121
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Forecasting cryptocurrencies log-returns: a LASSO-VAR and sentiment approach

ORCID Icon & ORCID Icon

References

  • Abraham, J., D. Higdon, J. Nelson, and J. Ibarra. 2018. “Cryptocurrency Price Prediction Using Tweet Volumes and Sentiment Analysis.” SMU Data Science Review 1 (3): 1.
  • Almuhimedi, H., S. Wilson, B. Liu, N. Sadeh, and A. Acquisti. 2013. “Tweets are Forever: A Large-Scale Quantitative Analysis of Deleted Tweets. In Proceedings of the 2013 conference on Computer supported cooperative work, San Antonio, Texas, USA, 897–908.
  • Aslanidis, N., A. F. Bariviera, and Ó. G. López. 2022. “The Link Between Cryptocurrencies and Google Trends Attention.” Finance Research Letters 47:102654. https://doi.org/10.1016/j.frl.2021.102654.
  • Auer, R., C. Monnet, and H. S. Shin. 2021. “Permissioned Distributed Ledgers and the Governance of Money.” Available at SSRN 3770075. https://doi.org/10.2139/ssrn.3770075.
  • Balcilar, M., E. Bouri, R. Gupta, and D. Roubaud. 2017. “Can volume predict bitcoin returns and volatility? a quantiles-based approach.” Economic Modelling 64:74–81. https://doi.org/10.1016/j.econmod.2017.03.019.
  • Bańbura, M., D. Giannone, and L. Reichlin. 2010. “Large Bayesian Vector Auto Regressions.” Journal of Applied Econometrics 25 (1): 71–92. https://doi.org/10.1002/jae.1137.
  • Baumgartner, J., S. Zannettou, B. Keegan, M. Squire, and J. Blackburn. 2020. “The Pushshift Reddit Dataset. In Proceedings of the international AAAI conference on web and social media, Atlanta, Georgia, USA, 14, 830–839.
  • Belloni, A., V. Chernozhukov, and C. Hansen. 2014. “High-Dimensional Methods and Inference on Structural and Treatment Effects.” Journal of Economic Perspectives 28 (2): 29–50. https://doi.org/10.1257/jep.28.2.29.
  • Bernanke, B. S., J. Boivin, and P. Eliasz. 2005. “Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (Favar) Approach.” The Quarterly Journal of Economics 120 (1): 387–422. https://doi.org/10.1162/qjec.2005.120.1.387.
  • Catalini, C., and J. S. Gans. 2020. “Some Simple Economics of the Blockchain.” Communications of the ACM 63 (7): 80–90. https://doi.org/10.1145/3359552.
  • Catania, L., and S. Grassi. 2021. “Forecasting Cryptocurrency Volatility.” International Journal of Forecasting 38:878–894. https://doi.org/10.1016/j.ijforecast.2021.06.005.
  • Catania, L., S. Grassi, and F. Ravazzolo. 2019. “Forecasting Cryptocurrencies Under Model and Parameter Instability.” International Journal of Forecasting 35 (2): 485–501. https://doi.org/10.1016/j.ijforecast.2018.09.005.
  • Chan, S., J. Chu, Y. Zhang, and S. Nadarajah. 2022. “An Extreme Value Analysis of the Tail Relationships Between Returns and Volumes for High Frequency Cryptocurrencies.” Research in International Business and Finance 59:101541. https://doi.org/10.1016/j.ribaf.2021.101541.
  • Ciaian, P., M. Rajcaniova, and D. A. Kancs. 2016. “The Economics of Bitcoin Price Formation.” Applied Economics 48 (19): 1799–1815. https://doi.org/10.1080/00036846.2015.1109038.
  • Clark, T., and M. McCracken. 2013. “Advances in Forecast Evaluation.” Handbook of Economic Forecasting 2 (Part B): 1107–1201.
  • Corbet, S., B. Lucey, A. Urquhart, and L. Yarovaya. 2019. “Cryptocurrencies as a Financial Asset: A Systematic Analysis.” International Review of Financial Analysis 62:182–199. https://doi.org/10.1016/j.irfa.2018.09.003.
