A FUZZY-BASED ROUGH SETS CLASSIFIER FOR FORECASTING QUARTERLY PGR IN THE STOCK MARKET (PART I)

作者：Chen You Shyang^*; Cheng Ching Hsue; Chen Da Ren

来源：International Journal of Innovative Computing Information and Control, 2011, 7(2): 555-569.

• Rough set theory
• Fuzzy-rule similarity
• Profit growth rate (PCR)
• Feature selection
• Discretization method
• Minimize entropy principal approach (MEPA)

For practitioners, a PG R (profit growth rate) is an effective evaluation indicator in the short- to midterm to see how big the potential power of future development is and measures the growth of future development for a target company that may be selected into investment portfolios by them. Although the PGR is one of the core financial ratios, it is used little in an academic study for researchers. Hence, the study aims to fill this knowledge gap. The study focuses mainly on solving the practical problems of forecasting quarterly PG R and offers three intelligent hybrid models to generate decision rules as knowledge-based systems for helping investors select appropriate investment portfolios. These proposed hybrid models are constituted differently by ski; basic components: experiential knowledge (EK), feature selection method (FSM), discretization method (DM), fuzzy set theory (FST), rule filter (RF), and rough set theory (RST), and the presentation of them are organized into two parts (papers). Part I groups the concepts, principles and expressions of them into this paper. Part II is intended to evaluate the proposed hybrid models, and an empirical case study is implemented, which will be presented in the next paper. A conclusion of the proposed hybrid models is given finally in this paper.

• 出版日期2011-2