Stock price prediction and trading strategy design based on investors' attention: Cross patterns perspective between stocks

WEI Qiang, ZHAO Xian, ZHANG Zunqiang, CHEN Guoqing

Systems Engineering - Theory & Practice ›› 2016, Vol. 36 ›› Issue (6) : 1361-1371.

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Systems Engineering - Theory & Practice ›› 2016, Vol. 36 ›› Issue (6) : 1361-1371. DOI: 10.12011/1000-6788(2016)06-1361-11

Stock price prediction and trading strategy design based on investors' attention: Cross patterns perspective between stocks

  • WEI Qiang, ZHAO Xian, ZHANG Zunqiang, CHEN Guoqing
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Abstract

With the rise of search engines, stock search volume has been well recognized as an important indicator of investor attention. In addition, stock price synchronicity also attracts many researchers' attention. To jointly consider these two phenomenons, this paper analyses the relationship between search volume of relevant stock and stock price. Unlike previous research that discusses the relationship at market level, this paper analyses the relationship at individual stock level. An after temporal associative analysis is applied to discover patterns between relevant stock volume variations and subsequent stock price changes, and trading strategy is further designed. To verify the proposed trading strategy, experiments are conducted on China A-share stocks under specific experimental setups. Results show that trading strategies which consider the relationship between search volumes and prices perform better than strategies without considering the relationship. Further, stock level strategy performs better than market level strategy.

Key words

investor attention / stock price synchronicity / search volume / stock price / after temporal association / stock trading strategy

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WEI Qiang , ZHAO Xian , ZHANG Zunqiang , CHEN Guoqing. Stock price prediction and trading strategy design based on investors' attention: Cross patterns perspective between stocks. Systems Engineering - Theory & Practice, 2016, 36(6): 1361-1371 https://doi.org/10.12011/1000-6788(2016)06-1361-11

