Chapter 6 Conclusion and Future Work
6.2 Future Work
(1) In this thesis, the model is applied in Taiwan stock market only. This model could be applied to other stock market data such as New York Stock Exchange (NYSE) and Nasdaq.
(2) Independent component analysis separates mixture signal into independent components.
In the future, the knowledge behinds each independent component deserves a further study. In other words, the meaning of each independent component could be discovered.
(3) In feature extraction method, the numbers of feature points are defined in advance. If the numbers of feature points are determined dynamically, it will be more suitable for different and complex stock trend.
(4) More factors should be considered to improve the prediction success rate and average profit.
(5) The proposed pattern matching can be applied to discover the relationship between volume and price.
References
[1] Aapo Hyvärinen and Erkki Oja, “Independent Component Analysis: Algorithms and Applications”, Neural Networks, Volume: 13, Issue: 4-5, pp411-430, 2000.
[2] Aapo Hyvärinen, “Fast and Robust Fixed-Point Algorithms for Independent Component Analysis”, Neural Networks, IEEE Transactions on, Volume: 10, Issue: 3, pp626-634 1999.
[3] Aapo Hyvärinen, Juha Karhunen, Errkki Oja, Independent Component Analysis, Wiley-Interscience, 2001.
[4] Andrew D. Back and Andreas S. Weigend, “A First Application of Independent Component Analysis to Extracting Structure from Stock Returns’, International Journal of Neural Systems, pp473-484, 1997.
[5] Andrew W. Lo, Harry Mamaysky, and Jiang Wang, “Foundations of Technical Analysis:
Computational Algorithms, Statistical Inference, and Empirical Implementation”, The Journal of Finance, Volume 55, No, pp1705-1765, 2000.
[6] Anil. K. Jain, M.N. Murty, and P.J. Flynn, “Data Clustering: A Review”, ACM Computing Surveys, pp264-323, 1999.
[7] Alau Seelenfreund, George G. C. Parker, James C. Van Horne, “Stock Price Behavior and Trading”, The Journal of Financial and Quantitative Analysis, Volume 3, No 3, pp263-281, 1968.
[8] Benjamin F. King, “Market and Industry Factors in Stock Price Behavior”, The Journal of Business, Volume 39, No 1, pp139-190, 1966.
[9] Bill Chiu, Eamonn Keogh, and Stefano Lonardi, “Probabilistic Discovery of Time Series Motifs” Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp483-498, 2003.
[10] Davood Rafiei and Alberto O. Mendelzon, “Querying Time Series Data Based on
Similarity”, Knowledge and Data Engineering, IEEE Transactions on, Volume: 12, Issue:
5, pp 675-693, 2000.
[11] Eamonn Keogh and Shruti Kasetty, ”On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration.”, Data Mining and Knowledge Discovery, pp349-371, 2003.
[12] Fu-Lai Chung, Tak-Chung Fu, Robert Luk, and Vincent Ng, “Evolutionary Time Series Segmentation for Stock Data Mining”, Data Mining, Proceedings. IEEE International Conference, pp83-90, 2002.
[13] Guoqing Chen, Qiang Wei, and Zhange, “Discovering Similar Time-Series Patterns with Fuzzy Clustering and DTW Methods”, IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th, Volume: 4, pp2160-2164, 2001.
[14] Henri Ralambondrainy, “A Conceptual Version of the K-means algorithm”, Pattern Recognition Letters, Volume: 16, Issue: 11, pp1147-1157, 1995.
[15] Jean-Jules Branlt and Réjean Plamondon, “Segmenting handwritten signatures at their perceptually important points”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Volume: 15, Issue: 9, pp953-957, 1993.
[16] Jiawei Han and Micheline Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann Publishers, 2001.
[17] Jiong Yang, Wei Wang, and Philip S. Yu, “Mining Asynchronous Periodic Patterns in Time Series Data”, Knowledge and Data Engineering, IEEE Transactions on , Volume:
15 Issue: 3, pp613-628, 2003.
[18] Jonh F. Roddick and Myra Spiliopoulou, “A Survey of Temporal Knowledge Discovery Paradigms and Methods”, Knowledge and Data Engineering, IEEE Transactions on, Volume: 14, Issue: 4, pp750-767, 2002.
[19] Jyh-Shing Roger Jang, Chuen-Tsai Sun, Eiji Mizutani, Neuro-Fuzzy and Soft Computing,
Prentice Hall, 1997.
