An application of Markov chains in stock price prediction and risk portfolio optimization

Kostadinova, Vyara; Georgiev, Ivan; Mihova, Vesela; Pavlov, Velizar

doi:10.1063/5.0041119

Cited by 13 publications

(5 citation statements)

References 2 publications

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“…If all the states of the chain are interconnected and non-periodic, the chain is called ergodic (long-term). For a given transition matrix P, the chain has a stable distribution if the vector π exists such that the following equation is true (Kostandinova et al, 2021):…”

Section: Methodsmentioning

confidence: 99%

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Markov Chain Model for Predicting the Stock Price of Paper and Paper Products Industry

ERSEN,

AKYÜZ,

AKYÜZ

2023

Turkish Journal of Forest Science

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Markov chains, which are a stochastic process, are a method for analyzing the current behavior of variables to predict their future behavior. With stochastic analysis, it is possible to make decisions in stock markets and make predictions about the future. In this study, with the help of Markov chains, the stock prices of the companies traded in the paper and paper products industry in BIST were predicted. In order to realize the purpose of this study, the closing prices of the seven companies traded in the BIST for the period 01.06.2022-31.05.2023 were used. While it was concluded that six stocks would likely decrease in the long term, it was concluded that the stock of TEZOL company would increase in the long term. When the expected stock returns of the companies are examined, it was determined that VIKING is the company with the highest expected stock return, while KARTN is the company with the lowest expected stock return.

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Section: Methodsmentioning

confidence: 99%

“…The vector π can be obtained as an arbitrary row from the matrix of the following boundary transition (Kostandinova et al, 2021):…”

Section: Methodsmentioning

confidence: 99%

Markov Chain Model for Predicting the Stock Price of Paper and Paper Products Industry

ERSEN,

AKYÜZ,

AKYÜZ

2023

Turkish Journal of Forest Science

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“…Efficient Monte Carlo (MC and quasi-Monte Carlo (QMC) methods [31][32][33] play a crucial role in conducting SA for large-scale computer models, ensuring optimal utilization of computational resources. These methods prove particularly valuable for analyzing intricate models characterized by a multitude of input parameters, as they can handle substantial volumes of data and yield rapid and accurate outcomes.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Unveiling the Power of Stochastic Methods: Advancements in Air Pollution Sensitivity Analysis of the Digital Twin

Todorov

Dimov

2023

Atmosphere

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Thorough examination of various aspects related to the distribution of air pollutants in a specific region and the factors contributing to high concentrations is essential, as these elevated levels can be detrimental. To accomplish this, the development and improvement of a digital twin that encompasses all relevant physical processes in the atmosphere is necessary. This tool, known as DIGITAL AIR, has been created, and it is now necessary to extend it with precise sensitivity analysis. DIGITAL AIR is gaining popularity due to its effectiveness in addressing complex problems that arise in intricate environments; this motivates our further investigations. In this paper, we focus on the preparation and further investigation of DIGITAL AIR through sensitivity analysis with improved stochastic approaches for investigating high-level air pollutants. We discuss and test the utilization of this digital tool in tackling the issue. The unified Danish Eulerian model (UNI-DEM) plays a crucial role within DIGITAL AIR. This mathematical model, UNI-DEM, is highly versatile and can be applied to various studies concerning the adverse effects caused by elevated air pollution levels.

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“…Leonel et al [34] discussed the risk control of hydro-market intelligence and stochastic optimization. Kostadinova et al [35] developed the theory of market chains for stock price and risk portfolio optimization. Sarfraz and Azeem [36] developed the theory of similarity measure.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Market Risk via Maclaurin Symmetric Mean Aggregation Operators: An Application of Interval-Valued Intuitionistic Fuzzy Sets in Multi-Attribute Group Decision-Making

Sarfraz,

Božanić

2024

JEMSE

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An application of Markov chains in stock price prediction and risk portfolio optimization

Cited by 13 publications

References 2 publications

Markov Chain Model for Predicting the Stock Price of Paper and Paper Products Industry

Markov Chain Model for Predicting the Stock Price of Paper and Paper Products Industry

Unveiling the Power of Stochastic Methods: Advancements in Air Pollution Sensitivity Analysis of the Digital Twin

Optimization of Market Risk via Maclaurin Symmetric Mean Aggregation Operators: An Application of Interval-Valued Intuitionistic Fuzzy Sets in Multi-Attribute Group Decision-Making

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