Online reviews can predict long-term returns of individual stocks

Online reviews are feedback voluntarily posted by consumers about their consumption experiences. This feedback indicates customer attitudes such as affection, awareness and faith towards a brand or a firm and demonstrates inherent connections with a company's future sales, cash flow and stock pricing. However, the predicting power of online reviews for long-term returns on stocks, especially at the individual level, has received little research attention, making a comprehensive exploration necessary to resolve existing debates. In this paper, which is based exclusively on online reviews, a methodology framework for predicting long-term returns of individual stocks with competent performance is established. Specifically, 6,246 features of 13 categories inferred from more than 18 million product reviews are selected to build the prediction models. With the best classifier selected from cross-validation tests, a satisfactory increase in accuracy, 13.94%, was achieved compared to the cutting-edge solution with 10 technical indicators being features, representing an 18.28% improvement relative to the random value. The robustness of our model is further evaluated and testified in realistic scenarios. It is thus confirmed for the first time that long-term returns of individual stocks can be predicted by online reviews. This study provides new opportunities for investors with respect to long-term investments in individual stocks.

[1]  Bin Gu,et al.  Content Contribution for Revenue Sharing and Reputation in Social Media: A Dynamic Structural Model , 2012, J. Manag. Inf. Syst..

[2]  Yong Liu.  Word-of-Mouth for Movies: Its Dynamics and Impact on Box Office Revenue , 2006 .

[3]  R. Litzenberger,et al.  QUARTERLY EARNINGS REPORTS AND INTERMEDIATE STOCK PRICE TRENDS , 1970 .

[4]  Shan Lu,et al.  Aggregating multiple types of complex data in stock market prediction: A model-independent framework , 2018, Knowl. Based Syst..

[5]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[6]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[7]  Philip S. Yu,et al.  Improving stock market prediction via heterogeneous information fusion , 2017, Knowl. Based Syst..

[8]  Zhi Xiao,et al.  A multiple support vector machine approach to stock index forecasting with mixed frequency sampling , 2017, Knowl. Based Syst..

[9]  Lukas Menkhoff.  The use of technical analysis by fund managers: International evidence , 2010 .

[10]  Wen Long,et al.  Deep learning-based feature engineering for stock price movement prediction , 2019, Knowl. Based Syst..

[11]  L. M. Capella,et al.  The Effect of Brand Attitude and Brand Image on Brand Equity , 2001 .

[12]  William Yang Wang,et al.  A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls , 2014, ACL.

[13]  Hamido Fujita,et al.  A novel forecasting method based on multi-order fuzzy time series and technical analysis , 2016, Inf. Sci..

[14]  Jia Wang,et al.  Predicting Stock Price Returns Using Microblog Sentiment for Chinese Stock Market , 2017, 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM).

[15]  Edward C. Malthouse,et al.  How Mobile Shopping Affects Customer Purchase Behavior: A Retailer’s Perspective , 2016 .

[16]  G. Tellis,et al.  The Value of Quality: Stock Market Returns to Published Quality Reviews , 2007 .

[17]  X. Zhang,et al.  Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics , 2010 .

[18]  S. Sénécal,et al.  The influence of online product recommendations on consumers' online choices , 2004 .

[19]  Arjan Durresi,et al.  Using Twitter trust network for stock market analysis , 2018, Knowl. Based Syst..

[20]  Nureize Arbaiy,et al.  A new procedure in stock market forecasting based on fuzzy random auto-regression time series model , 2018, Inf. Sci..

[21]  Zhi Da,et al.  In Search of Attention , 2009 .

[22]  Qifa Xu,et al.  Does Google search index really help predicting stock market volatility? Evidence from a modified mixed data sampling model on volatility , 2019, Knowl. Based Syst..

[23]  Lorin M. Hitt,et al.  Self Selection and Information Role of Online Product Reviews , 2007, Inf. Syst. Res..

