scholarly journals Regime-Switching Factor Investing with Hidden Markov Models

2020 ◽  
Vol 13 (12) ◽  
pp. 311
Author(s):  
Matthew Wang ◽  
Yi-Hong Lin ◽  
Ilya Mikhelson
Keyword(s):  
Regime Switching ◽  
Historical Data ◽  
Markov Models ◽  
Factor Model ◽  
Hidden Markov ◽  
Investment Strategy ◽  
Factor Models ◽  
Individual Factor ◽  
Market Regimes ◽  
The Individual

This study uses the hidden Markov model (HMM) to identify different market regimes in the US stock market and proposes an investment strategy that switches factor investment models depending on the current detected regime. We first backtested an array of different factor models over a roughly 10.5 year period from January 2007 to September 2017, then we trained the HMM on S&P 500 ETF historical data to identify market regimes of that period. By analyzing the relationship between factor model returns and different market regimes, we are able to establish the basis of our regime-switching investing model. We then back-tested our model on out-of-sample historical data from September 2017 to April 2020 and found that it both delivers higher absolute returns and performs better than each of the individual factor models according to traditional portfolio benchmarking metrics.

Using hidden Markov models to find discrete targets in continuous sociophonetic data

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Daniel Duncan
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Abstract Representation ◽  
Language Variation And Change ◽  
Novel Approach ◽  
Single Target ◽  
The Individual ◽  
Individual Speaker

Abstract Advances in sociophonetic research resulted in features once sorted into discrete bins now being measured continuously. This has implied a shift in what sociolinguists view as the abstract representation of the sociolinguistic variable. When measured discretely, variation is variation in selection: one variant is selected for production, and factors influencing language variation and change are influencing the frequency at which variants are selected. Measured continuously, variation is variation in execution: speakers have a single target for production, which they approximate with varying success. This paper suggests that both approaches can and should be considered in sociophonetic analysis. To that end, I offer the use of hidden Markov models (HMMs) as a novel approach to find speakers’ multiple targets within continuous data. Using the lot vowel among whites in Greater St. Louis as a case study, I compare 2-state and 1-state HMMs constructed at the individual speaker level. Ten of fifty-two speakers’ production is shown to involve the regular use of distinct fronted and backed variants of the vowel. This finding illustrates HMMs’ capacity to allow us to consider variation as both variant selection and execution, making them a useful tool in the analysis of sociophonetic data.

scholarly journals Two General Architectures for Intelligent Machine Performance Degradation Assessment

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Yanwei Xu ◽  
Aijun Xu ◽  
Tancheng Xie
Keyword(s):  
Markov Model ◽  
Case Studies ◽  
Historical Data ◽  
Markov Models ◽  
Hidden Markov ◽  
Economic Loss ◽  
Performance Degradation ◽  
Machine Performance ◽  
Good Ability ◽  
Random Events

Markov model is of good ability to infer random events whose likelihood depends on previous events. Based on this theory, hidden Markov model serves as an extension of Markov model to present an event from observations rather than states in Markov model. Moreover, due to successful applications in speech recognition, it attracts much attention in machine fault diagnosis. This paper presents two architectures for machine performance degradation assessment, which can be used to minimize machine downtime, reduce economic loss, and improve productivity. The major difference between the two architectures is whether historical data are available to build hidden Markov models. In case studies, bearing data as well as available historical data are used to demonstrate the effectiveness of the first architecture. Then, whole life gearbox data without historical data are employed to demonstrate the effectiveness of the second architecture. The results obtained from two case studies show that the presented architectures have good abilities for machine performance degradation assessment.

scholarly journals Averaged Chain Substitution Method – Applicability, Advantages, and Disadvantages

2021 ◽  
Vol 27 (2) ◽  
pp. 127-138
Keyword(s):  
Factor Analysis ◽  
Factor Models ◽  
Substitution Method ◽  
Individual Factor ◽  
Advantages And Disadvantages ◽  
Business Analysis ◽  
The Individual ◽  
Analytical Expressions ◽  
Determinant Factor

