Surveying effects of forward-backward P/E‎‎ ratios on stock's return and ‎fluctuation in Tehran's stock exchange

The aim of this study is to study the relationship between forward-backward effects on stock return, which normally depends on Price-Earnings ratio (P/E)‎ and stock fluctuation in stock exchange. Monthly time series pattern of Tehran stock exchange are used monthly from 2006 to 2010. The data contains all available companies in exchange where the shares were traded at the least 120 days during for the recent 12 months. The results of this research show that the independent variables investigated in this research have meaningful effects on the research's dependent variable. This means that the effects of company’s systematic risk and markets risk on companies’ stock return are positive

Forecasting economic activity with higher frequency targeted predictors

In this paper we explore the performance of bridge and factor models in forecasting quarterly aggregates in the very short-term subject to a pre-selection of monthly indicators. Starting from a large information set, we select a subset of targeted predictors using data reduction techniques as in Bai and Ng (2008). We then compare a Diffusion Index forecasting model as in Stock and Watson (2002), with a Bridge model specified with an automated General-To-Specific routine. We apply these techniques to forecasting Italian GDP growth and its main components from the demand side and find that Bridge models outperform naive forecasts and compare favorably against factor models. Results for France, Germany, Spain and the euro area confirm these findings.short-term GDP forecast, factor models, bridge models, General To Specific

Modelling dynamic portfolio risk using risk drivers of elliptical processes

The situation of a limited availability of historical data is frequently encountered in portfolio risk estimation, especially in credit risk estimation. This makes it, for example, difficult to find temporal structures with statistical significance in the data on the single asset level. By contrast, there is often a broader availability of cross-sectional data, i.e., a large number of assets in the portfolio. This paper proposes a stochastic dynamic model which takes this situation into account. The modelling framework is based on multivariate elliptical processes which model portfolio risk via sub-portfolio specific volatility indices called portfolio risk drivers. The dynamics of the risk drivers are modelled by multiplicative error models (MEM) - as introduced by Engle (2002) - or by traditional ARMA models. The model is calibrated to Moody's KMV Credit Monitor asset returns (also known as firm-value returns) given on a monthly basis for 756 listed European companies at 115 time points from 1996 to 2005. This database is used by financial institutions to assess the credit quality of firms. The proposed risk drivers capture the volatility structure of asset returns in different industry sectors. A characteristic temporal structure of the risk drivers, cyclical as well as a seasonal, is found across all industry sectors. In addition, each risk driver exhibits idiosyncratic developments. We also identify correlations between the risk drivers and selected macroeconomic variables. These findings may improve the estimation of risk measures such as the (portfolio) Value at Risk. The proposed methods are general and can be applied to any series of multivariate asset or equity returns in finance and insurance. --Portfolio risk modelling,Elliptical processes,Credit risk,multiplicative error model,volatility clustering

Essays in innovation, inequality and risk

Cette thèse s'articule autour de trois chapitres en économie de l'innovation et de la science. Pour ce faire, elle développe des modèles empiriques et théoriques pour analyser l'innovation technologique et scientifique et produire des recommandations politiques. Le premier chapitre utilise l'apprentissage automatique et les sciences de données pour construire un indicateur de l'innovation technologique. À l'aide d'une base de données unique sur les brevets au Canada, nous construisons un indice de qualité des brevets pour répondre à deux questions principales : l'absence d'une base de données systématique sur les brevets et leur valeur au Canada ainsi que l'évaluation du secteur pharmaceutique, l'un des principaux secteurs leaders de l'innovation au Canada . Les résultats révèlent que notre indice de qualité est lié à la performance économique des entreprises, à leur productivité et à la productivité agrégée. Le deuxième chapitre examine les innovations dans la recherche universitaire. Plus précisément, se focalisant sur les sciences économiques, ce chapitre vise à relier l'innovation et les inégalités en analysant la reconnaissance des idées des femmes. Des données bibliométriques issues de la recherche en économie sont utilisées pour étudier les biais de genre dans les citations. Sur la base des techniques d'apprentissage profond, on peut (1) établir les similitudes entre les articles (2) établir un lien entre les articles en identifiant les articles qui citent, les articles cités et les articles qui devraient être cités. Cette étude révèle qu'en moyenne, les articles qui ne sont pas cités sont 20% plus susceptibles d'être écrits par des femmes que par des hommes. Ce biais d'omission est plus répandu lorsqu'il n'y a que des hommes dans l'article citant. Dans l'ensemble, pour avoir le même niveau de citation que les articles rédigés par des hommes, les articles rédigés par des femmes doivent être supérieurs de 20 centiles dans la distribution du degré d'innovation de l'article. Enfin, le dernier chapitre analyse l'innovation dans une perspective plus macroéconomique, en se concentrant sur les entrepreneurs. En effet, les entrepreneurs sont au cœur du développement économique et de l'innovation. Cependant, l'activité entrepreneurial reste très risquée. Quelles sont donc les opportunités de diversification des risques d'investissement pour les entrepreneurs ? Pour répondre à cette question, nous étudions le rôle de l'intégration financière. Avec un modèle théorique en temps continu et avec des agents hétérogènes, nous montrons que l'ouverture financière produit des gains de bien-être substantiels pour les entrepreneurs et peut donc les aider à diversifier le risque d'investissement. Nos résultats sont également étayés par une analyse empirique.This thesis is organized into three chapters in the economics of innovation and science. In doing so, it develops empirical and theoretical models to analyze technological and scientific innovation and produce policy recommendations. The first chapter uses data science and big data techniques to build an indicator of technological innovation. Using a unique database on patents in Canada, we build a patent quality index to answer two main questions: the absence of a systematic database on patents and their value in Canada and the evaluation of the pharmaceutical sector, one of the leading innovating sectors in Canada. The results reveal that our quality index is linked to the economic performance of firms, their productivity, and aggregate productivity. The second chapter looks at innovations in academic research. Specifically, focusing on economics, this chapter aims to connect innovation and inequality by analyzing the recognition of women's ideas in the field. Bibliometric data from research in economics are used to investigate gender biases in citation patterns. Based on deep learning and machine learning techniques, one can (1) establish the similarities between papers (2) build a link between articles by identifying the papers citing, cited and that should be cited. This study finds that, on average, omitted papers are 20% more likely to be female-authored than male-authored. This omission bias is more prevalent when there are only males in the citing paper. Overall, to have the same level of citation as papers written by males, papers written by females need to be 20 percentiles upper in the distribution of the degree of innovativeness of the paper. Finally, the last chapter analyzes innovation from a more macroeconomic perspective, focusing on entrepreneurs. Indeed, entrepreneurs are at the core of economic development and innovation. However, entrepreneurship remains very risky. What are the opportunities for investment risk diversification for entrepreneurs? To answer this question, we investigate the role of financial integration. With a theoretical model featuring a continuous-time dimension with heterogeneous agents, we show that financial openness produces substantial welfare gains for entrepreneurs and therefore can help its agents to diversify the investment risk. Our results are also supported by empirical analysis

