摘要(英) |
In the perspective of investments, they desire to use the less financial variables to anticipate the most performance information. Every human being is increasingly faced with unmanageable amounts of financial data; hence, data mining or knowledge discovery apparently affects all of us. In this study of mining performance of company, we attempt to summarize the stronger characteristic rules of fundamental analysis using 81 financial statement variables. To address this problem, we proposed an effective method, a Char Algorithm, to automatically produce characteristic rules to describe the major characteristics of data in a table is proposed. To fit the data type of Char Algorithm, we proceed many steps to preprocess source data of financial statement from 2001-2003. In the first step, data compression, we adapt wavelet methods to preprocess time series data of several attributes from financial statement from 2001-2003. After data to be compressed by wavelet technique, the second step, sliding window, processes in order to increase the amount of virtual data. Thirdly, we use cluster method to do data discretization process categorizing data to fit the discrete data type.
It is a difficult task to construct a concept tree to describe the financial statement. In contrast to traditional Attribute Oriented Induction methods, the algorithm, named as Char Algorithm, does not need a concept tree and only requires setting a desired coverage threshold to generate a minimal set of characteristic rules to describe the given dataset. We develop a formal framework for financial data to adapt Char Algorithm and afford advisements to investors to extract characteristic rules, rapidly. It is also our observation that the dimension of growth rate is significant in circumstance of generalizing good performance companies. |
參考文獻 |
Reference in English
[1] Stefan Zemke. (2003).Data Mining for Prediction Financial Series Case. The Royal Institute of Technology, Sweden.
[2] Fama, E. (1965). The behavior of stock market prices. Journal of Business,
January, 34–105.
[3] Haughen, R. (1997). Modern investment theory. Prentice Hall.
[4] Monica Lam.(2004). Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decision Support Systems 37 ,pp 567– 581
[5] M.J. Pring. (1980). Technical Analysis Explained. McGraw-Hill, New York.
[8] Yu-Chin Liu , and Ping-Yu Hsu.(2004). Char : an Automatic Way to Describe Characteristics of Data
[9] B. Wuthrich, V. Cho, S. Leung, D. Permunetilleke, K. Sankaran, J. Zhang, W. Lam. (1998). Daily Stock Market Forecast from Textual Web Data. Knowledge Discovery and Data Mining
[10] R. L. Iman, W. J. Conover. ( 1989). Modern Business Statistics. Wiley.
[11] M. J. Pring. (1991). Technical Analysis Explained, McGraw-Hill
[12] N. Nazmi, (1993). Forecasting Cyclical Turning Points with an Index of Leading Indicators: A Probabilistic Approach, Journal of Forecasting. Vol 12, No. 3&4, pp. 216-226.
[13] Pictet et al. (1996). Genetic Algorithms with Collective Sharing for Robust Optimization in Financial Applications. TR Olsen & Associates Ltd., Seefeldstr. 233, 8008 Zurich.
[14] S.B. Reynolds and A. Maxwell. (1995). Box-Jenkins Forecast Model Identification. AI Expert, 10(6) pp. 15-28.
[15] H. Wong, W.C. Ip, Y.H. Luan and Z.J. Xie. (1996). Wavelet Detection of Jump Points And an Application to Exchange Rates. TR The Hong Kong Polytechnic University, Dept. of Applied Mathematics.
[16] D. Wood et al. (1996). Classifying trend movements in the MSCI USA Capital Market Index – a Comparison of Regression, ARIMA and Neural Network. Computers & Operations Research, Vol 23(6) pp. 611-622.
[17] Ou, J. and S. Penman. (1989). Financial Statement Analysis and the Prediction of Stock Returns. Journal of Accounting and Economics, pp295-329.
[18] V. Dropsy. (1996). Do macroeconomic factors help in predicting international equity risk premia? Journal of Applied Business Research 12 (3) 120–132.
[19] Richard G.Sloan. (2001). Discussion of :”Contextual Fundamental Analysis Through the Prediction of Extreme Returns”. Review of Accounting Studies ;Jun-Sep 2001:6,2-3;ABI/INFORM Global,pp191.
