Essays on the New Blockchain-Based Digital Financial Market : Risks and Opportunities

This doctoral thesis consists of five original essays on the risks and opportunities of the new blockchain-based digital financial market. The purpose of this dissertation is to analyze, identify, and, if possible, predict some of the major risks in the market for blockchain-based digital assets. It analyzes how crypto-specific characteristics are associated with solvency risk, sustainability risk, seclusion risk, and sentiment risk. On top of that, it also sheds light on the opportunity side of this financial innovation. The first essay of this dissertation specifically focuses on cryptocurrency for solvency risks. To forecast potential cryptocurrency default at an early stage, this study focuses on variables that are part of the information set of the investor 1 month at most after the start of trading for a cryptocurrency. The results of this research show that bankruptcies among cryptocurrencies are predictable. The second essay explores energy risk as a fundamental market-driving force for the pricing of cryptocurrency. Cryptocurrencies using a high-energy-consumption consensus protocol are riskier than others because their mining costs are more exposed to changes in energy price. Surprisingly, the study finds that energy consumption does not seem to play a role in pricing cryptocurrency. The third essay hypothesizes that privacy coins form a distinct submarket in the cryptocurrency market, shedding light on seclusion risk. It shows that privacy coins and non-privacy coins are two distinct asset markets within the cryptocurrency market. The fourth essay is about news media sentiment risk. It explores whether news media sentiments have an impact on Bitcoin volatility. It also differentiates financial sentiment and psychological sentiment and finds that financially optimistic investors are driving the Bitcoin market. On the other hand, the fifth essay in this dissertation analyzes opportunities, especially the funding opportunity in the widely known category of new digital assets defined as crypto tokens. It analyzes the determinants of the success of initial coin offerings and finds that initial-coin-offering investors are largely guided by their emotions when making investment decisions. Surprisingly, regulatory framework has not yet become a priority among policymakers. Therefore, this doctoral dissertation not only facilitates future research, but also helps regulators in shaping the future of blockchain-based financial technologies.Tämä väitöskirja koostuu viidestä esseestä, jotka käsittelevät uuden lohkoketjupohjaisen digitaalisen rahoitusmarkkinan riskejä ja mahdollisuuksia. Väitöskirjan tarkoituksena on analysoida, tunnistaa ja mahdollisuuksien mukaan ennustaa joitakin lohkoketjupohjaisten digitaalisten varojen markkinoiden suurimpia riskejä. Siinä analysoidaan, miten kryptovaluuttakohtaiset ominaisuudet liittyvät vakavaraisuusriskiin, kestävyysriskiin, eristäytymisriskiin ja sentimenttiriskiin. Tämän lisäksi se valottaa myös tämän rahoitusinnovaation mahdollisuuksia. Tämän väitöskirjan ensimmäisessä esseessä keskitytään erityisesti kryptovaluuttaan maksukyvyttömyysriskinä. Tässä tutkimuksessa keskitytään muuttujiin, jotka ovat sijoittajan saatavilla korkeintaan 1 kuukausi sen jälkeen, kun kaupankäynti kryptovaluutalla on alkanut. Tämän tutkimuksen tulokset osoittavat, että kryptovaluuttojen konkurssit ovat ennustettavissa. Toisessa esseessä tutkitaan energiariskiä markkinoita ohjaavana voimana kryptovaluutan hinnoittelussa. Kryptovaluutat, jotka käyttävät paljon energiaa kuluttavaa konsensusprotokollaa, ovat muita riskialttiimpia, koska niiden louhintakustannukset ovat alttiimpia energian hinnan muutoksille. Yllättäen tutkimuksessa todetaan, että energiankulutuksella ei näytä olevan merkitystä kryptovaluuttojen hinnoittelussa. Kolmannessa esseessä hypoteesina on, että yksityisyyskolikot muodostavat erillisen alamarkkinan kryptovaluuttamarkkinoilla, ja tutkimus tarkastelee näiden eristäytymisriskiä. Siinä osoitetaan, että yksityisyyskolikot ja ei-yksityisyyskolikot ovat kaksi erillistä omaisuuserämarkkinaa kryptovaluuttamarkkinoilla. Neljäs essee käsittelee uutismedian sentimenttiriskiä. Siinä tutkitaan, vaikuttaako uutismedian sentimentti Bitcoinin volatiliteettiin. Siinä myös erotetaan toisistaan taloudellinen sentimentti ja psykologinen sentimentti ja todetaan, että taloudellisesti optimistiset sijoittajat ohjaavat Bitcoin-markkinoita. Väitöskirjan viidennessä esseessä analysoidaan mahdollisuuksia, erityisesti rahoitusmahdollisuuksi, liittyen laajalti tunnettuihin digitaalisiin tokeneihin. Siinä havaitaan, että näihin omaisuuseriin sijoittavat sijoittajat toimivat pitkälti tunteidensa ohjaamina sijoituspäätöksiä tehdessään. Yllättävää kyllä, sääntelykehyksestä ei ole vielä tullut poliittisten päättäjien prioriteettia. Siksi tämä väitöskirja ei ainoastaan tue tulevaa tutkimusta, vaan auttaa myös viranomaisia lohkoketjupohjaisten rahoitusteknologioiden tulevaisuuden määrittelyssä.fi=vertaisarvioitu|en=peerReviewed