Three essays on quantile factor analysis

In the first chapter of this dissertation, I develop a method that extends quantile regressions to high dimensional factor analysis. In this context, the conditional quantile function of a panel of variables is endowed with a factor structure. Thus, both factors and factor loadings are allowed to be quantile-specific. I provide a set of conditions under which these objects are identied, and I propose a simple two-step iterative procedure called Quantile Principal Components (QPC) to estimate them. Uniform consistency of the estimators is established under general assumptions when both the cross-section and time dimensions (N and T, respectively) become large jointly. In the second chapter, I propose a novel measure to quantify systemic risk from the information contained in asset returns. In the context of the external habits formation model of Campbell and Cochrane (1999) and heteroskedastic stock returns, I show that the equilibrium risk premium has a factor structure where factors are a monotonic transformation of the systemic risk variable in the structural model, and one of the factors affects the variance of excess returns. I estimate the factor model using the QPC estimation procedure. Simulations of the model calibrated to the US show a good performance of the proposed metric computed at quantiles different than the median. When estimated using post-war data, the proposed measure displays signicant hikes that coincide with both several US recessions and financial market turbulence periods; and it can forecast extreme tight and loose financial conditions, and sharp shifts in both economic activity and industrial production up to one year ahead. The third chapter provides limiting distributions of the QPC estimators proposed in the first chapter. Under certain additional assumptions related to the density of the observations about the quantile of interest, and the relationship between N and T, I show that the QPC estimators are asymptotically normal with convergence rates similar to the ones derived in the traditional factor analysis literature. Monte Carlo simulations suggest that the proposed theory provides a good approximation to the finite sample distribution of the QPC estimates

The merit of high-frequency data in portfolio allocation

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a multi-scale spectral decomposition where volatilities, correlation eigenvalues and eigenvectors evolve on different frequencies. In an extensive out-of-sample forecasting study, we show that the proposed approach yields less risky and more diversified portfolio allocations as prevailing methods employing daily data. These performance gains hold over longer horizons than previous studies have shown

Modeling and Estimating Multi-Block Interactions for High-Dimensional Stationary Time Series

