Cardano Cryptocurrency Price from Twitter. A Prediction Algorithm from Machine Learning

International Journal of Electronics and Communication Engineering

© 2023 by SSRG - IJECE Journal

Volume 10 Issue 12

Year of Publication : 2023

Authors : Riccardo Piccarreta Acosta, Alejandra Zavala Arana

10.14445/23488549/IJECE-V10I12P104

How to Cite?

Riccardo Piccarreta Acosta, Alejandra Zavala Arana, "Cardano Cryptocurrency Price from Twitter. A Prediction Algorithm from Machine Learning," SSRG International Journal of Electronics and Communication Engineering, vol. 10,  no. 12, pp. 33-44, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I12P104

Abstract:

Cryptocurrencies are a growing market that has attracted the attention of many investors in recent years. While cryptocurrencies offer a secure and decentralized form of payment, this market is highly volatile. Factors influencing price changes include the balance of supply and demand, its utility, trading indicators, and market confidence. The present research aims to predict the price of the Cardano cryptocurrency by using machine learning techniques, specifically SVM, LSTM and BiLSTM models. In addition to accounting for financial indices, Twitter activity was used as a data source to measure market sentiment. The study analyzes various predictive horizons, including time ranges of 1 day, seven days, 14 days, 21 days and 30 days. The results obtained were validated with different performance indicators, and it was determined that the model predicts Cardano prices one month ahead with a MAPE of less than 22%, providing valuable information for investors interested in the volatile Cardano cryptocurrency market.

Keywords:

Cardano, Cryptocurrencies, Machine Learning, Neural Network, Twitter.

References:

