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), 2022. Published by Science Publishing Group |
Dynamic Programing, Stocks Portfolio, Reverse Algorithm, K-means Clustering, Dencision Making
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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
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
@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} }
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 -