Synergy Analysis on Cryptocurrency Returns and Investor Sentiment Using Bidirectional Encoder Representations from Transformers (BERT)

Reynaldy Hardiyanto; Zaäfri Ananto Husodo

doi:10.24014/ijaidm.v8i2.33315

Synergy Analysis on Cryptocurrency Returns and Investor Sentiment Using Bidirectional Encoder Representations from Transformers (BERT)

Reynaldy Hardiyanto, Zaäfri Ananto Husodo

Abstract

Cryptocurrencies have become prominent alternative investments. Unlike traditional financial assets, their intrinsic value is a subject of ongoing debate since they do not have a tangible backing asset. As a result, investor sentiment heavily influences price volatility and serves as a key indicator of perceived value based on collective investor beliefs. However, major events such as the FTX scandal can severely weaken investor confidence. Social media drives market discussions, making sentiment analysis vital for understanding behavior and predicting price movements. This study examined sentiment analysis techniques to construct an investor sentiment index and investigate its relationship with cryptocurrency returns during the FTX collapse. We employed DistilBERT and the AFINN lexicon method to develop sentiment index, finding that DistilBERT achieves an F1-score of 76.49%, significantly outperforming AFINN's 30.65%. Furthermore, our results indicate a positive correlation between investor sentiment and cryptocurrency returns during the FTX collapse. Our findings indicate that deep learning models can be more effective than lexicon-based approaches for sentiment analysis in financial markets

Keywords

BERT; Cryptocurrency; Deep Learning; Sentiment Analysis

References

S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” 2019, [Online]. Available: https://bitcoin.org/bitcoin.pdf

International Monetary Fund, Global Financial Stability Report, October 2021: COVID-19, Crypto, and Climate. S.l.: International Monetary Fund, 2021.

R. J. Shiller, Narrative economics: how stories go viral & drive major economic events. Princeton: Princeton University Press, 2019.

J. Bowden and R. Gemayel, “Sentiment and trading decisions in an ambiguous environment: A study on cryptocurrency traders,” J. Int. Financ. Mark. Inst. Money, vol. 80, p. 101622, Sep. 2022, doi: 10.1016/j.intfin.2022.101622.

Ş. Telli and H. Chen, “Multifractal behavior relationship between crypto markets and Wikipedia-Reddit online platforms,” Chaos Solitons Fractals, vol. 152, p. 111331, Nov. 2021, doi: 10.1016/j.chaos.2021.111331.

A. Bouteska, S. Mefteh-Wali, and T. Dang, “Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic,” Technol. Forecast. Soc. Change, vol. 184, p. 121999, Nov. 2022, doi: 10.1016/j.techfore.2022.121999.

I. Lee, “Social media analytics for enterprises: Typology, methods, and processes,” Bus. Horiz., vol. 61, no. 2, pp. 199–210, Mar. 2018, doi: 10.1016/j.bushor.2017.11.002.

S. Asur and B. A. Huberman, “Predicting the Future with Social Media,” in 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Toronto, AB, Canada: IEEE, Aug. 2010, pp. 492–499. doi: 10.1109/WI-IAT.2010.63.

A. P. Kirilenko and S. O. Stepchenkova, “Public microblogging on climate change: One year of Twitter worldwide,” Glob. Environ. Change, vol. 26, pp. 171–182, May 2014, doi: 10.1016/j.gloenvcha.2014.02.008.

S. Wooley, A. Edmonds, A. Bagavathi, and S. Krishnan, “Extracting Cryptocurrency Price Movements from the Reddit Network Sentiment,” in 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA: IEEE, Dec. 2019, pp. 500–505. doi: 10.1109/ICMLA.2019.00093.

H. Chen, P. De, Y. (Jeffrey) Hu, and B.-H. Hwang, “Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media,” Rev. Financ. Stud., vol. 27, no. 5, pp. 1367–1403, May 2014, doi: 10.1093/rfs/hhu001.

T. Loughran and B. Mcdonald, “When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks,” J. Finance, vol. 66, no. 1, pp. 35–65, Feb. 2011, doi: 10.1111/j.1540-6261.2010.01625.x.

C. Y. Chen, R. Despres, L. Guo, and T. Renault, “What Makes Cryptocurrencies Special? Investor Sentiment and Return Predictability During the Bubble,” SSRN Electron. J., 2019, doi: 10.2139/ssrn.3398423.

W. Antweiler and M. Z. Frank, “Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards,” J. Finance, vol. 59, no. 3, pp. 1259–1294, Jun. 2004, doi: 10.1111/j.1540-6261.2004.00662.x.

A.-D. Vo, “Sentiment Analysis of News for Effective Cryptocurrency Price Prediction,” Int. J. Knowl. Eng., vol. 5, no. 2, pp. 47–52, 2019, doi: 10.18178/ijke.2019.5.2.116.

D. Ider and S. Lessmann, “Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision,” 2022, doi: 10.48550/ARXIV.2204.05781.

E. C. Garrido-Merchan, R. Gozalo-Brizuela, and S. Gonzalez-Carvajal, “Comparing BERT Against Traditional Machine Learning Models in Text Classification,” Journal of Computational and Cognitive Engineering, vol. 2, no. 4. BON VIEW PUBLISHING PTE, pp. 352–356, Apr. 21, 2023. doi: 10.47852/bonviewjcce3202838.

X. Zhou, Y. Zhang, L. Cui, and D. Huang, “Evaluating Commonsense in Pre-trained Language Models,” arXiv.org, 2019. https://arxiv.org/abs/1911.11931

J.-W. Sun, J.-Q. Bao, and L.-P. Bu, “Text Classification Algorithm Based on TF-IDF and BERT,” 2022 11th International Conference of Information and Communication Technology (ICTech)). IEEE, pp. 1–4, Feb. 2022. doi: 10.1109/ictech55460.2022.00112.