[1] Basant Agarwal et al., “Prediction of Dogecoin Price Using Deep Learning and Social Media Trends,” EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, vol. 21, no. 29, pp. 1-12, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Aytaç Altan, Seçkin Karasu, and Stelios Bekiros, “Digital Currency Forecasting with Chaotic Meta-Heuristic Bio-Inspired Signal Processing Techniques,” Chaos, Solitons & Fractals, vol. 126, pp. 325-336, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Davide Chicco, Matthijs J. Warrens, and Giuseppe Jurman, “The Coefficient of Determination R-Squared is more Informative than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation,” PeerJ Computer Science, vol. 7, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Gil Cohen, “Forecasting Bitcoin Trends Using Algorithmic Learning Systems,” Entropy, vol. 22, no. 8, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Himansu Das et al., Applied Intelligent Decision Making in Machine Learning, 1st ed., CRC Press, 2022.
[Google Scholar] [Publisher Link]
[6] Paul Delfabbro, Daniel L. King, and Jennifer Williams, “The Psychology of Cryptocurrency Trading: Risk and Protective Factors,” Journal of Behavioral Addictions, vol. 10, no. 2, pp. 201-207, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Marion Dupire et al., “Non-Governmental Organization (NGO) Tweets: Do Shareholders Care?,” Business & Society, vol. 61, no. 2, pp. 419-456, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Luna: The Case of the Cryptocurrency that Collapsed in a Matter of Days, Gestión, 2003. [Online]. Available: https://gestion.pe/tendencias/luna-el-caso-de-la-criptomoneda-que-colapso-en-cuestion-de-dias-bitcoin-nnda-nnlt-noticia/
[9] Imen Hamraoui, and Adel Boubaker, “Impact of Twitter Sentiment on Stock Price Returns,” Social Network Analysis and Mining, vol. 12, no. 1, pp. 1-15, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Sepp Hochreiter, and Jürgen Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Richard A. Johnson, and Dean W. Wichern, Applied Multivariate Statistical Analysis, 6th ed., Pearson Education Inc., 2007.
[Google Scholar] [Publisher Link]
[12] Jasper Jolly, Price of Dogecoin Rises by 50% Following Elon Musk Tweet, Stock Markets, 2021. [Online]. Available: https://www.theguardian.com/business/2021/feb/04/price-of-dogecoin-rises-by-50-following-elon-musk-tweet
[13] Nicholas J. Kelley et al., “Nostalgia Confers Psychological Wellbeing by Increasing Authenticity,” Journal of Experimental Social Psychology, vol. 102, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Zeynep Hilal Kilimci, “Sentiment Analysis Based Direction Prediction in Bitcoin Using Deep Learning Algorithms and Word Embedding Models,” International Journal of Intelligent Systems and Applications in Engineering, vol. 8, no. 2, pp. 60-65, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Olivier Kraaijeveld, and Johannes De Smedt, “The Predictive Power of Public Twitter Sentiment for Forecasting Cryptocurrency Prices,” Journal of International Financial Markets, Institutions and Money, vol. 65, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Salim Lahmiri, Stelios Bekiros, and Frank Bezzina, “Complexity Analysis and Forecasting of Variations in Cryptocurrency Trading Volume with Support Vector Regression Tuned by Bayesian Optimization under Different Kernels: An Empirical Comparison from a Large Dataset,” Expert Systems with Applications, vol. 209, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Ricardo Leon-Ayala et al., “Predictive Analytics to Determine the Bitcoin Price Rise Using Machine Learning Techniques,” International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 275-283, 2022.
[CrossRef] [Publisher Link]
[18] Yuze Li et al., “Hybrid Data Decomposition-Based Deep Learning for Bitcoin Prediction and Algorithm Trading,” Financial Innovation, vol. 8, no. 1, pp. 1-24, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Scott E. Maxwell, Harold D. Delaney, and Ken Kelley, Designing Experiments and Analyzing Data: A Model Comparison Perspective, 3^rd ed., Routledge, 2017.
[Google Scholar] [Publisher Link]
[20] Mason McCoy, and Shahram Rahimi, “Prediction of Highly Volatile Cryptocurrency Prices Using Social Media,” International Journal of Computational Intelligence and Applications, vol. 19, no. 4, pp. 1-28, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Mojtaba Nabipour et al., “Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms via Continuous and Binary Data; A Comparative Analysis,” IEEE Access, vol. 8, pp. 150199-150212, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Douglas C. Montgomery, Design and Analysis of Experiments, 10th ed., Wiley, pp. 1-688, 2019.
[Google Scholar] [Publisher Link]
[23] Mohammed Mudassir et al., “Time-Series Forecasting of Bitcoin Prices Using High-Dimensional Features: A Machine Learning Approach,” Neural Computing and Applications, pp. 1-15, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Kingstone Nyakurukwa, and Yudhvir Seetharam, “Can a 280-Character Message Explain Stock Returns? Evidence from South Africa,” Managerial Finance, vol. 48, no. 4, pp. 663-686, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Pradhyumna Rao et al., “Crypto Currency Portfolio Allocation Using Machine Learning,” 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Noida, India, pp. 1522-1527, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Andrés Regal et al., “Cryptocurrency Price Projection Based on Tweets Using LSTM,” Ingeniare. Chilean Engineering Magazine, vol. 27, no. 4, pp. 696-706, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Saeed Rouhani, and Ehsan Abedin, “Crypto-Currencies Narrated on Tweets: A Sentiment Analysis Approach,” International Journal of Ethics and Systems, vol. 36, no. 1, pp. 58-72, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[28] David Valle-Cruz et al., “Does Twitter Affect Stock Market Decisions? Financial Sentiment Analysis during Pandemics: A Comparative Study of the H1N1 and the COVID19 Periods,” Cognitive Computation, vol. 14, no. 1, pp. 372-387, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[29] I Made Wirawan, Triyanna Widiyaningtyas, and Muchammad Maulana Hasan, “Short Term Prediction on Bitcoin Price Using ARIMA Method,” 2019 International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia, pp. 260-265, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Hirofumi Yamamoto et al., “Forecasting Crypto-Asset Price Using Influencer Tweets,” Advanced Information Networking and Applications, pp. 940-951, 2019.
[CrossRef] [Google Scholar] [Publisher Link]