Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models

TL;DR

This paper introduces hierarchical hidden Markov models to better detect market regime switches in financial time series, capturing both short- and long-term trends for improved trading strategy development.

Contribution

It presents a novel application of hierarchical hidden Markov models to model complex market regimes, enhancing the detection of bullish and bearish phases.

Findings

Effective modeling of two major stock indices

Improved detection of market regime changes

Potential for more sophisticated trading strategies

Abstract

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.