Weekly Research Recap
Latest research on investing and trading
This week’s recap highlights the key investing insights from academic research, industry analysis, blogs, and social media over the last seven days, with links to every source.
Asset Allocation
Tactical Asset Allocations of Large Asset Managers (Ibert)
Large asset managers’ tactical views closely follow their assessments of global growth and recession risk. When growth prospects deteriorate, they cut equity exposure and increase bond allocations. A move from overweight to underweight equities reduces equity weights by roughly 1.8–3.2 percentage points. Key takeaway: Institutional tactical shifts are relatively modest but consistently tied to changes in the economic growth outlook.
Crypto
Network-Based Detection of Wash Trading (Sirolly, Ma, Kanoria, and Sethi)
The authors introduce an algorithm that detects wash trading by spotting wallets that repeatedly open and close positions with similar counterparties. Applied to Polymarket, it flags about 25% of total volume as wash trading, with some markets reaching 90–95% artificial activity. Key takeaway: A significant share of volume on crypto prediction markets is estimated to be artificial, making raw trading volume an unreliable indicator of genuine information.
This paper compares MSTR and IBIT as institutional Bitcoin vehicles from January 2024 to November 2025. MSTR shows stronger returns and a higher Sharpe, but its large NAV premium, embedded leverage, dilution, and added complexity make it less suitable for fiduciaries. IBIT offers cleaner, lower-risk, and more precise Bitcoin exposure with near-perfect tracking. Key takeaway: Spot Bitcoin ETFs are the more efficient and defensible choice for institutions.
Equities
From Red to Blue: The Long-Run Inversion of Political Cycles and Stock Market Returns Over 153 Years (Han, Lu, Xu, and Zhou)
Using 153 years of U.S. data, the paper shows that the stock market’s preferred party changes across eras. Before the Great Depression, excess returns were about 7 percentage points higher under Republicans, reflecting stronger consumption growth. After 1929, Democrats outperform by roughly 9–10 points, especially when risk aversion is high. Key takeaway: Investors’ required risk compensation is affected by political regimes, and those regimes shift over time.
The Repurchase Effect and Asset Prices (Chen, Gu, Xiong, and Yu)
Investors avoid repurchasing stocks that rise after they sell, and the authors’ repurchase-effect metric captures this behavior. A long–short portfolio delivers annualized Fama-French 6-factor alphas of 23% (EW) and 11% (VW), with the effect concentrated in the next month and strongest in retail-driven and high-sentiment environments. China shows similar patterns. Key takeaway: Regret-driven demand gaps create possible short-horizon alpha opportunities.
Push-Response Anomalies in High-Frequency S&P 500 Price Series (Vlasiuk and Smirnov)
Using 2.6 billion SPY NBBO ticks (2018–2023), the paper examines how a push, the past price change over lag L, predicts the response, the next price change over the same lag. Short lags look efficient, but mid-range horizons (≈5,000–150,000 ticks) reveal clear predictability: Moderate pushes generate directional drift, and large negative pushes produce disproportionately strong positive rebounds. Key takeaway: SPY exhibits intraday predictability that standard autocorrelation tests struggle to detect.
Risk Factor Similarity and Economic Links (Chen and Yang)
Firms with similar 10-K risk disclosures show pronounced lead–lag return patterns. Sorting on peer-firm returns produces a 1.78% monthly long–short spread (before costs). The effect is not explained by other firm-link measures, strengthens when investor attention is limited or when disclosures change, and even extends to options. Key takeaway: Risk-disclosure similarity is a broad and significant source of cross-stock return predictability.
FX
Currency Speculation (Della Corte and Riddiough)
While many FX strategies, such as carry, value, and short-term momentum, deliver sizeable excess returns, their profitability weakens when the universe is restricted to liquid, floating, and open-capital-account currencies. Yet combining signals consistently boosts Sharpe ratios and yields stable performance across all samples. Key takeaway: In FX, sample selection matters, and diversified multi-signal portfolios are far more robust than any individual strategy.
Machine Learning and Large Language Models
Empirical Asset Pricing via Machine Learning: The Role of Research Design Choices (Jindal, Lalwani, and Meshram)
Machine-learning portfolio performance is highly sensitive to methodological choices. Across 5,000+ specifications, non-standard errors can be up to 5× traditional standard errors, and choices like micro-cap inclusion, weighting, and portfolio sorting materially affect outcomes. Still, over half of ML portfolios earn statistically significant net returns. Key takeaway: ML strategies can be profitable, but their realised performance is highly dependent on research design.
Growing Mimicking Portfolios: Estimating Nontraded Factor Risk Premia (Feng, He, Ma, and Robotti)
Traditional mimicking portfolios, based on projections onto benchmark portfolios, often weakly capture nontraded factors, producing unstable premium estimates. The paper’s “Mimicking Portfolio Tree” improves spanning by iteratively building characteristic-sorted portfolios that maximize correlation with each factor. Across 69 nontraded factors, about one-third show significant premia. Key takeaway: Stronger mimicking portfolios suggest that most nontraded factors lack meaningful risk compensation.
Options
Systematic Variance Risk Everywhere in Equity Option Markets (Dickerson, Fournier, Jacobs, and Orlowski)
Systematic variance risk, proxied by weekly changes in VIX², is the dominant priced factor in equity option returns. Using a conditional factor model and 12 million observations, the variance-risk premium is strongly negative and highly consistent across index, stock, and ETF options (roughly –18% to –20% per year, all statistically significant). Key takeaway: Variance shocks command a pervasive negative risk premium across the entire equity option universe.
Portfolio Construction
The Tranching Dilemma. A Cost-Aware Approach to Mitigate Rebalance Timing Luck in Factor Portfolios (Zarattini and Pagani)
The paper shows that rebalance timing luck, return differences caused solely by the chosen monthly rebalance date, is large in a concentrated momentum strategy, generating a 3.48-ppt CAGR gap from 1991 to 2024. Tranching, splitting the portfolio into sub-portfolios rebalanced on different days, reduces this dispersion roughly by 1 / (number of tranches), but only large AUMs benefit after trading costs. Key takeaway: Rebalance timing luck is real and persistent, but only worth addressing for investors with substantial AUM, given the transaction-cost drag.
Blogs
Wordle (TM) and the one simple hack you need to pass funded trader challenges (Rob Carver)
Implementing a TAA Model Using Futures Instead of ETFs (Quantseeker)
How to Design a Simple Multi-Timeframe Trend Strategy on Bitcoin (Quantpedia)
Macro scorecards for local-currency EM bonds (Macrosynergy)
Podcasts
From Data to Dollars: The Rise of Quant Investing with Carlos Morales (The Money Runner)
Chris Miller – Inside the Mechanics of Volatility Trading (Resonanz Capital)
Victor Haghani: From The Missing Billionaires to Smarter Risk and Better Decisions (Enterprising Investor)
Social Media & Industry Research
Why Hold Expensive Slow-Growing Stocks? (Research Affiliates)
Uncovering Alpha In The Networked Economy (Guido Baltussen, Northern Trust)
What AI Can (and Can’t Yet) Do for Alpha (Man Group)
Diversifying Alternatives (Cliff Asness, AQR)
Last Week’s Most Popular Links
Is predicting vol better worth the effort and does the VIX help? (Rob Carver)
Macro Risk Premia and the Business Cycle (Su and Yu)
Put Option Trading Efficiency (Huo)
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