A New Investor Sentiment Index Model and Its Application in Stock Price Prediction and Systematic Risk Estimation of Bull and Bear Market
International Journal of Finance and Banking Research
Volume 5, Issue 1, February 2019, Pages: 1-8
Received: Oct. 4, 2018; Accepted: Dec. 17, 2018; Published: Mar. 15, 2019
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Authors
Qiansheng Zhang, School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou, China
Sichuang Hu, School of Finance, Guangdong University of Foreign Studies, Guangzhou, China
Libo Chen, School of Finance, Guangdong University of Foreign Studies, Guangzhou, China
Ruixi Lin, School of Finance, Guangdong University of Foreign Studies, Guangzhou, China
Wan Zhang, School of Finance, Guangdong University of Foreign Studies, Guangzhou, China
Ruiying Shi, School of Finance, Guangdong University of Foreign Studies, Guangzhou, China
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Abstract
Many studies in recent years have shown that investor sentiment affects investor decision-making, which in turn affects stock market volatility and the direction of stock market prices. Since behavioral finance researchers find that linear combinations of stock turnover and popularity indices can greatly reflect stock investor sentiment, this paper aims to construct a new investor sentiment index that can be reasonably applied to predict stock market risk by selecting rational factors. A new investor sentiment index model is first proposed by combining specific monthly new account ratio (SNIA), monthly turnover rate (TOR), popularity index AR, delayed yield (DY) and using principal component analysis approach. Secondly, the indicator is statistically tested. The results of the correlation analysis show that the investor sentiment index is positively correlated with the monthly rate of return, and the result of causal analysis reveals that the investor sentiment index is the Granger cause of the change in yield. Thirdly, a new method is designed to predict the stock price trend by using the presented investor sentiment index. Finally, based on VaR and CoVaR model the investor sentiment index can be utilized to forecast and estimate of systematic risk in the bull or bear market.
Keywords
Investor Sentiment Index, Principal Component Analysis, Prediction of Stock Price, Systematic Risk, Condition at Risk
To cite this article
Qiansheng Zhang, Sichuang Hu, Libo Chen, Ruixi Lin, Wan Zhang, Ruiying Shi, A New Investor Sentiment Index Model and Its Application in Stock Price Prediction and Systematic Risk Estimation of Bull and Bear Market, International Journal of Finance and Banking Research. Vol. 5, No. 1, 2019, pp. 1-8. doi: 10.11648/j.ijfbr.20190501.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Yulei Rao, Hu Sheng, Behavioral Finance, Machinery Industry Press, 2010.
[2]
Ruowei Ma, Na Zhang, The Construction of SENT Sentiment Index of Chinese Stock Market — Based on panel data of Shanghai A-share companies, Journal of Central University of Finance & Economics, 2015 (7), pp. 42-49.
[3]
Lizhen Liang, An empirical study on the influence factors of investor sentiment, Finance Forum, 2010 (4), pp. 138-141.
[4]
Swaminathan B., Time-varying Expected Small Firm Returns and Closed-end Fund Discounts, Review of Financial Studies, 1996 (9), pp. 845-887.
[5]
Pontiff J, Excess Volatility and Closed-end Funds, American Economic Review, 1997 (87), pp. 155-169.
[6]
Shaoan Huang, Da Liu, Investor sentiment theory and China closed-end fund discount Nankai Economic Studies, 2005 (4), pp. 76-80.
[7]
Lily Qiu, Ivo Welch Brown, Investor Sentiment Measure, NBER Working Paper, 2004.
[8]
Baker M and Stein J, Market Liquidity as a Sentiment Indicator, Journal of Financial Markets, 2004 (7), pp. 271-299.
[9]
Caiting Dong, An empirical study on the impact of investor sentiment and corporate characteristics on stock portfolio returns, Yunnan University of Finance and Economics, 2016.
[10]
Neal Robert, Simon M W, Do Measures of Investor Sentiment Predict Returns, Journal of Financial and Quantitative Analysis, 1998, 33 (4), pp. 523-547.
[11]
F. A. de Oliveira, L. E. Zarate, M. De Azevedo Reis, C. N. Nobre, The use of artificial neural networks in the analysis and prediction of stock prices, 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2151-2155, 2011.
[12]
Ming Zhu and L. P. Wang, Intelligent trading using support vector regression and multilayer perceptrons optimized with genetic algorithms, 2010 International Joint Conference on Neural Networks (IJCNN 2010), 2010.
[13]
Shekhar Gupta and L. P. Wang, Stock Forecasting with Feedforward Neural Networks and Gradual Data Sub-Sampling, Australian Journal of Intelligent Information Processing Systems, vol. 11, pp. 14-17, 2010.
