single-jc.php

JACIII Vol.19 No.5 pp. 593-600
doi: 10.20965/jaciii.2015.p0593
(2015)

Paper:

Difference Between Chinese and US Stock Markets: Determinants, Mechanisms, and Impact

Bing Xu*, Qiuqin He*, Jun Qian*, and Jiangping Dong**

*Research Institute of Econometrics and Statistics, Zhejiang Gongshang University
18 Xuezheng Street, Xiasha Education Park, Hangzhou, Zhejiang 310018, China

**Graduate School of Information, Production and Systems, Waseda University
2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan

Received:
September 29, 2014
Accepted:
March 10, 2015
Published:
September 20, 2015
Keywords:
stock market, determinant variables, technical indicator, nonparametric path identification
Abstract
This study uses technical indicator data to propose a new data-driven approach called nonparametric path identification to investigate the differences in the determinants, mechanism, and impact of the Sino-US stock markets. First, MA_5 is relevant to NASDAQ, whereas MA_10 is relevant to SSEC, which indicates that the trend of NASDAQ is more stable than that of SSEC. Second, different nonlinear mechanisms exist in the two stock markets, such that MA_10 and SAR have a nonlinear correlation to SSEC and NASDAQ, respectively. This finding indicates that the volatility reversion of NASDAQ is faster than SSEC. In addition, the relationship of middle Bollinger Bands (mavg) with SSEC is linear, whereas that with NASDAQ is nonlinear. Third, the most significant impact on SSEC is from CMF, whereas that on NASDAQ is from Average Directional Index (ADX). This result indicates the existence of more speculative behavior in SSEC than in NASDAQ.
Cite this article as:
B. Xu, Q. He, J. Qian, and J. Dong, “Difference Between Chinese and US Stock Markets: Determinants, Mechanisms, and Impact,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.5, pp. 593-600, 2015.
Data files:
References
  1. [1] J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock market index using fusion of machine learning techniques,” Expert Systems with Applications, Vol.42, No.3, pp. 2162-2172, 2015.
  2. [2] A. W. Lo, H. Mamaysky, and J. Wang, “Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation,” The J. of Finance, Vol.LV, No.4, pp. 1705-1765, 2000.
  3. [3] B. R. Marshall, M. R. Young, and L. C. Rose, “Candlestick technical trading strategies: Can they create value for investors?” J. of Banking & Finance, Vol.30, No.5, pp. 2303-2323., 2006.
  4. [4] L. Menkhoff, “The use of Technical Analysis by Fund Managers: International Evidence,” J. of Banking and Finance, Vol.34, pp. 2573-2586, 2010.
  5. [5] S. K. Mitra, “How Rewarding is Technical Analysis in the Indian Stock Market?” Quantitative Finance, Vol.11, pp. 287, 2009.
  6. [6] A. I. Diler, “Predicting direction of ISE national-100 index with back propagation trained neural network,” J. of Istanbul Stock Exchange, Vol.7, No.25-26, pp. 65-81, 2003.
  7. [7] K. Manish and M. Thenmozhi, “Forecasting stock index movement: A comparison of support vector machines and random forest,” Proc. of 9th Indian Institute of Capital Markets Conf., http://ssrn.com/abstract=876544, 2005.
  8. [8] C. L. Huang and C. Y. Tsai, “A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting,” Expert Systems with Applications, Vol.36, No.2, pp. 1529-1539, 2009.
  9. [9] Y. Kara, M. Acar Boyacioglu, and ”O. K. Baykan, “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, Vol.38, pp. 5311-5319, 2011.
  10. [10] J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques,” Expert Systems with Applications, Vol.42, No.1, pp. 259-268, 2015.
  11. [11] C. J. Neely, D. E. Rapach, J. Tu, and G. Zhou, “Forecasting the equity risk premium: the role of technical indicators,” Management Science, Vol.60, No.4, pp. 1772-1791, 2014.
