臺灣博碩士論文加值系統

在近年股票交易市場中，出現了許多以預測未來股價為主的方法、系統，諸如類神經網路、基因演算法、甚至是行之有年的傳統統計方法…等。然而上述這些系統對於一般投資者卻有使用及判斷上的難處，如缺乏規則依循的判斷、計算預測曠日費時…等。因此本研究提出將一般指標轉化為技術指標作為一般屬性藉以作為趨勢、波動的基準；並且採用對於資料接納彈性大而且可以產生有效規則的粗集理論結合之。本研究方法初步是將股市一般數值轉換為技術指標，並從中選擇合適的技術指標作為資料集；其次透過模糊方法將指標離散化並採用粗集理論產生預測未來股價的規則；最後則是對預測語譯值反模糊化並且運用RMSE評估績效。在比較驗證中，本方法在績效評估上已優於文中所提出比較的模型。

In stock market, many systems or methods are used to predict stock prices such as neural network, genetic algorithm, and traditional statistic. However, there are some problems for investors among these methods such as no rules for making decision, high time complexity in computing outcome, or some strict mathematic distribution assumption for datasets. Therefore, this dissertation has proposed a new method which combines fuzzy theory and granular rough set algorithm in forecasting process and use technical indicators to produce effective rules for forecasts. Four main procedures are contained in the method: (1) transfer the basic index of dataset (time , open index, high index, low index, close index, and volume) into technical indicators(MA, RSI, PSY, STOD, VR, OBV, DIS, AR, ROC ); (2) use MEPA (Minimize Entropy Principle Approach)and CPDA(Cumulative probability distribution approach) to granulate the technical indicators; (3) employ rough set theory to extract effective rules from the granulated dataset of technical indicators for forecasting; and (4) use the extracted rules to produce forecasts and evaluate the performance of the forecasting model with RMSE(Root Mean Square Error). From model verification and comparison, the new forecasting method surpasses in accuracy the listing conventional fuzzy time-series models referred in this dissertation.

摘要 i
ABSTRACT ii
致謝 iv
1. Introduction 1
1.2 Motivation 1
1.3 Objective 4
1.4 Research Limitations 5
1.5 Thesis Organization 5
2. Related works 6
2.1 Rough set theory 6
2.2 Technical analysis 10
2.3 Minimize Entropy Principle Approach (MEPA) 13
2.4 Cumulative probability distribution approach (CPDA) 16
2.5 Defuzzification 17
2.6 Fuzzy Time Series 18
3. Proposed method 25
3.1 Research Framework 25
3.2 The algorithm 29
3.3 Detailed process of the algorithm with TAIEX dataset 36
4. Experiment and comparisons 42
4.1 Experiment dataset and performance measure 42
4.2 Verification and comparison 42
5. Conclusions and Future Research 46
Reference 48

