论文标题
隐藏的马尔可夫模型应用于日内动量交易，并提供侧面信息
Hidden Markov Models Applied To Intraday Momentum Trading With Side Information
论文作者
论文摘要
提出了一个隐藏的Markov模型，该模型指定了一个潜在的动量状态，负责产生观察到的证券的噪声回报。现有的动量交易模型由于数字过滤器的频率响应延迟而造成的时停滞。当市场改变趋势方向时，时间隔板会导致迹象错误的势头信号。该状态空间公式的一个关键特征是没有发生这样的滞后，从而可以在市场变化点上准确移动信号标志。模型中的潜在状态数量是使用三种技术，即交叉验证，惩罚的可能性标准和基于模拟的模型选择的边际可能性估算的。这三种技术都建议2或3个隐藏状态。然后使用Baum-Welch和Markov Chain Monte Carlo找到模型参数，同时假设发射矩阵的单个单变量高斯分布。势头交易者通常希望将其交易信号调节到其他信息上。为了反映这一点，在存在侧面信息的情况下，学习也是进行的。考虑了两组侧面信息，即实现的波动性和日内季节性的比率。结果表明，花键可用于从该信息中捕获统计上具有重要意义的关系，从而可以预测回报。输入输出隐藏的马尔可夫模型用于将这些单变量预测信号纳入过渡矩阵中，并提供了处理信号组合问题的可能解决方案。然后进行贝叶斯推断，以预测使用远期算法的证券$ t+1 $返回。对当前框架的简单修改允许使用异步预测的完全非参数模型。
A Hidden Markov Model for intraday momentum trading is presented which specifies a latent momentum state responsible for generating the observed securities' noisy returns. Existing momentum trading models suffer from time-lagging caused by the delayed frequency response of digital filters. Time-lagging results in a momentum signal of the wrong sign, when the market changes trend direction. A key feature of this state space formulation, is no such lagging occurs, allowing for accurate shifts in signal sign at market change points. The number of latent states in the model is estimated using three techniques, cross validation, penalized likelihood criteria and simulation-based model selection for the marginal likelihood. All three techniques suggest either 2 or 3 hidden states. Model parameters are then found using Baum-Welch and Markov Chain Monte Carlo, whilst assuming a single (discretized) univariate Gaussian distribution for the emission matrix. Often a momentum trader will want to condition their trading signals on additional information. To reflect this, learning is also carried out in the presence of side information. Two sets of side information are considered, namely a ratio of realized volatilities and intraday seasonality. It is shown that splines can be used to capture statistically significant relationships from this information, allowing returns to be predicted. An Input Output Hidden Markov Model is used to incorporate these univariate predictive signals into the transition matrix, presenting a possible solution for dealing with the signal combination problem. Bayesian inference is then carried out to predict the securities $t+1$ return using the forward algorithm. Simple modifications to the current framework allow for a fully non-parametric model with asynchronous prediction.
