Are crowds on the internet wiser than experts? The case of a stock prediction community

Understanding heterogeneity of investor sentiment on social media: A structural topic modeling approach

Investors nowadays post heterogeneous sentiments on social media about financial assets based on their trading preferences. However, existing works typically analyze the sentiment by its content only and do not account for investor profiles and trading preferences in different types of assets. This paper explicitly considers how investor sentiment about financial market events is shaped by the relative discussions of different types of investors. We leverage a large-scale financial social media dataset and employ a structural topic modeling approach to extract topical contents of investor sentiment across multiple finance-specific factors. The identified topics reveal important events related to the financial market and show strong heterogeneity in the social media content in terms of compositions of investor profiles, asset categories, and bullish/bearish sentiment. Results show that investors with different profiles and trading preferences tend to discuss financial markets with heterogeneous beliefs, leading to divergent opinions about those events regarding the topic prevalence and proportion. Moreover, our findings may shed light on the mechanism that underlies the efficient investor sentiment extraction and aggregation while considering the heterogeneity of investor sentiment across different dimensions.

Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions

A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote.

The Predictive Value of Data from Virtual Investment Communities

Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.

Deriving wisdom from virtual investing communities: an alternative strategy to stock recommendations

Purpose

Colossal information is available in cyberspace from a variety of sources such as blogs, reviews, posts and feedback. The mentioned sources have helped in improving various business processes from product development to stock market development. This paper aims to transform this wealth of information in the online medium to economic wealth. Earlier approaches to investment decision-making are dominated by the analyst's recommendations. However, their credibility has been questioned for herding behavior, conflict of interest and favoring underwriter's firms. This study assumes that members of the online crowd who have been reliable, profitable and knowledgeable in the recent past will continue to be so soon.

Design/methodology/approach

The authors identify credible members as experts using multi-criteria decision-making tools. In this work, an alternative actionable investment strategy is proposed and demonstrated through a mock-up. The experimental prototype is divided into two phases: expert selection and investment.

Findings

The created portfolio is comparable and even profitable than several major global stock indices.

Practical implications

This work aims to benefit individual investors, investment managers and market onlookers.

Originality/value

This paper takes into account factors: the accuracy and trustworthiness of the sources of stock market recommendations. Earlier work in the area has focused solely intelligence of the analyst for the stock recommendation. To the best of the authors’ knowledge, this is the first time that the combined intelligence of the virtual investment communities has been considered to make stock market recommendations.

Harnessing local ecological knowledge for conservation decision making via Wisdom of Crowds: the case of the Manus green tree snail Papustyla pulcherrima

The shell of the Manus green tree snail Papustyla pulcherrima is renowned for its beauty and is subject to international protection under CITES, having been harvested intensively in the past. To determine its threat status, and whether further conservation action is justified, an inexpensive Wisdom of Crowds approach was used to estimate the change in relative density of the snail between 1998 and 2013. Local men and women were approached around the main market on Manus Island, Papua New Guinea, and asked to map the relative abundance of the snail on an ordinal scale, based on their personal observations in 2013 and 1998 (a year of cultural significance). The spatial abundance data from 400 surveys were analysed using an information-theoretic approach. A suite of cumulative link models incorporating geographical factors was used to determine the magnitude of the change and to investigate possible biological influences underpinning the reported pattern. High abundance of the snail was associated with intact forested areas, high elevation and low population density. A slow decline was evident, with the median percentage of map cells where the snail was categorized as plentiful decreasing by c. 20% between the 2 years. On this basis a categorization of Near Threatened was advocated for the species. Although it is arguable that Wisdom of Crowds methods cannot be substituted for in situ quantification, the approach appears to have utility as a preliminary assessment for further conservation expenditure, and as a tool for determining threat status.