Publication:

Behavioral Anomalies in Cryptocurrency Markets

Date

Date

Date
2023
Working Paper

Citations

Citation copied

Yang, H. (2023). Behavioral Anomalies in Cryptocurrency Markets (No. 3174421; SSRN). https://doi.org/10.2139/ssrn.3174421

Abstract

Abstract

Abstract

If behavioral biases explain asset pricing anomalies, they should also materialize in cryptocurrency markets. I test more than 20 stock return anomalies based on daily cryptocurrency data, and document strong evidence of price momentum. Controlling for market and size, price momentum remains statistically significant, whereas price reversal and risk-based anomalies are weak. Cryptocurrency anomalies can be explained by behavioral theories that emphasize noise trader risks than fundamental risks.

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95 since deposited on 2023-09-22
89last week
Acq. date: 2025-11-12

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65 since deposited on 2023-09-22
64last week
Acq. date: 2025-11-12

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Additional indexing

Creators (Authors)

  • Yang, Hanlin

Series Name

Series Name

Series Name
SSRN

Institution

Institution

Institution

Item Type

Item Type

Item Type
Working Paper

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Scope

Scope

Scope
Discipline-based scholarship (basic research)

Language

Language

Language
English

Publication date

Publication date

Publication date
2023-04-05

Date available

Date available

Date available
2023-09-22

Number of pages

Number of pages

Number of pages
35

ISSN or e-ISSN

ISSN or e-ISSN

ISSN or e-ISSN
1556-5068

OA Status

OA Status

OA Status
Green

Free Access at

Free Access at

Free Access at
DOI

Other Identification Number

Other Identification Number

Other Identification Number
merlin-id:20868

Metrics

Downloads

95 since deposited on 2023-09-22
89last week
Acq. date: 2025-11-12

Views

65 since deposited on 2023-09-22
64last week
Acq. date: 2025-11-12

Citations

Citations

Citation copied

Yang, H. (2023). Behavioral Anomalies in Cryptocurrency Markets (No. 3174421; SSRN). https://doi.org/10.2139/ssrn.3174421

Green Open Access
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Files

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