[14]
Y. Fang, K. Fataliyev, L. P. Wang, X. J. Fu and Yaoli Wang, Improving the genetic-algorithm-optimized wavelet neural network approach to stock market prediction, 2014 International Joint Conference on Neural Networks (IJCNN 2014), pp. 3038-3042.
[15]
Y. Q. He, K. Fataliyev, and L. P. Wang, Feature selection for stock market analysis, the 20th International Conference on Neural Information Processing (ICONIP2013), Daegu, Korea, 3-10 November 2013, Part II, LNCS 8227, pp. 737-744, 2013.
[16]
Pengbo Lv, Qingju Luo, Construction of investor sentiment index — A new perspective for studying IPO first day returns, Journal of Shanxi Finance and Economics University, 2010 (3), pp. 55-61.
[17]
Wei Wang, Construction of the index system of investor sentiment and Empirical Analysis, Journal of Liaoning University, 2014.
[18]
Weiqi Liu, Xinxin Liu, Individual and institutional investor sentiment and stock returns — a study based on the Shanghai stock market A stock market, Journal of Management Sciences in China, 2014 (3), pp. 70-87.
[19]
Qingqing Wang, Principal Components Analysis of Composite Stock Score, Business Culture, 2009 (12), pp. 297-298.
[20]
Boping Tian, Yong Wang, Wenming Guo and Xipeng Ge, The Role of Principal Component Analysis in Comprehensive Evaluation of Chinese Listed Companies, Mathematics in Practice and Theory, 2004 (4), pp. 74-80.
[21]
Yingmei Zhang, Measurement of Internationalization of RMB and Countermeasures — Based on Principal Component Analysis in Matlab, Shanghai Finance, 2013 (2), pp. 32-37.
[22]
Jianjun Li, Yue Yu., Stock Investment Strategy, based on Principal Component Analysis, Journal of Changchun Normal University (Natural Science), 2009 (1), pp. 12-14.
[23]
C. Lee A., Shleifer R. H. Thaler, Investor Sentiment and the Closed-end Fund Puzzle, The Journal of Finance, 1995, 6 (1), pp. 75-109.
[24]
Victor Dragota, Mihai Caruntu and Andreea Stoian, An Analysis of Closed-end Fund Puzzle for Emerging Capital Markets, Theoretical and Applied Economics, 2008 (11), pp. 54-60.
[25]
BodurthaJ, E. Kim and C. Lee, Closed-End Country Funds and US Market Sentiment, Review of Financial Studies, 1995 (3), pp. 879-918.
[26]
Marc W. Simpson, Sanjay Ramchander., Is differential sentiment a cause of closed-end country fund premia? An empirical examination of the Australian case, Applied Economics Letters, 2002, 9 (9), pp. 615-619.
[27]
Kenneth L Fisher, Meir Statman, Investor Sentiment and Stock Returns, Financial Analysts Journal, 2000, 56 (2), pp. 16-23.
[28]
Chun Wang, The influence of investor sentiment on the earnings and volatility of the stock market — An Empirical Study Based on the net inflow of open stock fund funds, Chinese journal of management science, 2014 (9), pp. 49-56.
[29]
Agrawal. G, Global Financial Meltdown and the stock price behavior of underlying domestic shares of listed Indian ADRs / GDRs issues, Information and Financial Engineering, 2010 (2), pp. 788-792.
[30]
Xiaohui Qu, Linhua Huang, Investor Sentiment, Asset Securitization and Fair Value Information Content --- Empirical Evidence from IPO Approval Announcement of PE Company in A-share Market. Accounting Research, 2013 (9), pp. 14-21.
[31]
Yongkai Ma, Xiaowo Tang, Multi — factor behavior portfolio investment decision — making method, Journal of University of Electronic Science and Technology of China, 2005 (3), pp. 421-424.
[32]
Heshan Guan, Dan Zhou, Research on the Validity of Test Methods of Stationarity, Journal of Nanhua University, 2016 (1), pp. 63-68.
[33]
Shiwei Dai, Xi Luo, The measurement of systemic risk spillover effect in China’s financial industry—a study based on the GARCH_Copula_CoVaR model, Modern Economic Science, 2014 (6), pp. 30-38.
[34]
Wei, Sicong Lv, Xuncheng Mao, Comparison and application of CoVaR computing method based on Risk Spillover correlation characteristics, Economic Review, 2014 (4), pp. 148-160.
[35]
Mei, Lu Xu, China financial industry cross market risk measurement and analysis based on GARCH_Copula_CoVaR mode, Statistics & Information Forum, 2015 (4), pp. 28-32.
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