Chih-Fong Tsai
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Abstract
The aim of stock prediction is to effectively predict future stock market trends (or stock prices), which can lead to increased profit. One major stock analysis method is the use of candlestick charts. However, candlestick chart analysis has usually been based on the utilization of numerical formulas. There has been no work taking advantage of an image processing technique to directly analyze the visual content of the candlestick charts for stock prediction. Therefore, in this study we apply the concept of image retrieval to extract seven different wavelet-based texture features from candlestick charts. Then, similar historical candlestick charts are retrieved based on different texture features related to the query chart, and the “future” stock movements of the retrieved charts are used for stock prediction. To assess the applicability of this approach to stock prediction, two datasets are used, containing 5-year and 10-year training and testing sets, collected from the Dow Jones Industrial Average Index (INDU) for the period between 1990 and 2009. Moreover, two datasets (2010 and 2011) are used to further validate the proposed approach. The experimental results show that visual content extraction and similarity matching of candlestick charts is a new and useful analytical method for stock prediction. More specifically, we found that the extracted feature vectors of 30, 90, and 120, the number of textual features extracted from the candlestick charts in the BMP format, are more suitable for predicting stock movements, while the 90 feature vector offers the best performance for predicting short- and medium-term stock movements. That is, using the 90 feature vector provides the lowest MAPE (3.031%) and Theil’s U (1.988%) rates in the twenty-year dataset, and the best MAPE (2.625%, 2.945%) and Theil’s U (1.622%, 1.972%) rates in the two validation datasets (2010 and 2011).
- Andreopoulos, Y. 2009. Comments on “phase-shifting for nonseparable 2-D Haar wavelets. IEEE Trans. Image Process. 18, 8, 1897--1898. Google Scholar
Digital Library
- Araújo, R. D. A. 2010. Hybrid intelligent methodology to design translation invariant morphological operators for Brazilian stock market prediction. Neural Netw. 23, 10, 1238--1251. Google ScholarDigital Library
- Araújo, R. D. A. and Ferreira, T. A. E. 2013. A morphological-rank-linear evolutionary method for stock market prediction. Inform. Sci., 237, 3--17. Google ScholarDigital Library
- Ariss, R. T., Rezvanian, R., and Mehdian, S. M. 2011. Calendar anomalies in the Gulf Cooperation Council stock markets. Emerg. Markets Rev. 12, 3, 293--307.Google ScholarCross Ref
- Asadi, S., Hadavandi, E., Mehmanpazir, F., and Nakhostin, M. M. 2012. Hybridization of evolutionary Levenberg--Marquardt neural networks and data pre-processing for stock market prediction. Knowl.-Based Syst. 35, 245--258. Google ScholarDigital Library
- Bollerslev, T. 1986. Generalized autoregressive conditional heteroscedasticity. J. Econometrics 31, 3, 307--327.Google ScholarCross Ref
- Briza, A. C. and Naval Jr., P. C. 2011. Stock trading system based on the multi-objective particle swarm optimization of technical indicators on end-of-day market data. Appl. Soft Comput. 11, 1, 1191--1201. Google ScholarDigital Library
- Brock, W., Lakonishok, J., and LeBaron, B. 1992. Simple technical trading rules and the stochastic properties of stock returns. J. Finance 47, 5, 1731--1764.Google ScholarCross Ref
- Caginalp, G. and Laurent, H. 1998. The predictive power of price patterns. Appl. Math. Finance 5, 3--4, 181--205.Google ScholarCross Ref
- Chan, S. W. K. and Franklin, J. 2011. A text-based decision support system for financial sequence prediction. Decision Supp. Syst. 52, 1, 189--198. Google ScholarDigital Library
