211
Views
0
CrossRef citations to date
0
Altmetric
Research Papers

How does price (in)efficiency influence cryptocurrency portfolios performance? The role of multifractality

ORCID Icon & ORCID Icon
Pages 1637-1658 | Received 01 Mar 2023, Accepted 28 Sep 2023, Published online: 24 Oct 2023

References

  • Al-Yahyaee, K.H., Mensi, W., Ko, H.U., Yoon, S.M. and Kang, S.H., Why cryptocurrency markets are inefficient: The impact of liquidity and volatility. North Am. J. Econ. Finance, 2020, 52, 1–14.
  • Al-Yahyaee, K.H., Mensi, W. and Yoon, S.M., Efficiency, multifractality, and the long-memory property of the bitcoin market: A comparative analysis with stock, currency, and gold markets. Fin. Res. Lett., 2018, 27, 228–234.
  • Ali, S., Shahzad, S.J.H., Raza, N. and Al-Yahyaee, K.H., Stock market efficiency: A comparative analysis of islamic and conventional stock markets. Phys. A., 2018, 503, 139–153.
  • Arshad, S., Rizvi, S.A.R. and Ghani, G.M., Investigating stock market efficiency: A look at OIC member countries. Res. Int. Bus. Finance, 2016, 36, 402–413.
  • Bai, M.Y. and Zhu, H.B., Power law and multiscaling properties of the Chinese stock market. Phys. A., 2010, 389(9), 1883–1890.
  • Bodnar, T., Mazur, S. and Okhrin, Y., Bayesian estimation of the global minimum variance portfolio. Eur. J. Oper. Res., 2017, 256(1), 292–307.
  • Bodnar, T., Parolya, N. and Schmid, W., Estimation of the global minimum variance portfolio in high dimensions. Eur. J. Oper. Res., 2018, 266(1), 371–390.
  • Brouty, X. and Garcin, M., A statistical test of market efficiency based on information theory. Quant. Finance, 2023, 23(6), 1003–1018.
  • Caldeira, J.F., Moura, G.V., Perlin, M.S. and Santos, A.A.P., Portfolio management using realized covariances: Evidence from Brazil. EconomiA, 2017, 18(3), 328–343.
  • Cheng, Q., Liu, X. and Zhu, X., Cryptocurrency momentum effect: DFA and MF-DFA analysis. Phys. A., 2019, 526, 1–14.
  • Choi, S.Y., Analysis of stock market efficiency during crisis periods in the US stock market: Differences between the global financial crisis and COVID-19 pandemic. Phys. A: Stat. Mech. Appl., 2021, 574, 125988.
  • DeLong, J.B. and Magin, K., The U.S. equity return premium: Past, present, and future. J. Econ. Perspect., 2009, 23(1), 193–208.
  • Dewandaru, G., Masih, R., Bacha, O.I. and Masih, A.M.M., Developing trading strategies based on fractal finance: An application of MF-DFA in the context of Islamic equities. Phys. A: Stat. Mech. Appl., 2015, 438, 223–235.
  • Dimitrakopoulos, D.N., Kavussanos, M.G. and Spyrou, S.I., Value at risk models for volatile emerging markets equity portfolios. Q. Rev. Econ. Finance., 2010, 50(4), 515–526.
  • Diniz-Maganini, N., Diniz, E.H. and Rasheed, A.A., Bitcoin's price efficiency and safe haven properties during the COVID-19 pandemic: A comparison. Res. Int. Bus. Finance, 2021, 58, 101472.
  • Essid, H., Ganouati, J. and Vigeant, S., A mean-maverick game cross-efficiency approach to portfolio selection: An application to Paris stock exchange. Expert. Syst. Appl., 2018, 113, 161–185.
  • Fama, E., The behavior of stock-market prices. J. Bus., 1965, 38(1), 34–105.
  • Fernández, A. and Gómez, S., Portfolio selection using neural networks. Comput. Oper. Res., 2007, 34, 117–119.
  • Frahm, G. and Memmel, C., Dominating estimators for minimum-variance portfolios. J. Econom., 2010, 159(2), 289–302.
  • Hong, H., Torous, W. and Valkanov, R., Do industries lead stock markets?. J. Financ. Econ., 2007, 83(2), 367–396.
