Please enter verification code
Confirm
To Measure the Risk of Projects Financed from Structural Funds by a Fuzzy Logic System
International Journal of Finance and Banking Research
Volume 2, Issue 6, December 2016, Pages: 193-203
Received: Oct. 16, 2016; Accepted: Nov. 7, 2016; Published: Jan. 10, 2017
Views 2972      Downloads 144
Authors
Jianqiang Sun, College of Computer and Information Technology, China Three Gorges University, Yichang, China
Xingyu Chai, College of Economics and Management, China Three Gorges University, Yichang, China
Fenggang Zhang, College of Computer and Information Technology, China Three Gorges University, Yichang, China
Zhengying Cai, College of Computer and Information Technology, China Three Gorges University, Yichang, China
Article Tools
Follow on us
Abstract
To measure the risk of projects financed from structural funds is very difficult because there involved a great number of risks during the whole project process. Accordingly, a fuzzy logic system was applied to measure the risk of projects financed from a structural fund. First, the systematic structure of risk is also investigated, and the risk activities are analyzed for reflecting the finance problems, where the financing risk consists of basic risk element, project risk, and financing agreement in the second level. Second, a fuzzy risk measurement method is illustrated for risk management of projects. For each systematic part, the fuzzy logic system can be used to analyze and quantify different risks. At last, an experimental analysis was presented to verify the proposed model and some practical instructions are also indicated, as well as some interesting conclusions and future research directions.
Keywords
Projects Financed, Structural Funds, Fuzzy Logic System, Minimax
To cite this article
Jianqiang Sun, Xingyu Chai, Fenggang Zhang, Zhengying Cai, To Measure the Risk of Projects Financed from Structural Funds by a Fuzzy Logic System, International Journal of Finance and Banking Research. Vol. 2, No. 6, 2016, pp. 193-203. doi: 10.11648/j.ijfbr.20160206.12
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Wallenius, J; Dyer, JS; Fishburn, PC; Steuer, RE; Zionts, S; Deb, K. Multiple criteria decision making, multiattribute utility theory: recent accomplishments and what lies ahead, Management Science, 54 (7) (2008) 1336-1349.
[2]
Luce, TC; Humphreys, DA; Jackson, GL; Solomon, WM. Inductive flux usage and its optimization in tokamak operation, Nuclear Fusion, 54 (9) (2014) 093005.
[3]
Pratt, LR; Chaudhari, MI; Rempe, SB. Statistical analyses of hydrophobic interactions: a mini-review, Journal Of Physical Chemistry B, 120 (27) (2016) 6455-6460.
[4]
Fishburn, MW; Charbon, E. System tradeoffs in gamma-ray detection utilizing SPAD arrays and scintillators, Ieee Transactions On Nuclear Science, 57 (5) (2010) 2549-2557.
[5]
Smith, D; Schlaifer, A; Heldt, J; Anderson, K; Fargusson, J; Asplin, J; Heldt, J; Chen, C; Hodgkin, G; Baldwin, DD. A prospective randomized comparison of regular and diet lemonade upon risk for urinary stone formation, Journal Of Urology, 187 (4) (2012) E909-E909.
[6]
Linn, SC; Tay, NSP. Complexity and the character of stock returns: Empirical evidence and a model of asset prices based on complex investor learning, Management Science, 53 (7) (2007) 1165-1180.
[7]
Gonsalvez, DJA; Inman, RR. Supply Chain Shared Risk Self-Financing For Incremental Sales, Engineering Economist, 61 (1) (2016) 23-43.
[8]
Bolos, MI; Sabau-Popa, DC; Scarlat, E; Bradea, IA; Delcea, C. A business intelligence instrument for detection and mitigation of risks related to projects financed from structural funds. Economic Computation And Economic Cybernetics Studies And Research, 50 (2) (2016) 165-178.
[9]
Bertsimas, D; Brynjolfsson, E; Reichman, S; Silberholz, J. Or forum-tenure analytics: models for predicting research impact, Operations Research, 63 (6) (2015) 1246-1261.
[10]
Csoka, P; Havran, D; Szucs, N. Corporate financing under moral hazard and the default risk of buyers, Central European Journal Of Operations Research, 23 (4) (2015) 763-778.
[11]
Criscuolo, C; Menon, C. Environmental policies and risk finance in the green sector: cross-country evidence, Energy Policy, 83 (2015) 38-56.
[12]
Bolos, MI; Sabau-Popa, DC; Filip, P; Manolescu, A. Development of a fuzzy logic system to identify the risk of projects financed from structural funds, International Journal Of Computers Communications & Control, 10 (4) (2015) 480-491.
[13]
Frisari, G; Stadelmann, M. De-risking concentrated solar power in emerging markets: the role of policies and international finance institutions, Energy Policy, 82 (2015) 12-22.
[14]
Jensen, NR; Steffensen, M. Personal Finance And Life Insurance Under Separation Of Risk Aversion And Elasticity Of Substitution, Insurance Mathematics & Economics, 62 (2015) 28-41.
[15]
Card, D; Lee, DS; Pei, Z; Weber, A. Inference on causal effects in a generalized regression kink design, Econometrica, 83 (6) (2015) 2453-2483.
[16]
Verguet, S; Olson, ZD; Babigumira, JB; Desalegn, D; Johansson, KA; Kruk, ME; Levin, CE; Nugent, RA; Pecenka, C; Shrime, MG; Memirie, ST; Watkins, DA; Jamison, DT. Health gains and financial risk protection afforded by public financing of selected interventions in Ethiopia: An extended cost-effectiveness analysis, Lancet Global Health, 3 (5) (2015) E288-E296.
[17]
di Iasio, G; Gallegati, M; Lillo, F; Mantegna, RN. Special issue of quantitative finance on 'interlinkages and systemic risk' foreword, Quantitative Finance, 15 (4) (2015) 587-588.
[18]
Abdulkadiroglu, A; Angrist, J; Pathak, P. The elite illusion: achievement effects at boston and new york exam schools, Econometrica, 82 (1) (2014) 137-196.
[19]
Lee, CW; Zhong. Financing and risk management of renewable energy projects with a hybrid bond, Renewable Energy, 75 (2015) 779-787.
[20]
Sanford, A; Moosa, I. Operational risk modelling and organizational learning in structured finance operations: a bayesian network approach, Journal Of The Operational Research Society, 66 (1) (2015) 86-115.
[21]
Jongman, B; Hochrainer-Stigler, S; Feyen, L; Aerts, JCJH; Mechler, R; Botzen, WJW; Bouwer, LM; Pflug, G; Rojas, R; Ward, PJ. Increasing stress on disaster-risk finance due to large floods, Nature Climate Change, 4 (4) (2014) 264-268.
[22]
Guerra, ML; Magni, CA; Stefanini, L. Interval and fuzzy average internal rate of return for investment appraisal, Fuzzy sets and systems, 257 (2014) 217-241.
[23]
Jongman, B; Koks, EE; Husby, TG; Ward, PJ. Increasing flood exposure in the netherlands: implications for risk financing, Natural Hazards And Earth System Sciences, 14 (5) (2014) 1245-1255.
[24]
Tsetlin, I; Winkler, RL; Huang, RJ; Tzeng, LY. Generalized almost stochastic dominance, Operations Research 63 (2) (2015) 363-377.
ADDRESS
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
U.S.A.
Tel: (001)347-983-5186