Cryptocurrency price analysis

Cryptocurrencies have taken the world by storm when it surfaced as the world’s first encounter of digitalized currency alongside the conventional fiat money. Having an internet-based medium of exchange, it leverages on cryptographical functions for financial transactions by utilizing the blockchain...

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Bibliographic Details
Main Author: Neo, Kai Shuen
Other Authors: Anwitaman Datta
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/138075
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Institution: Nanyang Technological University
Language: English
Description
Summary:Cryptocurrencies have taken the world by storm when it surfaced as the world’s first encounter of digitalized currency alongside the conventional fiat money. Having an internet-based medium of exchange, it leverages on cryptographical functions for financial transactions by utilizing the blockchain technology. Cryptocurrencies are also known for being decentralized, transparent, immutable and unrestricted from the control of central authorities. Essentially, this means that cryptocurrencies are theoretically immune to traditional ways of government control and interference, and that a multitude of factors, ranging from demand and supply, mining, regulations, news and media, affect cryptocurrency price. The cryptocurrency in study would be bitcoin. This project seeks to build a better understanding of news events’ influence on bitcoin price by establishing correlations between bitcoin price and the types of news that actually have an effect on it. This is achieved by: (1) collecting bitcoin prices from 12 April 2019 to 13 March 2020, (2) crawling news titles of Business News, Market News and Bitcoin-related News from 12 April 2019 to 13 March 2020, (3) performing Sentiment Analysis on news titles to obtain their sentiment scores categorized to be positive, neutral or negative, (4) obtaining correlation coefficients of bitcoin price vs sentiment score of news events, (5) plotting time series of bitcoin prices vs sentiment scores of news events, and lastly, (6) analyzing and drawing inferences.