Egyptian Bitcoin Investments: A Comprehensive Examination of Investor Sentiment Effects on Bitcoin Returns

Ahmed H. Elgayar^1,*, Farouk F. Elgazzar², Noura Metawa^{3, 4}

¹Business Administration Department, Faculty of Commerce, Tanta University, Tanta, Egypt

²Economics and Public Finance Department, Faculty of Commerce, Tanta University, Tanta, Egypt

³Tashkent State University of Economics, Tashkent, Uzbekistan

⁴Business Administration Department, Faculty of Commerce, Mansoura University, Mansoura, Egypt

Emails: ahmed_elgayar@commerce.tanta.edu.eg; farouk.elgazar@commerce.tanta.edu.eg; n.metawa@tsue.uz

Abstract

This research investigates how Egyptian investor sentiment affects cryptocurrency returns, focusing specifically on Bitcoin. We utilized an enhanced investor sentiment index in Egypt, constructed through factor analysis of various literature-based variables. Our study's findings revealed a notable positive correlation between the investor sentiment index, lagged by one order, and Bitcoin returns, as per the estimation and analysis using VAR models. Analysis indicates that a one standard deviation change in the investor sentiment index leads to an alteration in the influence of each standard deviation of the original positive variable, resulting in a switch from positive to negative and vice versa in the medium and long term. Regarding variance decomposition, the short-term variance error of 100% is primarily explained by Bitcoin returns themselves. However, in the medium to long term, besides Bitcoin returns, the investor sentiment index emerges as the most influential variable affecting Bitcoin returns. Causality tests reveal a unidirectional short-term impact from the investor sentiment index to Bitcoin returns via Granger causality tests. Additionally, using the Toda-Yamamoto causality test, long-term bidirectional effects between Bitcoin returns and the investor sentiment index were observed.

Keywords: Cryptocurrency Behavior; Bitcoin, Egypt; Investor Sentiment; Factor Analysis; Vector Autoregressive (VAR) Model; Impulse Response Function (IRF); Forecast Error Variance Decomposition (FEVD); Granger Causality Test; and Toda-Yamamoto causality test