- Chang, P. H. K. and Osler, C. L. 1999. Methodical madness: Technical analysis and the irrationality of exchange-rate forecasts. Econ. J. 109, 458, 636--661.Google ScholarCross Ref
- Chen, D. R., Huang, Y. L., and Lin, S. H. 2011. Computer-aided diagnosis with textural features for breast lesions in sonograms. Comput. Med. Imaging Graphics 35, 3, 220--226.Google ScholarCross Ref
- Chen, P. C. and Pavlidis, T. 1983. Segmentation by texture using correlation. IEEE Trans. Pattern Anal. Mach. Intell. 5, 1, 64--69. Google ScholarDigital Library
- Cheng, C. H., Chen, T. L., and Wei, L. Y. 2010. A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Inform. Sci. 180, 9, 1610--1629. Google ScholarDigital Library
- Chianga, J. Y. and Cheng, S. R. 2009. Multiple-instance content-based image retrieval employing isometric embedded similarity measure. Pattern Recog. 42, 1, 158--166. Google ScholarDigital Library
- Colander, D. C. 2012. Macroeconomics 9th Ed. McGraw-Hill, New York, NY.Google Scholar
- Corder, G. W. and Foreman, D. I. 2009. Nonparametric Statistics for Non-Statisticians: A Step-By-Step Approach 1st Ed. Wiley, New York, NY.Google Scholar
- de Groot, B. and Franses, P. H. 2012. Common socio-economic cycle periods. Technol. Forecasting Soc. Change 79, 1, 59--68.Google ScholarCross Ref
- Doyle, J. R. and Chen, C. H. 2009. The wandering weekday effect in major stock markets. J. Banking Finance 33, 8, 1388--1399.Google ScholarCross Ref
- Edwards, R. D., Magee, J., and Bassetti, W. H. C. 2012. Technical Analysis of Stock Trends 10th Ed. CRC Press, Boca Raton, FL.Google Scholar
- Engle, R. F. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50, 4, 987--1007.Google ScholarCross Ref
- Fama, E. F. 1970. Efficient capital markets: A review of theory and empirical work. J. Finance 25, 2, 383--417.Google ScholarCross Ref
- Fiess, N. M. and MacDonald, R. 2002. Towards the fundamentals of technical analysis: Analysing the information content of high, low and close prices. Econ. Modell. 19, 3, 353--374.Google ScholarCross Ref
- Fock, J. H., Klein, C., and Zwergel, B. 2005. Performance of candlestick analysis on intraday futures data. J. Derivatives 13, 1, 28--40.Google ScholarCross Ref
- Friesen, G. C., Weller, P. A., and Dunham, L. M. 2009. Price trends and patterns in technical analysis: A theoretical and empirical examination. J. Banking Finance 33, 6, 1089--1100.Google ScholarCross Ref
- Fuertes, A. M., Izzeldin, M., and Kalotychou, E. 2009. On forecasting daily stock volatility: The role of intraday information and market conditions. Int. J. Forecasting 25, 2, 259--281.Google ScholarCross Ref
- Graña, M. and Veganzones, M. A. 2012. An endmember-based distance for content based hyperspectral image retrieval. Pattern Recog. 45, 9, 3472--3489. Google ScholarDigital Library
- Hagenau, M., Liebmann, M., and Neumann, D. 2013. Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision Supp. Syst. 55, 3, 685--697.Google ScholarCross Ref
- Hansen, P. R. and Lunde, A. 2005. A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? J. Appl. Econometrics 20, 7, 873--889.Google ScholarCross Ref
- He, D. C. and Wang, L. 1990. Texture unit, texture spectrum, and texture analysis. IEEE Trans. Geosci. Remote Sens. 28, 4, 509--512.Google ScholarCross Ref
- Hsia, C. H., Chiang, J. S., and Guo, J. M. 2013. Memory-efficient hardware architecture of 2-D dual-mode lifting-based discrete wavelet transforms. IEEE Trans. Circuits Syst. Video Technol. 23, 4, 671--683. Google ScholarDigital Library
- Hsia, C. H., Guo, J. M., and Chiang, J. S. 2009. Improved low-complexity algorithm for 2-D integer lifting-based discrete wavelet transform using symmetric mask-based scheme. IEEE Trans. Circuits Syst. Video Technol. 19, 8, 1202--1208. Google ScholarDigital Library