  • Daniel, K., D. Hirshleifer, and S. H. Teoh. 2002. “Investor Psychology in Capital Markets: Evidence and Policy Implications.” Journal of Monetary Economics 49 (1): 139–209. https://doi.org/10.1016/S0304-3932(01)00091-5.
  • Diebold, F. X., and R. S. Mariano. 2002. “Comparing Predictive Accuracy.” Journal of Business & Economic Statistics 20 (1): 134–144. https://doi.org/10.1198/073500102753410444.
  • Eichenauer, V. Z., R. Indergand, I. Z. Martínez, and C. Sax. 2022. “Obtaining Consistent Time Series from Google Trends.” Economic Inquiry 60 (2): 694–705. https://doi.org/10.1111/ecin.13049.
  • Fama, E. F. 1970. “Efficient Capital Markets: A Review of Theory and Empirical Work.” The Journal of Finance 25 (2): 383–417. https://doi.org/10.2307/2325486.
  • Friedman, J., T. Hastie, and R. Tibshirani. 2010. “Regularization paths for generalized linear models via coordinate descent.” Journal of Statistical Software 33 (1): 1. https://doi.org/10.18637/jss.v033.i01.
  • Gaffney, D., J. N. Matias, and C. M. Danforth. 2018. “Caveat Emptor, Computational Social Science: Large-Scale Missing Data in a Widely-Published Reddit Corpus.” PloS One 13 (7): e0200162. https://doi.org/10.1371/journal.pone.0200162.
  • Gandal, N., and H. Halaburda. 2016. “Can We Predict the Winner in a Market with Network Effects? Competition in Cryptocurrency Market.” Games 7 (3): 16. https://doi.org/10.3390/g7030016.
  • Garcia, D., C. J. Tessone, P. Mavrodiev, and N. Perony. 2014. “The Digital Traces of Bubbles: Feedback Cycles Between Socio-Economic Signals in the Bitcoin Economy.” Journal of the Royal Society Interface 11 (99): 20140623. https://doi.org/10.1098/rsif.2014.0623.
  • Georgoula, I., D. Pournarakis, C. Bilanakos, D. Sotiropoulos, and G. M. Giaglis. 2015. “Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices.” Available at SSRN 2607167.
  • Glenski, M., T. Weninger, and S. Volkova. 2019. “Improved Forecasting of Cryptocurrency Price Using Social Signals.” arXiv preprint arXiv:1907,00558.
  • Granger, C. W. 1969. “Investigating Causal Relations by Econometric Models and Cross-Spectral Methods.” Econometrica 37 (3): 424–438. https://doi.org/10.2307/1912791.
  • Granger, C. W. 1980. “Testing for Causality: A Personal Viewpoint.” Journal of Economic Dynamics and Control 2:329–352. https://doi.org/10.1016/0165-1889(80)90069-X.
  • Halaburda, H., G. Haeringer, J. S. Gans, and N. Gandal. 2020. “The Microeconomics of Cryptocurrencies.” Technical report. National Bureau of Economic Research.
  • Harvey, D., S. Leybourne, and P. Newbold. 1997. “Testing the Equality of Prediction Mean Squared Errors.” International Journal of Forecasting 13 (2): 281–291. https://doi.org/10.1016/S0169-2070(96)00719-4.
  • Hecq, A., L. Margaritella, and S. Smeekes. 2023. “Granger Causality Testing in High-Dimensional Vars: A Post-Double-Selection Procedure.” Journal of Financial Econometrics 21 (3): 915–958. https://doi.org/10.1093/jjfinec/nbab023.
  • Hitam, N. A., and A. R. Ismail. 2018. “Comparative Performance of Machine Learning Algorithms for Cryptocurrency Forecasting.” Indonesian Journal of Electrical Engineering and Computer Science 11 (3): 1121–1128. https://doi.org/10.11591/ijeecs.v11.i3.pp1121-1128.
  • Hutto, C., and E. Gilbert. 2014. “Vader: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text.” In Proceedings of the international AAAI conference on web and social media, Ann Arbor, Michigan USA, 8, 216–225.
  • Jain, P. C., and G.-H. Joh. 1988. “The Dependence Between Hourly Prices and Trading Volume.” The Journal of Financial and Quantitative Analysis 23 (3): 269–283. https://doi.org/10.2307/2331067.