Modeling and estimating interactions amongst multiple groups of variables is an important task for understanding the structure of complex system. In particular, for time series, the interdependence structure can be either on contemporaneous correlations, or on lead-lag cross-relations. This thesis addresses a number of topics related to such interdependence structures, under high-dimensional scaling. The first part of the thesis considers modeling and estimating interactions between observable blocks of variables, as well as their respective within-block dependence structures, in high-dimensional independent and identically distributed (iid), as well as temporal dependent settings. In the iid case, we model the blocks of variables of interest through a multi-layered Gaussian graphical model, and introduce a penalized maximum likelihood (MLE) procedure that provides both statistical and algorithmic guarantees, leveraging the structure of the log-likelihood function and its bi-convex nature. For the case where the data exhibit temporal dependence, the blocks are modeled through a stable Vector Autoregressive (VAR) system with group Granger-causal ordering. Building upon the work for the iid case, we estimate their lead-lag relationships, as well as the contemporaneous dependence structure using a penalized MLE criterion, under different structural assumptions of the transition matrices --- sparse or low rank. We establish theoretical properties for the estimates analogous to the iid case, modulo an additional cost due to the temporal dependence in the data. Moreover, we devise a testing procedure for the presence of such group Granger causality, tailoring it to the posited structural assumptions on the transition matrix that couples the blocks. The devised estimation and testing procedure are assessed via numerical experiments, and further illustrated on a real data example from economics that examines the impact of the stock market on major macroeconomic indicators. However, large stable VAR systems have the inherent limitation that the transition matrix needs to be very sparse or has small averaged magnitude to satisfy the stationary constraint. This further raises the issue of whether VAR model is the appropriate modeling framework for ultra large number of time series. To this end, we consider systems of time series that can be summarized by a small set of latent factors. In the second part of this thesis, we focus on estimating the interaction between an observable process and a dynamically evolving latent factor process. Specifically, we extend the popular in applied economics work, factor-augmented vector autoregressive (FAVAR) model to high dimensions and study estimation of the model parameters by formulating an optimization problem that involves a low-rank-plus-sparse type decomposition. Moreover, we investigate model identifiability issues and establish theoretical properties for the proposed estimator. The performance of the proposed method is evaluated through synthetic data, and the model is further illustrated on an economic data set that examines interlinkages between commodity prices and macroeconomic variables. Along a slightly different line of inquiry where the contemporaneous dependence is of prime interest rather than lead-lag relationships, we extend the approximate factor model where correlations amongst the idiosyncratic (error) component are assumed to be weak, to the case where moderate-to-strong correlations are allowed. Using a formulation similar to the FAVAR problem, we propose an algorithm to estimate the model parameters and investigate its statistical and algorithmic properties. The model and the quality of the resulting estimates are illustrated on log-returns of stock prices of large financial institutions.PHDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145976/1/jiahelin_1.pd

Three Essays in Financial Economics

This thesis encompasses three essays, each of which examines the role of information in a specific setting arising in financial economics. Thus, each essay contributes to the literature about the role of information in financial markets and to the debate whether financial markets are efficient or not. The first essay aims at understanding what market participants learn from corporate repurchase announcements and the objective is to deepen our understanding about the nature of information contained in repurchase announcements of firms. By applying a method from asset pricing we extract information about a firm’s cash flows and its risk from stock returns. We present evidence that repurchase announcements contain information about a firm’s risk when that firm is underpriced. More specifically, we show that market participants learn that their current assessment of the firm’s risk is inaccurate and too high given the information that is available to them. Importantly, no new fundamental information about the firm’s risk is contained in the repurchase announcement. This initiates a correction of the perceived risk ultimately leading to an appreciation in the firm’s stock price. The paper makes at least two contributions to the literature. First, it contributes to the literature on the anomalous behavior of stock returns around share repurchase announcements. Second, the paper adds to the literature on the information content of share repurchases. The main objective of the second essay is to provide novel insights on whether mutual fund managers possess skill and are not simply lucky when allocating their assets. To that end, I introduce a novel measure that captures whether fund managers can anticipate how stock prices will react to changes in the aggregate market’s expectations about the values of stocks, and adjust their fund holdings accordingly. In my setting, the market changes its expectations about the value of a stock due to firm-specific information and information about the entire financial market. I show that fund managers are able to anticipate changes in market expectations that are driven by firm-specific information but not those driven by information that affects the entire financial market. This suggests that mutual fund managers excel and are more precise at acquiring, processing, and using firm-related information for investment decisions. Furthermore, I show that this ability is only prevalent when a fund management consists of a team but not when it consists of one individual manager only. Finally, I show that firm-specific information in stock prices is less complete than market-wide information, Thus, I provide one possible mechanism that explains why anticipation of changes in the market’s expectations driven by firm-specific information is rendered possible. The contribution of this paper is at least threefold. First, it contributes to the vast literature on the skill of mutual fund managers. Second, it enriches the literature devoted to examining the skill difference between team-managed funds and single-managed funds. Third, the paper examines whether the informational inefficiency of stocks is related to managerial skill. The third paper proposes an information theoretic approach to measure the extent to which prices in financial markets reflect all available information. The measures draw on the idea of return predictability and are directly linked to the Efficient Market Hypothesis. The primary duty of the measures is to identify periods where assets or entire financial markets are inefficient in that they do not reflect all available information, such that active asset allocation might become profitable. Using these measures, we propose market timing strategies and provide timing measures for two of the most important and most established financial market phenomena, value and momentum. We also document that market efficiency is cyclical for the U.S. stock market and varies over time. The contribution of this study is severalfold. In general, the study contributes to the discussion about efficient markets. First, it contributes to the literature on market timing. Second, it adds to the literature that examines the performance of active investment. Third, it contributes to the literature of price efficiency measures. Finally, the paper contributes to the research that adopts ideas from information theory and maps it to financial markets. Collectively, this thesis contributes to the debate whether financial markets are efficient or not. The first essay finds that financial market participants have erroneous expectations about the risk of certain firms, suggesting that their information processing is flawed at times. The second essay shows that skilled fund management structures are able to anticipate how the aggregate market’s expectations about a stock will shift, implying that information is not always fully and instantly reflected in financial markets. The third essay finds that assets can be informationally incomplete such that financial markets can be timed. Thus, the thesis concludes in the spirit of Grossman and Stiglitz (1980): It is impossible for financial markets to be informationally efficient at all times

