We study investor sentiment on a non-classical asset such as cryptocurrency using machine learning methods. We account for context-specific information and word similarity by using efficient language modelling tools such as construction of featurized word representations (embeddings) and...

Cryptocurrencies, especially Bitcoin (BTC), which comprise a new revolutionary asset class, have drawn extraordinary worldwide attention. The characteristics of the cryptocurrency/ BTC include a high level of speculation, extreme volatility and price discontinuity. In this paper, we propose a...

The 2017 bubble on the cryptocurrency market recalls our memory in the dot-com bubble, during which hard-to-measure fundamentals and investors' illusion for brand new technologies led to overvalued prices. Benefiting from the massive increase in the volume of messages published on social media...

We propose how to quantify high-frequency market sentiment using high-frequency news from NASDAQ news platform and support vector machine classifiers. News arrive at markets randomly and the resulting news sentiment behaves like a stochastic process. To characterize the joint evolution of...

Cryptocurrencies come with benefits, such as anonymity of payments and positive network effects of user adoption, and transaction risks, such as unconfirmed transfers, hacks, and frauds. They compete with central-bank-regulated currencies as a means of payment but this competition is not...

We discover a novel flight-to-safety effect from cryptocurrency markets to stock markets. It occurs after a series of hacking attacks on crypto exchanges as greater uncertainty heightens investors' risk awareness and perception, which, in turn, induces herd-like behaviour in favor of safer stock...

We investigate the relationship between underlying blockchain mechanism of cryptocurren- cies and its distributional characteristics. In addition to price, we emphasise on using actual block size and block time as the operational features of cryptos. We use distributional charac- teristics such...

News move markets and contains incremental information about stock reactions. Future trading volumes, volatility and returns are a ected by sentiments of texts and opinions expressed in articles. Earlier work of sentiment distillation of stock news suggests that risk prole reactions might differ...

We distill tone from a huge assortment of NASDAQ articles to examine the predictive power of media-expressed tone in single-stock option markets and equity markets. We find that (1) option markets are impacted by media tone; (2) option variables predict stock returns along with tone; (3) option...