论文标题
使用赫斯特指数和Q学习在动量和平均回归策略上优化回报
Optimizing Returns Using the Hurst Exponent and Q Learning on Momentum and Mean Reversion Strategies
论文作者
论文摘要
动量和平均回归交易策略具有相反的特征。前者通常在趋势资产方面更好，而后者通常使用平均振兴资产更好。使用将时间序列归类为趋势或平均恢复的赫斯特指数，我们试图在每种策略平均产生更高回报时与每种策略进行交易。我们最终发现，与赫斯特指数的交易可以实现更高的回报，但风险也更高。最后，我们考虑研究的局限性，并提出一种使用Q学习的方法来改善我们的策略和实施单个算法。
Momentum and mean reversion trading strategies have opposite characteristics. The former is generally better with trending assets, and the latter is generally better with mean reverting assets. Using the Hurst exponent, which classifies time series as trending or mean reverting, we attempt to trade with each strategy when it is advantageous to generate higher returns on average. We ultimately find that trading with the Hurst exponent can achieve higher returns, but it also comes at a higher risk. Finally, we consider limitations of our study and propose a method using Q-learning to improve our strategy and implementation of individual algorithms.