On the implications of recent advancements in information technologies and high-dimensional modeling for financial markets and econometric frameworks

Around the turn of the millennium, the Organization for Economic Co-operation and Development (OECD) published an article, which summarizes the organizations expectations towards technological developments of the 21st century. Of particular interest to the authors are innovations in the area of information technology, highlighting their far-reaching impact on, amongst others, the financial sector. According to the article, the expected increasing interconnectedness of individuals, markets, and economies holds the potential to fundamentally change not only the flow of information in financial markets but also the way in which people interact with each other and with financial institutions. Looking back at the first two decades of the 21st century, these predictions appear to have been quite accurate: The rise of the internet to a platform of utmost relevance to industries and the economy as a whole profoundly impacts how people nowadays receive and process information and subsequently form, share, and discuss their opinions amongst each other. At the financial markets around the globe trading has become more and more accessible to individuals. Less financial and technical knowledge is required of retail investors to engage in trading, resulting in increased market participation and more heterogeneous trader profiles. This, in turn, influences the dynamics in the financial markets and challenges some of the conventional wisdom concerning market structures. In this context, the interdependencies between the media, retail investors, and the stock market are of particular interest for practitioners. However, the changed dynamics in the flow and exchange of data and information are also highly interesting from a researchers perspective, resulting in entire branches of the academic literature devoted to the topic. While these branches have grown in many different directions, this doctoral thesis explores two specific aspects of this field of research: First, it investigates the consequences of the increased interconnectedness of individuals and markets for the dynamics between the new information technologies and the financial markets. This entails gaining new insights about these dynamics and assessing how investors process certain company-related information for their investment decisions by means of sentiment analysis of large, publicly available data sets. Secondly, it illustrates how an advanced understanding of high-dimensional models, resulting from such analyses of large data sets, can be beneficial in re-thinking and improving existing econometric frameworks. Three independent but related research projects are presented in this thesis that address both of the aforementioned aspects to give a more holistic picture of the implications that the profoundly changed flow and exchange of data and information of the last decades hold for finance and econometrics. As such, the projects (i) highlight the importance of carefully assessing the dynamics between investor sentiment and stock market volatility in an intraday context, (ii) analyze how investors process newly available, rich sources of information on a firms environmental, social, and governance (ESG) practices for their investment decisions, and (iii) propose a new approach to detecting multiple structural breaks in a cointegrated framework enabled by new insights about high-dimensional models. The first original work of this doctoral thesis aims at closing an existing gap in the behavioral finance literature by taking an intraday perspective in assessing the relationship between investor sentiment and stock market volatility. More precisely, the paper titled "The Twitter myth revisited: Intraday investor sentiment, Twitter activity and individual-level stock return volatility", which is joint work with Simon Behrendt, takes a closer look at the dynamics of individual-level stock return volatility, measured by absolute 5-minute returns, and Twitter sentiment and activity in an intraday context. After accounting for the intraday periodicity in absolute returns, we discover some statistically significant co-movements of intraday volatility and information from stock-related Tweets for all constituents of the Dow Jones Industrial Average (DJIA). However, economically, the effects are of negligible magnitude, and out-of-sample forecast performance is not improved when including Twitter sentiment and activity as exogenous variables. From a practical point of view, this chapter finds that high-frequency Twitter information is not particularly useful for highly active investors with access to such data for intraday volatility assessment and forecasting when considering individual-level