Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Chen, Cathy Yi-Hsuan & Després, Roméo & Guo, Li & Renault, Thomas, 2019. "What makes cryptocurrencies special? Investor sentiment and return predictability during the bubble," IRTG 1792 Discussion Papers 2019-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
- Vytautas Karalevicius & Niels Degrande & Jochen De Weerdt, 2018. "Using sentiment analysis to predict interday Bitcoin price movements," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 19(1), pages 56-75, December.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
- Bouteska, Ahmed & Mefteh-Wali, Salma & Dang, Trung, 2022. "Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
- ?ikolaos A. Kyriazis, 2021. "Impacts of Stock Indices, Oil, and Twitter Sentiment on Major Cryptocurrencies during the COVID-19 First Wave," Bulletin of Applied Economics, Risk Market Journals, vol. 8(2), pages 133-146.
- Yousaf, Imran & Youssef, Manel & Goodell, John W., 2022. "Quantile connectedness between sentiment and financial markets: Evidence from the S&P 500 twitter sentiment index," International Review of Financial Analysis, Elsevier, vol. 83(C).
- Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
- Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
- Sapkota, Niranjan, 2022. "News-based sentiment and bitcoin volatility," International Review of Financial Analysis, Elsevier, vol. 82(C).
- Sheng‐Tun Li & Kuei‐Chen Chiu & Chien‐Chang Wu, 2023. "Apply big data analytics for forecasting the prices of precious metals futures to construct a hedging strategy for industrial material procurement," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(2), pages 942-959, March.
- Alla A. Petukhina & Raphael C. G. Reule & Wolfgang Karl Härdle, 2021.
"Rise of the machines? Intraday high-frequency trading patterns of cryptocurrencies,"
The European Journal of Finance, Taylor & Francis Journals, vol. 27(1-2), pages 8-30, January.
- Petukhina, Alla A. & Reule, Raphael C. G. & Härdle, Wolfgang Karl, 2019. "Rise of the Machines? Intraday High-Frequency Trading Patterns of Cryptocurrencies," IRTG 1792 Discussion Papers 2019-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Alla A. Petukhina & Raphael C. G. Reule & Wolfgang Karl Hardle, 2020. "Rise of the Machines? Intraday High-Frequency Trading Patterns of Cryptocurrencies," Papers 2009.04200, arXiv.org.
- Wu, Shue-Jen & Lee, Wei-Ming, 2015. "Predicting severe simultaneous bear stock markets using macroeconomic variables as leading indicators," Finance Research Letters, Elsevier, vol. 13(C), pages 196-204.
- Thomas Renault, 2020.
"Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages,"
Digital Finance, Springer, vol. 2(1), pages 1-13, September.
- Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205149, HAL.
- Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Post-Print hal-03205149, HAL.
- David G. McMillan, 2003. "Non‐linear Predictability of UK Stock Market Returns," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(5), pages 557-573, December.
- Kajtazi, Anton & Moro, Andrea, 2019. "The role of bitcoin in well diversified portfolios: A comparative global study," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 143-157.
- Matthew F Dixon, 2017. "A High Frequency Trade Execution Model for Supervised Learning," Papers 1710.03870, arXiv.org, revised Dec 2017.
- Jaydip Sen & Tamal Datta Chaudhuri, 2017. "A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector," Papers 1705.01144, arXiv.org.
- Harri Pönkä, 2017.
"Predicting the direction of US stock markets using industry returns,"
Empirical Economics, Springer, vol. 52(4), pages 1451-1480, June.
- Pönkä, Harri, 2014. "Predicting the direction of US stock markets using industry returns," MPRA Paper 62942, University Library of Munich, Germany.
- Gaies, Brahim & Nakhli, Mohamed Sahbi & Sahut, Jean-Michel & Schweizer, Denis, 2023. "Interactions between investors’ fear and greed sentiment and Bitcoin prices," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
- Ravi Kashyap, 2024. "The Concentration Risk Indicator: Raising the Bar for Financial Stability and Portfolio Performance Measurement," Papers 2408.07271, arXiv.org.
- Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
- Tania Morris & Jules Comeau, 2020. "Portfolio creation using artificial neural networks and classification probabilities: a Canadian study," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(2), pages 133-163, June.
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-AIN-2024-01-08 (Artificial Intelligence)
- NEP-BIG-2024-01-08 (Big Data)
- NEP-CMP-2024-01-08 (Computational Economics)
- NEP-PAY-2024-01-08 (Payment Systems and Financial Technology)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2311.14759. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.