- Hsieh, T. J., Hsiao, H. F., and Yeh, W. C. 2011. Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on a artificial bee colony algorithm. Appl. Soft Comput. 11, 2, 2510--2525. Google ScholarDigital Library
- Hsu, C. M. 2011. A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming. Expert Syst. Appl. 38, 11, 14026--14036.Google Scholar
- Hsu, P. H. and Kuan, C. M. 2005. Reexamining the profitability of technical analysis with data snooping checks. J. Financial Econ. 3, 4, 606--628.Google ScholarCross Ref
- Hung, J. C. 2009. A fuzzy asymmetric GARCH model applied to stock markets. Inform. Sci. 179, 22, 3930--3943. Google ScholarDigital Library
- Kang, M. 2010. Probability of information-based trading and the January effect. J. Banking Finance 34, 12, 2985--2994.Google ScholarCross Ref
- Krishnamachari, S. and Chellappa, R. 1997. Multiresolution Gauss-Markov random field models for texture segmentation. IEEE Trans. Image Proc. 6, 2, 251--267. Google ScholarDigital Library
- Kwon, Y. K. and Moon, B. R. 2007. A hybrid neurogenetic approach for stock forecasting. IEEE Trans. Neural Netw. 18, 3, 851--864. Google ScholarDigital Library
- Lee, C. H. L., Liu, A., and Chen, W. S. 2006. Pattern discovery of fuzzy time series for financial prediction employing an isometric embedded similarity measure. Pattern Recog. 42, 1, 158--166.Google Scholar
- Lee, C. H. L., Liu, A., and Chen, W. S. 2006. Pattern discovery of fuzzy time series for financial prediction. IEEE Trans. Knowl. Data Eng. 18, 5, 613--625. Google ScholarDigital Library
- Lee, D. J., Antani, S., Chang, Y., Gledhill, K., Long, L. R., and Christensen, P. 2009. CBIR of spine X-ray images on inter-vertebral disc space and shape profiles using feature ranking and voting consensus. Data Knowl. Eng. 68, 12, 1359--1369. Google ScholarDigital Library
- Lee, K. H. and Jo, G. S. 1999. Expert system for predicting stock market timing using a candlestick chart. Expert Syst. Appl. 16, 4, 357--364.Google ScholarCross Ref
- Leigh, W., Modani, N., Purvis, R., and Roberts, T. 2002. Stock market trading rule discovery using technical charting heuristics. Expert Syst. Appl. 23, 2, 155--159.Google ScholarCross Ref
- Levy, T. and Yagil, J. 2012. The week-of-the-year effect: Evidence from around the globe. J. Banking Finance 36, 7, 1963--1974.Google ScholarCross Ref
- Lilly, J. M. and Olhede, S. C. 2010. On the analytic wavelet transform. IEEE Trans. Inf. Theory 56, 8, 4135--4156. Google ScholarDigital Library
- Lin, M. C., Lee, A. J. T., Kao, R. T., and Chen, K. T. 2011. Stock price movement prediction using representative prototypes of financial reports. ACM Trans. Manage. Inf. Syst. 2, 3, 11--18. Google ScholarDigital Library
- Liu, C. F., Yeh, C. Y., and Lee, S. J. 2012. Application of type-2 neuro-fuzzy modeling in stock price prediction. Appl. Soft Comput. 12, 4, 1348--1358. Google ScholarDigital Library
- Liu, F. and Wang, J. 2012. Fluctuation prediction of stock market index by Legendre neural network with random time strength function. Neurocomput. 83, 12--21. Google ScholarDigital Library
- Luo, L. and Chen, X. 2013. Integrating piecewise linear representation and weighted support vector machine for stock trading signal prediction. Appl. Soft Comput. 13, 2, 806--816. Google ScholarDigital Library
- Marshall, B. R., Young, M. R., and Cahan, R. 2008. Are candlestick technical trading strategies profitable in the Japanese equity market? Rev. Quant. Finance Account. 31, 2, 191--207.Google ScholarCross Ref
- Marshall, B. R., Young, M. R., and Rose, L. C. 2006. Candlestick technical trading strategies: Can they create value for investors. J. Banking Finance 30, 8, 2303--2323.Google ScholarCross Ref
- Moller, N. and Zilca, S. 2008. The evolution of the January effect. J. Banking Finance 32, 3, 447--457.Google ScholarCross Ref
