Yoshiteru Nakamori

Forecasting stock market movement direction with support vector machine

Support vector machine (SVM) is a very specific type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of financial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare its performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms the other classification methods. Further, we propose a combining model by integrating SVM with the other classification methods. The combining model performs best among all the forecasting methods.

FORECASTING FOREIGN EXCHANGE RATES WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW

Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. Artificial neural networks (ANNs) have been widely used as a promising alternative approach for a forecasting task because of several distinguished features. Research efforts on ANNs for forecasting exchange rates are considerable. In this paper, we attempt to provide a survey of research in this area. Several design factors significantly impact the accuracy of neural network forecasts. These factors include the selection of input variables, preparing data, and network architecture. There is no consensus about the factors. In different cases, various decisions have their own effectiveness. We also describe the integration of ANNs with other methods and report the comparison between performances of ANNs and those of other forecasting methods, and finding mixed results. Finally, the future research directions in this area are discussed.

Systems thinking on knowledge and its management: systems methodology for knowledge management

KM is increasingly imperative as it is regarded as the key determinant of a firm, industry or country for survival and growth in knowledge era. Varieties of disciplines have made contributions to knowledge and knowledge management. Research focuses on one or more specific fields, but to understand which levels of knowledge processes knowledge management should concentrate on, should be more fundamental than advocacy of knowledge management. Knowledge‐related matters were examined from the viewpoint of systems science. Using critical systems thinking, soft systems thinking etc., a new systematic perspective on knowledge was proposed, aiming to provide a new way of thinking and a useful toolbox on different levels and phases of knowledge management for practical knowledge users.

Knowledge management and knowledge creation in academia: a study based on surveys in a Japanese research university

Purpose – This study aims to pose one major research question, i.e. why and how to use knowledge management methods in order to enhance knowledge creation in academia – at universities and research institutes?Design/methodology/approach – The paper defines KM in academia as any systematic activity related to support and enhancement of the creation of scientific knowledge and achievement of research goals, including both social process and relevant computer technology tools. Two surveys and case studies were carried out to achieve the research purpose at Japan Advanced Institute of Science and Technology (JAIST). The first survey focused on knowledge management in academia and investigated the current KM situations, special and diverse requirements from researchers. The second survey concentrated on supporting the creative processes of academic research and investigated which aspects of knowledge creation processes should be supported in particular. Based on survey findings, the practical solutions are fur...

Agent-based modeling on technological innovation as an evolutionary process

Abstract This paper describes a multi-agent model built to simulate the process of technological innovation, based on the widely accepted theory that technological innovation can be seen as an evolutionary process. The actors in the simulation include producers and a large number of consumers. Every producer will produce several types of products at each step. Each product is composed of several design parameters and several performance parameters (fitness components). Kauffman’s famous NK model is used to deal with the mapping from a design parameter space (DPS) to a performance parameter space (PPS). In addition to the constructional selection, which can be illustrated by the NK model, we added environmental selection into the simulation and explored technological innovation as the result of the interaction between these two kinds of selection.

Multiple-attribute decision making under uncertainty: the evidential reasoning approach revisited

In multiple-attribute decision making (MADM) problems, one often needs to deal with decision information with uncertainty. During the last decade, Yang and Singh (1994) have proposed and developed an evidential reasoning (ER) approach to deal with such MADM problems. Essentially, this approach is based on an evaluation analysis model and Dempsters rule of combination in the Dempster-Shafer (D-S) theory of evidence. This paper reanalyzes the ER approach explicitly in terms of D-S theory and then proposes a general scheme of attribute aggregation in MADM under uncertainty. In the spirit of such a reanalysis, previous ER algorithms are reviewed and two other aggregation schemes are discussed. Theoretically, it is shown that new aggregation schemes also satisfy the synthesis axioms, which have been recently proposed by Yang and Xu (2002) for which any rational aggregation process should grant. A numerical example traditionally examined in published sources on the ER approach is used to illustrate the discussed techniques

A roughness measure for fuzzy sets

Recently, an attempt of integration between the theories of fuzzy sets and rough sets has resulted in providing a roughness measure for fuzzy sets [M. Banerjee, S.K. Pal, Roughness of a fuzzy set, Information Sciences 93 (1996) 235-246]. Essentially, Banerjee and Pals roughness measure depends on parameters that are designed as thresholds of definiteness and possibility in membership of the objects to a fuzzy set. In this paper we first remark that this measure of roughness has several undesirable properties, and then propose a parameter-free roughness measure for fuzzy sets based on the notion of the mass assignment of a fuzzy set. Several interesting properties of this new measure are examined. Furthermore, we also discuss how the proposed approach is used to describe the rough approximation quality of a fuzzy classification.

NEURAL NETWORKS IN FINANCE AND ECONOMICS FORECASTING

Artificial neural networks (ANNs) have been widely applied to finance and economic forecasting as a powerful modeling technique. By reviewing the related literature, we discuss the input variables, type of neural network models, performance comparisons for the prediction of foreign exchange rates, stock market index and economic growth. Economic fundamentals are important in driving exchange rates, stock market index price and economic growth. Most neural network inputs for exchange rate prediction are univariate, while those for stock market index prices and economic growth predictions are multivariate in most cases. There are mixed comparison results of forecasting performance between neural networks and other models. The reasons may be the difference of data, forecasting horizons, types of neural network models and so on. Prediction performance of neural networks can be improved by being integrated with other technologies. Nonlinear combining forecasting by neural networks also provides encouraging results.

A Probability-Based Approach to Comparison of Fuzzy Numbers and Applications to Target-Oriented Decision Making

In this paper, we introduce a new comparison relation on fuzzy numbers based on their alpha-cut representation and comparison probabilities of interval values. Basically, this comparison process combines a widely accepted interpretation of fuzzy sets together with the uncertain characteristics inherent in the representation of fuzzy numbers. The proposed comparison relation is then applied to the issue of ranking fuzzy numbers using fuzzy targets in terms of target-based evaluations. Some numerical examples are used to illuminate the proposed ranking technique as well as to compare with previous methods. More interestingly, according to the interpretation of the new comparison relation on fuzzy numbers, we provide a fuzzy target-based decision model as a solution to the problem of decision making under uncertainty, with which an interesting link between the decision makers different attitudes about target and different risk attitudes in terms of utility functions can be established. Moreover, an application of the proposed comparison relation to the fuzzy target-based decision model for the problem of fuzzy decision making with uncertainty is provided. Numerical examples are also given for illustration.

A parametric representation of linguistic hedges in Zadeh's fuzzy logic

This paper proposes a model for the parametric representation of linguistic hedges in Zadeh?s fuzzy logic. In this model each linguistic truth-value, which is generated from a primary term of the linguistic truth variable, is identified by a real number r depending on the primary term. It is shown that the model yields a method of efficiently computing linguistic truth expressions accompanied with a rich algebraic structure of the linguistic truth domain, namely De Morgan algebra. Also, a fuzzy logic based on the parametric representation of linguistic truth-values is introduced.