Hybrid fuzzy neural network to predict price direction in the German DAX-30 index
Abstract
Intraday trading rules require accurate information about the future short term market evolution. For that reason, next-day market trend prediction has attracted the attention of both academics and practitioners. This interest has increased in recent years, as different methodologies have been applied to this end. Usually, machine learning techniques are used such as artificial neural networks, support vector machines and decision trees. The input variables of most of the studies are traditional technical indicators which are used by professional traders to implement investment strategies. We analyse if these indicators have predictive power on the German DAX-30 stock index by applying a hybrid fuzzy neural network to predict the one-day ahead direction of index. We implement different models depending on whether all the indicators and oscillators are used as inputs, or if a linear combination of them obtained through a factor analysis is used instead. In order to guarantee for the robustness of the results, we train and apply the HyFIS models on randomly selected subsamples 10,000 times. The results show that the reduction of the dimension through the factorial analysis generates more profitable and less risky strategies.
Keyword : Trend forecasting, stock exchange index, technical indicators, artificial neural networks, fuzzy rule-based systems, HyFIS
How to Cite
García, F., Guijarro, F., Oliver, J., & Tamošiūnienė, R. (2018). Hybrid fuzzy neural network to predict price direction in the German DAX-30 index. Technological and Economic Development of Economy, 24(6), 2161-2178. https://doi.org/10.3846/tede.2018.6394
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Kim, J., & Kasabov, N. (1999). HyFIS: adaptative neuro-fuzzy inference systems and their application to nonlinear dynamical systems. Neural Networks, 12(9), 1301-1319. https://doi.org/10.1016/S0893-6080(99)00067-2
Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1), 307-319. https://doi.org/10.1016/S0925-2312(03)00372-2
Kim, K. J., & Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications, 19, 125-132. https://doi.org/10.1016/S0957-4174(00)00027-0
Kim, J. S., Kim, D. H., & Seo, S. W. (2017). Individual mean-variance relation and stock-level investor sentiment. Journal of Business Economics and Management, 18(1), 20-34. https://doi.org/10.3846/16111699.2016.1252794
Leung, M. T., Daouk, H., & Chen, A. (2000). Forecasting stock indices: a comparison of classification and level estimation models. International Journal of Forecasting, 16(2), 173-1990. https://doi.org/10.1016/S0169-2070(99)00048-5
Lim, K. P., Brooks, R. (2011). The evolution of stock market efficiency over time: A survey of the empirical literature. Journal of Economic Surveys, 25(1), 69-108. https://doi.org/10.1111/j.1467-6419.2009.00611.x
Lu, T. H., & Shiu, Y. M. (2016). Can 1-day candlestick patterns be profitable on the 30 component stocks of the DJIA? Applied Economics, 48(35), 3345-3354. https://doi.org/10.1080/00036846.2015.1137553
Mamdani, E. H. (1974). Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers, 121(12), 1585-1588. https://doi.org/10.1049/piee.1974.0328
Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13. https://doi.org/10.1016/S0020-7373(75)80002-2
Metghalchi, M., Chang, Y. H., & Marcucci, J. (2008). Is the Swedish stock market efficient? Evidence from some simple trading rules. International Review of Financial Analysis, 17(3), 475-490. https://doi.org/10.1016/j.irfa.2007.05.001
Moghaddam, A. H., Moghaddam, M. H., & Esfandyari, M. (2016). Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science, 21, 89-93. https://doi.org./10.1016/j.jefas.2016.07.002
Mohd Adnan, M. R. H., Sarkheyli, A., Mohd Zain, A., & Haron, H. (2015). Fuzzy logic for modelling machining process: a review. Artificial Intelligence Review, 43, 345-379. https://doi.org/10.1007/s10462-012-9381-8
Nazario, R. T., Lima e Silva, J., Amorim OSbreiro, V., & Kimira, H. (2017). A literature review of technical analysis on stock markets. The Quarterly Review of Economics and Finance, 66, 115-126. https://doi.org/10.1016/j.qref.2017.01.014
Pla-María, M., & García, F. (2016). Stock market investment implementing technical analysis strategies: moving average crossover. Finance, Markets and Valuation, 1, 35-50.
Qiu, M., & Song, Y. (2016). Predicting the direction of stock market index movement using an optimized artificial neural network model. PLoS ONE, 11(5). https://doi.org/10.1371/journal.pone.0155133
Riza, S. L., Bergmeir, C., Herrera, F., & Benítez, J. M. (2015). Fuzzy rule-based systems for classification and regression in R. Journal of Statistical Software, 65(6), 1-30. https://doi.org/10.18637/jss.v065.i06
Schulmeister, S. (2009). Profitability of technical stock trading: has it moved from daily to intraday data? Review of Financial Economics, 18(4), 190-201. https://doi.org/10.1016/j.rfe.2008.10.001
Su, C. H., & Cheng, C. H. (2016). A hybrid fuzzy time series model based on ANFIS and integrated nonlinear feature selection method for forecasting stock. Neurocomputing, 205, 264-273. https://doi.org/10.1016/j.neucom.2016.03.068
Vaiciulyte, I., Kalsyte, Z., Sakalauskas, L., & Plikynas, D. (2017). Assessment of market reaction on the share performance on the basis of its visualization in 2D space. Journal of Business Economics and Management, 18(1), 309-318. https://doi.org/10.3846/16111699.2017.1285348
Wang, L. X., & Mendel, J. M. (1992). Generating fuzzy rules by learning from examples. EEE Transactions on Systems, Man, and Cybernetics, 22(6), 1414-1427. https://doi.org/10.1109/21.199466
Wu, B., & Duan, T. (2017). A performance comparison of neural networks in forecasting stock price trend. International Journal of Computational Intelligence Systems, 10, 336-346. https://doi.org/10.2991/ijcis.2017.10.1.23
Wu, M. E., Wang, C. H., & Chung, W. H. (2016). Using trading mechanisms to investigate large futures data and their implications to market trends. Soft Computing. https://doi.org/10.1007/s00500-016-2162-6
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
Arevalo, R., García, J., Guijarro, F., & Peris, A. (2017). A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Systems with Applications, 81, 177-192. https://doi.org/10.1016/j.eswa.2017.03.028
Bekiros, S. D. (2010). Fuzzy adaptative decision-making for boundedly rational traders in speculative stock markets. European Journal of Operational Research, 202, 285-293. https://doi.org/10.1016/j.ejor.2009.04.015
Buckley, J. J., & Hayashi, Y. (1994). Fuzzy neural networks: a survey. Fuzzy Sets and Systems, 66(1), 1-13. https://doi.org/10.1016/0165-0114(94)90297-6
Cervelló-Royo, R., Guijarro, F., & Michniuk, K. (2015). Stock market trading rule based on pattern recognition and technical analysis: forecasting the DJIA index with intraday data. Expert Systems with Applications, 42(14), 5963-5975. https://doi.org/10.1016/j.eswa.2015.03.017
Dash, R., & Dask, P. K. (2016). An evolutionary hybrid fuzzy computationally efficient EGARCH model for volatility prediction. Applied Soft Computing, 45, 40-60. https://doi.org/10.1016/j.asoc.2016.04.014
Dixit, G., & Roy, D. (2013). Predicting India Volatility Index: an application of artificial neural network. International Journal of Computer Applications, 70(4), 22-30. https://doi.org/10.5120/11950-7768
Fama, E. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25(2), 383-417. https://doi.org/10.2307/2325486
Gao, T., Li, X., Chai, Y., & Tang, Y. (2016). Deep learning with stock indicators and two-dimensional principal component analysis for closing price prediction system. IEEE Xplore. https://doi.org/10.1109/ICSESS.2016.7883040
García, F., Guijarro, F., & Moya, I. (2011). The curvature of the tracking frontier: a new criterion for the partial index tracking problem. Mathematical and Computer Modelling, 54, 1781-1784. https://doi.org/10.1016/j.mcm.2011.02.015
García, F., Guijarro, F., & Moya, I. (2013). A multiobjective model for passive portfolio management: an application on the S&P 100 index. Journal of Business Economics and Management, 14, 758-775. https://doi.org/10.3846/16111699.2012.668859
Guijarro, F. (2017). A similarity measure for the cardinality constrained frontier in the mean-variance optimization model. Journal of the Operational Research Society, 69(6), 928-945. https://doi.org/10.1057/s41274-017-0276-6
Göçken, M., Özçalici, M., Boru, A., & Dosdogru, A. (2016). Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Systems with Applications, 44, 320-331. https://doi.org/10.1016/j.eswa.2015.09.029
Holmberg, U., Lönnbark, C., & Lundström, C. (2012). Assessing the profitability of intraday opening range breakout strategies. Finance Research Letters, 10(1), 27-33. https://doi.org/10.1016/j.frl.2012.09.001
Hyup, T. (2007). Forecasting the volatility of stock price index. Expert Systems with Applications, 33, 916-922. https://doi.org/10.1016/j.eswa.2006.08.001
Ichihashi, H., & Watanabe, T. (1990). Learning control system by a simplified fuzzy reasoning model. Journal of Japan Society for Fuzzy Theory and Systems, 2(3), 429-437. https://doi.org/10.3156/jfuzzy.2.3_429
Inthachot, V., Boonjing, V., & Intakosum, S. (2016). Artificial neural network and genetic algorithm hybrid intelligence for predicting Thai stock price index trend. Computational Intelligence and Neuroscience, 2016, ID 3045254, 1-8. https://doi.org/10.1155/2016/3045254
Ishibuchi, H., Nozaki, K., & Tanaka, H. (1994). Empirical study on learning in fuzzy systems by rice taste analysis. Fuzzy Sets and Systems, 64(2), 129-144. https://doi.org/10.1016/0165-0114(94)90329-8
Jabbarzadeh, A., Shavvalpour, S., Khanjarpanah, H., & Dourvash, D. (2016). A multiple-criteria approach for forecasting stock price direction: nonlinear probability models with application in S&P 500 Index. International Journal of Applied Engineering Research, 11(6), 3870-3878.
