Emotions in Macroeconomic News and their Impact on the European Bond Market

We show how emotions extracted from macroeconomic news can be used to explain and forecast future behaviour of sovereign bond yield spreads in Italy and Spain. We use a big, open-source, database known as Global Database of Events, Language and Tone to construct emotion indicators of bond market affective states. We find that negative emotions extracted from news improve the forecasting power of government yield spread models during distressed periods even after controlling for the number of negative words present in the text. In addition, stronger negative emotions, such as panic, reveal useful information for predicting changes in spread at the short-term horizon, while milder emotions, such as distress, are useful at longer time horizons. Emotions generated by the Italian political turmoil propagate to the Spanish news affecting this neighbourhood market.Comment: Journal of International Money and Finance (to appear); 39 pages; 14 figure

Emotions in macroeconomic news and their impact on the European bond market

We show how emotions extracted from macroeconomic news can be used to explain and forecast future behaviour of sovereign bond yield spreads in Italy and Spain. We use a big, open-source, database known as Global Database of Events, Language and Tone to construct emotion indicators of bond market affective states. We find that negative emotions extracted from news improve the forecasting power of government yield spread models during distressed periods even after controlling for the number of negative words present in the text. In addition, stronger negative emotions, such as panic, reveal useful information for predicting changes in spread at the short-term horizon, while milder emotions, such as distress, are useful at longer time horizons. Emotions generated by the Italian political turmoil propagate to the Spanish news affecting this neighbourhood market

Customer and Employee Social Media Comments/Feedback and Stock Purchasing Decisions Enhanced by Sentiment Analysis

The U.S. Securities and Exchange Commission (SEC) warns professional investors that sentiment analysis tools may lead to impulsive investment decision-making. This warning comes despite evidence showing that aided social sentiment investment decision tools can increase accurate investment decision-making by 18%. Using Fama\u27s theory of efficient market hypothesis, the purpose of this quantitative correlational study was to examine whether customer Twitter comments and employee Glassdoor feedback sentiment predicted successful investing decisions measured by business stock prices. Two thousand records from 3 archival U.S. public NASDAQ 100 datasets from March 28, 2016, to June 15, 2016 (79 days) of 53 companies with over 100 comments were analyzed using multiple linear regression. The multiple regression analysis results indicated no significant predictability for successful investing decisions, F(10, 2993) = .295, p = .982, R2 = .001. The results indicated that the sentiment from both Twitter and Glassdoor was not necessarily an indicator for investors to make successful investment decisions for the 79 days in 2016. The knowledge about Artificial Intelligence (AI) sentiment usage may help professional investors gain profit or prevent losses. A recommendation to investors is to heed warnings from the SEC about tools for sentiment analysis investment decisions. Implications for positive social change include preventing an investor from using a risky sentiment tool for investment decision-making that may lead to losing capital

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

Biometeorology: review and analysis with regard to traumatic brain Injury acquisition in professional football, as well as traditional and digital economic markets

