Weekly Research Recap
Latest research on investing and trading
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
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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