  12. [12] F. A. de Oliveira, C. N. Nobre, and L. E. Záarate, “Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index–case study of PETR4, Petrobras, Brazil,” Expert Systems with Applications, Vol.40, No.18, pp. 7596-7606, 2013.
  13. [13] J. L. Ticknor, “A Bayesian regularized artificial neural network for stock market forecasting,” Expert Systems with Applications, Vol.40, No.14, pp. 5501-5506, 2013.
  14. [14] C. H. Park and S. H. Irwin, “What do we know about the profitability of technical analysis?” J. of Economic Surveys, Vol.21, No.3, pp. 786-826, 2007.
  15. [15] D. J. Henderson, C. Papageorgiou, and C. F. Parmeter, “Growth Empirics without Parameters,” The Economic J., Vol.122, No.559, pp. 125-154, 2012.
  16. [16] J. Racine and Q. Li, “Nonparametric estimation of regression functions with both categorical and continuous data,” J. of Econometrics, Vol.119, No.1, pp. 99-130, 2004.
  17. [17] P. Hall, Q. Li, and J. S. Racine, “Nonparametric estimation of regression functions in the presence of irrelevant regressors,” The Review of Economics and Statistics, Vol.89, No.4, pp. 784-789, 2007.
  18. [18] H. Amilon, “Garch estimation and discrete stock prices: an application to low-priced australian stocks,” Economics Letters, Vol.81, No.3, pp. 215-222, 2013.
  19. [19] T. Jeantheau, “A link between complete models with stochastic volatility and ARCH models,” Finance and Stochastics, Vol.8, pp. 111-131, 2004.
  20. [20] H. C. Liu, Y. H. Lee, and M. C. Lee, “Forecasting china stock markets volatility via GARCH models under skewed-GED distribution,” J. Money Investment Banking, pp. 5-14, 2009.
  21. [21] E. Hadavandi, H. Shavandi, and A. Ghanbari, “Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting,” Knowledge-Based Systems, Vol.23, No.5, pp. 800-808, 2010.
  22. [22] Y. S. Lee and L. I. Tong, “Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming,” Knowledge Based Systems, Vol.24, No.1, pp. 66-72, 2011.
  23. [23] M. M. Aldin, H. D. Dehnavi, M. Hajighasemi, and A. Hajighasemi, “Investigating the effect of fundamental accounting variables on the stock prices variations,” Int. J. of Economics & Finance, Vol.4, No.10, 2012.
  24. [24] A. A. Adebiyi, A. O. Adewumi, and C. K. Ayo, “Comparison of arima and artificial neural networks models for stock price prediction,” J. of Applied Mathematics, Vol.33, No.1, pp. 75-81, 2014.
  25. [25] R. Hafezi, J. Shahrabi, and E. Hadavandi, “A Bat-Neural Network Multi-Agent System (BNNMAS) For Stock Price Prediction: Case Study of DAX Stock Price,” Applied Soft Computing, 2015.
  26. [26] B. Xu, J. Zeng, and J. Watada, “Simulation of change of production efficiency based on FDI path identification,” ICIC Express Letters, No.3, pp. 461-466, 2011.
  27. [27] B. Xu, “Path Converged Design Application to Production Efficiency of FDI in Regions,” Economic Research J., Vol.2, pp. 44-54, 2010.
  28. [28] B. Xu, J. Zeng, and J. Watada, “Changes in Production Efficiency in China,” Springer, 2014.
  29. [29] M. Lam, “Neural network techniques for financial performance prediction: integrating fundamental and technical analysis,” Decision Support Systems, Vol.37, No.3, pp. 567-581, 2004.
  30. [30] M. T. Yamawaki and S. Tokuoka, “Adaptive use of technical indicators for the prediction of intra-day stock prices,” Physica A: Statistical Mechanics and its Applications, Vol.383, No.1, pp. 125-133, 2007.
  31. [31] P. C. Chang, “A novel model by evolving partially connected neural network for stock price trend forecasting,” Expert Systems with Applications, Vol.39, No.1, pp. 611-620, 2012.
  32. [32] P. C. Chang, C. H. Liu, J. L. Lin, C. Y. Fan, and C. S. Ng, “A neural network with a case based dynamic window for stock trading prediction,” Expert Systems with Applications, Vol.36, No.2, pp. 6889-6898, 2009.
  33. [33] T. H. Lu, “The profitability of candlestick charting in the Taiwan stock market,” Pacific-Basin Finance J., Vol.26, pp. 65-78, 2014.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 19, 2024