scholarly journals Pairs Trading Strategy for A and H Shares Based on Kalman-HMM Approach

2021 ◽  
Vol 4 (5) ◽  
pp. 8-16
Author(s):  
Ming Zang
Keyword(s):  
Markov Model ◽  
Financial Markets ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Real Data ◽  
State Transitions ◽  
Institutional Changes ◽  
Pairs Trading ◽  
Statistical Arbitrage ◽  
Arbitrage Strategy

Pairs trading is a statistical arbitrage strategy that takes advantage of unbalanced financial markets. A common difficulty for quantitative trading participants is the detection of market institutional changes in financial markets. In order to solve this issue, the hidden Markov model (HMM) is applied for status detection. The research objective is to use Kalman filter to predict and the hidden Markov model (HMM) to identify state transitions on the basis of screening transaction pairs with obvious co-integration relationship. This research would prove the profitability of the strategy and the ability to resist risk through the combination of these two methods with real data. The empirical results showed that compared with the traditional cointegration strategy, the holding yield increased from 1.6% to 16.2% and the maximum pullback reduced to 0.02%. Further research is required to improve trading rules.

Predicting the effect of Googling investor sentiment on Islamic stock market returns

2020 ◽  
Vol 13 (2) ◽  
pp. 165-193
Author(s):  
Yousra Trichilli ◽  
Mouna Boujelbène Abbes ◽  
Afif Masmoudi
Keyword(s):  
Stock Market ◽  
Markov Model ◽  
Financial Markets ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Investor Sentiment ◽  
Market Returns ◽  
Stock Market Returns ◽  
Content Type ◽  
The Relationship

Purpose The purpose of this paper is to evaluate the capability of the hidden Markov model using Googling investors’ sentiments to predict the dynamics of Islamic indexes’ returns in the Middle East and North Africa (MENA) financial markets from 2004 to 2018. Design/methodology/approach The authors propose a hidden Markov model based on the transition matrix to apprehend the relationship between investor’s sentiment and Islamic index returns. The proposed model facilitates capturing the uncertainties in Islamic market indexes and the possible effects of the dynamics of Islamic market on the persistence of these regimes or States. Findings The bearish state is the most persistent sentiment with the longest duration for all the MENA Islamic markets except for Jordan, Morocco and Qatar. In addition, the obtained results indicate that the effect of sentiment on predicting the future Islamic index returns is conditional on the MENA States. Besides, the estimated mean returns for each state indicates that the bullish and calm states are ideal for investing in Islamic indexes of Bahrain, Oman, Morocco, Kuwait, Saudi Arabia and United Arab Emirates. However, only the bullish state is ideal for investing Islamic indexes of Jordan, Egypt and Qatar. Research limitations/implications This paper has used data at a monthly frequency that can explain only short-term dynamics between Googling investor’s sentiment and the MENA Islamic stock market returns. Moreover, this work can be done on the stock markets while taking into account the specificity of each activity sector. Practical implications In fact, the findings of this paper are helpful for academics, analysts and practitioners, and more specifically for the Islamic MENA financial investors. Moreover, this study provides useful insights not only into the duration of the relationship between the indexes’ returns and the investors’ sentiments in the five states but also into the transition probabilities which have implications for how investors could be guided in their choice of future investment in a portfolio with Islamic indexes. Findings of this paper are important and valuable for policy-makers and investors. Thus, predicting the effect of Googling investors’ sentiment on the MENA Islamic stock market dynamics is important for portfolio diversification by domestic and international investors. Moreover, the results of this paper gave new insights into financial analysts about the dynamic relationship between Googling investors’ sentiment and Islamic stock market returns across market regimes. Therefore, the findings of this study might be useful for investors as they help them capture the unobservable dynamics of the changes in the investors’ sentiment regimes in the MENA financial markets to make successful investment decisions. Originality/value To the best of the authors’ knowledge, this paper is the first to use the hidden Markov model to examine changes in the Islamic index return dynamics across five market sentiment states, namely the depressed sentiment (S1), the bullish sentiment (S2), the bearish sentiment (S3), the calm sentiment (S4) and the bubble sentiment (S5).

scholarly journals Spatially Enhanced Differential RNA Methylation Analysis from Affinity-Based Sequencing Data with Hidden Markov Model

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Yu-Chen Zhang ◽  
Shao-Wu Zhang ◽  
Lian Liu ◽  
Hui Liu ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Statistical Significance ◽  
Simulated Data ◽  
Real Data ◽  
Differential Methylation ◽  
Methylation Site ◽  
Sequencing Data ◽  
Rna Methylation