This research explores Biometeorology, which is the relationship between the environment and human behavior. Previous research has indicated that meteorological events such as lunar cycles, solar activity, temperature, and humidity have been extensively documented to affect human psychophysiology through systemic variation. The purpose of this document is to explore the effects of environmental factors on typical everyday aggregate behaviors in distinct, unique and separate investigations that relate to participation in sporting events and economic activities in order to determine if these naturally occurring influences are genuine. For example, local weather related and extra-terrestrial phenomena were collected during timestamped Traumatic Brain Injury (TBI) acquisition in the National Football league (NFL) for 645 concussed players from 2012-2015. Components of the Earth’s geomagnetic field were also documented in relation to global search tendencies for highly emotional states in addition to stock market indices. Furthermore, solar and lunar cycles were recorded during the monumental rise in the cryptocurrency market in order to identify if these cyclical background patterns systemically altered interest in Bitcoin (BTC) and Ethereum (ETH) or influenced their price index in-and-of-itself. The results indicate that intrinsic capacities of the game of football inherently impacted injury severity and return to play considerations. TBI’s did however, vary as a function of geo-coordination and were most pronounced in the Northwest U.S. Injury severity was also found to be greatest during increased geomagnetic intensities. Lunar contributions also appeared to play a central role in injury acquisition insofar as TBI player height weight and injury severity were predicted by solar and geomagnetic variables of interest during the full moon. Aggregate search behavior on the Internet of Things (IoT) was found to correlate with magnetic variability, geomagnetic intensity as well as Dow Jones price movement and trading volume. Finally, traditional technical analysis indicators closely followed cryptocurrency price. However, Bitcoins Aroon up and down was found to cycle with the Moon, while Ethereum’s Heiken Ashi displayed a relationship with the Sun. Internet interest in Ethereum was found to have significant associations with the Earth’s geomagnetic field, the Sun and the Moon which was enhanced during specific alignments of these heavenly bodies. In summary, seemingly random events and aggregate group behaviors are intimately associated with external interconnected dynamics.Doctor of Philosophy (PhD) in Human Studie

Reading the Market

Americans pay famously close attention to "the market," obsessively watching trends, patterns, and swings and looking for clues in every fluctuation. In Reading the Market, Peter Knight explores the Gilded Age origins and development of this peculiar interest. He tracks the historic shift in market operations from local to national while examining how present-day ideas about the nature of markets are tied to past genres of financial representation.Drawing on the late nineteenth-century explosion of art, literature, and media, which sought to dramatize the workings of the stock market for a wide audience, Knight shows how ordinary Americans became both emotionally and financially invested in the market. He analyzes popular investment manuals, brokers’ newsletters, newspaper columns, magazine articles, illustrations, and cartoons. He also introduces readers to fiction featuring financial tricksters, which was characterized by themes of personal trust and insider information. The book reveals how the popular culture of the period shaped the very idea of the market as a self-regulating mechanism by making the impersonal abstractions of high finance personal and concrete.From the rise of ticker-tape technology to the development of conspiracy theories, Reading the Market argues that commentary on the Stock Exchange between 1870 and 1915 changed how Americans understood finance—and explains what our pervasive interest in Wall Street says about us now

Analysing behavioural factors that impact financial stock returns. The case of COVID-19 pandemic in the financial markets.

This thesis represents a pivotal advancement in the realm of behavioural finance, seamlessly integrating both classical and state-of-the-art models. It navigates the performance and applicability of the Irrational Fractional Brownian Motion (IFBM) model, while also delving into the propagation of investor sentiment, emphasizing the indispensable role of hands-on experiences in understanding, applying, and refining complex financial models. Financial markets, characterized by ’fat tails’ in price change distributions, often challenge traditional models such as the Geometric Brownian Motion (GBM). Addressing this, the research pivots towards the Irrational Fractional Brownian Motion Model (IFBM), a groundbreaking model initially proposed by (Dhesi and Ausloos, 2016) and further enriched in (Dhesi et al., 2019). This model, tailored to encapsulate the ’fat tail’ behaviour in asset returns, serves as the linchpin for the first chapter of this thesis. Under the insightful guidance of Gurjeet Dhesi, a co-author of the IFBM model, we delved into its intricacies and practical applications. The first chapter aspires to evaluate the IFBM’s performance in real-world scenarios, enhancing its methodological robustness. To achieve this, a tailored algorithm was crafted for its rigorous testing, alongside the application of a modified Chi-square test for stability assessment. Furthermore, the deployment of Shannon’s entropy, from an information theory perspective, offers a nuanced understanding of the model. The S&P500 data is wielded as an empirical testing bed, reflecting real-world financial market dynamics. Upon confirming the model’s robustness, the IFBM is then applied to FTSE data during the tumultuous COVID-19 phase. This period, marked by extraordinary market oscillations, serves as an ideal backdrop to assess the IFBM’s capability in tracking extreme market shifts. Transitioning to the second chapter, the focus shifts to the potentially influential realm of investor sentiment, seen as one of the many factors contributing to fat tails’ presence in return distributions. Building on insights from (Baker and Wurgler, 2007), we examine the potential impact of political speeches and daily briefings from 10 Downing Street during the COVID-19 crisis on market sentiment. Recognizing the profound market impact of such communications, the chapter seeks correlations between these briefings and market fluctuations. Employing advanced Natural Language Processing (NLP) techniques, this chapter harnesses the power of the Bidirectional Encoder Representations from Transformers (BERT) algorithm (Devlin et al., 2018) to extract sentiment from governmental communications. By comparing the derived sentiment scores with stock market indices’ performance metrics, potential relationships between public communications and market trajectories are unveiled. This approach represents a melding of traditional finance theory with state-of-the-art machine learning techniques, offering a fresh lens through which the dynamics of market behaviour can be understood in the context of external communications. In conclusion, this thesis provides an intricate examination of the IFBM model’s performance and the influence of investor sentiment, especially under crisis conditions. This exploration not only advances the discourse in behavioural finance but also underscores the pivotal role of sophisticated models in understanding and predicting market trajectories

