{"title":"编辑来信","authors":"H. Nachtmann","doi":"10.1080/0013791X.2023.2208505","DOIUrl":null,"url":null,"abstract":"Applying engineering methods such as machine learning and optimization to financial analyses can lead to powerful outcomes as presented in the first two articles published in this issue. We also feature a case analysis demonstrating major capital investment analysis within a unique industry. I would like to thank the reviewers who contributed to the peer-review process as well as area editors David Enke, Roy Kwon, and Karen Bursic for their contributions to this issue. The issue begins with an article entitled “Intraday Trend Prediction of Stock Indices with Machine Learning Approaches” by Tang, Tang, and Yu. The authors predict price movements of Shanghai Securities 50 Index and propose three kinds of trading strategies based on machine learning approaches. Specifically, decision tree analysis, eXtreme Gradient Boosting, random forest, support vector machines (SVM), and long short-term memory are constructed and compared at different time frequencies. The results show that SVM outperforms others at the same time frequency, and overall, the findings of this article further enrich quantitative trading strategies. In “Cash Holding Management for Self-Financing Phase-able and Non-Phase-able Project Portfolio Selection and Scheduling Problems” by Langaroudi, Khosravi, Davoodi, and Movahedifar, a novel mathematical model for managing the level of cash reserves for the self-financing phase-able project portfolio selection and scheduling problem is presented. An example demonstrating their mixed-integer programming approach illustrates the applicability and performance of their model. Amir, Efendy, and Hidayat coauthored “Economic Feasibility of Developing a Mini-Salt Industry Plant A Case Study of the University of Trunojoyo Madura.” Their case study provides an economic analysis of the development of a mini-salt industry plant at the University of Trunojoyo Madura. Demonstrating net present value, internal rate of return, benefit-cost ratio, and return on investment, the project is found to be economically attractive and a feasible option for mitigating Indonesia’ salt-import dependency. The Engineering Economist journal publishes articles, case studies, surveys, and book and software reviews that represent original research, current practice, and teaching involving problems of capital investment. For questions or inquiries, please contact me at hln@uark.edu.","PeriodicalId":49210,"journal":{"name":"Engineering Economist","volume":"68 1","pages":"59 - 59"},"PeriodicalIF":1.0000,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Letter from the editor\",\"authors\":\"H. Nachtmann\",\"doi\":\"10.1080/0013791X.2023.2208505\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Applying engineering methods such as machine learning and optimization to financial analyses can lead to powerful outcomes as presented in the first two articles published in this issue. We also feature a case analysis demonstrating major capital investment analysis within a unique industry. I would like to thank the reviewers who contributed to the peer-review process as well as area editors David Enke, Roy Kwon, and Karen Bursic for their contributions to this issue. The issue begins with an article entitled “Intraday Trend Prediction of Stock Indices with Machine Learning Approaches” by Tang, Tang, and Yu. The authors predict price movements of Shanghai Securities 50 Index and propose three kinds of trading strategies based on machine learning approaches. Specifically, decision tree analysis, eXtreme Gradient Boosting, random forest, support vector machines (SVM), and long short-term memory are constructed and compared at different time frequencies. The results show that SVM outperforms others at the same time frequency, and overall, the findings of this article further enrich quantitative trading strategies. In “Cash Holding Management for Self-Financing Phase-able and Non-Phase-able Project Portfolio Selection and Scheduling Problems” by Langaroudi, Khosravi, Davoodi, and Movahedifar, a novel mathematical model for managing the level of cash reserves for the self-financing phase-able project portfolio selection and scheduling problem is presented. An example demonstrating their mixed-integer programming approach illustrates the applicability and performance of their model. Amir, Efendy, and Hidayat coauthored “Economic Feasibility of Developing a Mini-Salt Industry Plant A Case Study of the University of Trunojoyo Madura.” Their case study provides an economic analysis of the development of a mini-salt industry plant at the University of Trunojoyo Madura. Demonstrating net present value, internal rate of return, benefit-cost ratio, and return on investment, the project is found to be economically attractive and a feasible option for mitigating Indonesia’ salt-import dependency. The Engineering Economist journal publishes articles, case studies, surveys, and book and software reviews that represent original research, current practice, and teaching involving problems of capital investment. 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Applying engineering methods such as machine learning and optimization to financial analyses can lead to powerful outcomes as presented in the first two articles published in this issue. We also feature a case analysis demonstrating major capital investment analysis within a unique industry. I would like to thank the reviewers who contributed to the peer-review process as well as area editors David Enke, Roy Kwon, and Karen Bursic for their contributions to this issue. The issue begins with an article entitled “Intraday Trend Prediction of Stock Indices with Machine Learning Approaches” by Tang, Tang, and Yu. The authors predict price movements of Shanghai Securities 50 Index and propose three kinds of trading strategies based on machine learning approaches. Specifically, decision tree analysis, eXtreme Gradient Boosting, random forest, support vector machines (SVM), and long short-term memory are constructed and compared at different time frequencies. The results show that SVM outperforms others at the same time frequency, and overall, the findings of this article further enrich quantitative trading strategies. In “Cash Holding Management for Self-Financing Phase-able and Non-Phase-able Project Portfolio Selection and Scheduling Problems” by Langaroudi, Khosravi, Davoodi, and Movahedifar, a novel mathematical model for managing the level of cash reserves for the self-financing phase-able project portfolio selection and scheduling problem is presented. An example demonstrating their mixed-integer programming approach illustrates the applicability and performance of their model. Amir, Efendy, and Hidayat coauthored “Economic Feasibility of Developing a Mini-Salt Industry Plant A Case Study of the University of Trunojoyo Madura.” Their case study provides an economic analysis of the development of a mini-salt industry plant at the University of Trunojoyo Madura. Demonstrating net present value, internal rate of return, benefit-cost ratio, and return on investment, the project is found to be economically attractive and a feasible option for mitigating Indonesia’ salt-import dependency. The Engineering Economist journal publishes articles, case studies, surveys, and book and software reviews that represent original research, current practice, and teaching involving problems of capital investment. For questions or inquiries, please contact me at hln@uark.edu.
Engineering EconomistENGINEERING, INDUSTRIAL-OPERATIONS RESEARCH & MANAGEMENT SCIENCE
CiteScore
2.00
自引率
0.00%
发文量
14
审稿时长
>12 weeks
期刊介绍:
The Engineering Economist is a refereed journal published jointly by the Engineering Economy Division of the American Society of Engineering Education (ASEE) and the Institute of Industrial and Systems Engineers (IISE). The journal publishes articles, case studies, surveys, and book and software reviews that represent original research, current practice, and teaching involving problems of capital investment.
The journal seeks submissions in a number of areas, including, but not limited to: capital investment analysis, financial risk management, cost estimation and accounting, cost of capital, design economics, economic decision analysis, engineering economy education, research and development, and the analysis of public policy when it is relevant to the economic investment decisions made by engineers and technology managers.