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

@article{Oelschlager2020DetectingBA,
  title={Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models},
  author={Lennart Oelschlager and Timo Adam},
  journal={Statistical Modelling},
  year={2020}
}
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 to bearish markets and vice versa. Popular tools for modelling 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… 
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References

SHOWING 1-10 OF 29 REFERENCES

A dynamic analysis of stock markets using a hidden Markov model

This paper proposes a framework to detect financial crises, pinpoint the end of a crisis in stock markets and support investment decision-making processes. This proposal is based on a hidden Markov

Hidden Markov Model for Stock Trading

This paper introduces the application of HMM in trading stocks (with S&P 500 index being an example) based on the stock price predictions and proves that the HMM outperforms this traditional method in predicting and trading stocks.

Dynamic portfolio optimization across hidden market regimes

Regime-based asset allocation has been shown to add value over rebalancing to static weights and, in particular, reduce potential drawdowns by reacting to changes in market conditions. The

Stylized Facts of Daily Return Series and the Hidden Markov Model

In two recent papers, Granger and Ding (1995a,b) considered long return series that are first differences of logarithmed price series or price indices. They established a set of temporal and

Regime Changes and Financial Markets

Regime switching models can match the tendency of financial markets to often change their behavior abruptly and the phenomenon that the new behavior of financial variables often persists for several

Mixture Hidden Markov Models in Finance Research

The Mixture Hidden Markov Model (MHMM) that takes into account time and space heterogeneity simultaneously is introduced that can deal with the specific features of financial time series data, such as asymmetry, kurtosis, and unobserved heterogeneity.

Hidden Markov Model for Financial Time Series and Its Application to S&P 500 Index

The R package ldhmm is developed for the study of financial time series using Hidden Markov Model (HMM) with the lambda distribution framework. In particular, S&P 500 index is studied in depth due to

Long Memory of Financial Time Series and Hidden Markov Models with Time-Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the

Markov-switching asset allocation: Do profitable strategies exist?

This article proposes a straightforward Markov-switching asset allocation model, which reduces the market exposure to periods of high volatility. The main purpose of the study is to examine the

Stylized facts of financial time series and hidden semi-Markov models