ChnStd  Vol.4 No.1 , February 2015
A Neuron Model with Dendritic Nonlinearity for Predicting the Influence of Overreaction in Shanghai Stock Market
Author(s) Sha Zijun, Hu Lin
On the basis of previous research, this study assembled trading data from January 2004 to October 2014 to verify the overreaction in Shanghai stock market. The influence of overreaction decreases with time from 2007 onwards and turns to disappear from 2011. The neuron model with dendritic nonlinearity (NMDN) proposed in this paper fits and predicts the variability of abnormal returns of ill-preforming and well-preforming stocks in the test period. This experiment demonstrates that NMDN possesses high computational ability and succeeds to predict trends in the influence of overreaction.

Cite this paper
Sha Zijun, & Hu Lin (2015) A Neuron Model with Dendritic Nonlinearity for Predicting the Influence of Overreaction in Shanghai Stock Market. Chinese Studies, 4, 1-9. doi: 10.4236/chnstd.2015.41001.
[1]   Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31, 307-327.

[2]   De Bondt, W. F. M., & Thaler, R. H. (1985). Does the Stock Market Overreact. Journal of Finance, 40, 793-808.

[3]   Hu Lin, Sha Zijun, Liu Xiuyi, & Chen Wenjun (2013). An Empirical Study on the Overreaction of Shanghai Stock Market. Chinese Studies, 2, 32.

[4]   Kitajima, T., & Hara, K. (1987). A Bipolar Model of the Nerve Cell—Dynamic Characteristics of Synaptic Film. Trans IEICE, J70-D, 818-826.

[5]   Koch, C., Poggio, T., & Torre, V. (1982). A Functional Interpretation of Dendritic Morphology. Philosophical Transactions of the Royal Society B, 298, 227-263.

[6]   Lehmann, B. (1990). Fads, Martingales, and Market Efficiency. Quarterly Journal of Economics, 105, 1-28.

[7]   Liao Zhe, & Wang Jun (2010). Forecasting Model of Global Stock Index by Stochastic Time Effective Neural Network. Expert Systems with Applications, 37, 834-841.

[8]   London, M., & Häusser, M. (2005). Dendritic Computation. Annual Review of Neuroscience, 28, 503-532.

[9]   McCulloch, W. S., & Pitts, W. (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity. The Bulletin of Mathematical Biophysics, 5, 115-133.

[10]   Nelson, D. B. (1991). Conditional Heterosdasticity in Asset Returns: A New Approach. Econometrica, 59, 347-370.

[11]   Song Xianzhong 宋献中, & Tang Sheng 汤胜 (2006). Zhongguo gushi “guodu fanying” yu “guimo fanying” de shizheng fenxi—Jiyu Zhongguo Shanghai A gu gupiao shichang de jianyan 中国股市“过度反应”与“规模效应”的实证分析——基于中国上海A股股票市场的检验. Jinan Xuebao, 3, 74-78.

[12]   Tamura, H., Tang, Z., & Ishii, M. (2001). A Model of Neuron with Dendrite Mechanisms Is Learning to the Movement Direction Selection Function. Denshi Joho Tsushin Gakkai Ronbunshi A, J84-A, 486-498.

[13]   Tang, Z., Tamura, H., Ishizuka, O., & Tanno, K. (2000). A Neuron Model with Interaction among Synapses. T.IEE Japan, 120-C, 1012-1019.

[14]   Tang, Z., Tamura, H., Kuratu, M., Ishizuka, O., & Tanno, K. (2000). A Model of the Neruon Based on Dendrite Mechanisms. Denshi Joho Tsushin Gakkai Ronbunshi, J83-A, 486-498.

[15]   Todo, Y., Tang, Z., Todo, H., Ji, J., & Yamashita, K. (2014). Neurons with Multiplicative Interactions of Nonlinear Synapses. Nautre, Submitted.

[16]   Zhang Renji 张人骥, Zhu Pingfang 朱平芳, & Wang Huaifang王怀芳 (1998). Shanghai zhengquan shichang guodu fanying de shizheng jianyan 上海证券市场过度反应的实证检验. Jingji Kexue, 5, 58-64.

[17]   Zhang Yanqing, & Wan Xuhui (2007). Statistical Fuzzy Interval Neural Networks for Currency Exchange Rate Time Series Prediction. Applied Soft Computing, 7, 1149-1156.

[18]   Zhu Shaoxing 朱少醒 (2000). Xingwei jinrong lilun kuangjia xia de jinrong shichang weiguan jiegou yanjiu 行为金融理论框架下的金融市场微观结构研究. Shanghai: Shanghai Jiao Tong University.

[19]   Zhu Xiaotian, Wang Hong, Xu Li, & Li Huaizu (2008). Predicting Stock Index Increments by Neural Networks: The Role of Trading Volume under Different Horizons. Expert Systems with Applications, 34, 3043-3054.