Momentum turning points
利用慢速和快速时间序列动量刻画股市的四个周期（牛市、修正、熊市、反弹），发现熊市下跌集中在高风险状态但预测负期望收益，混合速度的动量组合能产生正阿尔法。
We use slow and fast time-series momentum to characterize four stock market cycles—Bull, Correction, Bear, and Rebound. The steep market declines of Bears concentrate in high-risk states, yet predict negative expected returns, which is difficult to rationalize by most models of time-varying risk premia. Using a model to analyze slow and fast momentum strategies, we estimate both relatively high mean persistence and realization noise in U.S. stock market returns. Intermediate-speed momentum portfolios, formed by blending slow and fast momentum strategies, translate predictive information in market cycles into positive unconditional alpha, for which we propose a novel decomposition.