Collaborative Speculation and Overvaluation: Evidence from Social Media

I use data from StockTwits and Twitter to provide evidence that investor attention on social media in the period before earnings is related to short-term overvaluation, consistent with bullish investors herding around common information. In the 2 to 60 days after earnings, returns for companies in the highest quintile of pre-earnings announcement investor attention are 4.2 percent lower than those of companies in the lowest quintile. I find evidence that the negative post-earnings drift result found in this study is related to investors waiting until after earnings are announced to enact costly arbitrage strategies. I further examine intra- and inter-network herding and find evidence that social media influences investors beyond the population of active users. This study contributes to prior literature on herding, social media, and speculation and arbitrage

Representative agent earnings momentum models: the impact of sequences of earnings surprises on stock market returns under the influence of the Law of Small Numbers and the Gambler's Fallacy

This thesis examines the response of a representative agent investor to sequences (streaks) of quarterly earnings surprises over a period of twelve quarters using the United States S&P500 constituent companies sample frame in the years 1991 to 2006. This examination follows the predictive performance of the representative agent model of Rabin (2002b) [Inference by believers in the law of small numbers. The Quarterly Journal of Economics. 117(3).p.775 816] and Barberis, Shleifer, and Vishny (1998) [A model of investor sentiment. Journal of Financial Economics. 49. p.307 343] for an investor who might be under the influence of the law of small numbers, or another closely related cognitive bias known as the gambler s fallacy. Chapters 4 and 5 present two related empirical studies on this broad theme. In chapter 4, for successive sequences of annualised quarterly earnings changes over a twelve-quarter horizon of quarterly earnings increases or falls, I ask whether the models can capture the likelihood of reversion. Secondly, I ask, what is the representative investor s response to observed sequences of quarterly earnings changes for my S&P500 constituent sample companies? I find a far greater frequency of extreme persistent quarterly earnings rises (of nine quarters and more) than falls and hence a more muted reaction to their occurrence from the market. Extreme cases of persistent quarterly earnings falls are far less common than extreme rises and are more salient in their impact on stock prices. I find evidence suggesting that information discreteness; that is the frequency with which small information about stock value filters into the market is one of the factors that foment earnings momentum in stocks. However, information discreteness does not subsume the impact of sequences of annualised quarterly earnings changes, or earnings streakiness as a strong candidate that drives earnings momentum in stock returns in my S&P500 constituent stock sample. Therefore, earnings streakiness and informational discreteness appear to have separate and additive effects in driving momentum in stock price. In chapter 5, the case for the informativeness of the streaks of earnings surprises is further strengthened. This is done by examining the explanatory power of streaks of earnings surprises in a shorter horizon of three days around the period when the effect of the nature of earnings news is most intense in the stock market. Even in shorter windows, investors in S&P500 companies seem to be influenced by the lengthening of negative and positive streaks of earnings surprises over the twelve quarters of quarterly earnings announcement I study here. This further supports my thesis that investors underreact to sequences of changes in their expectations about stock returns. This impact is further strengthened by high information uncertainties in streaks of positive earnings surprise. However, earnings streakiness is one discrete and separable element in the resolution of uncertainty around equity value for S&P 500 constituent companies. Most of the proxies for earnings surprise show this behaviour especially when market capitalisation, age and cash flow act as proxies of information uncertainty. The influence of the gambler s fallacy on the representative investor in the presence of information uncertainty becomes more pronounced when I examine increasing lengths of streaks of earnings surprises. The presence of post earnings announcement drift in my large capitalised S&P500 constituents sample firms confirms earnings momentum to be a pervasive phenomenon which cuts across different tiers of the stock markets including highly liquid stocks, followed by many analysts, which most large funds would hold

