Notion of Neutrosophic Risk and Financial Markets Prediction

Presenting an application of the neutrosophic logic in the prediction of the financial markets

A synthetic protective put strategy for phased investment in projects without an outright deferral.

In this paper we propose and computationally demonstrate a synthetic protective put strategy for real options. Specifically, we deal with the problem of deferral option when an outright deferral is not permissible due to competitive pressures. We demonstrate that in such a situation an appropriate strategy would be to invest in the new project in phases rather than doing it all at once. By setting the owner’s equity in the project equal to the price of a call option on the value of the project, we set up the replicating portfolio for a protective put on the project. Our method is a logical extension of the financial protective put in the real options scenario and is rather simple and practicable for businesses to adopt and apply.synthetic protective put, replicating portfolio, deferral option

Conditional probability of actually detecting a financial fraud - a neutrosophic extension to Benford's law

This study actually draws from and builds on an earlier paper (Kumar and Bhattacharya, 2002). Here we have basically added a neutrosophic dimension to the problem of determining the conditional probability that a financial fraud has been actually committed, given that no Type I error occurred while rejecting the null hypothesis H0: The observed first-digit frequencies approximate a Benford distribution; and accepting the alternative hypothesis H1: The observed first-digit frequencies do not approximate a Benford distribution. We have also suggested a conceptual model to implement such a neutrosophic fraud detection system.Comment: 9 page

The Israel-Palestine Question – A Case for Application of Neutrosophic Game Theory

In our present paper, we have explored the possibilities and developed arguments for an application of principles of neutrosophic game theory as a generalization of the fuzzy game theory model to a better understanding of the Israel-Palestine problem in terms of the goals and governing strategies of either side. We build on an earlier attempted justification of a game theoretic explanation of this problem by Yakir Plessner (2001) and go on to argue in favour of a neutrosophic adaptation of the standard 2x2 zero-sum game theoretic model in order to identify an optimal outcomeIsrael-Palestine conflict, Oslo Agreement, fuzzy games, neutrosophic semantic space

Fuzziness and Funds Allocation in Portfolio Optimization

Each individual investor is different, with different financial goals, different levels of risk tolerance and different personal preferences. From the point of view of investment management, these characteristics are often defined as objectives and constraints. Objectives can be the type of return being sought, while constraints include factors such as time horizon, how liquid the investor is, any personal tax situation and how risk is handled. It's really a balancing act between risk and return with each investor having unique requirements, as well as a unique financial outlook - essentially a constrained utility maximization objective. To analyze how well a customer fits into a particular investor class, one investment house has even designed a structured questionnaire with about two-dozen questions that each has to be answered with values from 1 to 5. The questions range from personal background (age, marital state, number of children, job type, education type, etc.) to what the customer expects from an investment (capital protection, tax shelter, liquid assets, etc.). A fuzzy logic system has been designed for the evaluation of the answers to the above questions. We have investigated the notion of fuzziness with respect to funds allocation.Comment: 21 page

A comparative analysis of decision trees vis-a-vis other computational data mining techniques in automotive insurance fraud detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of - financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists