Multilayer Stock Forecasting Model Using Fuzzy Time Series

Hossein Javedani Sadaei,Muhammad Hisyam Lee

doi:10.1155/2014/610594

Abstract

After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting including five logical significant layers. Every single layer has its detailed concern to assist forecast development by reconciling certain problems exclusively. To verify the model, a set of huge data containing Taiwan Stock Index (TAIEX), National Association of Securities Dealers Automated Quotations (NASDAQ), Dow Jones Industrial Average (DJI), and S&P 500 have been chosen as experimental datasets. The results indicate that the proposed methodology has the potential to be accepted as a framework for model development in stock market forecasts using FTS.

Highlights

The statistical complex system model investigation of financial market index and return is an issue to understand and model the distribution of financial price fluctuation, which has long been an effort of economic study
Having discussed the key points in the introduction section, we proposed a multilayer model that could be beneficial for stock market forecasting by using FTS methods
A five-layer model was proposed for stock market forecasting

Summary

Introduction

The statistical complex system model investigation of financial market index and return is an issue to understand and model the distribution of financial price fluctuation, which has long been an effort of economic study. Chen and Chung [30] presented a method that modified the length of each interval in the universe of discourse to deal with the forecasting complications based on high-order fuzzy time They used historical enrolments of the University of Alabama to illustrate the forecasting process of their proposed method. Aladag et al [36] proposed another approach which used a single-variable constrained optimization to determine the ratio for the length of intervals Their method was successfully applied to the two case studies, which are the enrolment data at the University of Alabama and the inventory demand data. Egrioglu et al [38] proposed a new method which used MATLAB function that employs an algorithm based on golden section search to optimize a function with single-variable constraint for finding the effective length of intervals in high-order FTS.

Related Studies

The Framework of the Proposed Multilayer Stock Forecasting Model

Empirical Works

Findings

Conclusions and Future Works