Abstract
Stock prices are influenced by many external factors such as the oil prices, the exchange rates, the money interest rates, the certificate of deposit (CD), the gold prices, the exchange rates, the composite indexes in global markets, and so on. And the influence among these factors is reciprocal, cyclic, and often hierarchical, which can be naturally presented as a network. In this paper, a prediction method based on hierarchical structure of financial networks is proposed. Semi-supervised learning (SSL) is employed as a base algorithm, and revised to be suited for time series prediction. A network consists of nodes of the factors and edges of similarities between them. The layered structure of networks is implemented by reforming the existing integration method for multiple graphs. With the hierarchical structure of financial networks, it is able to reflect the complicated influences among the factors to prediction. The proposed method is applied to the stock price prediction from January 2007 to August 2008, using 16 global economic indexes and 200 individual companies listed to KOSPI200.
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References
Jeantheau, T.: A link between complete models with stochastic volatility and ARCH models. Finance Stochast. 8, 111–131 (2004)
Liu, H.C., Lee, Y.-H., Lee, M.-C.: Forecasting China Stock Markets Volatility via GARCH Models Under Skewed-GED Distribution. Journal of Money, Investment and Banking, 5–14 (2009)
Amilon, H.: GARCH estimation and discrete stock prices: an application to low-priced Australian stocks. Economics Letters 81, 215–222 (2003)
Chen, N.F., Roll, R., Ross, S.A.: Economic Forces and the Stock Market. Journal of Business 59, 383–403 (1986)
Kim, K.J.: Financial time series forecasting using support vector machines. Neurocomputing 55, 307–319 (2003)
Huang, W., Nakamori, Y., Wang, S.-Y.: Forecasting stock market movement direction with support vector machine. Computers & Operations Research 32, 2513–2522 (2005)
Cao, Q., Leggio, K.B., Schniederjans, M.J.: A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Computers & Operations Research 32, 2499–2512 (2005)
Chen, A.S., Leung, M.T., Daouk, H.: Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Computers & Operations Research 30, 901–923 (2003)
Tay, F.E.H., Cao, L.: Application of support vector machines in financial time series forecasting. Omega 29, 309–317 (2001)
Kanas, A.: Non-linear forecasts of stock returns. Journal of Forecasting 22, 299–315 (2003)
Yang, B., Li, L.X., Xu, J.: An early warning system for loan risk assessment using artificial neural networks. Knowledge-Based Systems 14, 303–306 (2001)
Bekiros, S., Georgoutsos, D.: Direction-of-Change Forecasting using a Volatility-Based Recurrent Neural Network. Journal of Forecasting 27, 407–417 (2008)
Tsang, P.M., Kwok, P., Choy, S.O., Kwan, R., Ng, S.C., Mak, J., Tsang, J., Koong, K., Wong, T.-L.: Design and implementation of NN5 for Hong Kong stock price forecasting. Engineering Applications of Artificial Intelligence 20, 453–461 (2007)
Zhu, X.: Semi-Supervised Learning with Graphs, PA 15213. Carnegie Mellon, Pittsburgh (2005)
Park, K., Shin, H.: Stock Price Prediction based on a Complex Interrelation Network of Economic Factors. Engineering Applications of Artificial Intelligence 26, 1550–1561 (2013)
Shin, H., Hou, T., Park, K., Park, C.-K., Choi, S.: Prediction of movement direction in crude oil prices based on semi-supervised learning. Decision Support Systems 55, 348–358 (2013)
Shin, H., Hill, N.J., Lisewski, A.M., Park, J.-S.: Graph sharpening. Expert Systems with Applications 37, 7870–7879 (2010)
Shin, H., Tsuda, K.: Prediction of Protein Function from Networks. In: Chapelle, O., et al. (eds.) Semi-Supervised Learning, pp. 339–352. MIT Press (2006)
Shin, H., Lisewski, A.M., Lichtarge, O.: Graph sharpening plus graph integration: a synergy that improves protein functional classification. Bioinformatics 23, 3217–3224 (2007)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with Local and Global Consistency. In: Advances in Neural Information Processing Systems 16 (NIPS), Whistler, Britishi Columbia, pp. 321–328 (2004)
Kim, K.J.: Artificial neural networks with evolutionary instance selection for financial forecasting. Expert Systems with Applications 30, 519–526 (2006)
Liu, Q., Sung, A.H., Chen, Z., Liu, J., Huang, X., Deng, Y.: Feature Selection and Classification of MAQC-II Breast Cancer and Multiple Myeloma Microarray Gene Expression Data. MAQC-II Gene Expression 4, 1–24 (2009)
Gribskov, M., Robinson, N.L.: Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching. Computers & Chemistry 20, 25–33 (1996)
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Park, K., Shin, H. (2013). Stock Price Prediction Based on Hierarchical Structure of Financial Networks. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_57
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DOI: https://doi.org/10.1007/978-3-642-42042-9_57
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