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Value and Misinformation in Collaborative Investing Platforms

Authors:

Divya Sambasivan,

Ben Y. ZhaoAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 11, Issue 2

Article No.: 8, Pages 1 - 32

https://doi.org/10.1145/3027487

Published: 04 May 2017 Publication History

Abstract

It is often difficult to separate the highly capable “experts” from the average worker in crowdsourced systems. This is especially true for challenge application domains that require extensive domain knowledge. The problem of stock analysis is one such domain, where even the highly paid, well-educated domain experts are prone to make mistakes. As an extremely challenging problem space, the “wisdom of the crowds” property that many crowdsourced applications rely on may not hold.

In this article, we study the problem of evaluating and identifying experts in the context of SeekingAlpha and StockTwits, two crowdsourced investment services that have recently begun to encroach on a space dominated for decades by large investment banks. We seek to understand the quality and impact of content on collaborative investment platforms, by empirically analyzing complete datasets of SeekingAlpha articles (9 years) and StockTwits messages (4 years). We develop sentiment analysis tools and correlate contributed content to the historical performance of relevant stocks. While SeekingAlpha articles and StockTwits messages provide minimal correlation to stock performance in aggregate, a subset of experts contribute more valuable (predictive) content. We show that these authors can be easily identified by user interactions, and investments based on their analysis significantly outperform broader markets. This effectively shows that even in challenging application domains, there is a secondary or indirect wisdom of the crowds.

Finally, we conduct a user survey that sheds light on users’ views of SeekingAlpha content and stock manipulation. We also devote efforts to identify potential manipulation of stocks by detecting authors controlling multiple identities.

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Cited By

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Value and Misinformation in Collaborative Investing Platforms

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Reviews

Reviewer: Salvatore F. Pileggi

Collaborative investing platforms often rely on the common "wisdom of the crowd" concept in a domain in which even highly paid, well-educated, and experienced professionals make mistakes, providing wrong or inaccurate evaluations. Under the realistic assumption of the coexistence between capable experts and average workers, the authors analyze data from two well-known platforms, focusing on the thin line that separates the direct and the indirect wisdom of crowds. Indeed, the empirical experiments, consisting in the correlation between sentiment analysis and historical performance of relevant stocks, clearly show that experts can be identified. Those people provide the real key value. The remaining part of the network plays a key role by helping to indirectly identify the valuable content through interactions. I appreciated this contribution. The authors provide clear results and seem aware of both the advantages as well as the limitations of the adopted approach. Moreover, the future work outlined in the paper looks very promising, especially concerning the possible implementation of systems underpinned by meta-reputation. Online Computing Reviews Service

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 11, Issue 2

May 2017

199 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3079924

Editors:
Brian D. Davison
Lehigh University, USA
,
Marianne Winslett
University of Illinois at Urbana-Champaign

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Copyright © 2017 ACM.

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Publication History

Published: 04 May 2017

Accepted: 01 December 2016

Revised: 01 September 2016

Received: 01 March 2016

Published in TWEB Volume 11, Issue 2

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