stocks. Inspired by this first research project, the second original work presented in this thesis keeps its focus on sentiment analysis in the context of the financial markets. Titled "Sustainable news - A sentiment analysis of the effect of ESG information on stock prices", it investigates the effect of ESG-related news sentiment on the stock market performance of the DJIA constituents. Relying on a large data set of news articles that were published online or in print media between the years of 2010 and 2018, each articles sentiment with respect to ESG-related topics is extracted using a dictionary approach from which a polarity-based sentiment index is calculated. Estimating autoregressive distributed lag models reveals significant effects of both temporary and permanent changes in ESG-related news sentiment on idiosyncratic returns for the vast majority of the DJIA constituents. According to the models results, one can assign the stocks to different groups depending on their investors apparent predisposition towards ESG news, which in turn seems to be linked with a stocks financial performance. The last original work presented is then concerned with the second aspect of this doctoral thesis - the question of how our enhanced understanding of the increasingly high dimensional datasets that occur in practice can produce new solutions to familiar problems in econometrics. The paper "Multiple structural breaks in cointegrating regressions: A model selection approach", which is joint work with Karsten Schweikert, introduces the least absolute shrinkage and selection operator (lasso) as a tool for consistent breakpoint estimation. In this paper, we propose a new approach to model structural change in cointegrating regressions using penalized regression techniques. First, we consider a setting with fixed breakpoint candidates and show that a modified adaptive lasso estimator can consistently estimate structural breaks in the intercept and slope coefficient of a cointegrating regression. In such a scenario, one could also perceive our method as performing an efficient subsample selection. Second, we extend our approach to a diverging number of breakpoint candidates and provide simulation evidence that timing and magnitude of structural breaks are consistently estimated. Third, we use the adaptive lasso estimation to design new tests for cointegration in the presence of multiple structural breaks, derive the asymptotic distribution of our test statistics and show that the proposed tests have power against the null of no cointegration. Finally, we use our new methodology to study the effects of structural breaks on the long-run PPP relationship.Um die Jahrtausendwende hat die Organisation für wirtschaftliche Zusammenarbeit und Entwicklung (OECD) einen Artikel publiziert, in welchem sie ihre Erwartungen bezüglich der technologischen Entwicklungen des 21. Jahrhunderts zusammenfasst. Hervorgehoben werden dabei insbesondere Innovationen im Bereich der Informationstechnologien und deren weitreichende Konsequenzen, die unter anderem auch den Finanzsektor betreffen. Dem Artikel zufolge birgt die zunehmende Vernetzung von Individuen, Märkten und Volkswirtschaften das Potential, den Informationsfluss auf den Finanzmärkten und die Interaktionen zwischen Marktteilnehmern und Finanzinstitutionen fundamental zu verändern. Blicken wir nun auf die ersten zwei Jahrzehnte des 21. Jahrhunderts zurück, so erscheinen diese Prognosen durchaus zutreffend: Die Etablierung des Internets zu einer der wichtigsten Plattformen für Unternehmen und ganze Industrien hat tiefgreifende Auswirkungen darauf, wie wir heutzutage an Informationen gelangen und diese verarbeiten, wie wir unsere Meinungen bilden, diese teilen und mit anderen diskutieren. Die Teilnahme am Handel wird an den Finanzmärkten weltweit einer immer breiteren Masse an Individuen ermöglicht, da diese heutzutage weit weniger Finanzwissen und technische Expertise für den Einstieg benötigen. Die somit steigende Anzahl von Kleinanlegern hat unmittelbare Auswirkungen auf die Dynamiken an den Finanzmärkten. In diesem Kontext ist vor allem die sich verändernde Beziehung zwischen den Medien, Kleinanlegern und den Aktienmärkten hervorzuheben. Jene ist nicht nur aus Anwendersicht, sondern auch aus Sicht der Wissenschaft äußerst interessant, was sich an neu entstehenden Forschungszweigen der akademischen Literatur zeigt, die sich mit diesem Thema beschäftigen. Während diese Zweige in die unterschiedlichsten Richtungen wachsen, ergründet die vorliegende Doktorarbeit zwei spezifische Aspekte dieses Forschungsbereiches näher: Erstens beschäftigt sie sich mit den Folgen der zunehmenden Vernetzung von Individuen und Märkten für die Dynamiken zwischen den neuen Informationstechnologien und den Finanzmärkten. Mittels Sentimentanalyse großer, öffentlich