Weekly Research Recap
Latest research on investing and trading
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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