A short term co-movement detection of financial data

This paper aims to apply statistical and the tree-based data mining techniques to build a model for predicting the movement of Thailand stock price index (SET). Predictors are stock price indexes of Hong Kong Hang Seng (HSI) and Nikkei 225. We also incorporate the index difference from the previous day closing price of HSI and Nikkei as additional predictors. The positive or negative movement of SET compared to the earlier closing index is a binary signal that is constructed and used as a target for our model. The co-movement among SET, HSI, and Nikkei is a short term detection in that it captures intra-day association. The original built model is manipulated to be concise and comprehensible through the application of feature subset selection techniques. We finally obtain a concise tree model to forecast index movement with accuracy as high as 70%.

[1]  Kyong Joo Oh,et al.  Analyzing Stock Market Tick Data Using Piecewise Nonlinear Model , 2022 .

[2]  Jun Wang,et al.  Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks , 2015, Neurocomputing.

[3]  Deron Liang,et al.  Novel feature selection methods to financial distress prediction , 2014, Expert Syst. Appl..

[4]  Prodromos D. Chatzoglou,et al.  An intelligent short term stock trading fuzzy system for assisting investors in portfolio management , 2016, Expert Syst. Appl..

[5]  Huaiqing Wang,et al.  A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network , 2015, PloS one.

[6]  Monica Lam,et al.  Neural network techniques for financial performance prediction: integrating fundamental and technical analysis , 2004, Decis. Support Syst..

[7]  Shu-Hsien Liao,et al.  Data mining investigation of co-movements on the Taiwan and China stock markets for future investment portfolio , 2013, Expert Syst. Appl..

[8]  Amiya Kumar Rath,et al.  A Naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices , 2015, Appl. Soft Comput..

[9]  Chih-Fong Tsai,et al.  Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches , 2010, Decis. Support Syst..

[10]  Chaker Aloui,et al.  Co-movements of GCC emerging stock markets: New evidence from wavelet coherence analysis , 2014 .

[11]  Mansur Masih,et al.  Linkages and co-movement between international stock market returns: Case of Dow Jones Islamic Dubai Financial Market index , 2015 .

[12]  L. Yao,et al.  Predictive ability and profitability of simple technical trading rules: Recent evidence from Southeast Asian stock markets , 2013 .

[13]  Wei-Sen Chen,et al.  Using neural networks and data mining techniques for the financial distress prediction model , 2009, Expert Syst. Appl..

[14]  Yanshan Wang,et al.  Stock price direction prediction by directly using prices data: an empirical study on the KOSPI and HSI , 2013, Int. J. Bus. Intell. Data Min..

[15]  N. Sopipan,et al.  Forecasting SET50 Index with Multiple Regression based on Principal Component Analysis , 2012 .

[16]  Chi-Jie Lu,et al.  Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes , 2012, Expert Syst. Appl..

[17]  Tobias Hahn,et al.  Creating trading systems with fundamental variables and neural networks: The Aby case study , 2012, Math. Comput. Simul..

[18]  Chih-Chou Chiu,et al.  Financial time series forecasting using independent component analysis and support vector regression , 2009, Decis. Support Syst..

[19]  Adriano Lorena Inácio de Oliveira,et al.  Expert Systems With Applications , 2022 .

[20]  Mehmet Özçalici,et al.  Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction , 2016, Expert Syst. Appl..

[21]  Milda Maria Burzała.  Contagion effects in selected European capital markets during the financial crisis of 2007–2009 , 2016 .

[22]  Jianxue Chen.  SVM application of financial time series forecasting using empirical technical indicators , 2010, 2010 International Conference on Information, Networking and Automation (ICINA).

[23]  Ying Wah Teh,et al.  Stock market co-movement assessment using a three-phase clustering method , 2014, Expert Syst. Appl..

[24]  Huang Li-li,et al.  Financial time series forecasting based on wavelet kernel support vector machine , 2012, 2012 8th International Conference on Natural Computation.

[25]  Lili Huang,et al.  Financial time series forecasting based on wavelet kernel support vector machine , 2012, ICNC.

[26]  Jinglu Hu,et al.  An SVM-based approach for stock market trend prediction , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[27]  Kazuhiro Seki,et al.  Predicting Stock Market Trends by Recurrent Deep Neural Networks , 2014, PRICAI.

[28]  Muh-Cherng Wu,et al.  An effective application of decision tree to stock trading , 2006, Expert Syst. Appl..