[20] Kelvin Kam Wing Chu, and Man Hon Wong, “Fast Time-Series Searching with Scaling and Shifting”, Proceedings of the 8th ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pp237-248, 1999.
[21] Laurent Foulloy and Sylvie Galichet, “Fuzzy Control With Fuzzy Input”, Fuzzy Systems, IEEE Transactions on, Volume: 11, Issue: 4, pp437-449, 2003.
[22] Lotfi A. Zadeh, Fuzzy sets. Information and Control, 8:338-353,1965.
[23] Mark L. Berenson and David M. Levine, Basic Business Statistics: Concepts and Applications, Prentice Hall, 1999.
[24] Mark Last, Yaron Klein, and Abraham Kandel, “Knowledge Discovery in Time Series Databases”, Systems, Man and Cybernetics, Part B, IEEE Transactions on, Volume: 31 Issue: 1, 2001, pp160-169.
[25] Martin Gavrilov, Dragomir Angueiov, Piotr Indyk, and Rajeev Motwani, “Mining The Stock Market: Which Measure Is Best? ”, Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp487-496, 2000.
[26] Poll Wan Po Man and Man Hon Wong, “Efficient and Robust Feature Extraction and Pattern Matching of Time Series by Lattice Structure”, Proceedings of the 10th International Conference on Information and Knowledge Management, pp271-p278, 2001.
[27] Richard J. Povinelli and Xin Feng, “A New Temporal Pattern Identification Method for Characterization and Prediction of Complex Time Series Events.” Knowledge and Data Engineering, IEEE Transactions on, Volume: 15, Issue: 2, pp339-352, 2003.
[28] Ray Tsaih, Yenshan Hsu, and Charles C. Lai, “Forecasting S&P 500 Stock Index Futures with A Hybrid AI”, Decision Support Systems, Volume: 23, Issue: 2, pp161-174, 1998.
[29] Robert A. Levy, “The Predictive Significance of Five-Point Chart Patterns”, Journal of
Business, Volume 44, No 3, pp316-323, 1971.
[30] Ronald Gallant, Peter E. Rossi, George Tauchen, “Stock Prices and Volume”, Review of Financial Studies, pp199-242, 1992.
[31] Salih N. Neftci, “Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of Technical Analysis”, The Journal of Business, pp549-571, 1991.
[32] Sanghyun Park, Sang-Wook Kim, and Wesley W. Chu, “Segment-Based Approach for Subsequence Searches in Sequence Databases”, Proceedings of the 2001 ACM symposium on Applied computing, pp248-252, 2001.
[33] Sati P. Bandyopadhyay, Lawrence D. Brown, and Gordon D. Richardson, “Analysts’ use of earning forecasts in predicting stock returns: Forecast horizon effects”, International Journal of Forecasting, Volume: 11, Issue: 3, pp429-445, 1995.
[34] Surapong Auwatanamongkol, “Pattern Recognition using Genetic Algorithm”, Evolutionary Computation, Proceedings of the 2000 Congress on, pp822-828, 2000.
[35] Tamer Kahveci and Ambuj K. Singh, “Optimizing Similarity Search for Arbitrary Length Time Series Queries”, Knowledge and Data Engineering, IEEE Transactions on, Volume:
16, Issue: 4, pp418-433, 2004.
[36] Te-Won Lee, Independent Component Analysis: Theory and Application, Kluwer Academic, 1998.
[37] Valery Guralnik and Jaideep Srivastava, “Event Detection from Time Series Data”, Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp33-42, 1999.
[38] Vassilios Petridis and Athaasios Kehagias, “Modular Neural Networks for MAP Classification of Time Series and the Partition Algorithm”, Neural Networks, IEEE Transactions on, Volume: 7, Issue: 1, pp73-86, 1996.
[39] Victor Niederhoffer, “The Analysis of World Events and Stock Prices”, The Journal of Business, Volume 44, No 2, pp 193-219, 1971.
[40] William Brock, Josef Lakonishok, and Blake LeBaron, “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns”, The Journal of Finance, pp1731-1764, 1992.
[41] Xavier Giannakopoulos, Juha Karhunen, and Erkki Oja, “An Experimental comparison of Neural ICA Algorithm” Proc. International. Conference. Artificial Neural Networks, pp651-656, 1998.
[42] Xianping Ge and Padhraci Smyth, “Deformable Markov Model Templates for Time-Series Pattern Matching”, Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp81-90, 2000.