References

[1] Daniel K, Hirshleifer D, Teoh S H. Investor psychology in capital markets: Evidence and policy implications[J]. Journal of Monetary Economics, 2002, 49(1): 139-209.
[2] Merton R C. A simple model of capital market equilibrium with incomplete information[J]. The Journal of Finance, 1987, 42(3): 483-510.
[3] Barber B M, Odean T. All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors[J]. Review of Financial Studies, 2008, 21(2): 785-818.
[4] Da Z, Engelberg J, Gao P. In search of attention[J]. The Journal of Finance, 2011, 66(5): 1461-1499.
[5] 俞庆进, 张兵. 投资者有限关注与股票收益-以百度指数作为关注度的一项实证研究[J]. 金融研究, 2012(8): 152-165. Yu Q J, Zhang B. Investors limited attention and stock return: An empirical study using Baidu Index as the measure of attention[J]. Journal of Financial Research, 2012(8): 152-165.
[6] Seasholes M S, Wu G. Predictable behavior, profits, and attention[J]. Journal of Empirical Finance, 2007, 14(5): 590-610.
[7] Kaniel R, Ozoguz A, Starks L. The high volume return premium: Cross-country evidence[J]. Journal of Financial Economics, 2012, 103(2): 255-279.
[8] Andrei D, Hasler M. Investor attention and stock market volatility[J]. Review of Financial Studies, 2015, 28(1): 33-72.
[9] 饶育蕾, 彭叠峰, 成大超. 媒体注意力会引起股票的异常收益吗? -来自中国股票市场的经验证据[J]. 系统工程理论与实践, 2010, 30(2): 287-297. Rao Y L, Peng D F, Cheng D C. Does media attention cause abnormal return?-Evidence from China's stock market[J]. Systems Engineering-Theory & Practice, 2010, 30(2): 287-297.
[10] 王建新, 饶育蕾, 彭叠峰. 什么导致了股票收益的"媒体效应": 预期关注还是未预期关注?[J]. 系统工程理论与实践, 2015, 35(1): 37-48. Wang J X, Rao Y L, Peng D F. What drives the stock market "media coverage effect": Expected media attention or unexpected media attention?[J]. Systems Engineering-Theory & Practice, 2015, 35(1): 37-48.
[11] Lou D. Attracting investor attention through advertising[J]. Review of Financial Studies, 2014, 27(6): 1797-1829.
[12] Joseph K, Babajide Wintoki M, Zhang Z. Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search[J]. International Journal of Forecasting, 2011, 27(4): 1116-1127.
[13] Preis T, Moat H S, Stanley H E. Quantifying trading behavior in financial markets using Google Trends[J]. Scientific Reports, 2013. DOI: 10. 1038/srep01684.
[14] Aouadi A, Arouri M, Teulon F. Investor attention and stock market activity: Evidence from France[J]. Economic Modelling, 2013, 35: 674-681.
[15] Takeda F, Wakao T. Google search intensity and its relationship with returns and trading volume of Japanese stocks[J]. Pacific-Basin Finance Journal, 2014, 27: 1-18.
[16] Ap Gwilym O, Kita A, Wang Q. Speculate against speculative demand[J]. International Review of Financial Analysis, 2014, 34: 212-221.
[17] Dzielinski M. Measuring economic uncertainty and its impact on the stock market[J]. Finance Research Letters, 2012, 9(3): 167-175.
[18] Roll R. R-squared[J]. Journal of Finance, 1988, 43(3): 541-566.
[19] Morck R, Yeung B, Yu W. The information content of stock markets: Why do emerging markets have synchronous stock price movements?[J]. Journal of Financial Economics, 2000, 58(1): 215-260.
[20] Jin L, Myers S C. R2 around the world: New theory and new tests[J]. Journal of Financial Economics, 2006, 79(2): 257-292.
[21] Ting J, Fu T C, Chung F. Mining of stock data: Intra-and inter-stock pattern associative classification[J]. Threshold, 2006, 5(100): 5-99.
[22] Ho G T S, Ip W H, Wu C H, et al. Using a fuzzy association rule mining approach to identify the financial data association[J]. Expert Systems with Applications, 2012, 39(10): 9054-9063.
[23] Paranjape-Voditel P, Deshpande U. A stock market portfolio recommender system based on association rule mining[J]. Applied Soft Computing, 2013, 13(2): 1055-1063.
[24] 张建林, 周超良. 关联规则在股票板块联动分析中的应用[J]. 计算机工程与应用, 2013, 49(2): 242-245. Zhang J L, Zhou C L. Application of association rules in stock plate cointegration analysis[J]. Computer Engineering and Applications, 2013, 49(2): 242-245.
[25] 陈艳, 褚光磊. 关联规则挖掘算法在股票预测中的应用研究-基于遗传网络规划的方法[J]. 管理现代化, 2014(3): 13-15. Chen Y, Chu G L. Association rules mining algorithm application in stock prediction-A genetic network programming approach[J]. Modernization of Management, 2014(3): 13-15.
[26] 中国证券投资者保护基金有限责任公司. 2012年中国证券投资者综合调查报告[R]. 北京:中国证券投资者保护基金有限责任公司, 2013. China Securities Investor Protection Fund Corporation. 2012 statistical report on securities investor in China[R]. Beijing: China Securities Investor Protection Fund Corporation, 2013.
[27] 王明涛, 路磊, 宋锴. 政策因素对股票市场波动的非对称性影响[J]. 管理科学学报, 2013, 15(12): 40-57. Wang M T, Lu L, Song K. Impacts of policy factors on volatility of stock markets[J]. Journal of Management Sciences in China, 2013, 15(12): 40-57.
[28] 游家兴, 汪立琴. 机构投资者, 公司特质信息与股价波动同步性-基于 R2 的研究视角[J]. 南方经济, 2012(11): 89-101. You J X, Wang L Q. Institutional investors, firm-specific information and the synchronicity of stock price variation: A R2-based perspective[J]. South China Journal of Economics, 2012(11): 89-101.