Market anomaly : 2014th World Cup effect

Inspired by the World Cup effect discovered by Kaplanski and Levy (2010), we decided to in-vestigate the 2014th edition. Our findings were conclusive. The average return on the U.S. stock market during the latest World Cup was +0.87%, compared to an average of -2.42% of all past World Cups; hence, the anomaly disappeared. We suggest its disappearance was driven by: (1) the growth popularity of Football in the U.S. and its influence on the local stock market, and by (2) the publication of Kaplanski and Levy (2010 and 2014) followed by an investment strategy, which allowed sophisticated investors to take advantage of the anomaly

3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information.As these sources, methods, and applications become more interdisciplinary, the 3rd International Conference on Advanced Research Methods and Analytics (CARMA) is an excellent forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Doménech I De Soria, J.; Vicente Cuervo, MR. (2020). 3rd International Conference on Advanced Research Methods and Analytics (CARMA 2020). Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/149510EDITORIA

Sentiment Analytics and Financial Markets

From major news outlets to social media and the general public, it is common to find mentions of the existence of relationships between narratives and economic outcomes. By definition, those narratives are forms of soft information, which until recently have been difficult to quantify and are often propagated through natural language and text in particular. This thesis seeks to leverage this soft information and harness one key dimension of text in particular: Sentiment. In the context of this thesis, sentiment is defined as the disposition of an entity toward another entity, expressed via a specific medium. In the first three chapters of this thesis, the medium of interest is “News”, specifically news stories published in the financial press. The first paper uses firm-specific news sentiment to understand why market anomalies earn a premium on earnings announcement days. News sentiment shows that this premium for value firms is concentrated on bad news events, which permits us to propose new avenues to understand this market anomaly. The second and third chapters investigate more generally how news can help understand drivers of market anomalies. Market anomalies have played a central role in asset pricing research over the past decades, and numerous competing theories seek to accommodate empirical observations that deviate from the classical model. Chapter two proposes a framework based on cash-flow and discount rate news, allowing us to capture the driving forces behind anomaly returns and disentangle competing explanations for anomalies. The third chapter investigates drivers of anomaly returns and characterizes news of momentum and value stocks, in particular, highlighting the strong negative correlation between the two. It is also the first to link cash-flow news, discount rate news, and news sentiment. The economic outcome of interest in the fourth chapter is to understand how changes in company ownership, especially following leveraged buy-outs, affect employee welfare. We gather millions of online reviews of employees about their employers and investigate the underlying text data to characterize the impact private equity firms have on those narratives. Overall, employees’ satisfaction drops sharper following leverage buy-outs than in other types of ownership changes and we can trace those problems back to specific issues related to lack of management care and fear of cost-cutting and layoffs

