Predicting Stock Prices with Text and Price Combined Model
Yu Chuanming1, Gong Yutian1, Wang Feng1, An Lu2()
1School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China 2School of Information Management, Wuhan University, Wuhan 430072, China
[Objective] This paper tries to predict stock price fluctuation with the help of big data, aiming to improve the accuracy of the forecasting and reduce the trading risks. [Methods] We proposed a new Text and Price Combined Model (TPCM) to process comments retrieved from a stock forum. Then, we employed deep representation learning algorithm to generate text feature matrix and utilized the K-means clustering method to generate text category. Finally, we used the Multi-Layer Perceptron (MLP) to predict stock price fluctuation based on the opening price, closing price and other 15 original price indicators. [Results] The accuracy of TPCM was 65.91%, which was 7.76% higher than that of the model (58.15%) employing price features only, and 11.37% higher than that of the model (54.54%) employing text features only. [Limitations] The study only used one stock to examine the proposed model. [Conclusions] Stock price forecasting could be improved through the combination of text and price, which creates novel perspectives for future studies.
(Zhuang Shutian. A Preliminary Analysis of Investment Psychology and Investment Behavior[J]. Journal of Southeast University: Philosophy and Social Science, 2015, 17(S2): 41, 46.)
(Zhang Jian.Economic Bubbles in the History of Modern Western Europe and Influences[J]. Forum of World Economics & Politics, 2010(4): 99-109.)
doi: 10.3969/j.issn.1007-1369.2010.04.009
(Shi Ping, Li Liqing, Yang Xun.A Game Theory Analysis Between Public Company and Audit Office in Securities Market[J]. Journal of Industrial and Engineering Management, 2004, 18(1): 44-47.)
doi: 10.3969/j.issn.1004-6062.2004.01.011
(Zou Huiwen.Combined Effects of Non-rational Trade Behavior of Investors and Fluctuation of Stock Prices[J]. Journal of Fuzhou University: Philosophy and Social Sciences, 2008, 22(1): 25-29.)
doi: 10.3969/j.issn.1002-3321.2008.01.005
(Shi Qingchun, Xu Luying.Empirical Research on the Listed Companies’ Stock Prices Affected by Negative Public Opinion[J]. Journal of Central University of Finance & Economics, 2014(10): 54-62.)
(Yu Jin, Hou Weixiang.Leveraged Transactions, the Behavior of Institutional Investor and the Risk of Asset Price Crash: Evidences from the Stock Market[J]. Financial Regulation Research, 2017(12): 17-34.)
[10]
岳衡, 赵龙凯. 股票价格中的数字与行为金融[J]. 金融研究, 2007(5): 98-107.
[10]
(Yue Heng, Zhao Longkai.Figures and Behavioral Finance in Stock Prices[J]. Journal of Financial Research, 2007(5): 98-107.)
(Lin Chuan. ExcessiveInvestment, Market Sentiment and Share Prices Crash: Empirical Evidence from GEM Listed Companies[J]. Journal of Central University of Finance & Economics, 2016(12): 53-64.)
(Guo Hongyu, Xu Zheng, Tong Jieran.The Characteristics of Japan’s Quantitative Easing Policy and Its Short-Term Impact on Stock Market——Based on Event Analysis[J]. Studies of International Finance, 2016(5): 38-47.)
(Lu Lei.Combinational Stock Price Forecasting Based on Multiple Regression and Technical Analysis[J]. Journal of Shanghai Institute of Technology: Natural Science, 2014, 14(3): 274-276.)
doi: 10.3969/j.issn.1671-7333.2014.03.020
(Chen Lulu.Based on Multivariate Linear Regression Analysis—— Forecasting Stock Prices in China Citic Bank[J]. Economic Research Guide, 2016(19): 75-76.)
doi: 10.3969/j.issn.1673-291X.2016.19.032
(Huang Hongyun, Wu Libin, Li Shizheng.Application of Neural Network in Prediction of Stock Index[J]. Journal of Tonghua Normal University, 2016, 37(5): 32-34.)
doi: 10.13877/j.cnki.cn22-1284.2016.10.011
(Cai Hong, Chen Rongyao.Stock Price Prediction Based on PCA and BP Neural Network[J]. Computer Simulation, 2011, 28(3): 365-368.)
doi: 10.3969/j.issn.1006-9348.2011.03.088
[20]
Göçken M, özçalıcı M, Boru A, et al.Integrating Metaheuristics and Artificial Neural Networks for Improved Stock Price Prediction[J]. Expert Systems with Applications, 2016, 44: 320-331.