With the development of new sequencing technology, the entire N6-methyl-adenosine (m6A) RNA methylome can now be unbiased profiled with methylated RNA immune-precipitation sequencing technique (MeRIP-Seq), making it possible to detect differential methylation states of RNA between two conditions, for example, between normal and cancerous tissue. However, as an affinity-based method, MeRIP-Seq has yet provided base-pair resolution; that is, a single methylation site determined from MeRIP-Seq data can in practice contain multiple RNA methylation residuals, some of which can be regulated by different enzymes and thus differentially methylated between two conditions. Since existing peak-based methods could not effectively differentiate multiple methylation residuals located within a single methylation site, we propose a hidden Markov model (HMM) based approach to address this issue. Specifically, the detected RNA methylation site is further divided into multiple adjacent small bins and then scanned with higher resolution using a hidden Markov model to model the dependency between spatially adjacent bins for improved accuracy. We tested the proposed algorithm on both simulated data and real data. Result suggests that the proposed algorithm clearly outperforms existing peak-based approach on simulated systems and detects differential methylation regions with higher statistical significance on real dataset.

scholarly journals A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque LFP and human EEG

2020 ◽  
Author(s):  
Indie C. Garwood ◽  
Sourish Chakravarty ◽  
Jacob Donoghue ◽  
Pegah Kahali ◽  
Shubham Chamadia ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Human Subjects ◽  
Slow Oscillation ◽  
State Transitions ◽  
State Process ◽  
The Mean ◽  
Eeg Recordings ◽  
Gamma Burst

AbstractKetamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes bursts of 30-50 Hz oscillations alternating with 0.1 to 10 Hz oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine’s neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in 10 Hz frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma burst and slow oscillation activity, as well as intermediate states in between. The mean duration of the gamma burst state was 2.5s([1.9,3.4]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.7s([1.9,3.8]s) for the human subjects. The mean duration of the slow oscillation state was 1.6s([1.1,2.5]s) and 0.7s([0.6,0.9]s) for the two NHPs, and 2.8s([1.9,4.3]s) for the human subjects. Our beta-HMM framework provides a useful tool for experimental data analysis. Our characterizations of the gamma-burst process offer detailed, quantitative constraints that can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.

scholarly journals Forecasting of Disassembly Waste Generation under Uncertainties Using Digital Twinning-Based Hidden Markov Model

2021 ◽  
Vol 13 (10) ◽  
pp. 5391
Author(s):  
Yinsheng Yang ◽  
Gang Yuan ◽  
Jiaxiang Cai ◽  
Silin Wei
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Prediction Models ◽  
Hidden Markov ◽  
Real Data ◽  
Gaussian Mixture ◽  
Waste Generation ◽  
Time Data ◽  
Proposed Model ◽  
Comparison Results

Disassembly waste generation forecasting is the foundation for determining disassembly waste treatment and process formulation and is also an important prerequisite for optimizing waste management. The prediction of disassembly waste generation is a complex process which is affected by potential time, environment, and economy characteristic variables. Uncertainty features, such as disassembly amount, disassembly component status, and workshop scheduling, play an important role in predicting the fluctuation of disassembly waste generation. We therefore focus on revealing the trend of waste generation in disassembly remanufacturing that faces significant influences of technology and economic changes to achieve circular industry sustainable development. To dynamically predict the generation of disassembly waste under uncertainty, this work proposes a statistical method driven by a probabilistic model, which integrates the digital twinning, Gaussian mixture, and the hidden Markov model (DG-HMM). First, digital twinning technology is used for real-time data interaction between simulation prediction and decision evaluation. Then, the Gaussian mixture and HMM are used to dynamically predict the generation of disassembly waste. In order to effectively predict the amount of disassembly waste generation, real data collected from a disassembly enterprise are used to train and verify the model. Finally, the proposed model is compared with other general prediction models to illustrate the correctness and feasibility of the proposed model. The comparison results show that DG-HMM has better prediction accuracy for the actual disassembly waste generation.

scholarly journals A lineage tree-based hidden Markov model to quantify cellular heterogeneity and plasticity

2021 ◽  
Author(s):  
Farnaz Mohammadi ◽  
Shakthi Visagan ◽  
Sean M Gross ◽  
Luka Karginov ◽  
JC Lagarde ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Single Cell ◽  
Cell Fate ◽  
Drug Response ◽  
Hidden Markov ◽  
Single Cells ◽  
State Transitions ◽  
Cycle Phase ◽  
Cell Heterogeneity

Cell plasticity, or the ability of cells within a population to reversibly alter their phenotype, is an important feature of tissue homeostasis during processes such as wound healing and cancer. Plasticity operates alongside other sources of cell-to-cell heterogeneity such as genetic mutations and variation in signaling. Ultimately these processes prevent most cancer therapies from being curative. The predominant methods of quantifying tumor-drug response operate on snapshot population-level measurements and therefore lack evolutionary dynamics, which are particularly critical for dynamic processes such as plasticity. Here we apply a tree-based adaptation of a hidden Markov model (tHMM) that employs single cell lineages as input to learn the characteristic patterns of single cell heterogeneity and state transitions in an unsupervised fashion. This model enables single cell classification based on the phenotype of individual cells and their relatives for improved specificity in pinpointing the structure and dynamics of variability in drug response. Integrating this model with a modular interface for defining observed phenotypes allows the model to easily be adapted to any phenotype measured in single cells. To benchmark our model, we paired cell fate with either cell lifetimes or individual cell cycle phase lengths (G1 and S/G2) as our observed phenotypes on synthetic data and demonstrated that the model successfully classifies cells within experimentally tractable dataset sizes. As an application, we analyzed experimental measurements of cell fate and phase duration in cancer cell populations treated with chemotherapies to determine the number of distinct subpopulations. In total, this tHMM framework allows for the flexible classification of single cell heterogeneity across lineages.

