[
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., McGrew, B. (2023). Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
]Search in Google Scholar
[
Bekaert, G., Hoerova, M. (2014). The VIX, the variance premium and stock market volatility. Journal of Econometrics, 183(2), 181-192.
]Search in Google Scholar
[
Chen, C.Y.H., Fengler, M.R., Härdle, W.K., Liu, Y. (2021). Media-expressed tone, Option Characteristics, and Stock Return Predictability, J Economic Dynamics and Control, https://doi.org/10.1016/j.jedc.2021.104290.
]Search in Google Scholar
[
Chen, Y., Yuan, J., You, Q., Luo, J. (2018). Twitter sentiment analysis via bi-sense emoji embedding and attention-based LSTM. In Proceedings of the 26th ACM international conference on Multimedia, 117-125.
]Search in Google Scholar
[
Devlin, J., Chang, M.W., Lee, K., Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
]Search in Google Scholar
[
Eisner, B., Rocktäschel, T., Augenstein, I., Bošnjak, M., Riedel, S. (2016). emoji2vec: Learning emoji representations from their description. arXiv preprint arXiv:1609.08359.
]Search in Google Scholar
[
Feng, S., Kirkley, A. (2021). Integrating online and offline data for crisis management: Online geolocalized emotion, policy response, and local mobility during the COVID crisis. Scientific Reports, 11(1), 8514.
]Search in Google Scholar
[
Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., Lehmann, S. (2017). Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. arXiv preprint arXiv:1708.00524.
]Search in Google Scholar
[
Härdle, W.K., Trimborn, S. (2015). CRIX or evaluating Blockchain based currencies. Oberwolfach Report No. 42/2015 „The Mathematics and Statistics of Quantitative Risk“. DOI: https://doi.org/10.4171/OWR/2015/42.
]Search in Google Scholar
[
Hou, A.J., Wang, W., Cheng, C.Y.H., Härdle, W.K. (2020). Pricing Cryptocurrency options. J Financial Econometrics, Vol. 18, No. 2, 250-279, https://doi.org/10.1093/jjfinec/nbaa006.
]Search in Google Scholar
[
Hogenboom, A., Bal, D., Frasincar, F., Bal, M., De Jong, F., Kaymak, U. (2013). Exploiting emoticons in sentiment analysis. In Proceedings of the 28th annual ACM symposium on applied computing, pp. 703-710.
]Search in Google Scholar
[
LI X, YAN R, ZHANG M (2017). Joint emoji classification and embedding learning. In Web and Big Data: First International Joint Conference, APWeb-WAIM 2017, Beijing, China, July 7–9, 2017, Proceedings, Part II 1 (pp. 48-63). Springer International Publishing.
]Search in Google Scholar
[
Kim, A., Trimborn, S., Härdle, W.K. (2021). VCRIX—A volatility index for crypto-currencies. International Review of Financial Analysis, 78, 101915.
]Search in Google Scholar
[
Kulakowski, M., Frasincar, F. (2023). Sentiment Classification of Cryptocurrency-Related Social Media Posts. IEEE Intelligent Systems, 38(4), 5-9.
]Search in Google Scholar
[
Liu, H., Zhang, P., Chien, E., Solomon, J., Bommes, D. (2018). Singularity-constrained octahedral fields for hexahedral meshing. ACM Trans. Graph., 37(4), 93-1.
]Search in Google Scholar
[
Liu, K.L., Li, W.J., Guo, M.Y. (2012). “Emoticon smoothed language models for twitter sentiment analysis.” Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 26, No. 1.
]Search in Google Scholar
[
Liu, F., Packham, N., Lu, M.J., Härdle, W.K. (2023). Hedging Cryptos with Bitcoin Futures, Quantitative Finance, DOI: 10.1080/14697688.2023.2187316.
]Search in Google Scholar
[
Matic, J.L., Packham, N., Härdle, W.K. (2023). Hedging Cryptocurrency Options. Review of Derivatives Research, https://doi.org/10.1007/s11147-023-09194-6.
]Search in Google Scholar
[
Nasekin, S., Chen, C.Y.H. (2020). Deep learning-based cryptocurrency sentiment construction. Digital Finance, 2(1-2), 39-67.
]Search in Google Scholar
[
Osman, M.B., Galariotis, E., Guesmi, K., Hamdi, H., Naoui, K. (2024). Are markets sentiment driving the price bubbles in the virtual?. International Review of Economics & Finance, 89, 272-285.
]Search in Google Scholar
[
Saif, H., He, Y., Alani, H. (2012). Alleviating data sparsity for twitter sentiment analysis. CEUR Workshop Proceedings (CEUR-WS. org).
]Search in Google Scholar
[
Sprenger, T.O., Sandner, P.G., Tumasjan, A., Welpe, I.M. (2014). News or Noise? Using Twitter to Identify and Understand Company-specific News Flow. Journal of Business Finance & Accounting, 41(7–8):791–830. doi: 10.1111/jbfa.12086.
]Search in Google Scholar
[
Trimborn, S., Härdle, W.K. (2018). CRIX an Index for cryptocurrencies. Journal of Empirical Finance, 49, 107-122.
]Search in Google Scholar
[
Zhang, J.L., Härdle, W.K., Chen, C.Y.H., Bommes, E. (2016). Distillation of news flow into analysis of stock reactions. Journal of Business & Economic Statistics, 34(4), 547-563.
]Search in Google Scholar
[
Zhao, J., Dong, L., Wu, J., & Xu, K. (2012, August). Moodlens: an emoticon-based sentiment analysis system for chinese tweets. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 1528-1531.
]Search in Google Scholar