verfügbarer Datensätze werden dabei neue Erkenntnisse über ebendiese Dynamiken erlangt und die Fragestellung untersucht, wie Investoren firmenspezifische Informationen in ihre Investitionsentscheidungen einbeziehen. Zweitens verdeutlicht sie, wie ein fortschrittliches Verständnis von hochdimensionalen Modellen, welches durch die Analyse solch großer Datensätze ermöglicht wird, vorteilhaft sein kann, um existierende ökonometrische Modelle zu verbessern. Diese beiden Aspekte werden in drei unabhängigen, jedoch miteinander verwandten Forschungsprojekten, die den Kern dieser Doktorarbeit bilden, näher betrachtet. Als solche (i) heben sie die Bedeutung einer sorgfältigen Analyse der Dynamiken zwischen Investoren Sentiment und der Volatilität am Aktienmarkt im Innertages-Kontext hervor, (ii) analysieren sie, wie Investoren neu verfügbare Informationsquellen über die Praktiken von Unternehmen im Bereich Umwelt, Soziales und Unternehmensführung (Englisch: environmental, social, and governance, kurz ESG) in ihren Investmententscheidungen verarbeiten und (iii) schlagen sie einen neuen Ansatz zum Erkennen von multiplen Strukturbrüchen in kointegrierten Systemen vor, der durch neue Erkenntnisse über hochdimensionale Modelle ermöglicht wird. Das erste Projekt dieser Doktorarbeit mit dem Titel "The Twitter myth revisited: Intraday investor sentiment, Twitter activity and individual-level stock return volatility", welches in Zusammenarbeit mit Simon Behrendt verfasst wurde, beleuchtet die Dynamiken zwischen Volatilität einzelner Aktien, gemessen als 5-minütige, absolute Renditen, und Twitter Sentiment und Aktivität im untertägigen Verlauf. Nachdem wir die untertägige Periodizität der absoluten Renditen berücksichtigt haben, finden wir statistisch signifikante Zusammenhänge zwischen der untertägigen Volatilität und Informationen von aktienbezogenen Tweets für alle Aktien im Dow Jones Industrial Average (DJIA). Allerdings sind diese Effekte, ökonomisch betrachtet, von vernachlässigbarer Größe und Prognosemodelle können keine besseren Ergebnisse erzielen, wenn Twitter Sentiment und Aktivität als exogene Variablen mit hinzugezogen werden. Aus Anwendersicht scheinen Twitter-Informationen nicht besonders wertvoll zu sein. Investoren, die Zugriff auf solche Daten haben, können ihr untertägiges Handeln auf Ebene individueller Aktien dadurch nicht optimieren. In Anlehnung an das erste Projekt beschäftigt sich auch das zweite Werk dieser Doktorarbeit mit Sentimentanalyse im Kontext der Finanzmärkte. Das Projekt mit dem Titel "Sustainable news - A sentiment analysis of the effect of ESG information on stock prices" untersucht den Effekt von ESG-bezogenen Nachrichten auf die Performance von Aktien des DJIA. Aus einem großen Datensatz öffentlich verfügbarer Nachrichtenartikel, die zwischen 2010 und 2018 erschienen sind, wird mit Hilfe eines Lexikon-Ansatzes ein Sentimentindex berechnet, der die im jeweiligen Artikel vertretene Meinung in Bezug auf ein ESG-Thema widerspiegelt. Autoregressive distributed lag Modelle zeigen signifikante Effekte von sowohl kurzfristigen als auch langfristigen Veränderungen in ESG-bezogenem Sentiment auf idiosynkratische Renditen für einen Großteil der Aktien im DJIA. Die Schätzergebnisse erlauben eine Einteilung der Aktien in verschiedene Gruppen, abhängig davon, wie die Investoren einer Aktie auf ESG-bezogene Informationen reagieren, was wiederum mit der finanziellen Performance der Aktien zusammenzuhängen scheint. Das letzte Projekt widmet sich dann dem zweiten Aspekt der Doktorarbeit - der Frage, wie ein erweitertes Verständnis von hochdimensionalen Modellen neue Erkenntnisse für bekannte ökonometrische Modelle liefern kann. Das Projekt "Multiple structural breaks in cointegrating regressions: A model selection approach", welches in Ko-Autorenschaft mit Karsten Schweikert entstanden ist, zeigt die Vorteile des least absolute shrinkage and selection operator (lasso) als Instrument zur konsistenten Schätzung von Strukturbrüchen in kointegrierten Systemen. Wir zeigen zunächst für den Fall einer fixen Anzahl an Strukturbruchkandidaten, dass eine modifizierte Version des adaptive lasso Schätzers Strukturbrüche in der Konstanten und im Steigungsparameter einer kointegrierten Regression konsistent schätzt. Auch für den Fall einer divergierenden Anzahl an Strukturbruchkandidaten zeigen wir durch Simulationen, dass der Zeitpunkt und die Größe von Strukturbrüchen konsistent geschätzt werden können. Wir leiten außerdem die asymptotische Verteilung der Teststatistik eines neuen Kointegrationstest im Falle multipler Strukturbrüche her und zeigen, dass der von uns vorgeschlagene Test erstrebenswerte Eigenschaften aufweist. Zuletzt zeigen wir den Mehrwert unserer Methode für die Praxis, um beispielsweise die Effekte von Strukturbrüchen in der langfristigen Kaufkraftparität zu analysieren

