40,167 research outputs found
Fuzzy interval net present value
In this paper we conjugate the operative usability of the net present value with the capability of the fuzzy and the interval approaches to manage uncertainty. Our fuzzy interval net present value can be interpreted, besides the usual present value of an investment project, as the present value of a contract in which the buyer lets the counterpart the possibility to release goods/services for money amounts that can vary, at time instants that can also vary. The buyer can reduce the widths of these variations by paying a cost. So, it is "natural" to represent the good/service money amounts and the time instants by means of triangular fuzzy numbers, and the cost of the buyer as a strictly increasing function of the level a in [0, 1] associated to the generic cut of the fuzzy interval net present value. As usual, the buyer is characterized by a utility function, depending on a and on the cost, that he/she has to maximize. As far the interest rates regard, we assume that the economic operators are only able to specify a variability range for each of the considered period interest rate. So, we represent the interest rates by means of interval numbers. Besides proposing our model, we formulate and solve the programming problems which have to be coped with to determine the extremals of the cut of the fuzzy interval net present value, and we deal with some questions related to the utility function of the buyer.net present value, fuzzy set theory, interval number theory, alpha-cut, utility function
Fuzzy Interval NEt present value
Il working paper è inserito nell'archivio RePEc.
http://ideas.repec.org/p/vnm/wpaper/170.htm
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Soft computing in investment appraisal
Standard financial techniques neglect extreme situations and regards large market shifts as too unlikely to matter. Such approach accounts for what occurs most of the time in the market, but does not reflect the reality, as major events happen in the rest of the time and investors are ‘surprised’ by ‘unexpected’ market movements. An
alternative fuzzy approach permits fluctuations well beyond the probability type of uncertainty and allows one to make fewer assumptions about the data distribution and market behaviour.
Fuzzifying the present value criteria, we suggest a measure of the risk associated with each investment opportunity and estimate the project’s robustness towards market uncertainty. The procedure is applied to thirty-five UK companies traded on the London Stock Exchange and a neural
network solution to the fuzzy criterion is provided to facilitate the decision-making process. Finally, we suggest a specific evolutionary algorithm to train a fuzzy neural net - the bidirectional incremental evolution will automatically identify the complexity of the problem and correspondingly adapt the parameters of the fuzzy network
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Investment Risk Appraisal
Standard financial techniques neglect extreme situations and regards large market shifts as too unlikely to matter. This
approach may account for what occurs most of the time in the market, but the picture it presents does not reflect the reality, as the
major events happen in the rest of the time and investors are ‘surprised’ by ‘unexpected’ market movements. An alternative fuzzy
approach permits fluctuations well beyond the probability type of uncertainty and allows one to make fewer assumptions about the
data distribution and market behaviour. Fuzzifying the present value criteria, we suggest a measure of the risk associated with each
investment opportunity and estimate the project’s robustness towards market uncertainty. The procedure is applied to thirty-five UK
companies and a neural network solution to the fuzzy criterion is provided to facilitate the decision-making process. Finally, we
discuss the grounds for classical asset pricing model revision and argue that the demand for relaxed assumptions appeals for another
approach to modelling the market environment
A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction
In spite of its importance, passenger demand prediction is a highly
challenging problem, because the demand is simultaneously influenced by the
complex interactions among many spatial and temporal factors and other external
factors such as weather. To address this problem, we propose a Spatio-TEmporal
Fuzzy neural Network (STEF-Net) to accurately predict passenger demands
incorporating the complex interactions of all known important factors. We
design an end-to-end learning framework with different neural networks modeling
different factors. Specifically, we propose to capture spatio-temporal feature
interactions via a convolutional long short-term memory network and model
external factors via a fuzzy neural network that handles data uncertainty
significantly better than deterministic methods. To keep the temporal relations
when fusing two networks and emphasize discriminative spatio-temporal feature
interactions, we employ a novel feature fusion method with a convolution
operation and an attention layer. As far as we know, our work is the first to
fuse a deep recurrent neural network and a fuzzy neural network to model
complex spatial-temporal feature interactions with additional uncertain input
features for predictive learning. Experiments on a large-scale real-world
dataset show that our model achieves more than 10% improvement over the
state-of-the-art approaches.Comment: https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.1
Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design
This paper addresses the problem of the optimal design of batch plants with imprecise demands in product amounts. The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must constitute a basic part of the design problem. Rather than resorting to a traditional probabilistic approach for modeling the imprecision on product demands, this work proposes an alternative treatment by using fuzzy concepts. The design problem is tackled by introducing a new approach based on a multiobjective genetic algorithm, combined wit the fuzzy set theory for computing the objectives as fuzzy quantities. The problem takes into account simultaneous maximization of the fuzzy net present value and of two other performance criteria, i.e. the production delay/advance and a flexibility index. The delay/advance objective is computed by comparing the fuzzy production time for the products to a given fuzzy time horizon, and the flexibility index represents the additional fuzzy production that the plant would be able to produce. The multiobjective optimization provides the Pareto's front which is a set of scenarios that are helpful for guiding the decision's maker in its final choices. About the solution procedure, a genetic algorithm was implemented since it is particularly well-suited to take into account the arithmetic of fuzzy numbers. Furthermore because a genetic algorithm is working on populations of potential solutions, this type of procedure is well adapted for multiobjective optimization
Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design
The design of such plants necessary involves how equipment may be utilized, which means that plant scheduling and production must form an integral part of the design problem. This work proposes an alternative treatment of the imprecision (demands) by using fuzzy concepts. In this study, we introduce a new approach to the design problem based on a multiobjective genetic algorithm, taking into account simultaneously maximization of the net present value NPV ~ and two other performance criteria, i.e. the production delay/advance and a flexibility criterion. The methodology provides a set of scenarios that are helpful to the decision’s maker and constitutes a very promising framework for taken imprecision into account in new product development stage. Besides, a hybrid selection method Pareto rank-tournament was proposed and showed a better performance than the classical Goldberg’s wheel, systematically leading to a higher number of non-dominated solutions
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