Capital budgeting problems with fuzzy cash flows

We consider the internal rate of return (IRR) decision rule in capital budgeting problems with fuzzy cash flows. The possibility distribution of the IRR at any r � 0, is defined to be the degree of possibility that the (fuzzy) net present value of the project with discount factor requals to zero. Generalizing our earlier results on fuzzy capital budegeting problems [5] we show that the possibility distribution of the IRR is a highly nonlinear function which is getting more and more unbalanced by increasing imprecision in the future cash flow. However, it is stable under small changes in the membership functions of fuzzy numbers representing the lingusitic values of future cash flows

Оцінка пріоритетності інноваційних проектів: комплексний підхід

Оцінка пріоритетності інноваційних проектів: комплексний підхід. Розробка засобу інтелектуальної підтримки рішень про якість інноваційних проектів на базі нечіткої логіки, яка дозволяє аналізувати як кількісні, так і лінгвістичні змінні. Для визначення доцільності залучення інвестицій частіше всього користуються методами оцінки, порівняння та відбору проектів. Основними серед них є такі: метод середньої ставки доходу; метод визначення періоду окупності; метод чистої теперішньої вартості; метод індексу прибутковості; метод внутрішньої ставки доходу.Assessment of priority inovacionns project: an integrated approach. The development of intellectual support decisions about the quality of innovative projects based on fuzzy logic, which allows to analyze both quantitative and linguistic variables. To determine the feasibility of investment most commonly used methods of evaluation, comparison and selection. The main among them are: the method of average rates of return, payback method, the method of clean present value, profitability index method, the method of internal rate of return

A fuzzy real option approach for investment project valuation

[[abstract]]The main purpose of this paper is to propose a fuzzy approach for investment project valuation in uncertain environments from the aspect of real options. The traditional approaches to project valuation are based on discounted cash flows (DCF) analysis which provides measures like net present value (NPV) and internal rate of return (IRR). However, DCF-based approaches exhibit two major pitfalls. One is that DCF parameters such as cash flows cannot be estimated precisely in the uncertain decision making environments. The other one is that the values of managerial flexibilities in investment projects cannot be exactly revealed through DCF analysis. Both of them would entail improper results on strategic investment projects valuation. Therefore, this paper proposes a fuzzy binomial approach that can be used in project valuation under uncertainty. The proposed approach also reveals the value of flexibilities embedded in the project. Furthermore, this paper provides a method to compute the mean value of a project’s fuzzy expanded NPV that represents the entire value of project. Finally, we use the approach to practically evaluate a project.[[incitationindex]]SCI[[booktype]]紙

Оцінювання внутрішньої форми доходності в ситуації нечітких грошових потоків

У публікації викладаються результати дослідження проблеми знаходження внутрішньої норми доходності реальних інвестицій у разі нечітких початкових даних. Проаналізовано метод на основі принципу відповідності нечіткому нулю. Запропоновано метод на основі принципу відповідності інтервальному нулю. Сформульовано метод на основі відтворення розподілу ступенів можливості. Для кожного методу наведено алгоритм, що супроводжується необхідними поясненнями. На прикладі умовного інвестиційного проекту здійснено апробацію зазначених підходів.This publication presents the results of research on evaluating the internal rate of return of a real investment under fuzzy initial data. Alternative approaches of fuzzy IRR assessment are considered. The method based on fuzzy zero matching is analyzed. «Matching interval to zero» method is proposed and the method based on a distribution of play opportunities degrees is developed. Each method is supported by an algorithm and necessary explanations. Application of approaches is illustrated using sample investment project data.Изложены результаты исследования проблемы определения внутренней нормы доходности реальных инвестиций в ситуации нечетких исходных данных. Проанализирован метод на основе принципа соответствия нечеткому нулю. Предложен метод на основе принципа соответствия интервальному нулю. Сформулирован метод на основе воспроизведения распределения степеней возможности. Для каждого метода приведен алгоритм, который сопровождается необходимыми пояснениями. На примере условного инвестиционного проекта осуществлена апробация указанных подходов

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

Adaptive probability scheme for behaviour monitoring of the elderly using a specialised ambient device

A Hidden Markov Model (HMM) modified to work in combination with a Fuzzy System is utilised to determine the current behavioural state of the user from information obtained with specialised hardware. Due to the high dimensionality and not-linearly-separable nature of the Fuzzy System and the sensor data obtained with the hardware which informs the state decision, a new method is devised to update the HMM and replace the initial Fuzzy System such that subsequent state decisions are based on the most recent information. The resultant system first reduces the dimensionality of the original information by using a manifold representation in the high dimension which is unfolded in the lower dimension. The data is then linearly separable in the lower dimension where a simple linear classifier, such as the perceptron used here, is applied to determine the probability of the observations belonging to a state. Experiments using the new system verify its applicability in a real scenario

Pricing life settlements in the secondary market using fuzzy internal rate of return

In this paper‎, ‎fuzzy set theory is implemented to model internal rate of return for calculating the price of life ‎settlements‎‎. ‎D‎eterministic‎, ‎probabilistic and stochastic ‎approaches ‎is ‎used ‎to ‎price life ‎settlements‎ in the secondary market for the Iranian insurance industry‎. ‎Research findings were presented and analyzed for whole life insurance policies using the interest rates announced in the supplement of Regulation No‎. ‎68 and Iranian life table‎, ‎which recently has been issued to be used by insurance companies‎. ‎Also‎, ‎the results of three approaches were compared with surrender value‎, ‎which indicates the surrender value is lower than ‎the fuzzy‎ price calculated based on the probabilistic and stochastic approaches and it is higher than the price calculated based on the deterministic approach‎. ‎Therefore‎, ‎selling life settlements in the secondary market ‎in ‎Iran‎ based on ‎calculated fuzzy price using ‎probabilistic and stochastic approaches will benefit the ‎policyholder‎. ‎Also‎,‎ ‎the price is obtained in the form of an interval using the fuzzy sets theory ‎and the investor can decide which price is suitable for this policy based on financial knowledge‎‎‎. ‎‎Furthermore, ‎in ‎order ‎to ‎show validity of the proposed fuzzy method,‎‎‎ the findings ‎are‎ ‎compared ‎to ‎the results of ‎using ‎‎the random ‎internal‎‎ ‎rate ‎of return‎.

"Can the neuro fuzzy model predict stock indexes better than its rivals?"

This paper develops a model of a trading system by using neuro fuzzy framework in order to better predict the stock index. Thirty well-known stock indexes are analyzed with the help of the model developed here. The empirical results show strong evidence of nonlinearity in the stock index by using KD technical indexes. The trading point analysis and the sensitivity analysis of trading costs show the robustness and opportunity for making further profits through using the proposed nonlinear neuro fuzzy system. The scenario analysis also shows that the proposed neuro fuzzy system performs consistently over time.