Abstract:
The realm of cryptocurrency has grown exponentially over the past decade, with the most rapid advances seen in the past few years as more and more parties around the world recognize the value of holding digital assets online. Statistics from Twitter support this statement where, approximately 1,500 Tweets about Bitcoin alone is recorded per hour. Consequently, many people are beginning to become more aware and accepting of the nature of digital currencies, and traders in particular seek to know how they can make profitable crypto-coin trades and investments. Although a number of research projects have been undertaken to develop systems that can effectively predict price movements in the cryptocurrency market, they display significant efficiency gaps, which this paper further explores. The authors then attempt to learn from past studies and construct a more holistic approach to a predictive price model for the cryptocurrency market. This focuses on assessing key factors that affect the volatility of the market - public perception, trading data, historic price data, and the interdependencies between Bitcoin and Altcoins - and how they can be best utilized from a technological aspect by applying sentiment analysis and machine learning techniques, to increase the efficiency of the process.
