Christos Bilanakos

Using Time-Series and Sentiment Analysis to Detect the Determinants of Bitcoin Prices

This paper uses time-series analysis to study the relationship between Bitcoin prices and fundamental economic variables, technological factors and measurements of collective mood derived from Twitter feeds. Sentiment analysis has been performed on a daily basis through the utilization of a state-of-the-art machine learning algorithm, namely Support Vector Machines (SVMs). A series of short-run regressions shows that the Twitter sentiment ratio is positively correlated with Bitcoin prices. The short-run analysis also reveals that the number of Wikipedia search queries (showing the degree of public interest in Bitcoins) and the hash rate (measuring the mining difficulty) have a positive effect on the price of Bitcoins. On the contrary, the value of Bitcoins is negatively affected by the exchange rate between the USD and the euro (which represents the general level of prices). A vector error-correction model is used to investigate the existence of long-term relationships between cointegrated variables. This kind of long-run analysis reveals that the Bitcoin price is positively associated with the number of Bitcoins in circulation (representing the total stock of money supply) and negatively associated with the Standard and Poors 500 stock market index (which indicates the general state of the global economy).

Do Dominant Firms Provide More Training

This paper examines the relationship between firm-specific training and product market competition. A canonical Cournot competition model shows that the profitability of training investments increases as the number of competitors decreases. Empirical evidence from British establishments in 1998, 2004 and 2011 confirms that a critical form of specific training, cross-training, is far more extensive in less competitive product markets. This persists within all three separate cross-sections and in two separate panel estimates and suggests that a dominant product market position increases the incentives to invest in specific human capital.