Research On The Value Of Crowdsourcing Stock Analysis And The Online Behavior Of Its Suppliers And Demanders

Recently,with the popularity of Internet crowdsourcing and the depression of the stock analysis industry,online investment communities have gradually crowdsourced stock analysis,the responsibility of sell-side analysts,to mass,allowing individuals with the capability of stock analysis(i.e.,amateur analysts)to actively participate in stock research,thereby meeting investors’ demand for stock analysis.Despite the development of crowdsourced stock analysis in practice,its related issues have not received sufficient attention from researchers.As the supply and demand sides of crowdsourced stock analysis,amateur analysts and investors are important market participants.As a result,their online behaviors not only influence the development of online investment communities,but also affect the health and stability of the stock market.This thesis focuses on Seeking Alpha,a leading online investment community whose main content is crowdsourced stock analysis,and explores the value of crowdsourced stock analysis and the factors of the online behavior of its suppliers and demanders(i.e.,amateur analysts and investors),thereby enriching the understanding of the value and activities of crowdsourced stock analysis from a theoretical perspective,and providing practical implications for investors,amateur analysts,and community managers.Specifically,this thesis includes:First,this work analyzes the value of two-level crowdsourced recommendations.This research focuses on crowdsourced recommendations in Seeking Alpha and checks their value with stocks’ future performance.The results show that bearish recommendations can predict future stock returns,and that their predictiveness maintains even after controlling for sell-side opinions,suggesting that amateurs can provide valuable information not covered by sell-side analysts.Also,the disagreement of recommendations is related to trading volume.Existing studies have checked the value of earnings forecasts,price targets,and reports issued by amateur analysts,but the research on the value of crowdsourced recommendations is limited.This work contributes to the literature on the value of crowdsourced stock analysis and provides new insights for investors to obtain investment advice.Second,this work explores the effects of improvements in the recommendation system on the content generation and interactive participation of crowdsourced stock analysis’ s suppliers and demanders(i.e.,amateur analysts and investors).Leveraging a natural experiment in which Seeking Alpha introduces a five-level mandatory recommendation system,this research finds that this improvement increases the information processing costs of amateur analysts generating analysis,leading them to publish fewer analyses.However,analyses’ quality improves due to the increased cognitive input.For investors,the improvement reduces their information acquisition costs when consuming stock analysis,and therefore their interactive participation decreases.Existing research has examined the effects of monetary incentives on the content generation of content providers,ignoring information processing costs,an important factor of online behavior.This work leverages the change in the recommendation system to examine the causal relationship between information processing costs and the content generation and interactive participation of content providers and demanders,thereby enriching the factors of users’ online behaviors.Also,the findings provide important practical implications for community managers to design better recommendation systems.Third,this work examines the herding behavior of amateur analysts,the providers of crowdsourced stock analysis,when issuing recommendations.This analysis finds that they engage in the herding behavior when issuing recommendations since the five-level recommendation system facilitates amateurs to mimic peers.Also,their busyness status promotes their herding behavior.In addition,this analysis finds that the stock market reacts to amateurs’ herding behavior,providing another evidence of the existence of herding behavior.Existing studies focus on the herding behavior of professional analysts,investors,and company managers,and pay less attention to amateur analysts,the emerging information intermediaries.This work confirms amateur analysts’ herding behavior when issuing recommendations,reveals the influence of the busyness status on the herding behavior based on the decision fatigue theory,and provides a new perspective for community managers to develop mechanisms to curb amateurs’ herding behavior.Finally,this work investigates the reaction of investors,the demanders of crowdsourced stock analysis,to amateur analysts’ herd behavior in the information acquisition process.The results show that analyses with larger rating deviations receive more investor attention.This work examines the causal relationship between rating deviations and investor comments with a natural experiment and a quasi-experiment.The research also finds that more deviating analyses do contain more valuable information,but investors,on average,disagree with the deviating opinions.In addition,the moderating effects of the busyness status and stock volatility reveal that rating deviations influence investor attention by the attraction of analysts’ private information.The moderating effects of investor experience and analyses’ accuracy confirm that investors cannot identify useful information contained in deviating analyses.This research enriches the literature on the effects of analysts’ herding behavior,deepens the understanding of investors’ attention allocation and information acquisition behavior,and helps amateur analysts and community managers to understand investors’ information preferences.