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Paper Type
ERF
Description
Online investment communities have been widely adopted by investors to disclose investment-related information, such as predictions of stock returns. Although it benefits both platforms by attracting more users and other investors by providing additional finely-processed information, it may hurt publishers due to the potential loss of their unique valuable private information. Therefore, understanding why users share their own predictions in online communities becomes an important issue. Drawing on the ability-motivation-opportunity framework, we seek to identify three important factors influencing users’ willingness to share predictions. Utilizing data obtained from StockTwits, our preliminary results show that the number of followers, prediction accuracy, and historical stock performance negatively affect users’ sharing of their predictions of stock returns. Our findings can contribute to the literature on information sharing and provide managerial implications for online investment communities.
Paper Number
1188
Recommended Citation
Fang, Bin; Yao, Yao; Shangguan, Wuyue; and Li, Ziru, "Why Do I Share My Predictions of Stock Returns in Online Communities? An Empirical Study on StockTwits" (2023). AMCIS 2023 Proceedings. 2.
https://aisel.aisnet.org/amcis2023/vcc/vcc/2
Why Do I Share My Predictions of Stock Returns in Online Communities? An Empirical Study on StockTwits
Online investment communities have been widely adopted by investors to disclose investment-related information, such as predictions of stock returns. Although it benefits both platforms by attracting more users and other investors by providing additional finely-processed information, it may hurt publishers due to the potential loss of their unique valuable private information. Therefore, understanding why users share their own predictions in online communities becomes an important issue. Drawing on the ability-motivation-opportunity framework, we seek to identify three important factors influencing users’ willingness to share predictions. Utilizing data obtained from StockTwits, our preliminary results show that the number of followers, prediction accuracy, and historical stock performance negatively affect users’ sharing of their predictions of stock returns. Our findings can contribute to the literature on information sharing and provide managerial implications for online investment communities.
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