Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer
2020-10-01
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| Series: | Journal of Statistical Theory and Applications (JSTA) |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/125945156/view |
| _version_ | 1811291128680415232 |
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| author | Imoh Udo Moffat James Augustine Ukpabio Emmanuel Alphonsus Akpan |
| author_facet | Imoh Udo Moffat James Augustine Ukpabio Emmanuel Alphonsus Akpan |
| author_sort | Imoh Udo Moffat |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T04:24:48Z |
| format | Article |
| id | doaj.art-42f47216760546c390711cd57f630a3a |
| institution | Directory Open Access Journal |
| issn | 2214-1766 |
| language | English |
| last_indexed | 2024-04-13T04:24:48Z |
| publishDate | 2020-10-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of Statistical Theory and Applications (JSTA) |
| spelling | doaj.art-42f47216760546c390711cd57f630a3a2022-12-22T03:02:34ZengSpringerJournal of Statistical Theory and Applications (JSTA)2214-17662020-10-0119310.2991/jsta.d.201006.001Modeling Investment Trends: A Logarithmic-Modified Markov Chain ApproachImoh Udo MoffatJames Augustine UkpabioEmmanuel Alphonsus AkpanThe 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.https://www.atlantis-press.com/article/125945156/viewConvergenceHeteroscdasticityLogarithmic transformationMarkov chainStochastic processTransition matrix |
| spellingShingle | Imoh Udo Moffat James Augustine Ukpabio Emmanuel Alphonsus Akpan Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach Journal of Statistical Theory and Applications (JSTA) Convergence Heteroscdasticity Logarithmic transformation Markov chain Stochastic process Transition matrix |
| title | Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach |
| title_full | Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach |
| title_fullStr | Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach |
| title_full_unstemmed | Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach |
| title_short | Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach |
| title_sort | modeling investment trends a logarithmic modified markov chain approach |
| topic | Convergence Heteroscdasticity Logarithmic transformation Markov chain Stochastic process Transition matrix |
| url | https://www.atlantis-press.com/article/125945156/view |
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