Prediction, Preference, and Idea Markets: How Corporations can Use the Wisdom of their Employees

Koen, Peter A.

doi:10.1002/9781118947166.ch4

Cited by 1 publication

(2 citation statements)

References 19 publications

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“…Prices reflect all available information in an efficient market. Therefore, more information increases the accuracy (Koen, 2014), and an investor can predict the variation of an asset more accurately.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The wisdom of the crowd is one of the essential characteristics of social networks (Mukherjee et al, 2021). In 2004, James Surowiecki authored a well-known book, "The Wisdom of the Crowds," which explores the concept of large groups of people exhibiting superior intelligence compared to an individual (Koen, 2014). He concludes that "large groups of untrained people are better at decision-making than a few elites in that domain."…”

Section: Literature Reviewmentioning

confidence: 99%

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Tehran Stock Exchange, Stocks Price Prediction, Using Wisdom of Crowd

Sohrabi,

Rouhani,

Yazdani

et al. 2023

IJFIFSA

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Two predominant methods for analyzing financial markets have been technical and fundamental analysis. However, the emergence of the Internet has altered the trading landscape. The availability of Internet and social media access plays a moderating role in information asymmetry, resulting in investors making informed decisions. Social media has turned into a source of information for investors. Through diverse communication channels on social media, investors articulate their perspectives on whether to buy or sell a stock. According to Surowiecki, the collective opinions gathered through social media frequently offer better predictions than individual opinions, a phenomenon referred to as the Wisdom of the Crowd. The wisdom of the crowd stands as an essential measure within social networks, with its potential to reduce errors and lessen information-gathering costs. In this study, we tried to evaluate the wisdom of the crowd's potential to improve stock price prediction accuracy. So, we developed a prediction model by Long Short-Term Memory based on the wisdom of the crowd. Users' opinions in Persian about the Tehran Stock Exchange (TSE) stocks were collected from SAHMETO for eight months. The Support Vector Machine classified them into buy, sell, and neutral classes. During the research period, people mentioned 823 stocks, and 52 stocks with over 100 signals were chosen. The results of the study show that although the model presented has achieved an acceptable level of accuracy, correlations between the actual and predicted values exceeded 90%. The accuracy metrics of the proposed model compared to the base model were not improved.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%