Sentiment-Induced Bubbles in the Cryptocurrency Market

Abstract

Cryptocurrencies lack clear measures of fundamental values and are often associated with speculative bubbles. This paper introduces a new way of testing for speculative bubbles based on StockTwits sentiment, which is used as the transition variable in a smooth transition autoregression. The model allows for conditional heteroskedasticity and fat tails of the conditional distribution of the error term, and volatility may depend on the constructed sentiment index. We apply the model to the CRIX index, for which several bubble periods are identified. The detected locally explosive price dynamics, given the specified bubble regime controlled by a smooth transition function, are more akin to the notion of speculative bubble that is driven by exuberant sentiment. Furthermore, we find that volatility increases as the sentiment index decreases, which is analogous to the commonly called leverage effect.

Price Movement Prediction of Cryptocurrencies Using Sentiment Analysis and Machine Learning

“…Another point of interest in the cryptocurrency market is the large-scale of available public sentiment data, particularly from social networks. This data can presumably be used to infer future human behavior, and therefore could be used to develop advantageous trading strategies [ 9 , 10 ] as has been shown in recent attempts to detect speculative bubbles in the cryptocurrency market using sentiment analysis [ 11 ].…”

A model of the optimal selection of crypto assets

“…tweets), information available on the Internet and prices [ 35 , 36 ]. Several socio-economic signals, such as volume of word-of-mouth communications and number of new adopters, appears to be intertwined with price dynamics and especially with price movements [ 37 , 38 ]. In our analysis as well, we will focus on and model the interplay between network effects (i.e.…”

A percolation model for the emergence of the Bitcoin Lightning Network

“…Moreover, data on users' behaviour and spending patterns have been used to understand the global state of the crypto-economy 22 and the drivers of the growth of the network 23 . More generally, our paper taps into the growing literature on quantitative investigations of the cryptocurrencies landscape, including models of pricing and adoption of tokens 24-27 , analysis of the market structure 28-34 , price prediction based on sentiment and social interactions [35][36][37][38][39][40][41][42][43] , dynamical analysis of informational efficiency 44 , and centralization of the Bitcoin economy 45 .In this paper, we investigate under which conditions in terms of blockchain and Lightning fees, average wealth and volume of transactions per user, a Lightning Network that spans a sizeable fraction of Bitcoin users -thus solving the scalability problem -emerges. We model the emergence of the Lightning Network as a (bond) percolation process on a graph, exploring how different conditions may impact its feasibility 46 .…”

“…pools and then arranged in the blocks data structure by miners: multiple miners compete using computational power to validate the next block of the chain -and therefore earn the associated reward for the service and transactions' fees-according to the Proof-of-Work consensus algorithm. Depending on the usage of the network and due to limitations in block size, waiting times can peak around 30 minutes (while the typical range is around 6-8 minutes), while blockchain fees per transaction exhibit a broad range of variability, from a few cents to [40][41][42][43][44][45][46][47][48][49][50] USD (data taken from https://www.blockchain.com/charts).…”