Jang, J. S. (1993). ANFIS: adaptative-netowrk-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685. https://doi.org/10.1109/21.256541
Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul Stock Exchange. Expert Systems with Applications, 38(5), 11-19. https://doi.org/10.1016/j.eswa.2010.10.027
Kim, J., & Kasabov, N. (1999). HyFIS: adaptative neuro-fuzzy inference systems and their application to nonlinear dynamical systems. Neural Networks, 12(9), 1301-1319. https://doi.org/10.1016/S0893-6080(99)00067-2
Kim, K. J. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1), 307-319. https://doi.org/10.1016/S0925-2312(03)00372-2
Kim, K. J., & Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications, 19, 125-132. https://doi.org/10.1016/S0957-4174(00)00027-0
Kim, J. S., Kim, D. H., & Seo, S. W. (2017). Individual mean-variance relation and stock-level investor sentiment. Journal of Business Economics and Management, 18(1), 20-34. https://doi.org/10.3846/16111699.2016.1252794
Leung, M. T., Daouk, H., & Chen, A. (2000). Forecasting stock indices: a comparison of classification and level estimation models. International Journal of Forecasting, 16(2), 173-1990. https://doi.org/10.1016/S0169-2070(99)00048-5
Lim, K. P., Brooks, R. (2011). The evolution of stock market efficiency over time: A survey of the empirical literature. Journal of Economic Surveys, 25(1), 69-108. https://doi.org/10.1111/j.1467-6419.2009.00611.x
Lu, T. H., & Shiu, Y. M. (2016). Can 1-day candlestick patterns be profitable on the 30 component stocks of the DJIA? Applied Economics, 48(35), 3345-3354. https://doi.org/10.1080/00036846.2015.1137553
Mamdani, E. H. (1974). Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers, 121(12), 1585-1588. https://doi.org/10.1049/piee.1974.0328
Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1-13. https://doi.org/10.1016/S0020-7373(75)80002-2
Metghalchi, M., Chang, Y. H., & Marcucci, J. (2008). Is the Swedish stock market efficient? Evidence from some simple trading rules. International Review of Financial Analysis, 17(3), 475-490. https://doi.org/10.1016/j.irfa.2007.05.001
Moghaddam, A. H., Moghaddam, M. H., & Esfandyari, M. (2016). Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science, 21, 89-93. https://doi.org./10.1016/j.jefas.2016.07.002
Mohd Adnan, M. R. H., Sarkheyli, A., Mohd Zain, A., & Haron, H. (2015). Fuzzy logic for modelling machining process: a review. Artificial Intelligence Review, 43, 345-379. https://doi.org/10.1007/s10462-012-9381-8
Nazario, R. T., Lima e Silva, J., Amorim OSbreiro, V., & Kimira, H. (2017). A literature review of technical analysis on stock markets. The Quarterly Review of Economics and Finance, 66, 115-126. https://doi.org/10.1016/j.qref.2017.01.014
Pla-María, M., & García, F. (2016). Stock market investment implementing technical analysis strategies: moving average crossover. Finance, Markets and Valuation, 1, 35-50.
Qiu, M., & Song, Y. (2016). Predicting the direction of stock market index movement using an optimized artificial neural network model. PLoS ONE, 11(5). https://doi.org/10.1371/journal.pone.0155133
Riza, S. L., Bergmeir, C., Herrera, F., & Benítez, J. M. (2015). Fuzzy rule-based systems for classification and regression in R. Journal of Statistical Software, 65(6), 1-30. https://doi.org/10.18637/jss.v065.i06
Schulmeister, S. (2009). Profitability of technical stock trading: has it moved from daily to intraday data? Review of Financial Economics, 18(4), 190-201. https://doi.org/10.1016/j.rfe.2008.10.001
Su, C. H., & Cheng, C. H. (2016). A hybrid fuzzy time series model based on ANFIS and integrated nonlinear feature selection method for forecasting stock. Neurocomputing, 205, 264-273. https://doi.org/10.1016/j.neucom.2016.03.068
Vaiciulyte, I., Kalsyte, Z., Sakalauskas, L., & Plikynas, D. (2017). Assessment of market reaction on the share performance on the basis of its visualization in 2D space. Journal of Business Economics and Management, 18(1), 309-318. https://doi.org/10.3846/16111699.2017.1285348
Wang, L. X., & Mendel, J. M. (1992). Generating fuzzy rules by learning from examples. EEE Transactions on Systems, Man, and Cybernetics, 22(6), 1414-1427. https://doi.org/10.1109/21.199466
Wu, B., & Duan, T. (2017). A performance comparison of neural networks in forecasting stock price trend. International Journal of Computational Intelligence Systems, 10, 336-346. https://doi.org/10.2991/ijcis.2017.10.1.23
Wu, M. E., Wang, C. H., & Chung, W. H. (2016). Using trading mechanisms to investigate large futures data and their implications to market trends. Soft Computing. https://doi.org/10.1007/s00500-016-2162-6
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X