Earticle

초록

영어

Financial markets are characterized by large numbers of complex and interacting factors which are ill-understood and frequently difficult to measure. Mathematical models developed in finance are precise formulations of theories of how these factors interact to produce the market value of financial asset. While these models are quite good at predicting these market values, because these forces and their interactions are not precisely understood, the model value nevertheless deviates to some extent from the observable market value. In this paper we propose a framework for augmenting the predictive capabilities of mathematical model with a learning component which is primed with an initial set of historical data and then adjusts its behavior after the event of prediction.

목차

Abstract
 1. Introduction
 2. A Review of Incremental Learning Approaches
  2.1 The Minimal Approach
  2.2 The Iterative Approach
  2.3 Comments on the Above Classification
 3. A Contingent Incremental Learning Approach
  3.1 Representation of Objects
  3.2 Representation of Class
  3.3 Discovery of Initial Classes
  3.4 Reconstruction of Classes
 4. Evaluation of Our Approach
  4.1 The Evaluation Approach
  4.2 Evaluation Results
 5. Conclusions
 References

키워드

저자

• Sung Kun Kim [ Professor of Information Systems, School of Business, Chung-Ang University, Seoul, KOREA ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005