A methodology for stochastic analysis of share prices as Markov chains with finite states

Mettle, Felix O.; Quaye, Enoch; Laryea, Ravenhill Adjetey

doi:10.1186/2193-1801-3-657

Cited by 18 publications

(15 citation statements)

References 4 publications

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“…The majority of Markov models are based on a sufficient data set, either in terms of sample size or frequency of time, as demonstrated by Fama (1965); Zhang and Zhang (2009); Mettle, Quaye, and Laryea (2014); Sarsour and Sabri (2020). However, these models would be difficult to calibrate for data that are characterized by a short frequency of time, which results in an unreliable estimation of the transition probability matrix.…”

Section: Asian Economic and Financial Reviewmentioning

confidence: 99%

A Simulation Study: Obtaining a Sufficient Sample Size of Discrete-Time Markov Chains of Investment in a Short Frequency of Time

Sarsour

Sabri

2020

Asian Economic and Financial Review

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This paper deals with the problem of determining the sufficient sample size needed to estimate the transition matrix in the Markov chain. In particular, this paper focuses on systems with insufficient data or a short frequency of time caused by the difficulty of acquiring data. This study developed a Markov chain simulation technique that achieves a sufficient sample and can be used to estimate the size of the transition probability, despite having a short frequency of time. It also shows how this technique can be used in the short-, medium-, and long-term, and how a sufficient sample size can be found in these three situations. More specifically, this study illustrates the proposed simulation Markov chain model that estimates the transition probability matrix of the return of assets (ROA) in the industrial sector in Malaysia between 2007 and 2018. In this study, we present a method of determining an adequate sample size using a Markov chain simulation model. This model uses data from a number of companies in the industrial sector in Malaysia in order to study the performance of ROA and assist investors in making investment decisions. However, each company only has yearly ROA values. In other words, the frequency of the values is low, which makes studying the performance of ROA in the industrial sector more difficult. This could be the case because companies don't publish financial yearly reports, or because they are emerging companies that don't have adequate financial reports to calculate their ROA. This study was able to compensate for the lack of data through the number of companies used. Contribution/Originality: This study is one of very few studies that has investigated how to determine an adequate sample size using a Markov chain simulation model. It presents a selection of companies, in order to study the performance of ROA in Malaysia's industrial sector, and to assist investors in their decision making.

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Section: Asian Economic and Financial Reviewmentioning

confidence: 99%

A Simulation Study: Obtaining a Sufficient Sample Size of Discrete-Time Markov Chains of Investment in a Short Frequency of Time

Sarsour

Sabri

2020

Asian Economic and Financial Review

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“…where n i is the total number of chain transitions from the state i over the time T [24,25]. Hence, the estimates of transition probabilities from the state i to state j are obtained as follows:…”

Section: Proposed Transformationmentioning

confidence: 99%

Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach

Moffat¹,

Ukpabio²,

Akpan³

2020

JSTA

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The study aimed at stabilizing the changing variance using the logarithmic transformation to achieve a significant proportion of stability and a faster rate of convergence of the steady state transition probability in Markov chains. The traditional Markov chain and logarithmic-modified Markov chain were considered. On exploring the yearly data on the stock prices from 2015 to 2018 as obtained from the Nigerian Stock Exchange, it was found that the steady state of logarithmic-modified Markov chain converged faster than the tradition Markov chain with efficiency in tracking the correct cycles where the stock movements are trending irrespective of which cycle it starts at time zero with differences in probability values by 1.1%, 0.7%, −0.41% and −1.37% for accumulation, markup, distribution and mark-down cycles, respectively. Thus, it could be deduced that the logarithmic modification enhances the ability of the Markov chain to tract the variation of the steady state probabilities faster than the traditional counterpart.

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“…[20] introduce Markov chain model to forecast stock market trend of Safaricom share in Nairobi Securities Exchange in Kenya. Mettle, [21] uses Markov chain model with finite states to analyze the share price changes for five different randomly selected equities on the Ghana Stock Exchange and [22] consider forecasting model based on non-homogenous index sequence. But, none of them have utilized Marcov Chain methodology for the application of algae growth prediction and stability.…”

Section: Introductionmentioning

confidence: 99%

Algae Growth Stabilization and Prediction Model Matrix of Soil Sludge

Siregar¹,

Pauzi²,

Kassim³

et al. 2020

IJIRMPS

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The pattern growth of the three algae species -PRK1-E01, C. Vulgaris and Nephrochlamys Subsolitaria which are found in the sludge within the Sabak Bernam region is not stable. The unstable distribution matrix of the algae growth is derived to determine a stable growth by modeling a transition matrix and stable distribution probability using Marcov Chain methodology. Thus, a stable algae growth can be predicted.

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A methodology for stochastic analysis of share prices as Markov chains with finite states

Cited by 18 publications

References 4 publications

A Simulation Study: Obtaining a Sufficient Sample Size of Discrete-Time Markov Chains of Investment in a Short Frequency of Time

A Simulation Study: Obtaining a Sufficient Sample Size of Discrete-Time Markov Chains of Investment in a Short Frequency of Time

Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach

Algae Growth Stabilization and Prediction Model Matrix of Soil Sludge

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