International Journal of Nonlinear Analysis and Applications

Predicting the expected returns of cryptocurrencies using CAPM and D-CAPM approaches

Articles in Press

Document Type : Research Paper

Authors

1 Department of Finance Engineering, Faculty of Management, North Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Financial Management, North Tehran Branch, Islamic Azad University, Tehran, Iran

10.22075/ijnaa.2024.32895.4890

Abstract

In the present study, the expected returns of cryptocurrencies were compared capital asset pricing model (CAPM) and downside capital asset pricing model (D-CAPM) approaches. For this purpose, fifty cryptocurrencies were studied as representative of risky assets in a five-year period from 2018 to 2022 with daily frequency. First, the panel was investigated using Levin-Lin-Chu, Im-Pesaran, and Shin and Dickey-Fuller's tests. Then, using paired t-statistics, the difference between the return estimates of the two models was investigated. Finally, using R2 and the generalized linear test model, the better model was selected to justify the changes in asset returns in these cryptocurrencies and portfolios. Based on the results, in almost 90% of the analyzed portfolios, the D-CAPM model was better than the CAPM model and had more justification power than the old CAPM model. In less than 1% of the models, the degree of justification and the appropriateness of the models were the same.

Keywords

[1] W.F. Agustini, I. Restu Affianti, and Endah RM Putri, Stock price prediction using geometric Brownian motion, J. Phys.: Conf. Ser. 974 (2018), 012047.
[2] A. Ang, J. Chen, and Y. Xing, Downside risk and the momentum effect, Working Paper 8643, 2001, http://www.nber.org/papers/w8643.
[3] C. Baek and M. Elbeck, Bitcoins as an investment or speculative vehicle? a first look, Appl. Econ. Lett. 22 (2015), no. 1, 30–34.
[4] S. Bagherzadeh, Medical factors on stock returns in Tehran Stock Exchange, J. Financ. Res. 19 (2016).
[5] Coin Market Cap, Coin market cap top 100 cryptocurrencies, Coinmarketcap, 2020.
[6] G.M. Caporale and A. Plastun, The day of the week effect in the cryptocurrency market, Finance Res. Lett. 31 (2019).
[7] K.D. Daniel, D. Hirshleifer, and A. Subrahmanyam, Overconfidence, arbitrage, and equilibrium asset pricing, J. Finance 56 (2001), no. 3, 921–965.
[8] J. Estrada, Systematic risk in emerging markets: The d-capm, Emerg. Markets Rev. 3 (2002), no. 4, 365–379.
[9] F. Eugene and K. French, The cross-section of expected stock returns, J. Finance 47 (1992), no. 2, 427–465.
[10] E.F. Fama, Foundations of Finance: Portfolio Decisions and Securities Prices, Wiley–Blackwell, 1976.
[11] K. Grobys and N. Sapkota, Cryptocurrencies and momentum, Econ. Lett. 180 (2019), 6–10.
[12] C.R. Harvey and A. Siddique, Conditional skewness in asset pricing tests, J. Finance 55 (2000), no. 3, 1263–1295.
[13] S. Hubrich, Know when to hodl’em, know when to fodl’em’: An investigation of factor-based investing in the cryptocurrency space, Know When to Fodl’Em’: An Investigation of Factor-Based Investing in the Cryptocurrency Space (October 28, 2017) (2017).
[14] N. Jegadeesh and S. Titman, Returns to buying winners and selling losers: Implications for stock market efficiency, J. Finance 48 (1993), no. 1, 65–91.
[15] B. Myers, Principles of Corporate Finance, McGraw-Hill Companies, 2002.
[16] H. Nikoo Maram and S. Shahverdiani, Strategic Financial Management (Value Creation), Kasa Kavosh Publications, 2006.
[17] F. Owhadi and Y. Taj, Using cryptocurrency and the risks of investing in it, Fifth Nat. Conf. Appl. Res. Manag. Account., 2018.
[18] L.M. Parsons, Is accounting information from nonprofit organizations useful to donors? A review of charitable giving and value-relevance, J. Account. Lit. 22 (2003), 104–129.
[19] R. Raei and A. Saeidi, Fundamentals of Financial Engineering and Risk Management, Second Edition ed., Semat Publications, 2016.
[20] K. Reddy and V. Clinton, Simulating stock prices using geometric Brownian motion: Evidence from Australian companies, Austr. Account. Bus. Finance J. 10 (2016), no. 3, 23–47.
[21] Reuters, Cryptocurrencies: Growing in number but falling in value, Crypto-Currencies-Conflicts, 2020.
[22] J. Rohrbach, S. Suremann, and J. Osterrieder, Momentum and trend following trading strategies for currencies revisited-combining academia and industry, Available at SSRN 2949379 (2017).
[23] W.F. Sharpe, Capital asset prices: A theory of market equilibrium under conditions of risk, J. Finance 19 (1964), no. 3, 425–442.
[24] D. Shen, A. Urquhart, and P. Wang, Does Twitter predict Bitcoin?, Econ. Lett. 174 (2019), 118–122.
[25] A. Shojaei and A. Heidarzadeh Hanzaei, Predicting cryptocurrency return using two approaches, geometric Brownian motion and wavelet transforms, Financ. Engin. Portfolio Manag. 12 (2021), no. 47, 92–111.
[26] A. Tavangar and M. Khosraviani, Test the model d-capm capm model to explain the relationship between risk and return, Financ. Knowledge Secur. Anal. 9 (2011), 25–42.
[27] P. Tzouvanas, R. Kizys, and B. Tsend-Ayush, Momentum trading in cryptocurrencies: Short-term returns and diversification benefits, Econ. Lett. 191 (2020), 108728.
[28] M.G. Yousefi, M.R. Tavakkoli Baghdadabad, and H. Nafar, Investigating the capital asset pricing model with d-capm to compare risk and return in the Tehran securities market, Econ. Res. J. 5 (2009), 95–123.
International Journal of Nonlinear Analysis and Applications

Articles in Press, Corrected Proof
Available Online from 02 July 2024
  • Receive Date: 05 December 2023
  • Accept Date: 21 February 2024