[24]  H. Stanley,et al.  Quantifying Trading Behavior in Financial Markets Using Google Trends , 2013, Scientific Reports.

[25]  Luís M. B. Cabral,et al.  Stretching Firm and Brand Reputation , 2000 .

[26]  Charu C. Aggarwal,et al.  Stock Price Prediction via Discovering Multi-Frequency Trading Patterns , 2017, KDD.

[27]  Stephen J. Hoch,et al.  Product Experience Is Seductive , 2002 .

[28]  Narasimhan Jegadeesh,et al.  Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency , 1993 .

[29]  Jianping Zeng,et al.  Posterior probability model for stock return prediction based on analyst's recommendation behavior , 2013, Knowl. Based Syst..

[30]  Neil A. Morgan,et al.  The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Business Performance , 2006 .

[31]  L. Harris.  A transaction data study of weekly and intradaily patterns in stock returns , 1986 .

[32]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[33]  T. Keiningham,et al.  The Long-Term Stock Market Valuation of Customer Satisfaction , 2008 .

[34]  Silke Bambauer-Sachse,et al.  Brand equity dilution through negative online word-of-mouth communication , 2011 .

[35]  Jiekun Huang.  The Customer Knows Best: The Investment Value of Consumer Opinions , 2018 .

[36]  Stephen J. Hoch,et al.  Managing What Consumers Learn from Experience , 1989 .

[37]  Brian T. Parker,et al.  A comparison of brand personality and brand user‐imagery congruence , 2009 .

[38]  Michael G. Madden,et al.  A neural network approach to predicting stock exchange movements using external factors , 2005, Knowl. Based Syst..

[39]  Huan Liu,et al.  Feature selection for classification: A review , 2014 .

[40]  Ligang Zhou,et al.  The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches , 2015, Knowl. Based Syst..

[41]  Jennifer Rowley,et al.  Mobile shopping behaviour: Insights into attitudes, shopping process involvement and location , 2013 .

[42]  S. Ross,et al.  Economic Forces and the Stock Market , 1986 .

[43]  Jie Jennifer Zhang,et al.  Social Media and Firm Equity Value , 2013, Inf. Syst. Res..

[44]  David Godes,et al.  Firm-Created Word-of-Mouth Communication: Evidence from a Field Test , 2009, Mark. Sci..

[45]  Ligang Zhou,et al.  Predicting the listing statuses of Chinese-listed companies using decision trees combined with an improved filter feature selection method , 2017, Knowl. Based Syst..

[46]  Dongsong Zhang,et al.  What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews , 2016, J. Manag. Inf. Syst..

[47]  Yong Liu,et al.  When do Third-Party Product Reviews Affect Firm Value and what can Firms Do? The Case of Media Critics and Professional Movie Reviews , 2012 .

[48]  M. Sirgy,et al.  Self-Concept in Consumer Behavior: A Critical Review , 1982 .

[49]  Dirk Neumann,et al.  Automated news reading: Stock price prediction based on financial news using context-capturing features , 2013, Decis. Support Syst..

[50]  R. Thaler,et al.  Does the Stock Market Overreact , 1985 .

[51]  Qiong Shen,et al.  Financial Time Series Forecasting Using Support Vector Machine , 2014, 2014 Tenth International Conference on Computational Intelligence and Security.

[52]  Ling Liu,et al.  The effect of news and public mood on stock movements , 2014, Inf. Sci..

[53]  Amiya Kumar Rath,et al.  A Naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices , 2015, Appl. Soft Comput..

[54]  T. Meenaghan.  The role of advertising in brand image development , 1995 .

[55]  Avanidhar Subrahmanyam,et al.  The Going‐Public Decision and the Development of Financial Markets , 1999 .

[56]  Ke Xu,et al.  Can Online Emotions Predict the Stock Market in China? , 2016, WISE.

