Federico D'Amario, Milos Ciganovic
arXiv 22 Sep 2022 · Finance — Statistical Finance · publishedApplied Economics (2023) · 6 citations (OpenAlex)
arXiv:2210.00883 · PDF · DOI · OpenAlex · Extracted main text
Cryptocurrencies have become a trendy topic recently, primarily due to their disruptive potential and reports of unprecedented returns. In addition, academics increasingly acknowledge the predictive power of Social Media in many fields and, more specifically, for financial markets and economics. In this paper, we leverage the predictive power of Twitter and Reddit sentiment together with Google Trends indexes and volume to forecast the log returns of ten cryptocurrencies. Specifically, we consider $Bitcoin$, $Ethereum$, $Tether$, $Binance Coin$, $Litecoin$, $Enjin Coin$, $Horizen$, $Namecoin$, $Peercoin$, and $Feathercoin$. We evaluate the performance of LASSO-VAR using daily data from January 2018 to January 2022. In a 30 days recursive forecast, we can retrieve the correct direction of the actual series more than 50% of the time. We compare this result with the main benchmarks, and we see a 10% improvement in Mean Directional Accuracy (MDA). The use of sentiment and attention variables as predictors increase significantly the forecast accuracy in terms of MDA but not in terms of Root Mean Squared Errors. We perform a Granger causality test using a post-double LASSO selection for high-dimensional VARs. Results show no "causality" from Social Media sentiment to cryptocurrencies returns
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | A. Hecq, L. Margaritella, and S. Smeekes (2019) Granger causality testing in high-dimensional vars: a post-double-selection procedure | 0.585 | 3 | 1 | 100% |
| 2 | C. W. Granger (1969) Investigating causal relations by econometric models and cross-spectral methods | 0.511 | 2 | 1 | 100% |
| 3 | N. Aslanidis, A. F. Bariviera, and Ó. G. López (2022) The link between cryptocurrencies and google trends attention | 0.405 | 1 | 1 | 100% |
| 4 | R. Auer, C. Monnet, and H. S. Shin (2021) Permissioned distributed ledgers and the governance of money | 0.405 | 1 | 1 | 100% |
| 5 | M. Balcilar, E. Bouri, R. Gupta, and D. Roubaud (2017) Can volume predict bitcoin returns and volatility? a quantiles-based approach | 0.405 | 1 | 1 | 100% |
| 6 | M. Bańbura, D. Giannone, and L. Reichlin (2010) Large bayesian vector auto regressions | 0.405 | 1 | 1 | 100% |
| 7 | J. Baumgartner, S. Zannettou, B. Keegan, M. Squire, and J. Blackburn (2020) The pushshift reddit dataset | 0.405 | 1 | 1 | 100% |
| 8 | A. Belloni, V. Chernozhukov, and C. Hansen (2014) High-dimensional methods and inference on structural and treatment effects | 0.405 | 1 | 1 | 100% |
| 9 | B. S. Bernanke, J. Boivin, and P. Eliasz (2005) Measuring the effects of monetary policy: a factor-augmented vector autoregressive (favar) approach | 0.405 | 1 | 1 | 100% |
| 10 | C. Catalini and J. S. Gans (2020) Some simple economics of the blockchain | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 38 scored citations.