论文标题
使用分层隐藏的马尔可夫模型在财务时间序列中检测看跌和看涨市场
Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models
论文作者
论文摘要
金融市场表现出价格上涨和下跌的交替时期。寻求做出盈利投资决策的股票交易者必须考虑到这些趋势，目标是准确预测从看涨的看跌市场的转变，反之亦然。用于建模财务时间序列的流行工具是隐藏的马尔可夫模型，其中潜在的状态流程用于在不同的市场制度之间明确模型开关。但是，以基本形式，隐藏的马尔可夫模型无法捕获短期和长期趋势，这可能会导致短期价格波动的误解，因为长期趋势的变化。在本文中，我们演示了如何使用分层隐藏的马尔可夫模型来绘制金融市场的全面图片，这可以有助于开发更复杂的交易策略。在两个实际数据应用程序中说明了建议的方法的可行性，我们在两个主要库存指数（Deutscher Aktienindex和Standard＆Poor的500）中对数据进行了建模。
Financial markets exhibit alternating periods of rising and falling prices. Stock traders seeking to make profitable investment decisions have to account for those trends, where the goal is to accurately predict switches from bullish towards bearish markets and vice versa. Popular tools for modeling financial time series are hidden Markov models, where a latent state process is used to explicitly model switches among different market regimes. In their basic form, however, hidden Markov models are not capable of capturing both short- and long-term trends, which can lead to a misinterpretation of short-term price fluctuations as changes in the long-term trend. In this paper, we demonstrate how hierarchical hidden Markov models can be used to draw a comprehensive picture of financial markets, which can contribute to the development of more sophisticated trading strategies. The feasibility of the suggested approach is illustrated in two real-data applications, where we model data from two major stock indices, the Deutscher Aktienindex and the Standard & Poor's 500.
