Application of Fuzzy Logic in Financial Modeling - Research Paper Example

Application of fuzzy logic in Financial Modeling Insert Insert Fuzzy logic is a concept dealing with the extent of truth other than the usual true or false. It includes a range of values between the two extremes for example .45 wide. It represents the way reasoning works and is inclusive of Boolean and binary. Financial modeling, on the other hand, refers to coming up with an economic representation of entities in an organization. It aims to transform the theories concerning markets to predict the nature of markets. The society at the present demands delving into new ways of understanding financial systems using different models in order to derive meaning. The cause of this is the ever-changing approaches to economic markets and commercial settings, in general. The aim here is to bring out how fuzzy logic is used to predict bankruptcy in organizations. Currently, many diverse factors are of the compound nature of businesses. Our concentration is in the prediction of the financial status of an organization within a particular period. In view of acquiring proper trading in these markets, there is a need to build financial models that can map the current situation in such markets. As a result, one can change their actions and steps they intend to take accordingly. In reality, it is very difficult for an individual or a company to be 100% bankrupt. It is very complex to define the extent of bankruptcy using traditional statistical means. An example of such a conventional means is an analysis function that is based on multivariate discriminant. Fuzzy logic will be used to define in exact facts in an organization. The model that results is used to predict the extent of risk as concerns to bankruptcy. Keywords: fuzzy logic, prediction, fuzzy rule, time series, analysis, probabilistic fuzzy systems. Introduction There have been numerous applications of fuzzy logic especially in robotics and general use of artificial systems. Our concern here is to employ fuzzy logic in coming up with a representation of its financial aspects. Such a prototype will enable it to make recommendations based on that information or even used to forecast the direction the market will follow in the future. There are many complexities emerging in the current business world as a result of an evolving world with different perspectives. As a consequence, there have been many factors influencing the status of items in a financial environment that makes predicting them very difficult. These factors are said to be fuzzy (Anderson, 2005). The core of our research is to analyze the prediction of the financial status of companies both for a duration of 1-2 years as an economic menace. It is almost inexact to foretell the business and business aspects of an organization. Factors affecting such a situation are many from within the company and external entities. It is almost impossible to declare an organization as being 100% bankrupt in the real market spheres since the very fact is considered to be inexact. Fuzzy logic is useful in accurately establishing the extent of bankruptcy because of its nature to define ambiguous scenarios as being either low or high. On the other hand, it is almost not possible to use backward means such as methods based on statistics to estimate the extent of bankruptcy. The previous case can pinpoint the degree of how positive the financial position of the company in case. The more there has been globalization, the more the economies worldwide have continued to worsen. For example, this is what has seen the United States drop to AA+ from an AAA prediction by supports since its existence. The result is that both the companies and the individuals become victims. What is now vital is the choice of method to counter the extent to which mistakes can be during this process. The way to approach this research is to explain the prediction mechanism to use and explore concepts of fuzzy logic and the way it relates to financing. The financial values belonging to clients in an insurance company are the basis of building our models. The research seeks to bring out the benefits that will be achieved including establishing systems that will advise the company in the initial state of the status of the finances worsens. It also endeavors to weigh risks concerning credit in the groups of interest. Further, it addresses issues on how to buy and sell shares that will in the overall affect the wealth of its supporters and clientele. Finally, it maps out how liquidation can be in the organization. Methodology The improvement in fuzzy logic forms the basis of understanding what has been applied to financial models. It exhibits the challenges that these economic models face, as a result. The aim here is in the use of reviewed journals to be able to explore the applications of fuzzy logic methodology to comprehend financial models. The search highly depends on of keywords to retrieve the relevant articles. The papers were analyzed for a review of fuzzy logic as a pillar for these financial systems. Although there has been an increase in the use of fuzzy logic, there are challenges with this kind of approach. Grouping of models used in financial forecasting The prediction models are mainly of three types including theoretical models, models that depend on computing means and statistical models. The computational means is based on artificial intelligence. The most frequently used model is the analytical, computing, and the other models are not as such. The mathematical model used here chooses financial ratios with values that are diagnostic, are approximated and put to use. The manner of selecting is dependent on studies on the past groups that comprise of consumers and institutions with good and bad financial conditions. Entries that contain similar data are avoided at all costs most are those that are. What follows is establishing the parameters of the model based on the initial diagnostic variables (From binary logic functions to fuzzy logic functions, 2013). The values selected receive weight of varied value. When the different individual sets are together, the result is the establishment of the bankruptcy prediction model. The milestone obtained is an indicator of a synthetic nature. To perform compaction, suitable econometrical and statistical methodologies are. To use such a prototype to evaluate the risk of a financial nature leads to replacement of the ratios used in the economic encounter and the way the indicator of the synthetic risk is. The financial circumstance surrounding an audited company is a reflection of the synthetic index in place. The variables to be in a statistical model have to concur with the assumptions the model has established. They include the independence of the indicator and normal dispersion of indicators. Further, it covers the manner in which insolvent entities are from solvent entities. It also involves separating one entity from the next and ensuring completion of observations made on an individual item. According to studies, the financial state predictions are majorly a result of soft computing, theoretical and statistical methods (From binary logic functions to fuzzy logic functions, 2013). The function for predicting bankruptcy as a risk is of number of ratios. It is an application of many different discriminants analyzes. Z = 1.2 * T1 + 1.4 * T2 + 3.3 * T3 + 0.6 * T4 + 0.999 * T5 From the above equation: T1 = working capital / total company assets T2 = retained earnings / total company assets T3 = earnings before taxes / total company assets T4 = market value of equity / total long term and short term business liabilities T5 = sales / total company assets The proposal from the above data is the use of three resolutions spheres based on the value of the Z score. First, if Z < 1.81 it means that the signal is one that has a high bankruptcy probability. Second, if 1.81 < Z < 2.99 it means that it is not possible to define the risk of failure in such a company. Commonly, such a situation is said to be a gray area. Thirdly, if Z > 2.99 it means that there is a very low level of bankruptcy to be in such a company. The idea is that the cause of failure in a business is affected by many factors that are as either external or internal. As a result, being able to foretell the bankruptcy in an enterprise is very vague and indefinite. Traditional means do not give the best returns when it comes to predicting the levels of bankruptcy in a business. When the threshold value is more than the discriminant function, the company involved is at a high risk of getting bankrupt. The use of fuzzy logic enables the explanation of vague concepts such as either small likelihood of bankruptcy or high probability of bankruptcy. In a non-empty space, the fuzzy set A can be described as: Where μA: X  [0, 1] is a function for every element of X that establishes its variation into belonging domain of set A. It is like a membership function of fuzzy set A. In a classical set theory, a company is a full member or it does not belong to a particular set wholly. However, in the fuzzy set theory the company may be somewhat members of a particular set, the connection may be expressed by means of an existing number that is in the interval 0, 1. The membership function is μA(x): U [0, 1] is as below: In, μA() of X to A is a subset of U. f() is that from an of 0, 1. The values in the function are the gradations of relationship. A membership function will assign the degree of membership of every element x X to a fuzzy set A, where we can define three existing situations as below: μA (x) = 1 describes the complete membership of the element X to the fuzzy set A μA (x) = 0 shows that there is no element X that belongs to fuzzy set A 0< μA (x) Read More