感知、预测和真实的投资专业知识:并非所有专家都提供专家建议

Amit Shavit, Sameena Shah
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引用次数: 2

摘要

社交网络使知识共享成为可能,这不可避免地回避了专业知识分析的问题。很多网上的个人资料都声称自己有专业知识,但拥有真正专业知识的人却很少。我们将专业知识描述为预测专业知识(一个人的主张),感知专业知识(人群如何看待个人)和真实专业知识(事实)。StockTwits是一个专注于投资者的微博平台,它让我们可以同时研究这三个方面。我们分析了1700天内超过1800万条推文。大的时间尺度允许我们分析专业知识及其类别,因为它们随着时间的推移而演变,这是StockTwits上的第一个此类研究。我们提出了一种方法,通过用户的追随者网络增长的显著程度和用户在对话中被提起的频率来捕捉感知专业知识。我们还根据用户的贸易和投资建议,量化实际的、基于市场的、真正的专业知识。最后,我们提供了一个分析,揭示了用户如何展示自己,人群如何感知他们,以及他们在市场上的实际表现之间的差异。我们的研究结果显示,把自己标榜为专家的用户是那些说话最多、推荐与推文比率最低的用户(也就是说,他们的大多数对话都是平淡无奇的)。来自用户的建议,谁投射新手专业知识略优于(≈5%)整体股票市场。另一方面,自称专家的交易建议的收益比中级交易员少80%。有趣的是,通过中心性测量,被其他人视为专家的用户在四年的交易期后产生了净负回报。我们的研究还着眼于专业知识的演变,并开始理解为什么以及是什么让用户改变了他们投射自己专业知识的方式。然而,对于这个课题,本文提出的问题多于回答的问题,这将为今后的研究奠定基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Perceived, Projected, and True Investment Expertise: Not All Experts Provide Expert Recommendations
Social networks enable knowledge sharing that inevitably begs the question of expertise analysis. Many online profiles claim expertise, but possessing true expertise is rare. We characterize expertise as projected expertise (claims of a person), perceived expertise (how the crowd perceives the individual) and true expertise (factual). StockTwits, an investor-focused microblogging platform, allows us to study all three aspects simultaneously. We analyze more than 18 million tweets spanning 1700 days. The large time scale allows us to also analyze expertise and its categories as they evolve over time, which is the first study of its kind on StockTwits. We propose a method to capture perceived expertise by how significantly a user's follower network grows and how often the user is brought up in conversations. We also quantify actual, market-based, true expertise based on the user's trade and investment recommendations. Finally we provide an analysis bringing out the differences between how users project themselves, how the crowd perceives them, and how they are actually performing on the market. Our results show that users who project themselves as experts are ones that talk the most and provide the least recommendation-to-tweet ratio (that is, most of their conversations are mundane). The recommendations from users who project novice expertise slightly outperform (≈5%) the overall stock market. On the other hand, the trade recommendations from self-proclaimed experts yield 80% less than those of intermediate traders. Interestingly, users who are perceived as experts by others, as measured by centrality measurements, resulted in net negative returns after a four year trading period. Our study also looks at the evolution of expertise, and begins to understand why and what makes users change the way they project their own expertise. For this topic, however, this paper introduces more questions than it answers, which will serve as the basis for future studies.
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