The article presents the applicability, advantages and disadvantages of the methods of determinant factor analysis, which are employed most commonly in practice for the needs of financial and business analysis. The averaged chain substitution method is developed and tested for approval for all possible combinations of factor models comprising two, three, and four factor variables. Analytical expressions are derived for the quantitative determination of the individual factor influences of the participating factor variables over the variation of their result indicator. It is characterized by universality and eliminates the only significant disadvantage of the chain substitution method, namely the ambiguity (non-accuracy) of the results obtained thereby regarding the quantitative measurement of the individual factor influences of the participating factor variables exerted on the variation of the result indicator when changing the order of substitutions of the factor variables in the construction of factor chains. The objective of the present study is to reveal the applicability, advantages, and disadvantages of the methods of determined factor analysis and to present the results of the approbation of the averaged chain substitution method for all possible combinations of factor models composed of two, three, and four factor variables.

scholarly journals MuBeFE: Multimodal Behavioural Features Extraction Method

2021 ◽  
Vol 27 (3) ◽  
pp. 254-284
Author(s):  
Alessia D'Andrea ◽  
Maria Chiara Caschera ◽  
Fernando Ferri ◽  
Patrizia Grifoni
Keyword(s):  
Extraction Method ◽  
Markov Models ◽  
Hidden Markov ◽  
Evaluation Process ◽  
Communication Style ◽  
Communicative Intention ◽  
The Social ◽  
Social Style ◽  
The Individual ◽  
The Voice

The paper aims to provide a method to analyse and observe the characteristics that distinguish the individual communication style such as the voice intonation, the size and slant used in handwriting and the trait, pressure and dimension used for sketching. These features are referred to as Communication Extensional Features. Observing from the Communication Extensional Features, the user’s behavioural features, such as the communicative intention, the social style and personality traits can be extracted. These behavioural features are referred to as Communication Intentional Features. For the extraction of Communication Intentional Features, a method based on Hidden Markov Models is provided in the paper. The Communication Intentional Features have been extracted at the modal and multimodal level; this represents an important novelty provided by the paper. The accuracy of the method was tested both at modal and multimodal levels. The evaluation process results indicate an accuracy of 93.3% for the Modal layer (handwriting layer) and 95.3% for the Multimodal layer.

scholarly journals When faced with increasing complexity: The effectiveness of AI assistance for drone design

2021 ◽  
pp. 1-38
Author(s):  
Binyang Song ◽  
Nicolas F Soria Zurita ◽  
Hannah Nolte ◽  
Harshika Singh ◽  
Jonathan Cagan ◽  
...  
Keyword(s):  
Design Process ◽  
Quantitative Methods ◽  
Search Strategy ◽  
Design Research ◽  
Markov Models ◽  
Hidden Markov ◽  
Parameter Design ◽  
Research Platform ◽  
Design Effectiveness ◽  
The Individual

Abstract As Artificial Intelligence (AI) assistance tools become more ubiquitous in engineering design, it becomes increasingly necessary to understand the influence of AI assistance on the design process and design effectiveness. Previous work has shown the advantages of incorporating AI design agents to assist human designers. However, the influence of AI assistance on the behavior of designers during the design process is still unknown. This study examines the differences in participants' design process and effectiveness with and without AI assistance during a complex drone design task using the HyForm design research platform. Data collected from this study is analyzed to assess the design process and effectiveness using quantitative methods, such as Hidden Markov Models and network analysis. The results indicate that AI assistance is most beneficial when addressing moderately complex objectives but exhibits a reduced advantage in addressing highly complex objectives. During the design process, the individual designers working with AI assistance employ a relatively explorative search strategy, while the individual designers working without AI assistance devote more effort to parameter design.

PORTFOLIO OPTIMIZATION, HIDDEN MARKOV MODELS, AND TECHNICAL ANALYSIS OF P&F-CHARTS

2002 ◽  
Vol 05 (04) ◽  
pp. 385-399 ◽  
Author(s):  
ROBERT ELLIOTT ◽  
JURI HINZ
Keyword(s):  
Markov Model ◽  
Portfolio Optimization ◽  
Hidden Markov Models ◽  
Stock Price ◽  
Historical Data ◽  
Markov Models ◽  
Hidden Markov ◽  
Recursive Algorithm ◽  
Adaptive Method ◽  
Portfolio Performance

In this work we introduce an adaptive method of portfolio optimization. The basic idea is to describe essential movements of the stock price using a hidden Markov model and to calculate the optimal portfolio using a recursive algorithm. The portfolio optimization is adaptive in the sense that the standard EM-algorithm fits the model to historical data, which improves the portfolio performance.