  • Hurst, H.E., Long-term storage capacity of reservoirs. Trans. Am. Soc. Civil Eng., 1951, 116(1), 770–799.
  • Jagannathan, R. and Ma, T., Risk reduction in large portfolios: Why imposing the wrong constraints helps. J. Finance, 2003, 58(4), 1651–1683.
  • Jiang, C., Du, J. and An, Y., Combining the minimum-variance and equally-weighted portfolios: Can portfolio performance be improved?. Econ. Model., 2019, 80, 260–274.
  • Kakinaka, S. and Umeno, K., Cryptocurrency market efficiency in short- and long-term horizons during COVID-19: An asymmetric multifractal analysis approach. Finance Res. Lett., 2022, 46, 102319.
  • Kantelhardt, J.W., Zschiegner, S., Koscielny-Bunde, E., Bunde, A., Havlin, S. and Stanley, E., Multifractal detrended fluctuation analysis of nonstationary time series. Phys. A: Stat. Mech. Appl., 2002, 316(1-4), 87–114.
  • Karmakar, M., Dependence structure and portfolio risk in Indian foreign exchange market: A GARCH-EVT-Copula approach. Q. Rev. Econ. Finance., 2017, 64, 275–291.
  • Lian, Y.M. and Chen, J.H., Portfolio selection in a multi-asset, incomplete-market economy. Q. Rev. Econ. Finance., 2019, 71, 228–238.
  • Lim, S., Oh, K.W. and Zhu, J., Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market. Eur. J. Oper. Res., 2014, 236(1), 361–368.
  • Maciel, L., A new approach to portfolio management in the Brazilian equity market: Does assets efficiency level improve performance?. Q. Rev. Econ. Finance., 2021, 81, 38–56.
  • Markiel, B. and Fama, E., Efficient capital markets: A review of theory and empirical work. J. Finance, 1970, 25(2), 383–417.
  • Markowitz, H.M., Mean–variance analysis in portfolio choice and capital markets. J. Finance, 1952, 7, 77–91.
  • Mashayekhi, Z. and Omrani, H., An integrated multi-objective Markowitz–DEA cross-efficiency model with fuzzy returns for portfolio selection problem. Appl. Soft. Comput., 2016, 38, 1–9.
  • Mensi, W., Hamdi, A., Shahzad, S.J.H., Shafiullah, M. and Al-Yahyaee, K.H., Modeling cross-correlations and efficiency of Islamic and conventional banks from Saudi Arabia: Evidence from MF-DFA and MF-DXA approaches. Phys. A., 2018, 502, 576–589.
  • Mensi, W., Lee, Y.J., Vinh Vo, X. and Yoon, S.M., Does oil price variability affect the long memory and weak form efficiency of stock markets in top oil producers and oil consumers? evidence from an asymmetric MF-DFA approach. North Am. J. Econ. Finance, 2021, 57, 101446.
  • Mensi, W., Sensoy, A., Vo, X.V. and Kang, S.H., Impact of COVID-19 outbreak on asymmetric multifractality of gold and oil prices. Resour. Policy, 2020, 69, 101829.
  • Merton, R.C., On estimation the expected return on the market: An exploratory investigation. J. Financ. Econ., 1980, 8(4), 323–361.
  • Mnif, E., Jarboui, A. and Mouakhar, K., How the cryptocurrency market has performed during COVID 19? A multifractal analysis. Fin. Res. Lett., 2020, 36, 101647.
  • Mnif, E., Salhi, B., Trabelsi, L. and Jarboui, A., Efficiency and herding analysis in gold-backed cryptocurrencies. Heliyon, 2022, 8(12), e11982.
  • Montasser, G.E., Charfeddine, L. and Benhamed, A., COVID-19, cryptocurrencies bubbles and digital market efficiency: Sensitivity and similarity analysis. Fin. Res. Lett., 2022, 46, 102362.
  • Naeem, M.A., Bouri, E., Peng, Z., Shahzad, S.J.H. and Vo, X.V., Asymmetric efficiency of cryptocurrencies during COVID19. Phys. A: Stat. Mech. Appl., 2021, 565, 125562.
