Demographic Characteristics and Behavioral Biases of Cryptocurrency adoptor: Evidence from an Online Survey in China*

This study aims to explore the social characteristics and behavioral biases of Chinese cryptocurrency investors, and how these factors impact their investment returns. Through an online survey, data from 274 respondents, including both investors and non-investors, were collected. The survey covered demographic characteristics, investment behaviors, and perceptions of the cryptocurrency market. The study found that typical Chinese cryptocurrency investors are usually male, have a STEM background, aged between 28 and 42, with higher education levels and income. These investors tend to take higher risks and have a deep understanding of blockchain technology. However, their investment performance typically lags behind the overall market, except for those who entered the market in 2021. The research concludes that education level, income, and overseas study experiences are significantly correlated with positive investment returns. Knowledge of blockchain technology and a positive attitude towards the future of cryptocurrencies are key factors for investment success. These findings are crucial for understanding the dynamics of the emerging cryptocurrency market, formulating policies aimed at protecting investors and stabilizing the market.

Authors

Institutions

Publication Details

Published
2024-11-26
DOI
https://doi.org/10.1109/bcca62388.2024.10844429
Primary Topic
Blockchain Technology Applications and Security
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Demographic Characteristics and Behavioral Biases of Cryptocurrency adoptor: Evidence from an Online Survey in China*

Giuliano Noci, Marco Mandolfo, Jiacheng Fu
Blockchain Technology Applications and Security
article

Demographic Characteristics and Behavioral Biases of Cryptocurrency adoptor: Evidence from an Online Survey in China*

Giuliano Noci, Marco Mandolfo, Jiacheng Fu
article en

Abstract

This study aims to explore the social characteristics and behavioral biases of Chinese cryptocurrency investors, and how these factors impact their investment returns. Through an online survey, data from 274 respondents, including both investors and non-investors, were collected. The survey covered demographic characteristics, investment behaviors, and perceptions of the cryptocurrency market. The study found that typical Chinese cryptocurrency investors are usually male, have a STEM background, aged between 28 and 42, with higher education levels and income. These investors tend to take higher risks and have a deep understanding of blockchain technology. However, their investment performance typically lags behind the overall market, except for those who entered the market in 2021. The research concludes that education level, income, and overseas study experiences are significantly correlated with positive investment returns. Knowledge of blockchain technology and a positive attitude towards the future of cryptocurrencies are key factors for investment success. These findings are crucial for understanding the dynamics of the emerging cryptocurrency market, formulating policies aimed at protecting investors and stabilizing the market.

Politecnico di Milano (IT)
Gender equality
Openalex Percentile: Top 58%
Blockchain Technology Applications and Security
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Demographic Characteristics and Behavioral Biases of Cryptocurrency adoptor: Evidence from an Online Survey in China* — Giuliano Noci, Marco Mandolfo, et al. · (2024) | TGRS Research Map | TGRS