[1]Arciszewski, T. and Ziarko, W.,1990. Inductive learning in civil engineering: a rough sets approach. Microcomputers in Civil Engineering, 5(1), 19–28.
[2]Azo, M.E., 1994:, Neural Network Time Series Forecasting of Financial Markets, Wiley, New York.
[3]Bauer Jr.,R.J., 1994, Genetic Algorithms and Investment Strategies, Wiley, New York.
[4]Ching-Hsue Cheng , Guang-Wei Cheng , Jia-Wen Wang ,2008, ” Multi-attribute fuzzy time series method based on fuzzy clustering”, Expert System with Application, 1237-1240
[5]Chung, S.C., 2002, “A study of fuzzy time series forecasting model”, Department of Industrial Engineering and Management, I-Shou University, Koahsiung, Taiwan, M.S. Thesis.
[6]Dubois, D., Prade, H., 1992, Putting rough sets and fuzzy sets together, Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht, 203–232.
[7]Dallal, G.E. and Wilkinson, L., 1986, “An analytic approximation to the distribution of Lilliefors'' test for normality”, The American Statistician, Vol. 40, pp. 294–296.
[8]Derry, J.F., 1997, Database mining/knowledge discovery in financial database: An overview, Journal of Computational Intelligence in Finance 5 (3), 5–9.
[9]Dimitras, A.I., Slowinski, R., Susmaga, R., Zopounidis, C., 1999, Business failure prediction using rough sets, European Journal of Operational Research 114, 263–280.
[10]Gately, E.,1996, Neural Networks for Financial Forecasting, Wiley, New York.
[11]Mani, G.., Quah, K.K., Mahfoud, S., Barr,D.,1995, An analysis of neural-network forecasts from a large-scale, real-world stock selection system, Proceedings of the IEEE/IAFE 1995 Conference on Computational Intelligence for Financial Engineering (CIFER95), IEEE, New Jersey, pp. 72– 78.
[12]Hurst, H.E., 1951, Long-term storage capacity of reservoirs, American Society of Civil Engineers 116, 770-799
[13]Lee, C.T., 1996, “A method for fuzzy time series analysis”, Department of Industrial Engineering, National Tsing-Hua University, Hsin-chu, Taiwan, M.S. Thesis.
[14]Luo, Z.Y., 1996, “Fuzzy Time Series Including Seasonal Factor – Forecasting the Monthly Population of Tourists in Taiwan”, Department of Industrial Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, M.S. Thesis.
[15]Lo, A.W., 1991, Long-term memory in market prices, Econometria 59, 1279-1313
[16]Kendall, M.G. , 1953, The analysis of economic time series, Part 1 : Price, Journal of the Royal Statistical Society 96, 11-25.
[17]Krusinska, E., Slowinski, R., and Stefanowski, J., 1992, Discriminant versus rough set approach to vague data analysis, Applied Stochastic Models and Data Analysis 8, 43–56.
[18]Huarng, K. H., and Yu T. H. K., 2006, "The application of neural networks to forecast fuzzy time series," Physica A, vol. 336, pp. 481-491.
[19]Krusinska, E., Slowinski, R., and Stefanowski, J., 1992, Discriminant versus rough set approach to vague data analysis, Applied Stochastic Models and Data Analysis 8, 43–56.
[20]Miller, G.A., 1956, “The magical number seven, plus or minus two: some limits on our capacity of processing information”, The Psychological Review, Vol. 63, pp. 81-97.
[21]Myoung-Jong Kim , Sung-Hwan Min , Ingoo Han, 2006, An evolutionary approach to the combination of multiple classifiers to predict a stock price index, Expert Systems with Application
[22]Mieko Tanaka-Yamawaki, Seiji Tokuoka, 2007, Adaptive use of technical indicators for the prediction of intra-day stock prices, PHSICA A, pp. 126
[23]Mrozek, A., 1992, Rough sets in computer implementation of rule-based control of industrial process, in Sowinski, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, Kluwer, Boston, MA, pp. 19–32.
[24]Pawlak, Z., and Skoworn, A.,2007. "Rudiments of rough sets."InformationScience 177.
[25]Pawlak, Z. Rough sets. Int. J. Computer and Infn. Sci., 1982, 11(5), 341–356.
[26]Pal, S.K. and Skowron, A., 1999, Rough Fuzzy Hybridization: A New Trend in Decision Making, Springer, Singapore.
[27]Refenes, A.N., Burgess, N., Bentz, Y., 1997,Neural networks in financial engineering: a study in methodology, IEEE Trans. Neural Networks 8 (6) 1222–1267
[28]Ross, T.J., 2000, Fuzzy logic with engineering applications, International edition, McGraw-Hill, USA.
[29]Sankar K. Pal a,1, B. Uma Shankar a,*, Pabitra Mitra. Granular computing, rough entropy and object extraction. Pattern Recognition Letters 26 (2005) 2509–2517
[30]Skalko, C., 1996, Rough sets help time the OEX, Journal of Computational Intelligence in Finance 4 (6) 20–27.
[31]Song, Q. and Chissom, B.S., 1993a, “Fuzzy time series and its models”, Fuzzy Sets and Systems, Vol. 54, pp. 269-277.
[32]Song, Q. and Chissom, B.S., 1993b, “Forecasting enrollments with fuzzy time series – Part Ⅰ”, Fuzzy sets and systems, Vol. 54, pp. 1-10.
[33]Song, Q. and Chissom, B.S, 1994, “Forecasting enrollments with fuzzy time series – Part Ⅱ”, Fuzzy sets and systems, Vol. 62, pp. 1-8.
[34]Slowinski, K. 1994 Rough classification of HSV patients, in Sowinski, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, Kluwer, Boston, MA, pp. 77–94.
[35]Skowron, A., and Grzymala-Busse, J.W., 1993, From the rough set theory to the evidence theory, Advances in the Dempster–Shafer Theory of Evidence, Wiley, New York, 295–305.
[36]Thomas A. Meyers, 192000, Technical analysis course, Toppan
[37]Thomas R. DeMark, 1994, The New Sicence of Technical Analysis, Wiley.
[38]Trippi, R.R., Turban (Ed.) E., 1993, Neural Network in Finance and Investing, Probus Publishing Company.
[39]Wong, J.-T. and Y.-S. Chung , 2007. "Rough set approach for accident chains exploration." Accident Analysis and Prevention 39: 629-637.
[40]Yager, R., and Filev, D., 1994, Template-based fuzzy system modeling, Intelligent and Fuzzy System, Vol. 2, 39-54.
[41]Yi-Fan Wang, 2003, Mining stock price using fuzzy rough set system, Expert Systems of Applications.
[42]Zadeh, L.A., 1965, “Fuzzy Sets”, Inform and Control, Vol. 8, pp. 338-353.
[43]Zadeh, L.A., 1975a, “The concept of a linguistic variable and its application to approximate reasoning Ⅰ”, Information Science, Vol. 8, pp. 199-249.
[44]Zadeh, L.A., 1975b, “The concept of a linguistic variable and its application to approximate reasoning Ⅱ”, Information Science, Vol. 8, pp. 301-357.
[45]Zadeh L.A., 1976, “The concept of a linguistic variable and its application to approximate reasoning Ⅲ”, Information Science, Vol. 9, pp. 43-80.