  • Kim, S.-J., K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky. 2007. “An interior-point method for large-scale ↕1-regularized least squares.” IEEE Journal of Selected Topics in Signal Processing 1 (4): 606–617. https://doi.org/10.1109/JSTSP.2007.910971.
  • Kraaijeveld, O., and J. De Smedt. 2020. “The Predictive Power of Public Twitter Sentiment for Forecasting Cryptocurrency Prices.” Journal of International Financial Markets, Institutions and Money 65:101188. https://doi.org/10.1016/j.intfin.2020.101188.
  • Kristoufek, L. 2013. “Bitcoin Meets Google Trends and Wikipedia: Quantifying the Relationship Between Phenomena of the Internet Era.” Scientific Reports 3 (1): 3415. https://doi.org/10.1038/srep03415.
  • Kristoufek, L., and E. Scalas. 2015. “What are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis.” PLoS One 10 (4): e0123923. https://doi.org/10.1371/journal.pone.0123923.
  • Li, X., and C. A. Wang. 2017. “The Technology and Economic Determinants of Cryptocurrency Exchange Rates: The Case of Bitcoin.” Decision Support Systems 95:49–60. https://doi.org/10.1016/j.dss.2016.12.001.
  • Llorente, G., R. Michaely, G. Saar, and J. Wang. 2002. “Dynamic Volume-Return Relation of Individual Stocks.” The Review of Financial Studies 15 (4): 1005–1047. https://doi.org/10.1093/rfs/15.4.1005.
  • Merediz-Solà, I., and A. F. Bariviera. 2019. “A Bibliometric Analysis of Bitcoin Scientific Production.” Research in International Business and Finance 50:294–305. https://doi.org/10.1016/j.ribaf.2019.06.008.
  • Miller, D., and J.-M. Kim. 2021. “Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies.” Journal of Risk and Financial Management 14 (10): 486. https://doi.org/10.3390/jrfm14100486.
  • Naeem, M., K. Saleem, S. Ahmed, N. Muhammad, F. Mustafa, and P. Vassilios. 2020. “Extreme return-volume relationship in cryptocurrencies: Tail dependence analysis.” Cogent Economics & Finance 8 (1): 1834175. https://doi.org/10.1080/23322039.2020.1834175.
  • Nakamoto, S. 2008. “Bitcoin: A Peer-To-Peer Electronic Cash System.” Decentralized Business Review .
  • Nasir, M. A., T. L. D. Huynh, S. P. Nguyen, and D. Duong. 2019. “Forecasting Cryptocurrency Returns and Volume Using Search Engines.” Financial Innovation 5 (1): 1–13. https://doi.org/10.1186/s40854-018-0119-8.
  • Polasik, M., A. I. Piotrowska, T. P. Wisniewski, R. Kotkowski, and G. Lightfoot. 2015. “Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry.” International Journal of Electronic Commerce 20 (1): 9–49. https://doi.org/10.1080/10864415.2016.1061413.
  • Sun, X., M. Liu, and Z. Sima. 2020. “A Novel Cryptocurrency Price Trend Forecasting Model Based on Lightgbm.” Finance Research Letters 32:101084. https://doi.org/10.1016/j.frl.2018.12.032.
  • Tibshirani, R. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society: Series B (Methodological) 58 (1): 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.
  • West, K. D. 1996. “Asymptotic Inference About Predictive Ability.” Econometrica: Journal of the Econometric Society 64 (5): 1067–1084. https://doi.org/10.2307/2171956.
  • Yi, S., Z. Xu, and G.-J. Wang. 2018. “Volatility Connectedness in the Cryptocurrency Market: Is Bitcoin a Dominant Cryptocurrency?” International Review of Financial Analysis 60:98–114. https://doi.org/10.1016/j.irfa.2018.08.012.
  • Zhang, W., P. Wang, X. Li, and D. Shen. 2018. “Multifractal Detrended Cross-Correlation Analysis of the Return-Volume Relationship of Bitcoin Market.” Complexity 2018:1–20. https://doi.org/10.1155/2018/8691420.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.