WhaleBot Alerts Telegram Channel and Google Alerts as Information Media for XRP Cryptocurrency Traders

Authors

  • Lady Joanne Tjahyana Petra Christian University

DOI:

https://doi.org/10.9744/petraijbs.7.2.125-133

Keywords:

XRP cryptocurrency, WhaleBot Alerts, Telegram channel, Google Alerts, trading information media

Abstract

Ripple (XRP) is one of the most significant cryptocurrencies that attracts many traders to invest. Traders often look for information from the media as a reference for their trading management strategy. This research aims to utilize WhaleBot Alerts Telegram channel and Google Alerts as trading information media by looking for the relationship between whale transactions, news sentiment, and price returns. The researcher collected XRP whale transactions from the WhaleBot Alerts Telegram channel and XRP news from Google Alerts within a crypto winter period, from November 2021 to January 2023, and analyzed the data using descriptive statistical analysis. This research also follows several variables and formulas from previous studies. After analyzing 457 XRP whale transaction datasets, the researcher finds a significant relationship between the number of trading days and trading return on investment. The relationship shows that the more days, the greater the chance of profit. The other variables give reference patterns, such as the number of whale transactions, total whale transaction value, the most significant transaction value, and news topic. Finally, this research develops a simple XRP short-term trading model using the WhaleBot Alerts Telegram channel data and Google Alerts. In conclusion, trading initiated by XRP whale's transactions and XRP news could give a 76.53% chance of making a profit. Therefore, XRP traders can use the WhaleBot Alerts Telegram channel and Google Alerts as their trading information media.

References

Abakah, E. J. A., Caporale, G. M., & Gil-Alana, L. A. (2022). The effects of us covid-19 policy re-sponses on cryptocurrencies, fintech and artificial intelligence stocks: A fractional integration analy-sis. Cogent Economics & Finance, 10(1), 2159736. https://doi.org/10.1080/23322039.2022.2159736

Agosto, A., Cerchiello, P., & Pagnottoni, P. (2022). Sentiment, Google queries and explosivity in the cryptocurrency market. Physica A: Statistical Mechanics and Its Applications, 605, 128016. https://doi.org/10.1016/j.physa.2022.128016

Aslanidis, N., Bariviera, A. F., & López, Ó. G. (2022). The link between cryptocurrencies and Google Trends attention. Finance Research Letters, 47, 102654. https://doi.org/10.1016/j.frl.2021.102654

Baker, J. S., Horowitz, H., Radha, S. K., Fernandes, S., Jones, C., Noorani, N., … Sanders, B. C. (2022, November 2). Quantum variational rewinding for time series anomaly detection. arXiv. https://doi.org/10.48550/arXiv.2210.16438

Banerjee, A. K., Akhtaruzzaman, M., Dionisio, A., Almeida, D., & Sensoy, A. (2022). Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment. Journal of Behav-ioral and Experimental Finance, 36, 100747. https://doi.org/10.1016/j.jbef.2022.100747

Chalkiadakis, I., Zaremba, A., Peters, G. W., & Chantler, M. J. (2022). On-chain analytics for sentiment-driven statistical causality in crypto–currencies. Blockchain: Research and Applica-tions, 3(2), 100063. https://doi.org/10.1016/j.bcra.2022.100063

Das, M. K., Singh, D., & Sharma, S. (2021). Media news on vaccines and vaccination: The content profile, sentiment and trend of the online mass media during 2015–2020 in India. Clinical Epi-demiology and Global Health, 10, 100691. https://doi.org/10.1016/j.cegh.2020.100691

Erdogan, S., Ahmed, M. Y., & Sarkodie, S. A. (2022). Analyzing asymmetric effects of cryptocurrency demand on environmental sustainability. Envi-ronmental Science and Pollution Research, 29(21), 31723–31733. https://doi.org/10.1007/s11356-021-17998-y

Fang, F., Ventre, C., Basios, M., Kanthan, L., Martinez-Rego, D., Wu, F., & Li, L. (2022). Cryptocurren-cy trading: A comprehensive survey. Financial Innovation, 8(1), 13. https://doi.org/10.1186/s40854-021-00321-6

Hamrick, J., Rouhi, F., Mukherjee, A., Feder, A., Gan-dal, N., Moore, T., & Vasek, M. (2021). An ex-amination of the cryptocurrency pump-and-dump ecosystem. Information Processing & Management, 58(4), 102506. https://doi.org/10.1016/j.ipm.2021.102506

Hamrick, J., Rouhi, F., Mukherjee, A., Vasek, M., Moore, T., & Gandal, N. (2021). Analyzing tar-get-based cryptocurrency pump and dump schemes. Proceedings of the 2021 ACM CCS Workshop on Decentralized Finance and Securi-ty, 21–27. Virtual Event Republic of Korea: ACM. https://doi.org/10.1145/3464967.3488591