[20] Jim Gray, Series Editor. (2000). Data Mining Techniques Morgan Kaufmann Publishers.
[21] Tao Li, Qi Li, Shenghuo Zhu, Mitsunori Ogihara. ().A Survey on Wavelet Applications in Data Mining.
[22] F. Abramovich, T. Bailey, and T. Sapatinas. (2000). Wavelet analysis and its statistical applications. JRSSD, (48):1–30.
[23] H. LIU, F. HUSSAIN, C. L. TAN, M. DASH. (2002). Discretization: An Enabling Technique. Data Mining and Knowledge Discovery, 6, pp393–423, 2002.
[24] Catlett, J. (1991). On changing continuous attributes into ordered discrete attributes. Fifth European Working Session on Learning. Berlin: Springer-Verlag, pp. 164–177.
[25] A.K. JAIN,M.N. MURTY, AND P.J. FLYNN. (1999). Data Clustering: A Review. ACM Computing Surveys, Vol. 31, No. 3, September.
[26] S. Michalski, J. G. Carbonell, and T. M. Mitchell. (1983). Machine Learning: An Artificial Intelligence Approach. Vol. 1. San Mateo, CA: Morgan Kaufmann.
[27] J. Han, M. Kamber. (2001). Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers.
[28] M.-S. Chen, J. Han and P. S. Yu. (1996). Data Mining: An Overview from a Database Perspective. IEEE Transactions on Knowledge and Data Engineering, 8(6): 866-883.
[29] Gupta, V. Harinarayan, and D. Quass. (1995). Aggregate-Query processing in data warehousing environment. Proc. 21st Int. Conf. Very Large Data Bases. Pages 358-369, Zurich, Switzerland, Sept.
[30] V. Harinarayan, J.D. Ullman, and A. Rajaraman. (1996). Implementing data cubes efficiently. Data Mining and Knowledge Discovery Portland, Oregon, August.
[31] Han and Y. Fu. (1994). Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases. Workshop on Knowledge Discovery in Databases, pages 158-168, Seattle, WA, July.
[32] J. Han and Y. Fu. Exploration of the power of attribute-oriented induction in data mining. In U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Ming, pages 399-421. AAAI/MIT Press.
[33] Jiawei Han’, Yandong Cai, and Nick Cerconet. (1992). Knowledge Discovery in Databases: An Attribute-Oriented Approach. Proceedings of the 18th VLDB Conference Vancouver, British Columbia, Canada 1992.
[34] Heikki Mannila. (1996). Data mining: machine learning, statistics, and databases. In Proceedings of the 8th International Conference on Scientific and statistical Database Management, pages 1-6, Stockholm, Sweden.
[35] MATLAB Help Library.
[36] Tao Li, Qi Li, Shenghuo Zhu, Mitsunori Ogihara. (2003). A Survey on Wavelet Applications in Data Mining. SIGKDD Explorations. Volume 4, Issue 2 , PP 49-68.
[37] F. Abramovich, T. Bailey, and T. Sapatinas. (2000). Wavelet analysis and its statistical applications. JRSSD 48, pp1–30.
[38] R. Polikar. The wavelet tutorial. Internet Resources:
http://engineering.rowan.edu/ polikar/ WAVELETS/WTtutorial.html.
[39] Boris Kovalerchuk, Evgenii Vityaev. (2000).DATA MINING IN FINANCE
Advances in Relational and Hybrid Methods. Kluwer Academic Publishers
Published
[40] Ying Yang, Geoffrey I. Webb. Discretization for Data Mining. University of Vermont ; Monash University Clayton Campus.
Reference in Chinese
[6] 李華玉. (1992). 台灣股票資訊內容之研究. 台灣大學會計研究所 碩士論文.
[7] 蕭義展.(2001). 財務報表資訊內涵與股票報酬率的關聯性. 中山大學經濟研究所碩論文 |