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10.3280/SPE2023-002002

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Abdi H., Williams L.J. (2010). Principal Component Analysis, Wiley Int. Rev. Comput. Stat., 2: 433-459.

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Ahn Y., Kim D. (2021). Emotional trading in the cryptocurrency market, Financ. Res. Lett., 42, 101912.

https://doi.org/10.1016/j.frl.2020.101912


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Akyildirim E., Aysan A.F., Cepni O., Darendeli S.P.C. (2021). Do investor sentiments drive cryptocurrency prices?, Econ. Lett. 206, 109980.

https://doi.org/10.1016/j.econlet.2021.109980


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Atkinson J., Escudero A. (2022). Evolutionary natural-language coreference resolution for sentiment analysis, Intern. Journ, Inform. Mang. Data Ins., 2, 100115.

https://doi.org/10.1016/j.jjimei.2022.100115


Unstructured Zitierung

Ba C.T., Zignani M., Gaito S. (2022). The role of cryptocurrency in the dynamics of blockchain-based social networks: The case of Steemit, PLoS ONE, 17(6), e0267612.

https://doi.org/10.1371/journal.pone.0267612


Unstructured Zitierung

Bariviera A.F., Merediz-Solà I. (2021). Where do we stand in cryptocurrencies economic Research? A survey based on hybrid analysis, J. Econ. Survey, 35(2): 377-407.

https://doi.org/10.1111/joes.12412


Unstructured Zitierung

Beh E.J., Lombardo R. (2014). Correspondence Analysis. Theory, Practice and New Strategies. Wiley, Chichester.

https://doi.org/10.1002/9781118762875


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Bhatt A., Joshipura M., Joshipura N. (2022). Decoding the trinity of Fintech, digitalization and financial services: An integrated bibliometric analysis and thematic literature review approach, Cog. Econ. Finance, 10, 2114160.

https://doi.org/10.1080/23322039.2022.2114160


Unstructured Zitierung

Bouteska A., Mefteh-Wali S., Dang T. (2022). Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic, Techn. Forec. Soc. Change, 184, 121999.

https://doi.org/10.1016/j.techfore.2022.121999


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Chen M.A., Wu D., Yang B. (2019). How Valuable Is FinTech Innovation?. Rev. Financ. Stud., 32(5).

https://doi.org/10.1093/rfs/hhy130


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Coulter K.A. (2022). The impact of news media on Bitcoin prices: modelling data driven discourses in the crypto-economy with natural language processing, Royal Soc. Open Sci., 9, 220276.

https://doi.org/10.1098/rsos.220276


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Dadar P. (2018). Decyphering cryptocurrencies: Sentiments and prices. SCSUG Paper.


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Egami N., Fong C.J., Grimmer J., Roberts M.E., Stewart B.M. (2018). How to Make Causal Inferences Using Texts, arXiv, 1802.02163v1.


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Elsayed A.H., Gozgor G., Yarovaya L. (2022). Volatility and return connectedness of cryptocurrency, gold, and uncertainty: Evidence from the cryptocurrency uncertainty indices, Financ Res. Lett., 47, 102732.

https://doi.org/10.1016/j.frl.2022.102732


Unstructured Zitierung

Garcia‑Corral F.J., Cordero‑Garcia J.A., de Pablo‑Valenciano J., Uribe‑Toril J. (2022). A bibliometric review of cryptocurrencies: how have they grown?, Financ. Innov., 8(2).

https://doi.org/10.1186/s40854-021-00306-5


Unstructured Zitierung

García-Medina A., Hernández J.B. (2020). Network Analysis of Multivariate Transfer Entropy of Cryptocurrencies in Times of Turbulence, Entropy, 22(7), 760.

https://doi.org/10.3390/e22070760


Unstructured Zitierung

Garriga M., Dalla Palma S., Arias M., De Renzis A., Pareschi R., Tamburri D.A (2020). Blockchain and cryptocurrencies: A classification and comparison of architecture drivers, Concurrency and Computation, 33(8).

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Greenacre M. (2007). Correspondence Analysis in Practice, Chapman & Hall, Boca Raton.

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Grimmer J., Stewart B. (2013). Text ad Data: The promise and Pitfalls of Automatic Content Analysis Methods for Political Texts, Political Analysis, 21: 267-97.

https://doi.org/10.1093/pan/mps028


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Guerrero Cusumano J.L. (2017). A Detection Mechanism with Text Mining Cross Correlation Approach, IEEE International Conference on Big Data Boston.


Unstructured Zitierung

Guo X., Donev P. (2020). Bibliometrics and Network Analysis of Cryptocurrency Research, J Syst Sci Complex, 33: 1933-1958.

https://doi.org/10.1007/s11424-020-9094-z


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Hill T., Lewicki P. (2006). Statistics. Methods and Applications, StatSoft, Tulsa. Hoover K.D. (2001). Causality in Macroeconomics, Cambridge University Press, Cambridge.

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Unstructured Zitierung

Jaquart P., Kopke S., Weinhardt C. (2022). Machine learning for cryptocurrency market prediction and trading, J. Financ. Data Sci., 8: 331-352.

https://doi.org/10.1016/j.jfds.2022.12.001


Unstructured Zitierung

Kim Y.B., Lee J., Park N., Choo J., Kim J-H., Kim (2017). When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation. PLoS ONE, 12(5), e0177630.

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Unstructured Zitierung

Kraaijeveld O., De Smedt J. (2020). The predictive power of public Twitter sentiment for forecasting cryptocurrency prices, J. Int. Financ. Mark. Inst. Money, 65, 101188 v.

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Unstructured Zitierung

Kufenko V., Geiger N. (2016). Business cycles in the economy and in economics: an econometric analysis, Scientometrics, 107: 43-69.

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Unstructured Zitierung

Kwapień J., Wątorek M., Drożdż S. (2021). Cryptocurrency Market Consolidation in 2020-2021, Entropy, 23(12), 1674.

https://doi.org/10.3390/e23121674


Unstructured Zitierung

Laskowski M., Kim H.M. (2016). Rapid Prototyping of a Text Mining Application for Cryptocurrency Market Intelligence, arXiv, 1611.00315v1.

https://doi.org/10.2139/ssrn.2798486