Wisdom of Crowds Algorithm for Stock Market Predictions

Velic, Marko; Grzinic, Toni; Padavic, Ivan

doi:10.2498/iti.2013.0542

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…The wisdom of the crowds will reduce error and cost and increase the amount of data (Raie et al, 2016). (Woolley et al, 2010) (Nofer & Hinz, 2014) (Velic et al, 2013) (Pan et al, 2012) (Zhang et al, 2011) (Hill & Ready-Campbell, 2011) (Saumya et al, 2016) (Eickhoff & Muntermann, 2016) (Al-Hasan, 2018) (Xu et al, 2017) (Sun et al, 2017) (Arnes & Copenhagen, 2014) (Reed, 2016) (Bari et al, 2019) (Wu et al, 2019) (T. Li et al, 2018) (Garcia-Lopez et al, 2018) (Hatefi et al, 2020) (Wu et al, 2020) (Breitmayer et al, 2019) (Chao et al, 2019) (Geng et al, 2022) studied the role of the wisdom of the crowd on the stock market and forecasting performance. Results have shown that the wisdom of the crowd has the potential to increase the accuracy of prediction models in the stock market and improve the performance of models.…”

Section: Introductionmentioning

confidence: 99%

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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