Herremans, D., & Low, K. W. (2022, October 6). Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Syn-thesizer Transformer models. arXiv. https://doi.org/10.48550/arXiv.2211.08281

Kakinaka, S., & Umeno, K. (2021). Exploring asym-metric multifractal cross-correlations of price–volatility and asymmetric volatility dynamics in cryptocurrency markets. Physica A: Statistical Mechanics and Its Applications, 581, 126237. https://doi.org/10.1016/j.physa.2021.126237

Katsiampa, P., Yarovaya, L., & Zięba, D. (2022). High-frequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis. Journal of Internation-al Financial Markets, Institutions and Money, 79, 101578. https://doi.org/10.1016/j.intfin.2022.101578

Lahmiri, S., Bekiros, S., & Bezzina, F. (2022). Com-plexity analysis and forecasting of variations in cryptocurrency trading volume with support vec-tor regression tuned by Bayesian optimization under different kernels: An empirical comparison from a large dataset. Expert Systems with Applica-tions, 209, 118349. https://doi.org/10.1016/j.eswa.2022.118349

Nghiem, H., Muric, G., Morstatter, F., & Ferrara, E. (2021). Detecting cryptocurrency pump-and-dump frauds using market and social signals. Ex-pert Systems with Applications, 182, 115284. https://doi.org/10.1016/j.eswa.2021.115284

Ortu, M., Uras, N., Conversano, C., Bartolucci, S., & Destefanis, G. (2022). On technical trading and social media indicators for cryptocurrency price classification through deep learning. Expert Sys-tems with Applications, 198, 116804. https://doi.org/10.1016/j.eswa.2022.116804

Ozer, F., & Okan Sakar, C. (2022). An automated cryptocurrency trading system based on the de-tection of unusual price movements with a Time-Series Clustering-Based approach. Expert Systems with Applications, 200, 117017. https://doi.org/10.1016/j.eswa.2022.117017

Powell, F., & Curry, B. (2022, September 2). What is crypto winter? – Forbes Advisor. Retrieved Feb-ruary 25, 2023, from https://www.forbes.com/advisor/investing/cryptocurrency/what-is-crypto-winter/

Qiu, T., Zhang, R., & Gao, Y. (2019). Ripple vs. SWIFT: Transforming cross border remittance us-ing blockchain technology. Procedia Computer Science, 147, 428–434. https://doi.org/10.1016/j.procs.2019.01.260

Quamara, S., & Singh, A. K. (2022). A systematic sur-vey on security concerns in cryptocurrencies: State-of-the-art and perspectives. Computers & Security, 113, 102548. https://doi.org/10.1016/j.cose.2021.102548

Rodeck, D., & Curry, B. (2022, May 31). What is rip-ple? What is XRP? – Forbes Advisor. Retrieved January 27, 2023, from https://www.forbes.com/advisor/investing/cryptocurrency/what-is-ripple-xrp/

Saggu, A. (2022). The intraday Bitcoin response to tether minting and burning events: Asymmetry, investor sentiment, and “whale alerts” on Twitter. Finance Research Letters, 49, 103096. https://doi.org/10.1016/j.frl.2022.103096

Sapkota, N. (2022). News-based sentiment and bitcoin volatility. International Review of Financial Anal-ysis, 82, 102183. https://doi.org/10.1016/j.irfa.2022.102183

Tan, X., & Tao, Y. (2023). Trend-based forecast of cryptocurrency returns. Economic Modelling, 124, 106323. https://doi.org/10.1016/j.econmod.2023.106323

Taylor, S., Kim, S. H., Zainol Ariffin, K. A., & Sheikh Abdullah, S. N. H. (2022). A comprehensive fo-rensic preservation methodology for crypto wal-lets. Forensic Science International: Digital In-vestigation, 42–43, 301477. https://doi.org/10.1016/j.fsidi.2022.301477

Tsegu, R. (2022). Cryptocurrency and security issues: The tide awaiting ripple’s decision. SMU Science and Technology Law Review, 25(1), 95. https://doi.org/10.25172/smustlr.25.1.7

Urbas, N. (2023). WhaleBot Alerts—T.me—Cryptocurrency telegram. Retrieved June 4, 2023, from CryptoLinks website: https://cryptolinks.com/348/whalebotalerts

XRP Ledger. (2023). What is XRP? - XRPL.org. Re-trieved November 16, 2023, from https://xrpl.org/what-is-xrp.html

Yousaf, I., & Yarovaya, L. (2022). The relationship between trading volume, volatility and returns of Non-Fungible Tokens: Evidence from a quantile approach. Finance Research Letters, 50, 103175. https://doi.org/10.1016/j.frl.2022.103175

Additional Files

Published

2024-12-31