Optimal Investment Timing with Investment Propensity Using Fuzzy Real Options Valuation

Kim, Yonggu

doi:10.1007/s40815-018-0493-4

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…The results obtained based on this method allow for a more accurate assessment and proposal of a plan for the development of the electric system of the Republic of Tajikistan. It should be noted that the effectiveness of this method today remains one of the most effective methods that is confirmed by numerous scientific publications in recent years [24][25][26][27][28][29][30][31]. It should also be noted that due to the simplicity of this method, it is often used in combination with artificial intelligence and machine learning methods [7,8,12,14].…”

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confidence: 75%

Forecasting of Electricity Consumption by Household Consumers Using Fuzzy Logic Based on the Development Plan of the Power System of the Republic of Tajikistan

et al. 2023

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Seasonal fluctuations in electricity consumption, and uneven loading of supply lines reduce not only the energy efficiency of networks, but also contribute to a decrease in the service life of elements of power supply systems. To solve the problem of forecasting power consumption, it is proposed to use the theory of fuzzy sets to assess the effective development of the energy system of the Republic of Tajikistan. According to the statistical data of power consumption for the previous period, a fuzzy logic model with membership functions is proposed, which makes it possible to evaluate consumer satisfaction using the criteria unsatisfactory, satisfactory, conditionally satisfactory, and satisfactory, as well as the efficiency of the consumption mode of compliance using the criteria high, medium, and low, allowing the evaluation of the efficiency plan for the development of the energy system of the Republic of Tajikistan. To obtain and set more accurate data on electricity consumption, calculations were made for the winter period of the year. Based on the proposed calculation model of fuzzy logic, a quantitative component of electricity consumption, the corresponding satisfaction of the consumer, and the efficiency of the regime for nine cities of the Republic of Tajikistan were proposed in the form of diagrams of seasonal electricity consumption. The obtained seasonal power consumption makes it possible to improve the accuracy of estimating power consumption, thereby equalizing the balance of consumption and generation.

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confidence: 75%

Forecasting of Electricity Consumption by Household Consumers Using Fuzzy Logic Based on the Development Plan of the Power System of the Republic of Tajikistan

et al. 2023

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“…When a sponsor chooses to execute and finance the construction and operation of an international steel plant, it can play a variety of roles such as a licensor, material provider, operation and maintenance agency, and/or a contractor [70]. The diversity of necessary expertise and general lack of experience in international work exposes managerial teams to unknown risks with unknown magnitudes.…”

Section: Discussion: Industry Implicationsmentioning

confidence: 99%

Risk Assessment and Mitigation Model for Overseas Steel-Plant Project Investment with Analytic Hierarchy Process—Fuzzy Inference System

et al. 2018

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This paper presents an analytic hierarchy process (AHP)-fuzzy inference system (FIS) model to aid decision-makers in the risk assessment and mitigation of overseas steel-plant projects. Through a thorough literature review, the authors identified 57 risks associated with international steel construction, operation, and transference of new technologies. Pairwise comparisons of all 57 risks by 14 subject-matter experts resulted in a relative weighting. Furthermore, to mitigate human subjectivity, vagueness, and uncertainty, a fuzzy analysis based on the findings of two case studies was performed. From these combined analyses, weighted individual risk soring resulted in the following top five most impactful international steel project risks: procurement of raw materials; design errors and omissions; conditions of raw materials; technology spill prevention plan; investment cost and poor plant availability and performance. Risk mitigation measures are also presented, and risk scores are re-assessed through the AHP-FIS analysis model depicting an overall project risk score reduction. The model presented is a useful tool for industry performing steel project risk assessments. It also provides decision-makers with a better understanding of the criticality of risks that are likely to occur on international steel projects.

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“…An effective method for calculating fuzzy real option is proposed.Liu Xiaojing et al (2015) [10] discussed the real option characteristics of enterprise design innovation from the perspective of economic management, and made a scientific evaluation on the flexibility of management decisionmaking. Yonggu Kim(2018) [11] et al used real options with fuzzy logic to carry out valuation and give the best investment timing to support the decisions of investors with different investment tendencies.Wang Xiandong (2020) [12] used the fuzzy compound real option method to study the investment decision-making of R & D projects, established the fuzzy compound option model of value evaluation and investment decision-making, and gave the arbitrary level cut set of fuzzy value.…”

Section: Literature Review 21 Fuzzy Real Option Theorymentioning

confidence: 99%

Research on Income evaluation of photovoltaic agricultural investment model based on fuzzy real option

Wang¹,

Fu²

2022

BCPBM

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With the proposal of emission peak and carbon neutrality, in view of the actual development of China's energy economy , photovoltaic agriculture becomes an important means to promote the economic development of poor and remote areas. Aiming at the investment modes of photovoltaic agriculture projects such as agricultural land leasing and farmers' participation in investment, this paper constructs a fuzzy real option investment model, and uses Monte Carlo simulation method to evaluate and analyze the income of both photovoltaic power generation project modes. The research shows that compared with the traditional real option income evaluation, the fuzzy real option model is more suitable for the investment evaluation of photovoltaic power generation project modes, and the project with farmers’ participation has higher returns. The income evaluation results show that the best time to benefit from the photovoltaic power generation project is in the next seven years or so, which provides reference for policy guidance and investment decision-making, and helps to promote the development of photovoltaic agriculture projects.

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Optimal Investment Timing with Investment Propensity Using Fuzzy Real Options Valuation

Cited by 12 publications

References 27 publications

Forecasting of Electricity Consumption by Household Consumers Using Fuzzy Logic Based on the Development Plan of the Power System of the Republic of Tajikistan

Forecasting of Electricity Consumption by Household Consumers Using Fuzzy Logic Based on the Development Plan of the Power System of the Republic of Tajikistan

Risk Assessment and Mitigation Model for Overseas Steel-Plant Project Investment with Analytic Hierarchy Process—Fuzzy Inference System

Research on Income evaluation of photovoltaic agricultural investment model based on fuzzy real option

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