Reddit as a prediction tool for crypto-assets Other Languages

ID:
66434
Abstract:
Cryptocurrencies, such as Bitcoin and Ethereum, have recently become a conversation topic among the general population. This paper will explore the information available in Reddit regarding crypto assets. Unlike other social platforms, Reddit allows analyzing the general population sentiment while conveniently organizing information by topic. We study the benefit of sentiment variables derived from Reddit's crypto forums to forecast volatilities and returns. While volatility forecasts seem to benefit from Reddit sentiment variables consistently, results are not statistically different from a benchmark. In contrast, returns present mixed forecasting results but show statistical differences from the proposed benchmark. We also offer evidence that the Reddit variables gain importance in market-wide and asset-specific events.
ABNT Citation:
CAMOU, L. A. L.Reddit as a prediction tool for crypto-assets. Revista Brasileira de Finanças, v. 20, n. 1, p. 1-39, 2022.
APA Citation:
Camou, L. A. L.(2022). Reddit as a prediction tool for crypto-assets. Revista Brasileira de Finanças, 20(1), 1-39.
DOI:
https://doi.org/10.12660/rbfin.v20n1.2022.83888
Permalink:
https://www.spell.org.br/documentos/ver/66434/reddit-as-a-prediction-tool-for-crypto-assets/i/en
Document type:
Artigo
Language:
Inglês
References:
Ahn, Y. and Kim, D. (2020). Emotional trading in the cryptocurrency market, Finance Research Letters p. 101912.

Alexa Internet (2021). Alexa internet, inc. Competitive Analysis, Marketing Mix and Traffic for reddit.com. Accessed May 2nd, 2021. URL: https://www.alexa.com/siteinfo/reddit.com.

Anamika, Chakraborty, M. and Subramaniam, S. (2021). Does sentiment impact cryptocurrency?, Journal of Behavioral Finance pp. 1–17.

Andersen, T. G., Bollerslev, T., Diebold, F. X. and Ebens, H. (2001). The distribution of realized stock return volatility, Journal of financial economics 61(1): 43–76.

Antweiler, W. and Frank, M. Z. (2004). Is all that talk just noise? the information content of internet stock message boards, The Journal of finance 59(3): 1259–1294.

Apopo, N. and Phiri, A. (2021). On the (in) efficiency of cryptocurrencies: have they taken daily or weekly random walks?, Heliyon 7(4): e06685.

Baumgartner, J., Zannettou, S., Keegan, B., Squire, M. and Blackburn, J. (2020). The pushshift reddit dataset, Proceedings of the International AAAI Conference on Web and Social Media, Vol. 14, pp. 830–839.

Bitcoinaverage. Accessed Jan 26th, 2022. URL: https://bitcoinaverage.com/.

Bloomberg (2021). Bloomberg. Ethereum Becoming More Than Crypto Coder Darling, Grayscale Says. Accessed May 2nd, 2021. URL: https://www.bloomberg.com/news/articles/2020 -12-04/ethereum-becoming-more-than-crypto-coderdarling-grayscale-says.

Bouri, E., Gkillas, K., Gupta, R. and Pierdzioch, C. (2021). Forecasting realized volatility of bitcoin: The role of the trade war, Computational Economics 57(1): 29–53.

Breiman, L. (1996). Bagging predictors, Machine learning 24(2): 123–140.

Breiman, L. (1996). Bagging predictors, Machine learning 24(2): 123–140. CNBC (2021). Cnbc. Reddit frenzy pumps up Dogecoin, a cryptocurrency started as a joke. Accessed May 2nd, 2021. URL: https://www.cnbc.com/2021/01/29/dogecoin-cry ptocurrency-rises-over-400percent-after-reddit-g roup-talks-it-up.html.

CNBC (2021). Cnbc. Reddit frenzy pumps up Dogecoin, a cryptocurrency started as a joke. Accessed May 2nd, 2021. URL: https://www.cnbc.com/2021/01/29/dogecoin-cry ptocurrency-rises-over-400percent-after-reddit-g roup-talks-it-up.html.

CoinDesk (2021). Coindesk. XRP Pump Fails to Materialize as Price Crashes 40% From Day’s High. Accessed May 2nd, 2021. URL: https://www.coindesk.com/xrp-pump-fails-to-m aterialize-as-price-crashes-40-from-days-high.

CoinMarketCap (2021a). Coinmarketcap. Historical Snapshot 03 January 2021. Accessed May 2nd, 2021. URL: https://coinmarketcap.com/historical/2021010 3/.

CoinMarketCap (2021b). Coinmarketcap. Today’s Cryptocurrency Prices by Market Cap. Accessed May 2nd, 2021. URL: https://coinmarketcap.com/.

CoinMarketCap (2021c). Coinmarketcap. Top Cryptocurrency Spot Exchange. Accessed May 2nd, 2021. URL: https://coinmarketcap.com/rankings/exchanges/

CoinMarketCap (2022). Coinmarketcap blog. Accessed Jan 26th, 2022. URL: https://blog.coinmarketcap.com/2020/05/29/co inmarketcap-revamps-market-pairs-ranking-to-empo wer-users-against-volume-inflation/.

Cointelegraph (2021). Cointelegraph. Bitcoin’s Twitter-volume spikes to new all-time highs on Elon pump. Accessed May 2nd, 2021. URL: https://cointelegraph.com/news/bitcoin-s-twi tter-volume-spikes-to-new-all-time-highs-on-elon -pump.