doi: 10.1016/j.eswa.2015.09.029
(Guo Jianfeng, Li Yu, An Dong.Prediction for Short-term Stock Price Based on LM-GA-BP Neural Network[J]. Computer Technology and Development, 2017, 27(1): 152-155.)
doi: 10.3969/j.issn.1673-629X.2017.01.034
[22]
Adebiyi A A, Adewumi A, Ayo C.Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction[J]. Journal of Applied Mathematics, 2014(1): 1-7.
doi: 10.1155/2014/614342
[23]
Evangelopoulos N, Magro M, Sidorova A.The Dual Micro/Macro Informing Role of Social Network Sites: Can Twitter Macro Messages Help Predict Stock Prices?[J]. Informing Science: The International Journal of an Emerging Transdiscipline, 2012, 15: 247-269.
doi: 10.28945/1739
(Wang Jianjun, Yin Linsen, Ye Wenjing. Investor Sentiment, Leveraged Fund and Stock Price: Reflection on the Cause of Stock Crash in 2015-2016[J]. Financial Economics Research, 2017, 32(1): 85-98.)
(Shi Yong, Tang Jing, Guo Kun.The Study of Social Media Investor Attention and Sentiment’s Influence on Chinese Stock Market[J]. Journal of Central University of Finance & Economics, 2017(7): 45-53.)
(Dong Li, Wang Zhongqing, Xiong Deyi.Stock Index Prediction Based on Text Information[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2017, 53(2): 273-278.)
doi: 10.13209/j.0479-8023.2017.037
(Huang Runpeng, Zuo Wenming, Bi Lingyan.Predicting the Stock Market Based on Microblog Mood[J]. Journal of Industrial Engineering and Engineering Management, 2015, 29(1): 47-52.)
[29]
Yan D F, Zhou J, Zhao X, et al.Predicting Stock Using Microblog Moods[J]. China Communications, 2016, 13(8): 244-257.
doi: 10.1109/CC.2016.7563727
[30]
Nguyen T H, Shirai K, Velcin J.Sentiment Analysis on Social Media for Stock Movement Prediction[J]. Expert Systems with Applications, 2015, 42(24): 9603-9611.
doi: 10.1016/j.eswa.2015.07.052
[31]
Li X, Xie H, Chen L, et al.News Impact on Stock Price Return via Sentiment Analysis[J]. Knowledge-Based Systems, 2014, 69(1): 14-23.
doi: 10.1016/j.knosys.2014.04.022
(Su Zhi, Lu Man, Li Dexuan.Deep Learning in Financial Empirical Application: Dynamics, Contributions and Prospects[J]. Journal of Financial Research, 2017(5): 111-126.)
(Liu Xiangqiang, Li Qinyang, Sun Jian.Internet Media Coverage and Stock Returns: Investor Recognition or Over Attention[J]. Journal of Central University of Finance & Economics, 2017(7): 54-62.)
(Duan Jiangjiao, Liu Hongzhong, Zeng Jianping.Analysis on the Information Content of China’s Internet Stock Message Boards[J]. Journal of Financial Research, 2017(10): 178-192.)
(Yang Xiaolan, Shen Hanbin, Zhu Yu.The Effect of Local Bias in Investor Attention and Investor Sentiment on Stock Markets: Evidence from Online Forum[J]. Journal of Financial Research, 2016(12): 143-158.)
[38]
Huang Y, Qiu H, Wu Z.Local Bias in Investor Attention: Evidence from China’s Internet Stock Message Boards[J]. Journal of Empirical Finance, 2016, 38: 338-354.
doi: 10.2139/ssrn.2050232
[39]
Rätsch G, Onoda T, Müller K R.Soft Margins for AdaBoost[J]. Machine Learning, 2001, 42(3): 287-320.
doi: 10.1023/A:1007618119488
[40]
Safavian S R, Landgrebe D.A Survey of Decision Tree Classifier Methodology[J]. IEEE Transactions on Systems, Man and Cybernetics, 2002, 21(3): 660-674.
doi: 10.1109/21.97458
[41]
Guo G, Wang H, Bell D, et al.KNN Model-Based Approach in Classification[J]. Lecture Notes in Computer Science, 2003, 2888: 986-996.
doi: 10.1007/b94348
[42]
Rish I.An Empirical Study of The Naive Bayes Classifier[C]// Proceedings of the 2001 Workshop on Empirical Methods in Artificial Intelligence. 2001, 3(22): 41-46.
[43]
Hearst M A, Dumais S T, Osuna E, et al.Support Vector Machines[J]. IEEE Intelligent Systems & Their Applications, 1998, 13(4): 18-28.