Weekly Research Recap
Latest research on investing and trading
Hi there, this week’s research recap highlights the key investing insights from the past seven days, drawn from academic papers, industry reports, social media, and blogs, with direct links to every source.
Bonds
What 200 Years of Data Tell Us About the Predictive Variance of Long-Term Bonds (Della Corte, Gao, Preve, and Valente)
Over two centuries of data show that the risk of holding long-term foreign bonds without currency hedging increases with the investment horizon rather than mean-reverting. The dominant sources of uncertainty are exchange rate fluctuations and shifts in monetary and interest-rate regimes, which are difficult to forecast and overwhelm any gradual mean reversion in bond yields. Key takeaway: Long-horizon international bond allocations face structural regime risk that is not visible in standard historical volatility measures.
Commodities
The Monetary Policy–Commodities Nexus: A Survey (Bohl, Humann, and Siklos)
This survey paper discusses how commodity prices and monetary policy now move together. When central banks raise rates, commodity prices such as oil and metals often fall as borrowing becomes more expensive and the dollar strengthens. Moreover, rising food or energy prices push up inflation expectations, affecting monetary policy. Since the mid-2000s, rising financial investment in commodities has made these swings larger and more connected across markets. Key takeaway: Central banks must treat commodity prices as a core part of how policy affects the economy, not just as outside shocks.
Crypto
From Time-Series Momentum to Size-Factor Comovement: Bitcoin’s Continuing Evolution as a Financial Asset (Rosen and Wang)
Early Bitcoin returns showed strong time-series momentum from 2011 to 2018, but this effect disappears after 2018, indicating greater market efficiency. Since 2019, Bitcoin loads positively on the SMB size factor, behaving more like a small-cap equity, while still responding to VIX and short-rate conditions. Key takeaway: Bitcoin has become integrated into traditional equity risk, reducing its diversification value.
Equities
Long-Term Risk-Reward Tradeoffs and Sharpe Ratios (Welch)
The standard way of annualizing Sharpe ratios is to multiply by the square root of time T, assuming mean returns scale with T and volatility by √T. However, this paper shows that this breaks down over long horizons. For the U.S. equity premium (annual Sharpe of about 0.40), the Sharpe rises only up to about 10–15 years, then declines as volatility compounds faster than returns. Key takeaway: Sharpe ratios are misleading for long-horizon decisions as long-term risk comes from potential shifts in the return distribution, not from short-term volatility.
Machine Learning and Large Language Models
BondBERT: What we learn when assigning sentiment in the bond market (Barter, Gao, Christodoulaki, Chen, and Cartlidge)
BondBERT is a transformer model fine-tuned on bond-market news. It corrects the issue where general financial (equity-leaning) sentiment models sometimes interpret macro news in the wrong direction for bonds. Across UK sovereign bonds, BondBERT achieves roughly 57–58% next-day directional accuracy (vs. roughly 55–56% for FinBERT and <40% for FinGPT). Key takeaway: Bond-specific sentiment training improves sign prediction and signal quality in fixed income.
Macroeconomic Reports and the Cross-section of Industry Returns (Breitung, Kruthof, and Muller)
Central bank and OECD reports contain clues about which industries will outperform. Using GPT-4.1 and Gemini 2.5 Pro, the authors convert each report into 15 macro sentiment dimensions and then forecast six-month industry returns using a range of ML models. An equal-weighted long–short industry portfolio earns roughly 0.9% per month in alpha. Key takeaway: Markets are slow to process nuanced macro language and its implications, especially for smaller firms, creating a potentially tradable signal.
Technical indicators and the cross-section of corporate bond returns in a machine learning era (Chin, Guo, Lin, and Mei)
Technical indicators based on bond prices, yields, and volume predict corporate bond returns better than standard bond characteristics. Portfolios formed on 146 technical signals earn 0.4–0.6% per month net, with Sharpe ratios of about 0.5, and complex ML models add no improvement over simple OLS. Key takeaway: In corporate bonds, trend and momentum dominate fundamentals, and simple linear models are enough to capture the signal.
Macro
Macro Risk Premia and the Business Cycle (Su and Yu)
Cyclical (high macro-beta) stocks underperform countercyclical stocks for several months after recessions begin as investors revise macro expectations slowly. The gap between machine-learning and survey GDP forecasts captures this effect and predicts the cyclical–countercyclical return spread. Using this to time exposure generates roughly 5% annual alpha, and combining it with an investor sentiment signal increases alpha to about 8% annually. Key takeaway: Macro expectations adjust slowly, and that lag is tradable.
Options
Put Option Trading Efficiency (Huo)
The author forms a market-timing signal by regressing put open interest on mispricing scores across stocks each month. A one-standard-deviation increase in the resulting slope predicts about –0.6% lower excess market returns the following month, with the effect persisting up to a year and strengthening in more volatile periods. Call-based analogs show no forecasting power. Key takeaway: Concentrated bearish put positioning signals lower future market returns.
Blogs
Is predicting vol better worth the effort and does the VIX help? (Rob Carver)
NBER Asset Pricing Lessons (John H. Cochrane)
Causation Does not Imply Variation (John H. Cochrane)
Podcasts
You’re Focusing on the Wrong Question | Victor Haghani on Why Static Allocation Fails (Excess Returns)
Why Trend Thrives When Inflation Returns ft. Yoav Git (Top Traders Unplugged)
The Most Powerful Investing Tool You Aren’t Using | Four Lessons from Michael Mauboussin (Excess Returns)
Ted Cadsby: The Power of Index Funds, and Being Human (Rational Reminder)
Social Media & Industry Research
A story of data in the age of data deluge (Transtrend)
‘Total Portfolio Approach’ Is Shaking Up How Trillions Get Managed (Justina Lee, Bloomberg)
Great reads from Man Group (Man Group)
Great interview with Cliff Asness (Bloomberg TV, via “Ptuomov”)
Last Week’s Most Popular Links
Dynamic Dragon: Integrating Regime Detection into Strategic Asset Allocation (Ni)
The Lazy Man’s Momentum Strategy (Estrada)
R squared and Sharpe Ratio (Rob Carver)
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