  • Naeem, M.A., Farid, S., Ferrer, R. and Shahzad, S.J.H., Comparative efficiency of green and conventional bonds pre- and during COVID-19: An asymmetric multifractal detrended fluctuation analysis. Energy. Policy., 2021, 153, 112285.
  • Ozkan, O., Impact of COVID-19 on stock market efficiency: Evidence from developed countries. Res. Int. Bus. Fin., 2021, 58, 101445.
  • Paiva, F.D., Cardoso, R.T.N., Hanaoka, G.P. and Duarte, W.M., Decision-making for financial trading: A fusion approach of machine learning and portfolio selection. Expert. Syst. Appl., 2019, 115, 635–655.
  • Petukhina, A., Trimborn, S., Härdle, W.K. and Elendner, H., Investing with cryptocurrencies–Evaluating their potential for portfolio allocation strategies. Quant. Finance, 2021, 21(11), 1825–1853.
  • Pinto, D.D.D., Monteiro, J.G.M.S. and Nakao, E.H., An approach to portfolio selection using an ARX predictor for securities' risk and return. Expert. Syst. Appl., 2011, 38(12), 15009–15013.
  • Rizvi, S.A.R. and Arshad, S., How does crisis affect efficiency? An empirical study of east Asian markets. Borsa Istanb. Rev., 2016, 16(1), 1–8.
  • Rizvi, S.A.R. and Arshad, S., Analysis of the efficiency-integration nexus of Japanese stock market. Phys. A., 2017, 470, 296–308.
  • Rizvi, S.A.R. and Arshad, S., Analysis of the efficiency-integration nexus of Japanese stock market. Phys. A., 2017, 470, 296–308.
  • Sant'Anna, L.R., Filomena, T.P. and Caldeira, J.F., Index tracking and enhanced indexing using cointegration and correlation with endogenous portfolio selection. Q. Rev. Econ. Finance., 2017, 65, 146–157.
  • Shahzad, S.J.H., Nor, S.F., Mensi, W. and Kumar, R.R., Examining the efficiency and interdependence of US credit and stock markets through MF-DFA and MF-DXA approaches. Physi. A., 2017, 471, 351–363.
  • Silva, Y.L.T.V., Herthel, A.B. and Subramanian, A., A multi-objective evolutionary algorithm for a class of mean-variance portfolio selection problems. Expert. Syst. Appl., 2019, 133, 225–241.
  • Sukpitak, J. and Hengpunya, V., Efficiency of Thai stock markets: Detrended fluctuation analysis. Phys. A., 2016, 458, 204–209.
  • Titan, A.G., The efficient market hypothesis: Review of specialized literature and empirical research. Procedia Econ. Finance, 2015, 32, 442–449.
  • Tiwari, A.K., Albulescu, C.T. and Yoon, S.M., A multifractal detrended fluctuation analysis of financial market efficiency: Comparison using Dow Jones sector ETF indices. Phys. A., 2019, 483, 182–192.
  • Tiwari, A.K., Aye, G.C. and Gupta, R., Stock market efficiency analysis using long spans of data: A multifractal detrendend fluctuation approach. Fin. Res. Lett., 2019, 28, 398–411.
  • Tran, V.L. and Leirvik, T., A simple but powerful measure of market efficiency. Fin. Res. Lett., 2019, 29, 141–151.
  • Vidal-Tomás, D., An investigation of cryptocurrency data: The market that never sleeps. Quant. Finance, 2021, 21(12), 2007–2024.
  • Xing, X., Hu, J. and Yang, Y., Robust minimum variance portfolio with L-infinity constraints. J. Bank. Financ., 2014, 46, 107–117.
  • Yoshida, Y., An estimation model of value-at-risk portfolio under uncertainty. Fuzzy Sets and Syst., 2009, 160(22), 3250–3262.
  • Yu, L., Wang, S. and Lai, K.K., Neural network-based mean-variance-skewness model for portfolio selection. Comput. Oper. Res., 2008, 35(1), 34–46.
  • Zhu, H. and Zhang, W., Multifractal property of Chinese stock market in the CSI 800 index based on MF-DFA approach. Phys. A: Stat. Mech. Appl., 2018, 490, 497–503.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.