scholarly journals A whole-genome-based approach for estimation and characterization of individual inbreeding

2017 ◽  
Author(s):  
T. Druet ◽  
M. Gautier
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Real Data ◽  
Genetic Distances ◽  
Error Rates ◽  
Base Population ◽  
Sequencing Data ◽  
Model Framework ◽  
Marker Allele

AbstractInbreeding results from the mating of related individuals and has negative consequence because it brings together deleterious variants in one individual. Inbreeding is associated with recessive diseases and reduced production or fitness. In general, inbreeding is estimated with respect to a base population that needs to be defined. Ancestors in generations anterior to the base population are considered unrelated. We herein propose a model that estimates inbreeding relative to multiple age-based classes. Each inbreeding distribution is associated to a different time in the past: recent inbreeding generating longer homozygous stretches than more ancient. Our model is a mixture of exponential distribution implemented in a hidden Markov model framework that uses marker allele frequencies, genetic distances, genotyping error rates and the sequences of observed genotypes. Based on simulations studies, we show that the inbreeding coefficients and the age of inbreeding are correctly estimated. Mean absolute errors of estimators are low, the efficiency depending on the available information. When several inbreeding classes are simulated, the model captures them if their ages are sufficiently different. Genotyping errors or low-fold sequencing data are easily accommodated in the hidden Markov model framework. Application to real data sets illustrate that the method can reveal recent different demographic histories among populations, some of them presenting very recent bottlenecks or founder effects. The method also clearly identifies individuals resulting from extreme consanguineous matings.

scholarly journals Generalized Poisson Hidden Markov Model for Overdispersed or Underdispersed Count Data

2020 ◽  
Vol 43 (1) ◽  
pp. 71-82
Author(s):  
Sebastian George ◽  
Ambily Jose
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Count Data ◽  
Markov Models ◽  
Hidden Markov ◽  
Real Life ◽  
Simulated Data ◽  
Real Data ◽  
Good Convergence ◽  
Finite Mixture Of Distributions

The most suitable statistical method for explaining serial dependency in time series count data is that based on Hidden Markov Models (HMMs). These models assume that the observations are generated from a finite mixture of distributions governed by the principle of Markov chain (MC). Poisson-Hidden Markov Model (P-HMM) may be the most widely used method for modelling the above said situations. However, in real life scenario, this model cannot be considered as the best choice. Taking this fact into account, we, in this paper, go for Generalised Poisson Distribution (GPD) for modelling count data. This method can rectify the overdispersion and underdispersion in the Poisson model. Here, we develop Generalised Poisson Hidden Markov model (GP-HMM) by combining GPD with HMM for modelling such data. The results of the study on simulated data and an application of real data, monthly cases of Leptospirosis in the state of Kerala in South India, show good convergence properties, proving that the GP-HMM is a better method compared to P-HMM.

REVERSE ENGINEERING A SOCIAL AGENT-BASED HIDDEN MARKOV MODEL — ViSAGE

2008 ◽  
Vol 18 (06) ◽  
pp. 491-526 ◽  
Author(s):  
HUNG-CHING (JUSTIN) CHEN ◽  
MARK GOLDBERG ◽  
MALIK MAGDON-ISMAIL ◽  
WILLIAM A. WALLACE
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Social Evolution ◽  
Driving Forces ◽  
Hidden Markov ◽  
Group Structure ◽  
Identification Problem ◽  
State Transitions ◽  
Agent Based ◽  
Social Agent

We present a machine learning approach to discover the agent dynamics that drives the evolution of the social groups in a community. We set up the problem by introducing an agent-based hidden Markov model for the agent dynamics: an agent's actions are determined by micro-laws. Nonetheless, We learn the agent dynamics from the observed communications without knowing state transitions. Our approach is to identify the appropriate micro-laws corresponding to an identification of the appropriate parameters in the model. The model identification problem is then formulated as a mixed optimization problem. To solve the problem, we develop a multistage learning process for determining the group structure, the group evolution, and the micro-laws of a community based on the observed set of communications among actors, without knowing the semantic contents. Finally, to test the quality of our approximations and the feasibility of the approach, we present the results of extensive experiments on synthetic data as well as the results on real communities, such as Enron email and Movie newsgroups. Insight into agent dynamics helps us understand the driving forces behind social evolution.

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