a profitable long-short allocation strategy with Sharpe ratios above 0.5. The strongest performance is achieved by blending multiple trend horizons rather than optimizing a single parameter. Key takeaway: Global allocation may depend less on forecasting macro turning points and more on adapting to persistent relative-performance trends.

Commodities

On the Comovement of Contango and Backwardation Across Futures Commodity Markets (Luisi, Roccazzella, and Triantafyllou)

Contango and backwardation tend to move together across energy, metals, and agricultural futures markets, especially during crises. Gold is the outlier: When other commodity curves become more contangoed, gold often moves the opposite way. Key takeaway: Macro shocks increasingly dominate commodity term structures, meaning diversification across commodities may be far weaker than expected.

Dual Momentum Allocation Between Physical Gold and Bitcoin (Digital Gold) (Vojtko and Dujava)

A simple momentum strategy rotating weekly between GLD and Bitcoin based on relative and absolute strength improves risk-adjusted performance versus buy-and-hold. The strongest variant deliver 80% annualized returns with a Sharpe of 1.64. Key takeaway: There is potential alpha in systematically adapting to when investors prefer digital versus physical stores of value.

Equities

The Crash Risk in Individual Stocks Embedded in Skewness Swap Returns (Pederzoli)

Investors appear willing to pay heavily for protection against crashes in individual stocks. Skewness-based option strategies earn roughly 20% monthly average returns with Sharpe ratios near 0.5, though punctuated by rare but severe losses during market stress. The effect strengthened materially after 2008 as deep OTM put protection became more expensive. Key takeaway: Stock-specific tail risk appears to command a large positive risk premium in option markets.

Economic Uncertainty and the Beta Anomaly in G10 Countries (Atilgan, Demirtas, Gunaydin, and Tosun)

Across G10 equity markets, low-beta stocks outperform high-beta stocks primarily during periods of low economic uncertainty. In calmer environments, the performance gap between low- and high-beta portfolios is often around 1% per month. During high-uncertainty periods, the relationship weakens substantially. Key takeaway: The beta anomaly appears highly regime dependent.

The Difficulty of Market Timing: Proximity Matters More Than You Think (Lars N. Kestner)

Getting the direction right isn’t enough in market timing; precision matters. Using 22 years of SPY data, this paper shows that even investors who correctly identify every major turning point lose most of the edge if trades are early or late by more than a few weeks. Timing errors of 8 to 16 weeks quickly converge toward buy-and-hold performance, while only trades within roughly 4 weeks retain meaningful outperformance. Key takeaway: Market timing is less about being right and more about being precisely right; small timing errors can eliminate most of the edge.

Why the Standard Sharpe Ratio Misleads for Market-timing Strategies with Many Zero Return Days (Lars N. Kestner)

Market-timing strategies often look worse on paper than they actually are. This paper shows that the standard Sharpe ratio mechanically declines as time out of the market increases, even when in-market performance is unchanged. Zero-return days create a mixture distribution that depresses Sharpe and increases kurtosis. But when measured only on invested days, Sharpe remains stable. Key takeaway: Standard Sharpe ratios can penalize low-exposure strategies for being inactive, not for having worse returns.

Machine Learning and Large Language Models

On the predictability of ETF returns with technical predictors (Gong and Muller)

A random forest model trained only on global stock technical indicators generates long-short ETF return spreads of 0.76% per month (t=2.76), with volatility and momentum signals carrying most of the predictive power. The edge is strongest in less efficient, lower-liquidity markets like China and fade quickly at longer horizons. Key takeaway: Cross-sectional patterns in individual stocks appear to carry over into ETF returns.

A Comparative Analysis of Regressor Machine Learning Models in Forecasting SPDR S&P 500 ETF Trust (SPY) Movements (Lee and Haldankar)

This paper tests five machine-learning regressors on short-term SPY prediction and finds that adding technical indicators consistently lifts directional accuracy from roughly random levels (46 to 49%) to about 54%. Random Forest benefited the most from added features, while Prophet achieved the highest hit rate at 54.6%. Key takeaway: In short-term market forecasting, feature engineering may matter more than model complexity.

A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective (Zhang and Zhang)

Most investors think LLMs will easily unlock alpha in markets. The reality may be far messier. This review paper argues that many impressive LLM trading results may overstate real-world performance due to data leakage, short sample periods, and illiquidity. In some cases, strategies with extremely high reported Sharpe ratios deteriorate sharply once realistic trading frictions are considered. Key takeaway: The biggest challenge for AI in investing may not be prediction; it’s surviving contact with real markets.

Portfolio Construction

Volatility Scaling in Multi-Asset Portfolios: Evidence from a Systematic Risk-Targeting Strategy (Almeida and Farias)

A simple multi-asset vol-scaled portfolio nearly doubles the Sharpe ratio of a 60/40 benchmark during calm market regimes (0.75 vs 0.41) by mechanically increasing exposure when realized volatility is low. But the tradeoff is severe during crises: De-leveraging leads to underperformance during sharp V-shaped recoveries like 2020. Key takeaway: Volatility scaling works best when markets stay calm but struggles during violent rebounds.

Blogs

Don’t Be Too Smart About History (Quantseeker)

Reinforcement Learning for Optimal Execution (Jonathan Kinlay)

Bad Month for Your Strategy? Should You Change It? (Alvarez Quant Trading)

The Anatomy of a Successful Trend Program (Concretum Group)

Book Review: Wrong Number (CFA Institute)

Podcasts

John Gu – Crypto Market Making & The Cold Start Problem (Flirting with Models)

Martyn Tinsley - 1 of 2 - Building Robust Trading Strategies - The Masterclass (The Algorithmic Advantage)

“If it is easy and obvious, there is no edge in it” - TD Quant Matt Schrager (Odds on Open)

Crisis Alpha, Cocoa Trends, and Correlated Trendlessness: Inside Aspect’s Strategy with Chris Reeve (RCM Alternatives)

Social Media & Industry Research

Why Momentum Investing Has Been Struggling—And What Volatility Has to Do With It (Alpha Architect)

Reads You May Have Missed (Man Group)

Last Week’s Most Popular Links

Variance and Skewness Risk Premium and Expected Equity Returns (Ito)

Multi-Asset Commodities Volatility Portfolio (Dottin)

Getting the Target Right in Return Prediction (Cakici and Zaremba)

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Don't Be Too Smart About History

QuantSeeker — Sat, 09 May 2026 22:47:40 GMT

Statistical models used to predict returns typically make a quiet assumption: That the past is uniformly informative about the future. A standard predictive regression assigns the same weight to turbulent periods, such as October 2008, as to calm periods. The data goes in, the estimate comes out, and the differences between market regimes are averaged away.

The natural fix is to condition on relevance, to identify which historical episodes most resemble today and place greater weight on those observations when forming forecasts. It is an intuitive idea with an elegant mathematical foundation, and it has attracted interest in both academic research and applied quantitative investing.

But there is a deeper question beneath the intuition:

Does conditioning on “relevant” history actually improve forecasts, or does it simply amplify noise precisely when markets become most unstable?