Decision Support System for Stock Portfolio Selection Using Artificial Intelligence and Machine Learning

Patalay, Sandeep; Bandlamudi, Madhusudhan Rao

doi:10.18280/isi.260109

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…Based on the FCM clustering algorithm, a fuzzy decision model is established to intelligently evaluate and analyze the financial performance of enterprises [3]. Building models based on artificial intelligence and machine learning involves financial analysis and decision-making policies [4]. For the application of distress prediction and evaluation cases in financial decision-making, a multistandard decision-making assistant model is proposed, and random forest and bagging CART perform well [5].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Financial Data Analysis and Decision Management Based on Edge Computing

Cheng

2022

Journal of Sensors

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In this era of data explosion, in order to make better use of data, people need to collect information to analyze and judge future decisions. For enterprises, the management of time, capital, and decision-making can directly affect the acquisition of enterprise profits. The sooner we analyze financial data, the earlier we use an intelligent system to assist management and promote operators to make reasonable business decisions. Traditional enterprise data management is very backward and has few functions. In order to achieve the purpose of optimal allocation of enterprise resources, information-based financial management. According to the financial status and functional requirements of enterprises, the specific functions of the system are designed. The method based on edge computing has a high reference value. This paper puts forward a financial management system of intelligent analysis. It can provide rich solutions to financial problems. Data mining and analysis of financial situation provide support for decision-making management of enterprises. The research results show that (1) after 10 times of inspection and adjustment, the system grade is all D grade, and there is no defect. (2) There is no abnormality in system response time; the average response time of F1, F2, F3, and F4 functions is in line with expectations. The fluctuation of time range is no more than 3 s; the overall average response time is 1 s. (3) After CPU detection, the CPU of the system is less than 70% of the expected utilization rate. The system is qualified. (4) The whale algorithm is used to combine new and old tasks. After comparison, it has an excellent performance in computing resources and delays energy consumption. Some follow-up improvement work needs to be optimized.

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Section: Introductionmentioning

confidence: 99%

Intelligent Financial Data Analysis and Decision Management Based on Edge Computing

Cheng

2022

Journal of Sensors

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“…Research continues to focus on improving decision support systems (Gupta et al, 2021;Patalay & Bandlamudi, 2021). AI is used in "big data" management processes, among others, in the integration of knowledge generated throughout the product life cycle.…”

Section: Artificial Intelligence In the 21st Centurymentioning

confidence: 99%

The state of development of artificial intelligence in polish industry: opinions of employees

Kądzielawski

2022

International Journal of Contemporary Management

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Purpose The purpose of the article is to show how employees of industrial organizations perceive the development of artificial intelligence (AI) within them and to gather their opinions on what AI solutions are most commonly used in Polish industry. The literature review pointed to the lack of knowledge on how employees of Polish industrial companies perceive the development of AI in their respective companies and what AI solutions they already use. Design/methodology/approach Literature review and surveys were used to collect the data. The study was carried out using a survey questionnaire. The sample was taken with a specific aim in mind: first, 30 entities were selected for that purpose, while in the second stage the employees (managers and specialists) were chosen from among those. Findings In most cases, employees are not afraid of losing their jobs due to the development of AI systems in their industries. They are positive about the use of solutions that include AI elements. In the opinion of the vast majority of respondents, modern technologies, including AI, help them in their work and facilitate it. Most popular current industrial applications are: robotic process automation technologies, Cognex cameras using neural networks, machine-learning and data technologies, distributed control systems (DSCs), enterprise resource planning (ERP)) systems, and security information and event management (SIEM) systems. Practical implication-Results of this research can be useful for developing programs aimed at reducing the fear and anxiety associated with the ongoing Industrial Revolution. Originality/value The presented research results are the only ones that show the opinions of employees regarding artificial intelligence in Polish organizations.

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“…Jalota et al [27] proposed a decision support system (DSS) for generating a suitable portfolio for an investor in an uncertain multiple-criteria framework and modeled the parameters using L-R fuzzy numbers. Patalay and Bandlamudi [28] proposed a DSS for stock portfolio selection using AI and ML; it was completed using a mathematical optimization model. Xidonas et al [29] established a decision support system for multiplecriteria portfolio selection using Python.…”

Section: Introductionmentioning

confidence: 99%

A Web-Based Decision Support System for Project Evaluation with Sustainable Development Considerations Based on Two Developed Pythagorean Fuzzy Decision Methods

Mahmoudian Azar Sharabiani,

Mousavi

2023

Sustainability

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Decision support systems are being developed as attractive tools to help organizations make better decisions. These systems assist decision-makers in making the best decisions. The widespread application of the internet has transformed the development of decision support systems into a web-based challenge. On the other hand, project selection has always been a significant issue for organizations. The limitation of resources and the existence of different criteria while selecting projects cause organizations to face the challenges of multiple-criteria decision making. In this research, a new approach is introduced for the selection of criteria. It also presents a new web-based decision support system for selecting projects considering uncertainty and various criteria, including organizational strategies, the seventh edition of project management standard, and sustainable development. Therefore, the economic, social, and environmental dimensions of sustainable development were included as project evaluation indicators. The proposed approach was developed using Pythagorean fuzzy sets, MEREC, and MARCOS methods to examine uncertainty and solution methods. In this approach, a new version of the MARCOS method was developed, with Pythagorean fuzzy sets for rankings. Also, a new development was presented using the Pythagorean fuzzy (PF)-MEREC method, which was used for weighting. The effectiveness of the proposed method is discussed through a real case study conducted on one of the mineral holdings in Iran. Among the mining projects introduced to the company, finally, the second project was selected. In the comparison made using PF-Entropy-TOPSIS and PF-Entropy-VIKOR methods, the superior project provided similar results. By changing the weights of the criteria for four different types of states, sensitivity analysis was used to determine the reliability of the final rankings. In these states, the weights of the criteria were moved together or assigned equal weights, and, in all four states, the ranking results were the same.

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Decision Support System for Stock Portfolio Selection Using Artificial Intelligence and Machine Learning

Cited by 13 publications

References 19 publications

Intelligent Financial Data Analysis and Decision Management Based on Edge Computing

Intelligent Financial Data Analysis and Decision Management Based on Edge Computing

The state of development of artificial intelligence in polish industry: opinions of employees

A Web-Based Decision Support System for Project Evaluation with Sustainable Development Considerations Based on Two Developed Pythagorean Fuzzy Decision Methods

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