Strategies focused on US-listed assets.
This paper shows that the part of the variance risk premium tied to downside tail risk—jumps in asset prices—is what really predicts market returns. It's a cleaner measure of investor fear than the VIX.
It’s not volatility, but the anticipation of risk that drives returns. This paper shows how timing macro events reveals the hidden premium of uncertainty.
Option volume can be an early signal for takeover events
Equity market factors like size, profitability, and past returns can predict corporate bond returns,
Want better bond return forecasts? This paper finds a new factor that improves risk premium estimates, making Treasury bond investing more precise.
Most stock market gains happen during a four-hour window before European markets open. This paper finds that almost all returns come from this short time period, likely because investors are reacting to overnight news. A simple strategy that trades during this window beats buy-and-hold.
Sharpe ratio up to 7.2. A powerful AI-driven approach to price trends, demonstrating that machine learning can outperform traditional technical indicators.
Adding a seasonality factor to a portfolio of market, size, value, and momentum increases the Sharpe ratio from 1.04 to 1.67.
The study highlights greater returns in markets with high trading frictions, such as non-US developed and emerging markets.
Taking advantage of limited attention in anomaly trading: The average Sharpe ratio documented in the paper is 1.09 (min: 0.62 ROE, max: 4.45 MOM). Top anomalies: MOM (4.45), ROA (2.40), PEAD (1.60), PERF (1.68)—all stronger in low media coverage stocks.
Sharpe Ratio (H-L Portfolio): 4.80