Investor Sentiment and Attention in Capital Markets - A (Social) Media Perspective

This dissertation examines the impact of social and traditional media on capital markets. The empirical tests focus on investor sentiment which, for example, can be captured by postings on social media platforms, innovative news databases and the textual analysis of traditional media press. The research direction of this dissertation implicitly questions the assumptions stated by the traditional finance theory. Our new empirical findings and their explanations are, hence, closely linked with the behavioral finance theory. The Efficient Market Hypothesis constitutes one of the fundamental pillars of the traditional finance theory. In this concept, the availability of information is the basic requirement for the functionality of efficient capital markets. New information is quickly and correctly incorporated into an asset’s price. The new price of an asset, therefore, immediately reflects the updated fundamental value (Fama, 1969; 1970). However, various studies have recently shown that stock market movements are not always associated with rational information about an asset’s value. The observation of over- and underreaction of asset prices to news signals or distinctive return patterns gave reason for the gaining importance of the behavioral finance theory since the 1990’s. The changing availability and the easier access to information for institutional and individual investors play an important role in this recent development. For example, Figure 1 1 (p. 3) depicts the circulation of US newspapers between 1970 and 2017. The number of households covered by traditional media press decreased from more than 60 million to around 30 million households in 2017. The establishment of the internet, on the other hand, parallelly accelerated the digital development in the media landscape. Figure 1 3 (p. 5) describes the global development of social media users since 2010. The number of social media users is expected to increase from 1 billion users in 2010 to around 3 billion users in 2021. This development not only affects the society but also a specific focus group of this dissertation: the financial investors. The way investors gather, process, and disseminate information also experienced a significant change in recent decades (Puppis et al., 2017). In this connection, the development of investor attention and sentiment for individual assets is sustainably impacted by the digitalization of media channels. Consequently, we derive four fundamental research questions, which accompany the empirical analyses of this dissertation: 1. What role does investor sentiment play in financial markets? Do investors solely follow the market, or do beliefs of investors predict future returns or other market variables? 2. How does (social) media relate to financial markets in the general daily context and specifically around news events, such as earnings or M&A announcements? 3. What kind of firms are more sensitive to investor sentiment than others? 4. Does arbitrage stabilize financial markets against noise traders? The following structure of this dissertation aims to answer these questions in the best possible way: The first chapter introduces the reader to the relevance of the topic and the leading research questions of the dissertation. The second chapter lays the theoretical foundation and describes the fundamental concepts of the traditional and also the behavioral finance theory, which aims to comprehensively explain selected market anomalies. Also, we summarize selected psychological concepts that help to explain irrational actions of investors, which potentially cause market volatility and asset prices to deviate from their fundamental value. Literature reviews on investor sentiment in close relationship with traditional and social media complete the second chapter. The third chapter encompasses the first empirical work of this dissertation and primarily explores the impact of social media on capital markets. The empirical analysis falls back to more than 4.5 million posts on the leading Australian financial internet message board HotCopper between January 2008 and May 2016. The findings suggest that social media activity is price relevant for capital markets. Positive investor sentiment, for example, is in this connection contemporaneously and significantly correlated with a stock’s abnormal return. However, the effect diminishes after one month. Arbitrage of presumably informed investors only partially countervail this effect. Postings by individual investors on social media, hence, cause capital markets to overreact to potentially non-relevant information in the short-term. However, negative investor sentiment expressed in internet message boards provides a differentiated picture. Negative investor sentiment is significantly related with the next month’s abnormal returns. Also, an increasing rate of agreement on negative investor sentiment before earnings announcements forecasts negative earnings surprises. Both findings support the information hypothesis that negative internet message board postings contain value-relevant information. The question whether social media activity induces market volatility remains ambiguous. The Granger-tests and the reactions of the impulse-response functions show a bilateral relationship between return volatility and the number of internet message board postings. However, we find in this context that individual investors react more sensitive to market volatility on social media than the other way around. In summary, the results of the first empirical work provide evidence for the economic significance of investor sentiment measured on social media and its asymmetric role in capital markets. We extend the empirical analysis