[57]  Forrest V. Morgeson,et al.  Stock Returns on Customer Satisfaction Do Beat the Market: Gauging the Effect of a Marketing Intangible , 2016 .

[58]  Mehmet Özçalici,et al.  Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction , 2016, Expert Syst. Appl..

[59]  Paul A. Pavlou,et al.  Overcoming Self-Selection Biases in Online Product Reviews , 2008 .

[60]  M. Holbrook.  The Millennial Consumer in the Texts of Our Times: Experience and Entertainment , 2000 .

[61]  Huan Liu,et al.  Feature Selection for Classification: A Review , 2014, Data Classification: Algorithms and Applications.

[62]  Joseph T. Plummer.  How Personality Makes a Difference , 2000, Journal of Advertising Research.

[63]  E. Clemons,et al.  When Online Reviews Meet Hyperdifferentiation: A Study of the Craft Beer Industry , 2006 .

[64]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[65]  R. Belk.  Possessions and the Extended Self , 1988 .

[66]  Mu-Yen Chen,et al.  A hybrid fuzzy time series model based on granular computing for stock price forecasting , 2015, Inf. Sci..

[67]  Xueming Luo,et al.  Quantifying the Long-Term Impact of Negative Word of Mouth on Cash Flows and Stock Prices , 2009, Mark. Sci..

[68]  Ying Wah Teh,et al.  Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment , 2015, Expert Syst. Appl..

[69]  Jun Yang,et al.  Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression , 2016 .

[70]  Yue Zhang,et al.  Using Structured Events to Predict Stock Price Movement: An Empirical Investigation , 2014, EMNLP.

[71]  Ke Xu,et al.  Tales of emotion and stock in China: volatility, causality and prediction , 2017, World Wide Web.

[72]  Kyoung-jae Kim,et al.  Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[73]  Milad Jasemi,et al.  A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick , 2011, Expert Syst. Appl..

[74]  Tomer Geva,et al.  Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news , 2014, Decis. Support Syst..

[75]  J. Malbon.  Taking Fake Online Consumer Reviews Seriously , 2013 .

[76]  Luca Cagliero,et al.  Discovering profitable stocks for intraday trading , 2017, Inf. Sci..

[77]  Linda L. Price,et al.  River Magic: Extraordinary Experience and the Extended Service Encounter , 1993 .

[78]  Trevor Hastie,et al.  Additive Logistic Regression : a Statistical , 1998 .

[79]  A. O'Cass,et al.  Examining service experiences and post-consumption evaluations , 2004 .

[80]  Yue Zhang,et al.  Deep Learning for Event-Driven Stock Prediction , 2015, IJCAI.

[81]  Jie Jennifer Zhang,et al.  How Do Consumer Buzz and Traffic in Social Media Marketing Predict the Value of the Firm? , 2013, J. Manag. Inf. Syst..

[82]  Yoav Freund,et al.  Automated trading with boosting and expert weighting , 2010 .

[83]  Sahil Shah,et al.  Predicting stock market index using fusion of machine learning techniques , 2015, Expert Syst. Appl..

[84]  E. Anderson,et al.  Dual Emphasis and the Long-Term Financial Impact of Customer Satisfaction , 2005 .

[85]  Kyoung-jae Kim,et al.  Simultaneous optimization of artificial neural networks for financial forecasting , 2012, Applied Intelligence.

[86]  Chih-Ming Hsu,et al.  A hybrid procedure for stock price prediction by integrating self-organizing map and genetic programming , 2011, Expert Syst. Appl..

[87]  Gerard J. Tellis,et al.  Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance , 2011, Mark. Sci..

[88]  M. Urde.  Core value-based corporate brand building , 2003 .

[89]  M. J. Houston,et al.  Goal-Oriented Experiences and the Development of Knowledge , 1993 .

[90]  Prem C. Jain,et al.  The Dependence between Hourly Prices and Trading Volume , 1988, Journal of Financial and Quantitative Analysis.