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Forecasting Returns of Major Cryptocurrencies: Evidence from Regime-Switching Factor Models. (2022). GUPTA, RANGAN ; Bouri, Elie ; Christou, Christina.
In: Working Papers.
RePEc:pre:wpaper:202213.

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Cites: 21

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  1. Cryptocurrency Management from the Beginning to the Present. (2025). Razvan, Dobrea Catalin ; Cristina, Dima ; Laurentiu, Ion Eduard ; Ioana, Moncea Madalina.
    In: Proceedings of the International Conference on Business Excellence.
    RePEc:vrs:poicbe:v:19:y:2025:i:1:p:331-340:n:1004.

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  2. A hybrid deep learning model for cryptocurrency returns forecasting: Comparison of the performance of financial markets and impact of external variables. (2025). Jirou, Ismail ; Jebabli, Ikram ; Lahiani, Amine.
    In: Research in International Business and Finance.
    RePEc:eee:riibaf:v:73:y:2025:i:pa:s0275531924003684.

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  3. Analyzing clustered factors in the cryptocurrency market with Random Matrix Theory. (2025). Mattera, Raffaele ; Gonzlez, Laura Molero ; Cerqueti, Roy ; Snchez, Miguel Ngel ; Trinidad, Juan Evangelista.
    In: Physica A: Statistical Mechanics and its Applications.
    RePEc:eee:phsmap:v:665:y:2025:i:c:s0378437125001256.

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  4. Markov regime-switching in pricing equity-linked securities: An empirical study for losses in HSCEI-linked products. (2025). Kim, Hongjoong ; Park, Sungwon ; Moon, Kyoung-Sook.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:76:y:2025:i:c:s154461232500193x.

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  5. Forecasting of cryptocurrencies: Mapping trends, influential sources, and research themes. (2024). Peiulis, Tomas ; Ahmad, Nisar ; Bibi, Aqsa ; Menegaki, Angeliki N.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:43:y:2024:i:6:p:1880-1901.

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  6. Assessing Cryptomarket Risks: Macroeconomic Forces, Market Shocks and Behavioural Dynamics. (2024). Thlissaint, Josu.
    In: Economics Working Paper Archive (University of Rennes & University of Caen).
    RePEc:tut:cremwp:2024-14.

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  7. Regime-Specific Dynamics and Informational Efficiency in Cryptomarkets: Evidence from Gaussian Mixture Models. (2024). Rondeau, Fabien ; Tuffry, Stphane ; Thlissaint, Josu ; Martin, Franck ; Jamhamed, Fayssal.
    In: Economics Working Paper Archive (University of Rennes & University of Caen).
    RePEc:tut:cremwp:2024-13.

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  8. Regime switching and causal network analysis of cryptocurrency volatility: evidence from pre-COVID and post-COVID analysis. (2024). Kayal, Parthajit ; Dutta, Sumanjay.
    In: Digital Finance.
    RePEc:spr:digfin:v:6:y:2024:i:2:d:10.1007_s42521-023-00104-x.

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  9. Forecasting cryptocurrency returns with machine learning. (2023). NEKHILI, Ramzi ; Liu, Yujun ; Sultan, Jahangir.
    In: Research in International Business and Finance.
    RePEc:eee:riibaf:v:64:y:2023:i:c:s0275531923000314.

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  10. Predicting cryptocurrency returns for real-world investments: A daily updated and accessible predictor. (2023). Zhang, Yaojie ; He, Mengxi ; Shen, Lihua.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:58:y:2023:i:pa:s154461232300778x.

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  11. Risk-weighted cryptocurrency indices. (2023). Zhang, Zhengjun ; Feng, Wenjun.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:51:y:2023:i:c:s1544612322006158.

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References

References cited by this document

  1. Aslanidis, N., Bariviera, A.F., and Perez-Laborda, A. (2021). Are cryptocurrencies becoming more interconnected? Economics Letters, 199, 109725.

  2. Bouri, E., and Gupta, R. (2021). Predicting Bitcoin Returns: Comparing the Roles of Newspaper- and Internet Search-Based Measures of Uncertainty. Finance Research Letters, 38, 101398.

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  15. Kraaijeveld, O., and De Smedt, J. (2020). The predictive power of public Twitter sentiment for forecasting cryptocurrency prices. Journal of International Financial Markets, Institutions and Money, 65, 101188.

  16. Nasir, M.A., Huynh, T.L.D., Nguyen, S.P., and Duong, D. (2019). Forecasting cryptocurrency returns and volume using search engines. Financial Innovation, 5(1), 1-13.

  17. Plakandaras, V., Bouri, E., and Gupta, R. (2021). Forecasting Bitcoin Returns in a Machine Learning Framework: Is there a Role for the U.S. – China Trade War. The Journal of Risk, 23(3), 75-93.
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  18. Sebastião, H., and Godinho, P. (2021). Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financial Innovation, 7(1), 1-30.

  19. Shahzad, S.J.H., Bouri, E., Kang, S.H., and Saeed, T. (2021). Regime specific spillover across cryptocurrencies and the role of COVID-19. Financial Innovation, 7(1), 1-24.

  20. Sun, X., Liu, M., and Sima, Z. (2020). A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Research Letters, 32, 101084.

  21. Xu, Q., Zhang, Y., and Zhang, Z. (2021). Tail-risk spillovers in cryptocurrency markets. Finance Research Letters, 38, 101453.

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  4. Sentiment matters: the effect of news-media on spillovers among cryptocurrency returns. (2024). Cepni, Oguzhan ; Serbest, Ozge ; Akyildirim, Erdinc ; Aysan, Ahmet Faruk.
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  6. Do design features explain the volatility of cryptocurrencies?. (2024). Shi, Yanghua ; Uhrig-Homburg, Marliese ; Eska, Fabian E ; Theissen, Erik.
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