in the fourth chapter of this dissertation and investigate the impact of traditional and social media on target price run-ups before bid announcements. The literature previously documented an increase in the target stock price two months prior to the official bid announcement (e.g., Keown and Pinkerton, 1981). This phenomenon is also referred to as the target run-up. One group of researchers find explanations within the insider hypothesis (leakage of insider information prior to the bid announcement). Another group argues based on the market expectation hypothesis (the market anticipates publicly available information to predict upcoming mergers). Our second empirical work considers 2,765 bid announcements in Australia between January 2008 and August 2015. We use more than 15 thousand news articles, more than 80 thousand posts on the internet message board HotCopper, analyst recommendations, and Google search queries to analyze their relationship with target run-ups before official bid announcements. Thus, we specifically examine the varying impact of investor attention of different investor groups (institutional and individual investors) on target run-ups. The results let us conclude that target run-ups of smaller, unprofitable, and growth firms are significantly related with social media coverage on HotCopper. On the contrary, similar firms that lack media coverage do not experience a significant target run-up prior to a bid announcement. Target run-ups of larger capitalization stocks are, on the other hand, more sensitive to analyst recommendations. The results are consistent with the anecdotal evidence that smaller firms are usually less covered by analysts. Social media closes the information gap for small firms in this perspective. Google search inquiries for target firms are not found to be significantly related to target run-ups. The overall findings of the second empirical work support the market expectation hypothesis. In this regard, social media contributes to the increase of market efficiency and partially closes informational blind spots for smaller firms which might exist due to inefficient allocations of resources or costly information sourcing for smaller firms. The fifth chapter comprises the last empirical work of this dissertation and explores the relationship between media press sentiment and capital markets. We specifically examine the im-pact of aggregated news sentiment indices on the cross-section of returns in the asset pricing context. The literature around asset pricing especially focuses on the determination of risk premia that help to explain stock returns. A central question of our third empirical work is, therefore, whether stock returns are associated with their underlying risk or whether these returns are just a result of irrational market movements in the spirit of the behavioral finance theory. We calculate monthly aggregated news sentiment indices based on more than 120 million unique classified news articles from the Ravenpack News Analytics database between 2000 and 2017. Thus, we construct monthly zero-investment portfolios that go long on (sell) stocks which exhibit on average positive (negative) news sentiment in the previous month. The portfolio yields an annual return of 7.5% even if we control for widely-accepted risk fac-tors, such as market, size, momentum, liquidity, profitability, and investments. The results are mainly driven by positive news sentiment. Hence, we refer this premium to the “premium on optimism”. One possible explanation could be the persistent positive news coverage in the respective time period. The probability of the publication of good news is in particularly high-er if a firm experienced positive news in the prior months. The total results of our third empirical work support the view that news sentiment reflects a risk factor. The overall results of this dissertation have several implications for firms, investors, regulators and researchers in the field of behavioral finance. Firms must learn today to early anticipate crowd movements on (social) media and to deal with putatively fake news. The investor relations department of a firm must engage in this topic more sophistically content-wise and in the communicative interaction with its stakeholders. Selective communication strategies for specific firm events are required to early prevent a potentially negative public perception of the firm. Fake news and volatile markets are also gaining in importance for regulators. The identification of manipulative activities or the stabilization of financial markets in the presence of ambiguous information is of special interest for regulators. This task is even more relevant in the time of increased digitalization of media channels and the networks behind them. The more important is, hence, a better understanding of the stakeholders in financial markets and their actions for the functionality of efficient markets. Finally, the results of this dissertation create new connection points for future research. The asymmetric role of investor sentiment and its underlying mechanism are still controversial and elusive. Current studies especially fail to shed light on the long-term impact of investor sentiment on capital markets. This dissertation, hence, provides a substantiated baseline for future empirical work. Also, this work could not fully answer the question in which situation investors specifically use different media channels for information sourcing and dissemination. An intraday-based analysis on various media channels could provide new answers to this question. In summary, this dissertation shows that investor sentiment is an integral part of today’s financial markets and its important role cannot be anymore neglected by advocates of the traditional finance theory

