Testing herd behavior in the cryptocurrency market in the framework of behavioral finance

This thesis aims to find consistency between cyrptocurrency market and literature related to prejudgment and to analyze cyrptocurrency market from the perspective of behavioral finance. Therefore, in this study, behavioral convergence is examined under the assumption that there is a "coordination mechanism" in the prices of the cryptocurrency market. For this task, an experimental herd model is used and extending the model is proposed under both asymmetric and symmetric conditions. In the study, the daily and weekly closing values of the 30 cryptocurrencies with the highest market value in US Dollars between the years 2021-2023 were used. Methods based on "cross-sectional distributions of returns" developed by Christie and Huang (1995) and Chang, Cheng and Khorana (2000) were used to determine herd behavior. There were no findings related to the existence of herd behavior as a result of the analysis from Christie and Huang (1995) method using the daily and weekly dataset. Similarly, there was no evidence of the existence of herd behavior in periods of rise or fall as a result of the analysis from Chang, Cheng, and Khorana (2000) method. The main reasons of the failure to observe the herd behavior effect in the cryptocurrency market can be counted as the ongoing discussions on what exactly crypto currency is and it's untrusted condition causing it not to consider as an investment tool like stocks. As a result, although the crypto money market is interesting for savers, it is not yet fully reliable as a market where they can evaluate their savings as in the stock market. Keywords: Behavioral Finance, Crypto Money, Herd Behavior.