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While venture capital firms are increasingly relying on recommendation models in investment decisions, existing startup recommendation models fail to consider the uniqueness of venture capital context, including two-sided matching between investing and investee firms and a lack of information disclosure requirements on startups. Following the design science research paradigm and guided by the proximity principle from social psychology, we develop a novel framework called SocioLink by depicting and analyzing various relations in a knowledge graph via machine learning. Our experimental results show that SocioLink significantly outperforms state-of-the-art startup recommendation methods in both accuracy and quality. This improvement is driven by not only the inclusion of social relations but also the superiority of modelling relations via knowledge graph. We also develop a web-based prototype to demonstrate explainable artificial intelligence. This work contributes to the FinTech literature by adding an innovative design artifact—SocioLink—for decision support in the investment context.

A large literature in neuroscience and social psychology shows that humans are wired to be meticulous about how they are perceived by others. In this paper, we propose that impression management considerations can also end up guiding the content that investors transmit via word of mouth and inadvertently lead to the propagation of noise. We analyze server log data from one of the largest investment-related websites in the United States. Consistent with our proposition, we find that investors more frequently share articles that are more suitable for impression management despite such articles less accurately predicting returns. Additional analyses suggest that high levels of sharing can lead to overpricing.

汇纳科技正在为整个实体商业构建一个新的基础坐标系。 对商业地产来说，数字化转型早已是大势所趋且探索已久，而今年的疫情黑天鹅更使这一进程加快了。 一直以来，数字化转型最大的难度就是思想意识上的改变，疫情改变了这一现状。数字化好处在于，企业在面临疫情影响的时候，业务波动性会比较低。 疫情期间对居民外出的限制，为实体渠道带来严重冲击。如果说此前的数字化转型对商业地产来说是锦上添花，那如今已是迫在眉睫。商业地产开始思索这场危机给行业带来的改变。

Prior studies have shown that social media discussions can be helpful in predicting price movements in financial markets. With the increasingly large amount of social media data, how to effectively distinguish value-relevant information from noise remains an important question. We study this question by investigating the role of network cohesion in the relationship between social media sentiment and price changes in the Bitcoin market. As network cohesion is associated with information correlation within the discussion network, we hypothesize that less cohesive social media discussion networks are better at predicting the next-day returns than more cohesive networks. Both regression analyses and trading simulations based on data collected from Bitcointalk.org confirm our hypothesis. Our findings enrich the literature on the role of social media in financial markets and provide actionable insights for investors to trade based on social media signals.

Digital distribution introduces many new strategic questions for the creative industries—notably, how the use of new digital channels will impact sales in established channels. We analyze this question in the context of e-book and hardcover sales by exploiting a natural experiment that exogenously delayed the release of a publisher’s new Kindle e-books in April and May 2010. Using new books released simultaneously in e-book and print formats in March and June 2010 as the control group, we find that delaying e-book availability results in a 43.8% decrease in e-book sales but no increase in print book sales on Amazon.com or among other online or offline retailers. We also find that the decrease in e-book sales is greater for books with less prerelease buzz. Together, we find no evidence of strong cannibalization between print books and e-books in the short term and no support for the sequential distribution of books in print versions followed by e-book versions.