Nowcasting real economic activity in the euro area: Assessing the impact of qualitative surveys. National Bank of Belgium Working Paper No. 331

This paper analyses the contribution of survey data, in particular various sentiment indicators, to nowcasts of quarterly euro area GDP. It uses a genuine real-time dataset that is constructed from original press releases in order to transform the actual dataflow into an interpretable flow of news. The latter is defined as the difference between the released values and the prediction of a mixedfrequency dynamic factor model. Our purpose is twofold. First, we aim to quantify the specific value added for nowcasting GDP from a set of heterogeneous data releases including not only sentiment indicators constructed by Eurostat, Markit, the National Bank of Belgium, IFO, ZEW, GfK or Sentix, but also hard data regarding industrial production or retail sales in the aggregate euro area and individually in some of the largest euro area countries. Second, our quantitative analysis is used to draw up an overall ranking of the indicators, on the basis of their average contribution to updates of the nowcast. Among the survey indicators, we find the strongest impact for the Markit Manufacturing PMI and the Business Climate Indicator in the euro area, and the IFO Business Climate and IFO Expectations in Germany. The widely monitored consumer confidence indicators, on the other hand, typically do not lead to significant revisions of the nowcast. In addition, even if euro area industrial production is a relevant predictor, hard data generally contribute less to the nowcasts: they may be more closely correlated with GDP but their relatively late availability implies that they can to a large extent be anticipated by nowcasting on the basis of survey data and, hence, their ‘news’ component is smaller. Finally, we also show that, in line with the previous literature, the NBB’s own business confidence indicator appears to be useful for predicting euro area GDP. The prevalence of survey data remains also under a counterfactual scenario in which hard data are released without any delay. This finding confirms that, in addition to being available in a more timely manner, survey data also contain relevant information that does not seem to be captured by hard data

The Information Content of Financial Textual Data: Creating News Measures for Volatility Modeling and for the Analysis of Price Jumps

We retrieve news stories and earnings announcements of the S&P 100 constituents from two professional news providers, along with ten macroeconomic indicators. We also gather data from Google Trends about these firms' assets as an index of retail investors' attention. Thus, we create an extensive and innovative database that contains precise information with which to analyze the link between news and asset price dynamics. We detect the sentiment of news stories using a dictionary of sentiment-related words and negations and propose a set of more than five thousand information-based variables that provide natural proxies for the information used by heterogeneous market players. We first shed light on the impact of information measures on daily realized volatility and select them by penalized regression. Then we use these measures to forecast volatility and obtain superior results with respect to the results of models that omit them. Thereafter, we detect intraday price jumps in the S&P 100 constituents' stocks and we build high frequency news indicators from news stories released by two professional news providers, earnings announcements, and twenty-three US macroeconomic indicators. We investigate the extent to which statistically significant intraday jumps are associated with the news indicators and select them by penalized logistic regression. We compare the economic significance of jumps and we find effects on returns and volatility at both high frequency and daily level, and that these effects vary depending on the type of news to which jumps are associated. We also find that future quarterly and yearly returns seem to be exposed to jump risk measures built using jumps related to macro-announcements. A common method to detect the sentiment of a text is the so-called bag-of-words approach. Finally, we extend the method in three directions, by using: 1) an extended negations list of single words, two-word sequences, and three-word sequences; 2) lists of sentiment-related expressions; 3) lists of sentiment-related words combinations. The aim is creating a general method suitable for detecting the sentiment of a financial text of any type