Does Beck's Hopelessness Scale really measure several components?

2001 ◽  
Vol 31 (2) ◽  
pp. 367-372 ◽  
Author(s):  
ANNE-MARIE AISH ◽  
DANUTA WASSERMAN
Keyword(s):  
Factor Model ◽  
Factor Models ◽  
Factor Analyses ◽  
Confirmatory Factor Analyses ◽  
Suicide Attempters ◽  
Confirmatory Factor ◽  
Exploratory Factor Analyses ◽  
Scale Measure ◽  
The Individual ◽  
Item Scale

Background. Much of the interest in hopelessness stems from the key role it plays in the prediction of suicidal behaviour. To measure hopelessness, Beck et al.(1974)developed a 20-item scale (BHS), applied exploratory factor analysis and argued that the scale measures three specific components (affective, motivational and cognitive). Subsequent exploratory factor analyses identified two, three or more factors underlying the scale.Method. Several confirmatory factor analyses (LISREL) were run on data on 324 suicide attempters in Sweden in order to test the hypothesized factorial structures and to investigate the psychometric properties of the individual items.Results. Neither three- nor two-factor models fitted the data. A simpler structure was sufficient to account for the observed correlations between most of the items. This led to the development of several variants of a one-factor model, each a combination of affective, motivational and cognitive items. The number of items varied between four and 15.Conclusions. Our findings suggest that most of the items (15) of Beck's Hopelessness Scale measure one factor. They further suggest that the number of items could considerably be reduced. A four-item scale produced an excellent fit. It includes positive and negative items describing the perception of the future in terms of success, darkness, lack of opportunity and faith. It might even be possible to replace the total scale with one item only, ‘my future seems dark to me’.

scholarly journals Individual factor structure of qualified volleyball players' preparedness

2019 ◽  
Vol 5 (1) ◽  
pp. 56
Author(s):  
Zh.L. Kozina ◽  
S.B. Khrapov ◽  
S. Yevstratov ◽  
N.A. Kolomiets ◽  
S.V. Hryshchenko ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Factor Structure ◽  
Factor Models ◽  
Relative Strength ◽  
The Body ◽  
Individual Factor ◽  
Starting Position ◽  
Flexion Extension ◽  
Volleyball Players ◽  
The Individual

<p><em>The purpose</em> of the study was to develop individual factor models of physical preparedness of high-level volleyball players of different game roles. <em>Matherial and methods</em>. The study was attended by 12 players of the Kharkiv woman women team in Kharkiv Volleyball players were tested for 10 indicators of pedagogical pedagogical testing: Running to 4 points; Running for determining the speed of movement along the volleyball court at a distance of 30 m (9-3-6-3-9 m); Jump in height from place; Jump in height from run; Throwing a stuffed ball from the starting position sitting; Throwing a stuffed ball from the starting position is lying; Lifting of the body from the position lying on the back for 20 s (number of times); Elevation of body from the position of lying on the stomach for 20 s (number of times); The flexion-extension of the hands in the emphasis lying for 20 s (number of times); Leap length from space. The obtained data were mathematically processed using factor analysis methods, on the basis of which the individual factor models of players were constructed. <em>Results</em>. On the basis of factor analysis, the structure of qualified volleyball players' preparedness was determined, in which four expressed factors were expressed: speed-strength training, relative strength, special endurance, high endurance. The model of the belonging of the players to the role is determined according to the determined factor structure: in the attackers of the first pace, the following factors are factors such as speed-power preparedness and relative strength, at attackers of the second pace - speed-power preparedness and fast endurance, fast endurance and special endurance, in Libero - relative strength and special endurance. <em>Conclusion.</em> Using the developed scheme, the individual factor models of players and the developed scale of assessments of the test indicators, one can determine the individual potential opportunities of different players to the game in different roles.</p>

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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