Each week, I share interesting findings from recent investment research, including academic papers, industry reports, and investment blogs. Links to the original sources are included.
Calendar Effects
Trading the Multi-Asset Drift Around U.S. Elections (Vojtko and Dujava)
Risk assets tend to decline the day after US elections while intermediate-term Treasuries recover. Shorting an equal-weighted basket of seven ETFs the day after elections (2008–2024) produced a Sharpe ratio of 0.31 and a maximum drawdown of 4.2%. A complementary Treasury strategy, short before elections and long afterward, achieved a Sharpe ratio of 0.48. However, results are based on relatively few elections.
Crypto
State-Dependent Market (In)Efficiency in Cryptocurrency Markets (Barak, Razmi, and Mousavi)
This paper tests an adaptive strategy across 50 cryptocurrencies, using machine learning to adjust trend-following parameters based on on-chain data, market conditions, and recent strategy performance. Out-of-sample gross Sharpe reached 1.34 versus 0.59 for the best static strategy, with 37.5% lower maximum drawdown. However, performance is sensitive to trading costs.
Equities
Synthesizing Information-Driven Insider Trade Signals (Heckmann, Jacobs, and Schwarz)
Not all insider trades are equally informative. Analyzing 3.7 million insider transactions across 34 countries, the authors combine 33 signals to identify informed trades. The resulting long-short strategy delivers monthly six-factor alphas exceeding 1% in equal-weighted portfolios, outperforming simple insider-trading strategies by 3–4% annually. The edge is strongest in small stocks and insider purchases, but trading costs pose a significant challenge.
Not All Beats Are Equal: Anchoring on the Nominal Earnings Surprise (Ferreira, Partridge, and Wang)
Do investors react to earnings surprises in percentages or cents? Analyzing 250,000 quarterly earnings announcements (1990–2024), this paper finds that cents matter. For the same percentage surprise, stock price reactions more than double across earnings-per-share levels. Each additional cent is associated with roughly 5.3 basis points of abnormal return over three days.
Synthetic Sector Classification for Statistical Relative Valuation (Leite)
Is traditional sector classification optimal? The author groups stocks by their historical correlations with sector ETFs rather than GICS labels, then uses valuation regressions to select stocks. Across six models (2019–2023), the ETF-based approach reported average terminal cumulative returns of 141%, versus 117% for GICS and 129% without sector controls.
Does mean reversion work better on stocks that historically revert quickly? This paper tests an Ornstein-Uhlenbeck mean-reversion strategy on S&P 500 stocks over 2006–2025. Selecting stocks with the shortest estimated half-lives produced a Sharpe ratio of 0.55, versus 0.31 for trending stocks. However, the estimated half-lives did not persist, and trading costs eliminated the strategy’s advantage over SPY.
ETFs
Measuring Longer-Horizon Leveraged ETF Performance (Murray and Sammon)
Volatility drag is only half the story for leveraged ETFs. This paper highlights an offsetting “convexity bonus” that can allow leveraged ETFs to outperform their stated multiple over longer horizons. For example, NVIDIA gained 171% in 2024, while its 2× ETF (NVDX) returned 382%. The authors also show why arithmetic average returns can be misleading: For long-term investors, compounded wealth matters more.
Portfolio Management
The Hidden Sharpe Ratio (Chernov, Kelly, Malamud, and Schwab)
How high is the maximum achievable Sharpe ratio in US equities? The authors estimate 7.4, or 5.0 excluding micro-caps, far above existing portfolio-based benchmarks of 1.0–4.0. Using 132 stock characteristics and random matrix theory, the authors find that conventional portfolio optimization captures only a fraction of the available risk-adjusted return potential. These are theoretical estimates, before trading costs.
Blogs
Outperforming the S&P500 with macro factors (Macrosynergy)
This time would have to be very different. Part II (Hanno Lustig)
The Part Time Trader · Part 5: Half a Million Backtests & a Coin Flip (The Algorithmic Advantage)
Podcasts
Systematic Trading Redux with Michael Covel on Trend Following Radio (Michael Covel)
Why Your Trading Strategy Might Be Wrong for You · Alex Temiz and Bao Nguyen (Chat with Traders)
$100M fund manager: how to stop living like a robot that just saves money (My First Million)
Social Media & Industry Research
The Passive Equity Trap (Man Group)
Corporate Credit and the Institutional Portfolio: Reasserting First Principles (Acadian Asset Management)
Global Views: Downside Growth Risks (Goldman Sachs)
Value Traps Are Painful ... Read the Footnotes (Syzygy Asset Management)
Last Week’s Most Popular Links
Daily mean Reversion in Global Equity Indices: Serial Dependence, Tail Risk, and Exploitation (Bertola)
Simple Portfolios that Deliver Consistent Decent Returns (Eden Wallberg and Karolin)
Expected and Realized Returns on Volatility (Hu and Jacobs)
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Adaptive trend following going from a 0.59 to a 1.34 Sharpe with 37.5% lower max drawdown is a big jump. Curious how much of that survives once you add real trading costs.