Composed Volatility based Time Series Momentum 中文
傳統的動量因子將股票依過去的表現分成贏家和輸家，並認為贏家會比輸家在未來有更多的報酬。然而，因為市場資訊的流動越來越迅速，以及目前的高通膨情境，導致最近十年的動量因子表現不佳。我們致力於研究不同的動量因子以因應目前的局勢。在本篇報告中，我們發現在考慮了不同時間維度的市場波動性之後，我們可以顯著改善動量因子的報酬。
The traditional momentum factor (cross mom) is a strategy that buys past winners and short sells past losers. It has been shown in the paper that this strategy can gain significant positive returns. But in recent years, we found that this factor has lost its effect, and the reason might be because the more efficient the market becomes and the high inflation. To deal with the high inflation nowadays the possible bubble crisis that may follow, We will further study the momentum factor to make it more adaptive. Thus, we start by constructing a slightly different momentum strategy called Time Series Momentum, which assumes that stocks with positive past returns can earn consistent profit in the future, and vice versa.
At first glance, the TSMOM outperforms the Cross Mom, but we still can make improvements to make it more stable. Then we introduce two indicators established by volatility in different time dimensions. They are volatility indicators and the Sierra Signal. Inspired by Daniel, Moskowitz (2016), we found that the previous month’s market volatility is predictive of the return of TSMOM. And Y.C. Lin (2021) shows that the Sierra Signal considered the spread between historical realized volatility and intraday volatility could capture the coming soon shock. By adding these two indicators we make the strategy more dynamics and flexible.
Based on the adjustments, we increase TSMOM’s ability to withstand risks and stability in the face of market fluctuations. And also improve the Sharpe ratio to 1.35 and the max drawdown to 11.6%.
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CY Yang
Quantitative Research Intern, Quantitative Finance, Gamma Paradigm
