scholarly journals Pattern recognition using hidden Markov models in financial time series

2017 ◽  
Vol 21 (1) ◽  
pp. 25
Author(s):  
Sara Rebagliati ◽  
Emanuela Sasso
Keyword(s):  
Time Series ◽  
Pattern Recognition ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Financial Time Series ◽  
Hidden Markov ◽  
Financial Time

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

2021 ◽  
pp. 1471082X2110340
Author(s):  
Lennart Oelschläger ◽  
Timo Adam
Keyword(s):  
Time Series ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Financial Time Series ◽  
Hidden Markov ◽  
Financial Time ◽  
State Process ◽  
Bank Stock ◽  
Hierarchical Hidden Markov Models

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 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 article, we demonstrate how hierarchical hidden Markov models can be used to draw a comprehensive picture of market behaviour, 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 the Deutscher Aktienindex and the Deutsche Bank stock. The proposed methodology is implemented in the R package fHMM, which is available on CRAN.

scholarly journals A Hidden Markov Model for the Linguistic Analysis of the Voynich Manuscript

2019 ◽  
Vol 24 (1) ◽  
pp. 14 ◽  
Author(s):  
Luis Acedo
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Mathematical Linguistics ◽  
Underlying Structure ◽  
Internal States ◽  
Consonant Sounds

Hidden Markov models are a very useful tool in the modeling of time series and any sequence of data. In particular, they have been successfully applied to the field of mathematical linguistics. In this paper, we apply a hidden Markov model to analyze the underlying structure of an ancient and complex manuscript, known as the Voynich manuscript, which remains undeciphered. By assuming a certain number of internal states representations for the symbols of the manuscripts, we train the network by means of the α and β -pass algorithms to optimize the model. By this procedure, we are able to obtain the so-called transition and observation matrices to compare with known languages concerning the frequency of consonant andvowel sounds. From this analysis, we conclude that transitions occur between the two states with similar frequencies to other languages. Moreover, the identification of the vowel and consonant sounds matches some previous tentative bottom-up approaches to decode the manuscript.

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