Factor Model in Cryptocurrency Market

§ University of California, Berkeley University of California, Berkeley

Send Message

To: Author

Factor Model in Cryptocurrency Market

Article Fingerprint

ReserarchID

MBR1YQ76

Factor Model in Cryptocurrency Market Banner

Key Research Insights

Synthesized scholarly intelligence & interactive research assistant
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu
Reading Preferences
Font Size
Line Spacing
Background

Abstract

In our paper, we investigate the explanatory power to the crypto currency return premium of market factor and size factor. We tested both the value-weighted and the equally weighted market factor and a big minus small Fama-French size factor. We found the market and size together can explain 33% of the premium. We also used UMAP to find a non-linear transformation of the crypto returns to create two factors, who can explain over 80% of the premium in both training and testing periods. However, further analysis and research needs to be carried out to decipher what these two factors represent.

References

10 Cites in Article
  1. P Benigno (2019). Monetary policy in a world of cryptocurrencies.
  2. Chan Chu,Y Zhang (2019). High frequency momentum trading with cryptocurrencies.
  3. Abeer Elbahrawy,Laura Alessandretti,Anne Kandler,Romualdo Pastor-Satorras,Andrea Baronchelli (2017). Evolutionary dynamics of the cryptocurrency market.
  4. Y Liu,A Tsyvinski,X Wu (2019). Common Risk Factors in Cryptocurrency.
  5. Klaus Grobys,Niranjan Sapkota (2019). Cryptocurrencies and momentum.
  6. Z Khamisa (2019). An analysis of the factors driving performance in the cryptocurrency market.
  7. Y Sovbetov (2018). Factors Influencing Cryptocurrency Prices: Evidence from Bitcon, Ethereum, Dash, Litcoin, and Monero.
  8. Yecheng Yao,Jungho Yi,Shengjun Zhai,Yuwen Lin,Taekseung Kim,Guihongxuan Zhang,Leonard Yoonjae Lee (2018). Predictive Analysis of Cryptocurrency Price Using Deep Learning.
  9. M Siddharth,Stefanos Bhambhwani,George Delikouras,Korniotis (2019). Do Fundamentals 10. Drive Cryptocurrency Prices?.
  10. Yukun Liu,Aleh Tsyvinski (2018). Risk and Returns of Cryptocurrency.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Saket Kumar, Mike Zeng, Ruinan Lu. 2020. "Factor Model in Cryptocurrency Market". Global Journal of Management and Business Research - C: Finance GJMBR-C Volume 20 (GJMBR Volume 20 Issue C3).

Download Citation

Journal Specifications

Crossref Journal DOI 10.17406/GJMBR

Print ISSN 0975-5853

e-ISSN 2249-4588

Keywords
Classification
GJMBR-C Classification JEL Code: G20
Version of record

v1.2

Issue date
July 10, 2020

Language
English
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

Article Matrices
Total Views: 634
Total Downloads: 31
All Trends

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Factor Model in Cryptocurrency Market

Saket Kumar
Saket Kumar University of California, Berkeley
Mike Zeng
Mike Zeng
Ruinan Lu
Ruinan Lu