Table 11.
Research on cryptocurrency bubble analysis
| Models | Data | Results | Innovation | References |
|---|---|---|---|---|
| Network measures of complexity, Random matrix theory |
Daily returns of 24 global cryptocurrency price time series from 2013 to 2018 | The market effect as a whole is captured by the highest eigenvalue, which is very susceptible to crash occurrences. Both the largest eigenvalue of the correlation matrix and the new economic quality can serve as quantum indicators that foretell the market fall for Bitcoin. | The possibility of using recursive measures of complexity, entropy measures, network measures and quantum measures to detect dynamic changes in complex time series is explored. | Soloviev and Belinskiy163 |
| LASSO, Network analysis | Data for 50 cryptocurrencies from January 1, 2015 to September 30, 2020 | The connectivity of cryptocurrency returns under extreme shocks may be stronger and more complex. | Reveal media-based and right-tail-based return interdependence between cryptocurrencies under normal and extreme market conditions. | Shahzad et al.164 |
| Quantile vector autoregressive model |
Closing prices of six cryptocurrencies from June 1, 2016 to May 31, 2021 | Explains the existence of cryptocurrency asset interdependence and contagion effects during bubble and crash periods. | Separating dependency, contagion, and asset rotation effects by concentrating on currencies with higher market capitalization measures directional spillovers. | Chowdhury et al.165 |
| Ensemble learning, CNN, LSTM |
BTC, ETH and XRP from January 1, 2018 to August 31, 2019 | Ensemble learning and deep learning can effectively benefit each other to develop powerful, stable, and reliable predictive models. | Ensemble learning strategies are combined with advanced deep learning models for predicting hourly prices and movements of cryptocurrencies. | Livieris et al.166 |
| RNN, GRU, LSTM, MLP | BTC, ETH, USDT, and BNB data over the years |
The neural network’s input data has the best ability to predict prices, and the use of adaptive feature selection approaches significantly enhances classification performance. | A prediction model based on deep learning during the bubble period is proposed to predict and classify the price of cryptocurrency and its movement direction. | El-Berawi et al.167 |
| CNN, Ensemble models | 20 stocks from 2000 to 2021 | The experimental results show that the integrated CNN model using GAF greatly improves the prediction accuracy of the model under key market conditions. | An integrated CNN-based model is proposed that is highly resilient to stock market crashes, especially in the early stage of the COVID-19 pandemic | Ghasemieh and Kashef168 |
| ZI/MI traders, DRL | January 1, 2015 to December 31, 2018 BTC, ETH | The ZI/MI factor is easier to interpret than the CI factor, and GGSMZ proves to be a decision support tool for investors. | A trading agent mechanism GGSMZ based on neurofuzzy mechanism is introduced. The behavior of ZI/MI trading agents during financial bubbles is analyzed. | Guarino et al.169 |
| Security model |
Ethereum and Litecoin | Consumers could be holding economies with essentially useless bitcoin, and a systemic attack could jeopardize bitcoin’s ability to move. | Show how Bitcoin’s security-model can embed price volatility amplification and develop models that let consumers choose between Bitcoin and fiat currencies | Pagnotta170 |
| NLP, Hyperbolic learning | Prices from March 1, 2016 to April 7, 2021 | The utility of CryptoBubbles is demonstrated, and CryptoBubbles and our hyperbolic model are publicly released. | The multi-span crypto bubble prediction task and dataset of CryptoBubble are proposed to explore the power-law dynamics of social media hype. | Sawhney et al.171 |
| Time-varing parameter model |
Bitcoin, Ethereum, Litecoin, and XRP data during the 2017–2018 cryptocurrency bubble | During the 2018 crypto crash, the negative news effect was a significant driver of crypto returns. | It generalizes the asset pricing model with correlation between return and volatility, considers time-varying parameters, and explains the importance of time-varying returns and volatility maybe formally verified in this new model. | Cross et al.172 |
| Dynamic Time Warping(DTW) | Daily closing prices of 18 major cryptocurrencies for 2017 2018 | Comparing the cryptocurrency bubble period to the COVID 19 pandemic, the results suggest that this one had the greatest impact on cryptocurrency market efficiency. | DTW paths are used to study the lead lag relationship between different cryptocurrencies | Montasser et al.173 |