Application of Dynamic Programming to Revenue Management: The Optimum Validity Model’s Test (S)

Felix Obi Ohanuba; Everestus Okafor Ossai; Precious Ndidiamaka Ezra; Martin Nnaemeka Eze

doi:doi:10.11648/j.mcs.20220706.15

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Application of Dynamic Programming to Revenue Management: The Optimum Validity Model’s Test (S)

Felix Obi Ohanuba, Everestus Okafor Ossai, Precious Ndidiamaka Ezra, Martin Nnaemeka Eze

Published in Mathematics and Computer Science (Volume 7, Issue 6)

Received: 4 December 2022 Accepted: 19 December 2022 Published: 29 December 2022

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Abstract

A suitable decision plan is followed by an effective financial management to achieve optimality while investing in a competing stock portfolio. This study altered a Dynamic Programming (DP) model of Bellman. The modified model was used to solve a business problem. The problems of choosing a stock portfolio for optimal return among investors in financial markets have resulted in a financial crisis. Most financial analysts provide investors with incorrect and unvalidated investment information. The consequences were minimal optimum, no return, and an investment problem. The goals are to ensure optimality in investor returns, validate the results using two validity tests, and select the test that best validated the model. The silhouette and Dunn tests were used to validate the outcome result. The results of using Silhouette reduced computational complexity and produced a more robust and validated return. The k-means clustering (an aspect of unsupervised machine learning) provided better statistical evaluation and information on the investment pattern. In comparison to previous work, the introduction of variables allowed for the best return at stage one. Finally, a validated investment report can help to avoid mistakes made by market analysts and investors when making investment decisions.

Published in	Mathematics and Computer Science (Volume 7, Issue 6)
DOI	10.11648/j.mcs.20220706.15
Page(s)	130-143
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Dynamic Programing, Stocks Portfolio, Reverse Algorithm, K-means Clustering, Dencision Making

References

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Felix Obi Ohanuba, Everestus Okafor Ossai, Precious Ndidiamaka Ezra, Martin Nnaemeka Eze. (2022). Application of Dynamic Programming to Revenue Management: The Optimum Validity Model’s Test (S). Mathematics and Computer Science, 7(6), 130-143. https://doi.org/10.11648/j.mcs.20220706.15

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ACS Style

Felix Obi Ohanuba; Everestus Okafor Ossai; Precious Ndidiamaka Ezra; Martin Nnaemeka Eze. Application of Dynamic Programming to Revenue Management: The Optimum Validity Model’s Test (S). Math. Comput. Sci. 2022, 7(6), 130-143. doi: 10.11648/j.mcs.20220706.15

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AMA Style

Felix Obi Ohanuba, Everestus Okafor Ossai, Precious Ndidiamaka Ezra, Martin Nnaemeka Eze. Application of Dynamic Programming to Revenue Management: The Optimum Validity Model’s Test (S). Math Comput Sci. 2022;7(6):130-143. doi: 10.11648/j.mcs.20220706.15

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@article{10.11648/j.mcs.20220706.15,
  author = {Felix Obi Ohanuba and Everestus Okafor Ossai and Precious Ndidiamaka Ezra and Martin Nnaemeka Eze},
  title = {Application of Dynamic Programming to Revenue Management: The Optimum Validity Model’s Test (S)},
  journal = {Mathematics and Computer Science},
  volume = {7},
  number = {6},
  pages = {130-143},
  doi = {10.11648/j.mcs.20220706.15},
  url = {https://doi.org/10.11648/j.mcs.20220706.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20220706.15},
  abstract = {A suitable decision plan is followed by an effective financial management to achieve optimality while investing in a competing stock portfolio. This study altered a Dynamic Programming (DP) model of Bellman. The modified model was used to solve a business problem. The problems of choosing a stock portfolio for optimal return among investors in financial markets have resulted in a financial crisis. Most financial analysts provide investors with incorrect and unvalidated investment information. The consequences were minimal optimum, no return, and an investment problem. The goals are to ensure optimality in investor returns, validate the results using two validity tests, and select the test that best validated the model. The silhouette and Dunn tests were used to validate the outcome result. The results of using Silhouette reduced computational complexity and produced a more robust and validated return. The k-means clustering (an aspect of unsupervised machine learning) provided better statistical evaluation and information on the investment pattern. In comparison to previous work, the introduction of variables allowed for the best return at stage one. Finally, a validated investment report can help to avoid mistakes made by market analysts and investors when making investment decisions.},
 year = {2022}
}

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TY  - JOUR
T1  - Application of Dynamic Programming to Revenue Management: The Optimum Validity Model’s Test (S)
AU  - Felix Obi Ohanuba
AU  - Everestus Okafor Ossai
AU  - Precious Ndidiamaka Ezra
AU  - Martin Nnaemeka Eze
Y1  - 2022/12/29
PY  - 2022
N1  - https://doi.org/10.11648/j.mcs.20220706.15
DO  - 10.11648/j.mcs.20220706.15
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 130
EP  - 143
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20220706.15
AB  - A suitable decision plan is followed by an effective financial management to achieve optimality while investing in a competing stock portfolio. This study altered a Dynamic Programming (DP) model of Bellman. The modified model was used to solve a business problem. The problems of choosing a stock portfolio for optimal return among investors in financial markets have resulted in a financial crisis. Most financial analysts provide investors with incorrect and unvalidated investment information. The consequences were minimal optimum, no return, and an investment problem. The goals are to ensure optimality in investor returns, validate the results using two validity tests, and select the test that best validated the model. The silhouette and Dunn tests were used to validate the outcome result. The results of using Silhouette reduced computational complexity and produced a more robust and validated return. The k-means clustering (an aspect of unsupervised machine learning) provided better statistical evaluation and information on the investment pattern. In comparison to previous work, the introduction of variables allowed for the best return at stage one. Finally, a validated investment report can help to avoid mistakes made by market analysts and investors when making investment decisions.
VL  - 7
IS  - 6
ER  -