[29] 余秋玲, 朱宏泉. 宏观经济信息与股价联动-基于中国市场的实证分析[J]. 管理科学学报, 2014, 17(3): 15-26. Yu Q L, Zhu H Q. Macroeconomic information and stock price synchronicity: Empirical analysis in Chinese stock markets[J]. Journal of Management Sciences in China, 2014, 17(3): 15-26.
[30] 中国互联网络信息中心. 第33次中国互联网络发展状况统计报告[R]. 北京: 中国互联网络信息中心, 2014. China Internet Network Information Center (CNNIC). 33rd statistical report on internet development in China[R]. Beijing: China Internet Network Information Center, 2014.
[31] Ginsberg J, Mohebbi M H, Patel R S, et al. Detecting influenza epidemics using search engine query data[J]. Nature, 2009, 457(7232): 1012-1014.
[32] Choi H, Varian H. Predicting the present with Google Trends[J]. Economic Record, 2012, 88(s1): 2-9.
[33] Fondeur Y, Karame F. Can Google data help predict French youth unemployment?[J]. Economic Modelling, 2013, 30: 117-125.
[34] Goel S, Hofman J M, Lahaie S, et al. Predicting consumer behavior with web search[C]// Proceedings of the National Academy of Sciences, 2010, 107(41): 17486-17490.
[35] 王炼, 贾建民. 基于网络搜索的票房预测模型-来自中国电影市场的证据[J]. 系统工程理论与实践, 2014, 34(12): 3079-3090. Wang L, Jia J M. Forecasting box office performance based on online search: Evidence from Chinese movie industry[J]. Systems Engineering-Theory & Practice, 2014, 34(12): 3079-3090.
[36] Bangwayo-Skeete P F, Skeete R W. Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach[J]. Tourism Management, 2015, 46: 454-464.
[37] Vosen S, Schmidt T. A monthly consumption indicator for Germany based on Internet search query data[J]. Applied Economics Letters, 2012, 19(7): 683-687.
[38] Chen T, So E P K, Wu L, et al. The 2007-2008 US recession: What did the real-time Google Trends data tell the United States?[J]. Contemporary Economic Policy, 2015, 33(2): 395-403.
[39] Gao L, Mei B. Investor attention and abnormal performance of timberland investments in the United States[J]. Forest Policy and Economics, 2013, 28(10): 60-65.
[40] 宋双杰, 曹晖, 杨坤. 投资者关注与 IPO 异象-来自网络搜索量的经验证据[J]. 经济研究, 2011, 1: 145-155. Song S J, Cao H, Yang K. Investor attention and IPO anomalies-Evidence from Google trend volume[J]. Economic Research Journal, 2011, 1: 145-155.
[41] Vaughan L, Chen Y. Data mining from web search queries: A comparison of Google Trends and Baidu Index[J]. Journal of the Association for Information Science and Technology, 2015, 66(1): 13-22.
[42] Zhang W, Shen D, Zhang Y, et al. Open source information, investor attention, and asset pricing[J]. Economic Modelling, 2013, 33: 613-619.
[43] 刘锋, 叶强, 李一军. 媒体关注与投资者关注对股票收益的交互作用:基于中国金融股的实证研究[J]. 管理科学学报, 2014, 17(1): 72-85. Liu F, Ye Q, Li Y J. Impacts of interactions between news attention and investor attention on stock returns: Empirical investigation on financial shares in China[J]. Journal of Management Sciences in China, 2014, 17(1): 72-85.
[44] 张安宁, 金德环. 牛市和熊市下投资者关注对股票收益影响的非对称性分析[J]. 投资研究, 2014(10): 132-148. Zhang A N, Jin D H. The asymmetric effect of investor attention on stock returns in bull and bear markets[J]. Review of Investment Studies, 2014(10): 132-148.
[45] 陈梦根, 毛小元. 中国证券市场价格联动效应的实证研究[J]. 财贸经济, 2007(5): 93-99. Chen M G, Mao X Y. Empirical study on price co-movement effect in Chinese securities market[J]. Finance & Trade Economics, 2007(5): 93-99.
[46] Wang C Y, Wei Q, Guo X H, et al. A study on after-temporal association between online search volume and stock price with an intelligent ATARII method[C]// Proceedings of the 11th International FLINS Conference, World Scientific, 2014: 614-619.
[47] Agrawal R, Imieliński T, Swami A. Mining association rules between sets of items in large databases[C]// ACM SIGMOD Record, 1993, 22(2): 207-216.
[48] Allen J F. Maintaining knowledge about temporal intervals[J]. Communications of the ACM, 1983, 26(11): 832-843.
[49] Tan P N, Kumar V, Srivastava J. Selecting the right interestingness measure for association patterns[C]// Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002: 32-41.
[50] 中国互联网络信息中心. 2013年中国搜索引擎市场研究报告[R]. 北京: 中国互联网络信息中心, 2013. China Internet Network Information Center. 2013 Chinese search engine market research report[R]. Beijing: China Internet Network Information Center, 2013.
[51] 何诚颖. 中国股市"板块现象"分析[J]. 经济研究, 2001, 12: 82-87. He C Y. Analysis of industrial effect in Chinese stock market[J]. Economic Research Journal, 2001, 12: 82-87.
[52] 杨赟劼, 尚建辉, 林建忠. 上市公司股票成交量与收益率板块效应的面板数据模型分析[J]. 经济研究导刊, 2013(36): 129-133. Yang Y J, Shang J H, Lin J Z. Panel data model analysis of industrial effect on Chinese listed company's stock trading volume and returns[J]. Economic Research Guide, 2013(36): 129-133.

Funding

National Natural Science Foundation of China (71110107027, 71490724, 71372044); MOE Project of Key Research Institute of Humanities and Social Sciences at Universities (12JJD630001)
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