- Morris, G. L. 2006. Candlestick Charting Explained: Timeless Techniques for Trading Stocks and Futures 3rd Ed. McGraw-Hill, New York, NY.Google Scholar
- Murphy, J. J. 1999. Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications 1st Ed. New York Institute of Finance, New York, NY.Google Scholar
- O’Connor, N. and Madden, M. G. 2006. A neural network approach to predicting stock exchange movements using external factors. Knowl.-Based Syst. 19, 5, 371--378.Google ScholarDigital Library
- Oh, S. K., Pedrycz, W., and Park, H. S. 2006. Genetically optimized fuzzy polynomial neural networks. IEEE Trans. Fuzzy Syst. 14, 1, 125--144. Google ScholarDigital Library
- Park, K. and Shin, H. 2013. Stock price prediction based on a complex interrelation network of economic factors. Eng. Appl. Artif. Intell. 26, 5--6, 1550--1561. Google ScholarDigital Library
- Qi, M. and Zhang, G. P. 2008. Trend time-series modeling and forecasting with neural networks. IEEE Trans. Neural Netw. 19, 5, 808--816. Google ScholarDigital Library
- Quellec, G., Lamard, M., Cazuguel, G., Cochener, B., and Roux, C. 2010. Adaptive nonseparable wavelet transform via lifting and its application to content-based image retrieval. IEEE Trans. Image Proc. 19, 1, 25--35. Google ScholarDigital Library
- Rapach, D. E. and Wohar, M. E. 2006. In-sample vs. out-of-sample tests of stock return predictability in the context of data mining. J. Empirical Finance 13, 2, 231--247.Google ScholarCross Ref
- Rui, Y., Huang, T. S., and Chang, S. F. 1999. Image retrieval: Current techniques, promising directions and open issues. J. Visual Commun. Image Rep. 10, 1, 39--62. Google ScholarDigital Library
- Sapankevych, N. I. and Sankar, R. 2009. Time series prediction using support vector machines: A survey. IEEE Comput. Intell. Mag. 4, 2, 24--38. Google ScholarDigital Library
- Schumaker, R. P. and Chen, H. 2009. Textual analysis of stock market prediction using breaking financial news: The AZFinText system. ACM Trans. Inf. Syst. 27, 2, 1--19. Google ScholarDigital Library
- Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., and Jain, R. 2000. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22, 12, 1349--1380. Google ScholarDigital Library
- Tsai, C. F. and Hsiao, Y. C. 2010. Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches. Decis. Supp. Syst. 50, 1, 258--269. Google ScholarDigital Library
- Wang, Y. F., Cheng, S., and Hsu, M. H. 2010. Incorporating the Markov chain concept into fuzzy stochastic prediction of stock indexes. Appl. Soft Comput. 10, 2, 613--617. Google ScholarDigital Library
- Wink, A. M. and Roerdink, J. B. T. M. 2010. Polyphase decompositions and shift-invariant discrete wavelet transforms in the frequency domain. Signal Proc. 90, 6, 1779--1787. Google ScholarDigital Library
- Yue, J., Li, Z., Liu, L., and Fu, Z. 2011. Content-based image retrieval using color and texture fused features. Math. Comput. Modell. 54, 3--4, 1121--1127. Google ScholarDigital Library
- Zarandi, M. H. F., Zarinbal, M., Ghanbari, N., and Turksen, I. B. 2013. A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing application: Stock price prediction. Inf. Sci. 222, 213--228. Google ScholarDigital Library
- Zarb, F. G. and Kerekes, G. T. 1970. The Stock Market Handbook: Reference Manual for the Securities Industry 1st Ed. Dow Jones-Irwin, Homewood, IL.Google Scholar
- Zheng, P. and Huang, J. 2013. Discrete wavelet transform and data expansion reduction in homomorphic encrypted domains. IEEE Trans. Image Proc. 22, 6, 2455--2468. Google ScholarDigital Library
- Zhu, Y. and Zhou, G. 2009. Technical analysis: An asset allocation perspective on the use of moving averages. J. Financial Econ. 92, 3, 519--544.Google ScholarCross Ref
Index Terms
- Stock Prediction by Searching for Similarities in Candlestick Charts
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