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Author Information

Felix Obi Ohanuba

Department of Statistics, University of Nigeria, Nsukka, Nigeria
Everestus Okafor Ossai

Department of Statistics, University of Nigeria, Nsukka, Nigeria
Precious Ndidiamaka Ezra

Department of Statistics, University of Nigeria, Nsukka, Nigeria
Martin Nnaemeka Eze

Department of Statistics, University of Nigeria, Nsukka, Nigeria

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APA Style

Felix Obi Ohanuba, Everestus Okafor Ossai, Precious Ndidiamaka Ezra, Martin Nnaemeka Eze. (2022). Application of Dynamic Programming to Revenue Management: The Optimum Validity Model’s Test (S). Mathematics and Computer Science, 7(6), 130-143. https://doi.org/10.11648/j.mcs.20220706.15

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ACS Style

Felix Obi Ohanuba; Everestus Okafor Ossai; Precious Ndidiamaka Ezra; Martin Nnaemeka Eze. Application of Dynamic Programming to Revenue Management: The Optimum Validity Model’s Test (S). Math. Comput. Sci. 2022, 7(6), 130-143. doi: 10.11648/j.mcs.20220706.15

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AMA Style

Felix Obi Ohanuba, Everestus Okafor Ossai, Precious Ndidiamaka Ezra, Martin Nnaemeka Eze. Application of Dynamic Programming to Revenue Management: The Optimum Validity Model’s Test (S). Math Comput Sci. 2022;7(6):130-143. doi: 10.11648/j.mcs.20220706.15

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@article{10.11648/j.mcs.20220706.15,
  author = {Felix Obi Ohanuba and Everestus Okafor Ossai and Precious Ndidiamaka Ezra and Martin Nnaemeka Eze},
  title = {Application of Dynamic Programming to Revenue Management: The Optimum Validity Model’s Test (S)},
  journal = {Mathematics and Computer Science},
  volume = {7},
  number = {6},
  pages = {130-143},
  doi = {10.11648/j.mcs.20220706.15},
  url = {https://doi.org/10.11648/j.mcs.20220706.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20220706.15},
  abstract = {A suitable decision plan is followed by an effective financial management to achieve optimality while investing in a competing stock portfolio. This study altered a Dynamic Programming (DP) model of Bellman. The modified model was used to solve a business problem. The problems of choosing a stock portfolio for optimal return among investors in financial markets have resulted in a financial crisis. Most financial analysts provide investors with incorrect and unvalidated investment information. The consequences were minimal optimum, no return, and an investment problem. The goals are to ensure optimality in investor returns, validate the results using two validity tests, and select the test that best validated the model. The silhouette and Dunn tests were used to validate the outcome result. The results of using Silhouette reduced computational complexity and produced a more robust and validated return. The k-means clustering (an aspect of unsupervised machine learning) provided better statistical evaluation and information on the investment pattern. In comparison to previous work, the introduction of variables allowed for the best return at stage one. Finally, a validated investment report can help to avoid mistakes made by market analysts and investors when making investment decisions.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Application of Dynamic Programming to Revenue Management: The Optimum Validity Model’s Test (S)
AU  - Felix Obi Ohanuba
AU  - Everestus Okafor Ossai
AU  - Precious Ndidiamaka Ezra
AU  - Martin Nnaemeka Eze
Y1  - 2022/12/29
PY  - 2022
N1  - https://doi.org/10.11648/j.mcs.20220706.15
DO  - 10.11648/j.mcs.20220706.15
T2  - Mathematics and Computer Science
JF  - Mathematics and Computer Science
JO  - Mathematics and Computer Science
SP  - 130
EP  - 143
PB  - Science Publishing Group
SN  - 2575-6028
UR  - https://doi.org/10.11648/j.mcs.20220706.15
AB  - A suitable decision plan is followed by an effective financial management to achieve optimality while investing in a competing stock portfolio. This study altered a Dynamic Programming (DP) model of Bellman. The modified model was used to solve a business problem. The problems of choosing a stock portfolio for optimal return among investors in financial markets have resulted in a financial crisis. Most financial analysts provide investors with incorrect and unvalidated investment information. The consequences were minimal optimum, no return, and an investment problem. The goals are to ensure optimality in investor returns, validate the results using two validity tests, and select the test that best validated the model. The silhouette and Dunn tests were used to validate the outcome result. The results of using Silhouette reduced computational complexity and produced a more robust and validated return. The k-means clustering (an aspect of unsupervised machine learning) provided better statistical evaluation and information on the investment pattern. In comparison to previous work, the introduction of variables allowed for the best return at stage one. Finally, a validated investment report can help to avoid mistakes made by market analysts and investors when making investment decisions.
VL  - 7
IS  - 6
ER  -

Copy | Download