Business cycles, interest rates and market volatility : estimation and forecasting using DSGE macroeconomic models under partial information

Even long before the recent financial and economic crisis of 2007/2008 economists were more than aware of the insufficiencies and a lack of realism in macroeconomic modelling and model calibration methods, including those with DSGE methods and models, and spelled the need for further enhancements. The issues this research started addressing even before the 2008 crisis imposed demand for improvements, was use of single, fully informed rational agents in those modes. Consequently, the first part of this research project was aiming to improve the DSGE econometric methods by introducing novel solution for DSGE models with imperfect, partial information about the current values of deep variables and shocks, and apply this solution to imperfectly informed multiple agents with their different, inner-rationality models. Along these lines, this research also shows that DSGE models can be extended and suited to both, fitting and estimation of long-term yield curve, and to estimating with rich data sets by extending further its inner-mechanism. In the aftermath of the 2008 crises, which struck at the beginning of this research project, and the subsequent, extensive criticism of DSGE models, this research analyses the alternative causes of the crisis. It then focuses on identifying its possible causes, such as yet unknown debt accelerator mechanism and the related, probable model miss-specifications, rational inattention, and as well, a role of institutional policies in both the development of the crisis and its resolution. And finally, in a response to many of the critiques of the, usually monetary policy oriented DSGE models, this research project provides another set of novel extensions to such models, aiming to bring more of Keynesian characteristics suited to a more active, endogenous fiscal policy deemed needed in the aftermath of the crisis. This project, henceforth, extends the NK-Neo-Classical synthesis monetary DSGE models with a novel, endogenous, counter-cyclical fiscal policy rule driven by news and unemployment changes. It then also shows overall benefits of the resulting, mutually active, monetary-fiscal policy for both capital utilisation and overall economic stability

Platformization of Urban Life: Towards a Technocapitalist Transformation of European Cities

The increasing platformization of urban life needs critical perspectives to examine changing everyday practices and power shifts brought about by the expansion of digital platforms mediating care-services, housing, and mobility. This book addresses new modes of producing urban spaces and societies. It brings both platform researchers and activists from various fields related to critical urban studies and labour activism into dialogue. The contributors engage with the socio-spatial and normative implications of platform-mediated urban everyday life and urban futures, going beyond a rigid techno-dystopian stance in order to include an understanding of platforms as sites of social creativity and exchange

Platformization of Urban Life

The increasing platformization of urban life needs critical perspectives to examine changing everyday practices and power shifts brought about by the expansion of digital platforms mediating care-services, housing, and mobility. This book addresses new modes of producing urban spaces and societies. It brings both platform researchers and activists from various fields related to critical urban studies and labour activism into dialogue. The contributors engage with the socio-spatial and normative implications of platform-mediated urban everyday life and urban futures, going beyond a rigid techno-dystopian stance in order to include an understanding of platforms as sites of social creativity and exchange