N. O. Adelakun and A. B. Adebisi, “Sentiment Analysis of Financial News Using The BERT Model” ITEGAM-Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA, vol. 10, no. 48. GN1 Sistemas e Publicacoes Ltd., 2024. doi: 10.5935/jetia.v10i48.1029.

M. G. Sousa, K. Sakiyama, L. de S. Rodrigues, P. H. Moraes, E. R. Fernandes, and E. T. Matsubara, “BERT for Stock Market Sentiment Analysis,” 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, pp. 1597–1601, Nov. 2019. doi: 10.1109/ictai.2019.00231.

V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter,” Mar. 2020. Available: https://arxiv.org/pdf/1910.01108

Venkatesh, S. U. Hegde, Z. A. S, and N. Y, “Hybrid CNN-LSTM Model with GloVe Word Vector for Sentiment Analysis on Football Specific Tweets,” 2021 International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, pp. 1–8, Feb. 19, 2021. doi: 10.1109/icaect49130.2021.9392516.

H. Anand and A. Arya, “An Empirical Study of Financial BERT Models for Sentiment Analysis and Cryptocurrency Price Correlation,” 2024 IEEE 9th International Conference for Convergence in Technology (I2CT). IEEE, pp. 1–7, Apr. 05, 2024. doi: 10.1109/i2ct61223.2024.10543560.

A. Bozanta, S. Angco, M. Cevik, and A. Basar, “Sentiment Analysis of StockTwits Using Transformer Models,” 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, pp. 1253–1258, Dec. 2021. doi: 10.1109/icmla52953.2021.00204.

D. Vidal-Tomás, A. Briola, and T. Aste, “FTX’s downfall and Binance’s consolidation: the fragility of Centralized Digital Finance,” 2023, doi: 10.48550/ARXIV.2302.11371.

KPMG, “The collapse of FTX: Lessons and implications for stakeholders in the crypto industry.” KPMG, Nov. 2022. [Online]. Available: https://assets.kpmg.com/content/dam/kpmg/sg/pdf/2022/12/the-collapse-of-ftx.pdf

P. Seroyizhko, Z. Zhexenova, M. Z. Shafiq, F. Merizzi, A. Galassi, and F. Ruggeri, “A Sentiment and Emotion Annotated Dataset for Bitcoin Price Forecasting Based on Reddit Posts,” in Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP), Abu Dhabi, United Arab Emirates (Hybrid): Association for Computational Linguistics, Dec. 2022, pp. 203–210. [Online]. Available: https://aclanthology.org/2022.finnlp-1.27

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” 2018, doi: 10.48550/ARXIV.1810.04805.

A. Vaswani et al., “Attention Is All You Need,” Jun. 2017. Available: https://arxiv.org/pdf/1706.03762

S. Kula, R. Kozik, and M. Choraś, “Implementation of the BERT-derived architectures to tackle disinformation challenges,” Neural Computing and Applications, Jul. 2021, doi: https://doi.org/10.1007/s00521-021-06276-0.

Sahana V et al., “The DistilBERT Model: A Promising Approach to Improve Machine Reading Comprehension Models,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 8, pp. 293–309, Sep. 2023, doi: https://doi.org/10.17762/ijritcc.v11i8.7957.

S. Rouhani and E. Abedin, “Crypto-currencies narrated on tweets: a sentiment analysis approach,” Int. J. Ethics Syst., vol. 36, no. 1, pp. 58–72, Oct. 2019, doi: 10.1108/IJOES-12-2018-0185.

J. Zhang and C. Zhang, “Do cryptocurrency markets react to issuer sentiments? Evidence from Twitter,” Res. Int. Bus. Finance, vol. 61, p. 101656, Oct. 2022, doi: 10.1016/j.ribaf.2022.101656.

C. Sun, X. Qiu, Y. Xu, and X. Huang, “How to Fine-Tune BERT for Text Classification?” 2019, doi: 10.48550/ARXIV.1905.05583.

Q. Liu, W.-S. Lee, M. Huang, and Q. Wu, “Synergy between stock prices and investor sentiment in social media,” Borsa Istanb. Rev., vol. 23, no. 1, pp. 76–92, Jan. 2023, doi: 10.1016/j.bir.2022.09.006.

P. Rosen, “Here’s the latest timeline of the FTX saga from Sam Bankman-Fried’s shady balance sheet and a battle with Binance to pleading not guilty to fraud,” Business Insider, Jan. 05, 2023. [Online]. Available: https://markets.businessinsider.com/news/currencies/ftx-timeline-sam-bankman-fried-bankruptcy-trial-alameda-sec-doj-2023-1

Ehsanur Rahman Rhythm, Rajvir Ahmed Shuvo, Md Sabbir Hossain, Md. Farhadul Islam, and Annajiat Alim Rasel, “Sentiment Analysis of Restaurant Reviews from Bangladeshi Food Delivery Apps,” 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Mar. 2023, doi: https://doi.org/10.1109/esci56872.2023.10100214.

E. Akyildirim, T. Conlon, S. Corbet, and J. W. Goodell, “Understanding the FTX exchange collapse: A dynamic connectedness approach,” Finance Res. Lett., vol. 53, p. 103643, May 2023, doi: 10.1016/j.frl.2023.103643.

I. Yousaf, Y. Riaz, and J. W. Goodell, “What do responses of financial markets to the collapse of FTX say about investor interest in cryptocurrencies? Event-study evidence,” Finance Res. Lett., vol. 53, p. 103661, May 2023, doi: 10.1016/j.frl.2023.103661.

DOI: http://dx.doi.org/10.24014/ijaidm.v8i2.33315

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