Corsi, F. (2009). A simple approximate long-memory model of realized volatility, Journal of Financial Econometrics 7(2): 174–196.

DeMarzo, P. M., Vayanos, D. and Zwiebel, J. (2003). Persuasion bias, social influence, and unidimensional opinions, The Quarterly journal of economics 118(3): 909–968.

Diebold, F. X. and Mariano, R. S. (2002). Comparing predictive accuracy, Journal of Business & economic statistics 20(1): 134–144.

Dogecoin (2021). Dogecoin. Official website. Accessed May 2nd, 2021. URL: https://dogecoin.com/.

Engle, R. F., Hansen, M., Lunde, A. et al. (2011). And now, the rest of the news: Volatility and firm specific news arrival, Unpublished Working Paper, CREATES.

Fleming, J., Kirby, C. and Ostdiek, B. (2003). The economic value of volatility timing using “realized” volatility, Journal of Financial Economics 67(3): 473–509.

Friedman, J., Hastie, T., Tibshirani, R. et al. (2001). The elements of statistical learning, Vol. 1, Springer series in statistics New York. Google Trends (2021). Google trends. Trending Searches. Accessed May 2nd, 2021. URL: https://trends.google.com/trends/trendingsea rches/daily?geo=US.

Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods, Econometrica: journal of the Econometric Society pp. 424–438. Henriques, I. and Sadorsky, P. (2018). Can bitcoin replace gold in an investment portfolio?, Journal of Risk and Financial Management 11(3): 48.

Hutto, C. and Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text, Proceedings of the International AAAI Conference on Web and Social Media, Vol. 8.

Karalevicius, V., Degrande, N. and De Weerdt, J. (2018). Using sentiment analysis to predict interday bitcoin price movements, The Journal of Risk Finance .

Kayal, P. and Balasubramanian, G. (2021). Excess volatility in bitcoin: extreme value volatility estimation, IIM Kozhikode Society & Management Review p. 2277975220987686.

Kraaijeveld, O. and De Smedt, J. (2020). The predictive power of public twitter sentiment for forecasting cryptocurrency prices, Journal of International Financial Markets, Institutions and Money 65: 101188.

Kristoufek, L. (2013). Bitcoin meets google trends and wikipedia: Quantifying the relationship between phenomena of the internet era, Scientific reports 3(1): 1–7.

Kristoufek, L. (2018). On bitcoin markets (in) efficiency and its evolution, Physica A: statistical mechanics and its applications 503: 257–262.

Liu, J., Ma, F., Yang, K. and Zhang, Y. (2018). Forecasting the oil futures price volatility: Large jumps and small jumps, Energy Economics 72: 321– 330.

Manela, A. and Moreira, A. (2017). News implied volatility and disaster concerns, Journal of Financial Economics 123(1): 137–162. Markets Insider (2021).

Markets insider. A new wave of institutional interest has boosted bitcoin. Here are the key players getting involved, from Morgan Stanley to Tesla. Accessed May 2nd, 2021. URL: https://markets.businessinsider.com/currenci es/news/bitcoin-btc-institutional-interest-crypt ocurrencies-wall-street-tesla-microstrategy-jpmo rgan-2021-3-1030194067.

Martin, B. and Koufos, N. (2018). Sentiment analysis on reddit news headlines with python’s natural language toolkit (nltk). Accessed May 2nd, 2021. URL: https://www.learndatasci.com/tutorials/senti ment-analysis-reddit-headlines-pythons-nltk/.

McAleer, M. and Medeiros, M. C. (2008). Realized volatility: A review, Econometric Reviews 27(1-3): 10–45.

Naeem, M. A., Mbarki, I., Suleman, M. T., Vo, X. V. and Shahzad, S. J. H. (2021). Does twitter happiness sentiment predict cryptocurrency?, International Review of Finance 21(4): 1529–1538.

Pesaran, M. H. and Timmermann, A. (2005). Small sample properties of forecasts from autoregressive models under structural breaks, Journal of Econometrics 129(1-2): 183–217.

Prajapati, P. (2020). Predictive analysis of bitcoin price considering social sentiments, arXiv preprint arXiv:2001.10343 .

Qiu, Y., Zhang, X., Xie, T. and Zhao, S. (2019). Versatile har model for realized volatility: A least square model averaging perspective, Journal of Management Science and Engineering 4(1): 55–73.

Racine, J. (2000). Consistent cross-validatory model-selection for dependent data: hv-block cross-validation, Journal of econometrics 99(1): 39–61.

Rosol, M., Młynczak, M. and Cybulski, G. (2022). Granger causality test with ´ nonlinear neural-network-based methods: Python package and simulation study, Computer Methods and Programs in Biomedicine.

Shen, D., Urquhart, A. and Wang, P. (2019). Does twitter predict bitcoin?, Economics Letters 174: 118–122.

Urquhart, A. (2016). The inefficiency of bitcoin, Economics Letters 148: 80– 82.

W. (1986). Stability comparison of estimators, Econometrica: Journal of the Econometric Society pp. 1207–1235.

Wooley, S., Edmonds, A., Bagavathi, A. and Krishnan, S. (2019). Extracting cryptocurrency price movements from the reddit network sentiment, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), IEEE, pp. 500–505.

Yu, M. (2019). Forecasting bitcoin volatility: The role of leverage effect and uncertainty, Physica A: Statistical Mechanics and Its Applications 533: 120707.

Zipf, G. K. (2016). Human behavior and the principle of least effort: An introduction to human ecology, Ravenio Books.