In this paper, I analyze the relation between daily bitcoin returns and sentiment, using a dataset reaching from 2013 to 2018. I find that daily bitcoin returns are not only affected contemporaneous by the bitcoin-sentiment measures, but also in the next three days - while established stock-market sentiment measures provide no explanatory power.
Additionally, the negative emotions show return-reversal patterns as often observed in sentiment-induced mispricing literature, resulting in higher returns the next two days, after affecting returns negatively today. I further find that trading volume affects re-turns positively today, in the next four days, and that it is connected with stock-market measures. For what I wisdom, this is also the first academic research that uses the recently introduced Thomson Reuters MarketPsych Indices on cryptocurrencies.
Table of Contents
I. Introduction
II. Stock Sentiment Literature
A. Bitcoin Framework
A.1. Bitcoin Sentiment Literature
III. Data and Methodology
A. Thomson Reuters MarketPsych Indices
A.1. Constructing Sentiment Measures
A.2. Measures
IV. Empirical Results
A. Bitcoin Sentiment Measures and Future Returns
B. Drivers of Bitcoin Tradevolume
V. Conclusions
Research Objectives and Themes
This master thesis investigates the relationship between investor sentiment and daily bitcoin returns, utilizing a comprehensive five-year dataset ranging from 2013 to 2018. The study aims to determine how various sentiment measures, particularly the newly introduced Thomson Reuters MarketPsych Indices (TRMI), influence contemporaneous and future bitcoin prices, while also analyzing the drivers behind bitcoin trading volume.
- The impact of positive and negative emotions on daily bitcoin returns.
- Analysis of return-reversal patterns induced by sentiment in the cryptocurrency market.
- The influence of media attention (Buzz) and trading volume on bitcoin price dynamics.
- Comparison of cryptocurrency sentiment versus established stock-market sentiment indicators.
- Investigation of macro-economic and supply-demand factors in bitcoin pricing models.
Excerpt from the Thesis
I. Introduction
In the short era of nine years Bitcoin - the first digital peer-to-peer cryptocurrency - made its way into mainstream media and consciousness by potentially disrupting the financial mechanisms as we know them. Unlike with traditional fiat money (i.e. USD, EUR etc.), no banks or other financial institutions are needed. Neither for issuing money, nor for transferring or validating transactions. The blockchain technology keeps track of every single transaction in a public distributed ledger which encompasses the double spending problem - leaving financial intermediaries out of the process - and paving the way for industry-shaping blockchain innovations. While this sounds like the next financial revolution, Bitcoins market capitalization with 116.6 billion USD and the whole cryptocurrency market capitalization of 274 billion USD is still far from being adopted as a mainstream asset class compared to the equity market capitalization of 69 trillion USD in equity markets. Mainly the lacking regulation and dramatic fluctuations in Bitcoins price keeps institutional investors and the broad mass away, e.g. after closing at the all-time high of 19.497 USD on 17th December 2017, one bitcoin is trading at 6.100 USD at the 27th of June 2018 - losing 70% of its value in six months. Price dynamics like this fueled researchers’ attention on Bitcoin.
Chapter Summaries
I. Introduction: This chapter provides the context for the emergence of Bitcoin, its market challenges, and the motivation for investigating its pricing mechanisms through investor sentiment.
II. Stock Sentiment Literature: This section reviews existing academic literature on noise trader risk and investor sentiment in traditional financial markets, establishing the theoretical basis for the study.
A. Bitcoin Framework: This sub-chapter details the unique characteristics of Bitcoin and its role as a speculative asset, reviewing prior research on its fundamental value and market dynamics.
A.1. Bitcoin Sentiment Literature: An overview of existing research specifically focused on Bitcoin sentiment, highlighting the shift from basic social media analysis to more advanced sentiment metrics.
III. Data and Methodology: This chapter describes the datasets used, including the Thomson Reuters MarketPsych Indices, and the empirical models applied to test the relationship between sentiment and returns.
A. Thomson Reuters MarketPsych Indices: An explanation of the TRMI data collection, covering how sentiment, buzz, and emotional variables are calculated from large-scale media and web sources.
A.1. Constructing Sentiment Measures: Details the application of Principal Component Analysis (PCA) to aggregate various emotional indices into usable sentiment vectors.
A.2. Measures: Provides definitions for all variables used in the econometric models, including Bitcoin log returns, blockchain-related supply-demand factors, and macroeconomic controls.
IV. Empirical Results: Presents the findings from the regression analyses, focusing on how sentiment measures correlate with daily Bitcoin returns and future market movements.
A. Bitcoin Sentiment Measures and Future Returns: Discusses evidence of return-reversal patterns where current sentiment predicts future price adjustments in the Bitcoin market.
B. Drivers of Bitcoin Tradevolume: Examines factors influencing trading volume, highlighting the significant role of media attention and market sentiment.
V. Conclusions: Summarizes the study’s findings, suggesting that sentiment-induced mispricing is a key driver of Bitcoin returns and noting the potential of Bitcoin as a unique asset class.
Key Keywords
Bitcoin, Returns, Reversal, Sentiment, TRMI, Cryptocurrency, Blockchain, Investor Sentiment, MarketPsych, Trading Volume, Speculative Asset, Principal Component Analysis, Noise Traders, Financial Markets, Empirical Analysis
Frequently Asked Questions
What is the core focus of this research?
The thesis examines the relationship between investor sentiment—measured through textual analysis—and daily price returns of Bitcoin, specifically investigating if sentiment influences prices contemporaneously or over subsequent days.
What are the primary thematic areas?
Key themes include sentiment-induced mispricing, the role of noise traders, the predictive power of media and social sentiment (TRMI), and the influence of blockchain-specific supply-demand factors on Bitcoin pricing.
What is the primary research goal?
The study aims to quantify the impact of psychological sentiments (such as joy, fear, and optimism) on Bitcoin returns and determine whether these metrics can explain return-reversal patterns commonly observed in speculative markets.
Which scientific methods are utilized?
The researcher uses OLS (Ordinary Least Squares) regressions, Principal Component Analysis (PCA) for constructing sentiment indices, and vector autoregression models to control for various macroeconomic and supply-demand variables.
What does the main body of the paper cover?
The main body covers the literature review on stock and Bitcoin sentiment, the methodology for processing Thomson Reuters MarketPsych Indices, detailed regression results regarding price returns, and an analysis of the drivers of Bitcoin trading volume.
What are the key terms that define this work?
The most important terms include Bitcoin returns, TRMI (Thomson Reuters MarketPsych Indices), return reversal, investor sentiment, and trading volume.
How does the author define 'Buzz' in the context of this study?
Buzz is defined as the sum of all references to Bitcoin found in news and social media sources, serving as a measure of media attention and popularity that is analyzed for its influence on price and trading volume.
What is the significance of the return reversal findings?
The finding that negative sentiment results in lower returns today but higher returns in the following two days is significant because it suggests a temporary mispricing driven by noise traders, which is then corrected by more experienced participants.
- Citation du texte
- Andreas Bialek (Auteur), 2018, Bitcoin Pricing. An Empirical Analysis, Munich, GRIN Verlag, https://www.grin.com/document/459668
