Temporal Association Rule Mining: With Application to US Stock Market
DOI:
https://doi.org/10.14738/tmlai.35.1051Keywords:
Temporal data mining, financial time series, Knowledge discovery, events, DJIA, hypothesis testing, multi-period portfolio optimizationAbstract
A modified framework, that applies temporal association rule mining to financial time series, is proposed in this paper. The top four components stocks (stock price time series, in USD) of Dow Jones Industrial Average (DJIA) in terms of highest daily volume and DJIA (index time series, expressed in points) are used to form the time-series database (TSDB) from 1994 to 2007. The main goal is to generate profitable trades by uncovering hidden knowledge from the TSDB. This hidden knowledge refers to temporal association rules, which represent the repeated relationships between events of the financial time series with time-parameter constraints: sliding time windows. Following an approach similar to Knowledge Discovery in Databases (KDD), the basic idea is to use frequent events to discover significant rules. Then, we propose the Multi-level Intensive Subset Learning (MIST) algorithm and use it to unveil the finer rules within the subset of the corresponding significant rules. Hypothesis testing is later applied to remove rules that are deemed to occur by chance. After which, multi-period portfolio optimization is done to demonstrate the practicality of using the rules in the real world.
References
(1) Burton G Malkiel. The efficient market hypothesis and its critics. The Journal of Economic Perspectives, 17(1):59–82, 2003.
(2) Burton Gordon Malkiel. A random walk down Wall Street: including a lifecycle guide to personal investing. WW Norton & Company, 1999.
(3) Herbert A Edelstein. Introduction to data mining and knowledge discovery.Two Crows Corporation, 2, 1999.
(4) Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. From data mining to knowledge discovery in databases. AI magazine, 17(3):37, 1996.
(5) Boris Kovalerchuk and Evgenii Vityaev. Data mining in finance: advances in relational and hybrid methods. Springer, 2000.
(6) Conti Dante and J Martinez De Pison Francisco. Finding temporal associative rules in financial time-series: a case of study in madrid stock exchange (igbm). In Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics, pages 60–68. World Scientific and Engineering Academy and Society (WSEAS).
(7) Trevor Hastie, Robert Tibshirani and Jerome Friedman. The Elements of Statistical Learning: Data mining, Inference and Prediction, Second Edition. In chapter 14, pages 490.
(8) http://en.wikipedia.org/wiki/Association_rule_learning
(9) Jiawei Han, Micheline Kamber, and Jian Pei. Data mining: concepts and techniques. Morgan Kaufmann, 2006.
(10) David J Hand. Principles of data mining. Drug safety, 30(7):621–622, 2007.
(11) Mark Last, Horst Bunke, and Abraham Kandel. Data mining in time series database. 2004.
(12) Heikki Mannila, Hannu Toivonen, and A Inkeri Verkamo. Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1(3):259–289, 1997.
(13) Sherri K Harms, Jitender Deogun, Jamil Saquer, and Tsegaye Tadesse. Discovering representative episodal association rules from event sequences using frequent closed episode sets and event constraints. In Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on, pages 603–606. IEEE.
(14) Sherri K Harms, Jitender Deogun, and Tsegaye Tadesse. Discovering sequential association rules with constraints and time lags in multiple sequences, pages 432–441. Springer, 2002.
(15) Kuo-Yu Huang and Chia-Hui Chang. Efficient mining of frequent episodes from complex sequences. Information Systems, 33(1):96–114, 2008.
(16) Edi Winarko and John F Roddick. Armadaâ˘A ¸San algorithm for discovering richer relative temporal association rules from interval-based data. Data & Knowledge Engineering, 63(1):76–90, 2007.
(17) Kittipong Warasup and Chakarida Nukoolkit. Discovery association rules in time series data. 29(4):447–462, 2012.
(18) Hongjun Lu, Jiawei Han, and Ling Feng. Stock movement prediction and n-dimensional inter-transaction association rules. In Proc. of the ACM SIGMOD Workshop on Research Issues in Data Mining and
Knowledge Discovery, page 12.
(19) Jessica Lin, Eamonn Keogh, Stefano Lonardi, and Bill Chiu. A symbolic representation of time series, with implications for streaming algorithms. In Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, pages 2–11. ACM.
(20) Battuguldur Lkhagva, Yu Suzuki, and Kyoji Kawagoe. Extended sax: extension of symbolic aggregate approximation for financial time series data representation. DEWS2006 4A-i8, 7, 2006.
(21) https://sg.finance.yahoo.com/q?s=%5EDJI
(22) Markowitz, H. M. (1952), Portfolio selection, Journal of Finance 7, 77–91.
(23) Markowitz, H. M. (1959), Portfolio Selection: Efficient Diversification of Investments, Wiley, New York, NY.
(24) R. Merton, Optimum Consumption and Portfolio Rules in a Continuous Time Model, Journal of Economic Theory, 3(4), 373–413, 1971.
(25) D. Li, and W. Ng, Optimal Dynamic Portfolio Selection: Multi-period Mean-Variance Formulation, Mathematical Finance, 10(3), 387–406, 2000.
(26) Jianping He, Qing-Guo Wang, Peng Cheng, Jiming Chen and Youxian Sun, Multi-period Mean-variance Portfolio Optimization with High-order Coupled Asset Dynamics, 2013
(27) David G. Luenberger, Investment Science, 1998
(29) http://www.investopedia.com/terms/c/central_limit_theorem.asp