On the implications of recent advancements in information technologies and high-dimensional modeling for financial markets and econometric frameworks

Around the turn of the millennium, the Organization for Economic Co-operation and Development (OECD) published an article, which summarizes the organizations expectations towards technological developments of the 21st century. Of particular interest to the authors are innovations in the area of information technology, highlighting their far-reaching impact on, amongst others, the financial sector. According to the article, the expected increasing interconnectedness of individuals, markets, and economies holds the potential to fundamentally change not only the flow of information in financial markets but also the way in which people interact with each other and with financial institutions. Looking back at the first two decades of the 21st century, these predictions appear to have been quite accurate: The rise of the internet to a platform of utmost relevance to industries and the economy as a whole profoundly impacts how people nowadays receive and process information and subsequently form, share, and discuss their opinions amongst each other. At the financial markets around the globe trading has become more and more accessible to individuals. Less financial and technical knowledge is required of retail investors to engage in trading, resulting in increased market participation and more heterogeneous trader profiles. This, in turn, influences the dynamics in the financial markets and challenges some of the conventional wisdom concerning market structures. In this context, the interdependencies between the media, retail investors, and the stock market are of particular interest for practitioners. However, the changed dynamics in the flow and exchange of data and information are also highly interesting from a researchers perspective, resulting in entire branches of the academic literature devoted to the topic. While these branches have grown in many different directions, this doctoral thesis explores two specific aspects of this field of research: First, it investigates the consequences of the increased interconnectedness of individuals and markets for the dynamics between the new information technologies and the financial markets. This entails gaining new insights about these dynamics and assessing how investors process certain company-related information for their investment decisions by means of sentiment analysis of large, publicly available data sets. Secondly, it illustrates how an advanced understanding of high-dimensional models, resulting from such analyses of large data sets, can be beneficial in re-thinking and improving existing econometric frameworks. Three independent but related research projects are presented in this thesis that address both of the aforementioned aspects to give a more holistic picture of the implications that the profoundly changed flow and exchange of data and information of the last decades hold for finance and econometrics. As such, the projects (i) highlight the importance of carefully assessing the dynamics between investor sentiment and stock market volatility in an intraday context, (ii) analyze how investors process newly available, rich sources of information on a firms environmental, social, and governance (ESG) practices for their investment decisions, and (iii) propose a new approach to detecting multiple structural breaks in a cointegrated framework enabled by new insights about high-dimensional models. The first original work of this doctoral thesis aims at closing an existing gap in the behavioral finance literature by taking an intraday perspective in assessing the relationship between investor sentiment and stock market volatility. More precisely, the paper titled "The Twitter myth revisited: Intraday investor sentiment, Twitter activity and individual-level stock return volatility", which is joint work with Simon Behrendt, takes a closer look at the dynamics of individual-level stock return volatility, measured by absolute 5-minute returns, and Twitter sentiment and activity in an intraday context. After accounting for the intraday periodicity in absolute returns, we discover some statistically significant co-movements of intraday volatility and information from stock-related Tweets for all constituents of the Dow Jones Industrial Average (DJIA). However, economically, the effects are of negligible magnitude, and out-of-sample forecast performance is not improved when including Twitter sentiment and activity as exogenous variables. From a practical point of view, this chapter finds that high-frequency Twitter information is not particularly useful for highly active investors with access to such data for intraday volatility assessment and forecasting when considering individual-level stocks. Inspired by this first research project, the second original work presented in this thesis keeps its focus on sentiment analysis in the context of the financial markets. Titled "Sustainable news - A sentiment analysis of the effect of ESG information on stock prices", it investigates the effect of ESG-related news sentiment on the stock market performance of the DJIA constituents. Relying on a large data set of news articles that were published online or in print media between the years of 2010 and 2018, each articles sentiment with respect to ESG-related topics is extracted using a dictionary approach from which a polarity-based sentiment index is calculated. Estimating autoregressive distributed lag models reveals significant effects of both temporary and permanent changes in ESG-related news sentiment on idiosyncratic returns for the vast majority of the DJIA constituents. According to the models results, one can assign the stocks to different groups depending on their investors apparent predisposition towards ESG news, which in turn seems to be linked with a stocks financial performance. The last original work presented is then concerned with the second aspect of this doctoral thesis - the question of how our enhanced understanding of the increasingly high dimensional datasets that occur in practice can produce new solutions to familiar problems in econometrics. The paper "Multiple structural breaks in cointegrating regressions: A model selection approach", which is joint work with Karsten Schweikert, introduces the least absolute shrinkage and selection operator (lasso) as a tool for consistent breakpoint estimation. In this paper, we propose a new approach to model structural change in cointegrating regressions using penalized regression techniques. First, we consider a setting with fixed breakpoint candidates and show that a modified adaptive lasso estimator can consistently estimate structural breaks in the intercept and slope coefficient of a cointegrating regression. In such a scenario, one could also perceive our method as performing an efficient subsample selection. Second, we extend our approach to a diverging number of breakpoint candidates and provide simulation evidence that timing and magnitude of structural breaks are consistently estimated. Third, we use the adaptive lasso estimation to design new tests for cointegration in the presence of multiple structural breaks, derive the asymptotic distribution of our test statistics and show that the proposed tests have power against the null of no cointegration. Finally, we use our new methodology to study the effects of structural breaks on the long-run PPP relationship.Um die Jahrtausendwende hat die Organisation für wirtschaftliche Zusammenarbeit und Entwicklung (OECD) einen Artikel publiziert, in welchem sie ihre Erwartungen bezüglich der technologischen Entwicklungen des 21. Jahrhunderts zusammenfasst. Hervorgehoben werden dabei insbesondere Innovationen im Bereich der Informationstechnologien und deren weitreichende Konsequenzen, die unter anderem auch den Finanzsektor betreffen. Dem Artikel zufolge birgt die zunehmende Vernetzung von Individuen, Märkten und Volkswirtschaften das Potential, den Informationsfluss auf den Finanzmärkten und die Interaktionen zwischen Marktteilnehmern und Finanzinstitutionen fundamental zu verändern. Blicken wir nun auf die ersten zwei Jahrzehnte des 21. Jahrhunderts zurück, so erscheinen diese Prognosen durchaus zutreffend: Die Etablierung des Internets zu einer der wichtigsten Plattformen für Unternehmen und ganze Industrien hat tiefgreifende Auswirkungen darauf, wie wir heutzutage an Informationen gelangen und diese verarbeiten, wie wir unsere Meinungen bilden, diese teilen und mit anderen diskutieren. Die Teilnahme am Handel wird an den Finanzmärkten weltweit einer immer breiteren Masse an Individuen ermöglicht, da diese heutzutage weit weniger Finanzwissen und technische Expertise für den Einstieg benötigen. Die somit steigende Anzahl von Kleinanlegern hat unmittelbare Auswirkungen auf die Dynamiken an den Finanzmärkten. In diesem Kontext ist vor allem die sich verändernde Beziehung zwischen den Medien, Kleinanlegern und den Aktienmärkten hervorzuheben. Jene ist nicht nur aus Anwendersicht, sondern auch aus Sicht der Wissenschaft äußerst interessant, was sich an neu entstehenden Forschungszweigen der akademischen Literatur zeigt, die sich mit diesem Thema beschäftigen. Während diese Zweige in die unterschiedlichsten Richtungen wachsen, ergründet die vorliegende Doktorarbeit zwei spezifische Aspekte dieses Forschungsbereiches näher: Erstens beschäftigt sie sich mit den Folgen der zunehmenden Vernetzung von Individuen und Märkten für die Dynamiken zwischen den neuen Informationstechnologien und den Finanzmärkten. Mittels Sentimentanalyse großer, öffentlich verfügbarer Datensätze werden dabei neue Erkenntnisse über ebendiese Dynamiken erlangt und die Fragestellung untersucht, wie Investoren firmenspezifische Informationen in ihre Investitionsentscheidungen einbeziehen. Zweitens verdeutlicht sie, wie ein fortschrittliches Verständnis von hochdimensionalen Modellen, welches durch die Analyse solch großer Datensätze ermöglicht wird, vorteilhaft sein kann, um existierende ökonometrische Modelle zu verbessern. Diese beiden Aspekte werden in drei unabhängigen, jedoch miteinander verwandten Forschungsprojekten, die den Kern dieser Doktorarbeit bilden, näher betrachtet. Als solche (i) heben sie die Bedeutung einer sorgfältigen Analyse der Dynamiken zwischen Investoren Sentiment und der Volatilität am Aktienmarkt im Innertages-Kontext hervor, (ii) analysieren sie, wie Investoren neu verfügbare Informationsquellen über die Praktiken von Unternehmen im Bereich Umwelt, Soziales und Unternehmensführung (Englisch: environmental, social, and governance, kurz ESG) in ihren Investmententscheidungen verarbeiten und (iii) schlagen sie einen neuen Ansatz zum Erkennen von multiplen Strukturbrüchen in kointegrierten Systemen vor, der durch neue Erkenntnisse über hochdimensionale Modelle ermöglicht wird. Das erste Projekt dieser Doktorarbeit mit dem Titel "The Twitter myth revisited: Intraday investor sentiment, Twitter activity and individual-level stock return volatility", welches in Zusammenarbeit mit Simon Behrendt verfasst wurde, beleuchtet die Dynamiken zwischen Volatilität einzelner Aktien, gemessen als 5-minütige, absolute Renditen, und Twitter Sentiment und Aktivität im untertägigen Verlauf. Nachdem wir die untertägige Periodizität der absoluten Renditen berücksichtigt haben, finden wir statistisch signifikante Zusammenhänge zwischen der untertägigen Volatilität und Informationen von aktienbezogenen Tweets für alle Aktien im Dow Jones Industrial Average (DJIA). Allerdings sind diese Effekte, ökonomisch betrachtet, von vernachlässigbarer Größe und Prognosemodelle können keine besseren Ergebnisse erzielen, wenn Twitter Sentiment und Aktivität als exogene Variablen mit hinzugezogen werden. Aus Anwendersicht scheinen Twitter-Informationen nicht besonders wertvoll zu sein. Investoren, die Zugriff auf solche Daten haben, können ihr untertägiges Handeln auf Ebene individueller Aktien dadurch nicht optimieren. In Anlehnung an das erste Projekt beschäftigt sich auch das zweite Werk dieser Doktorarbeit mit Sentimentanalyse im Kontext der Finanzmärkte. Das Projekt mit dem Titel "Sustainable news - A sentiment analysis of the effect of ESG information on stock prices" untersucht den Effekt von ESG-bezogenen Nachrichten auf die Performance von Aktien des DJIA. Aus einem großen Datensatz öffentlich verfügbarer Nachrichtenartikel, die zwischen 2010 und 2018 erschienen sind, wird mit Hilfe eines Lexikon-Ansatzes ein Sentimentindex berechnet, der die im jeweiligen Artikel vertretene Meinung in Bezug auf ein ESG-Thema widerspiegelt. Autoregressive distributed lag Modelle zeigen signifikante Effekte von sowohl kurzfristigen als auch langfristigen Veränderungen in ESG-bezogenem Sentiment auf idiosynkratische Renditen für einen Großteil der Aktien im DJIA. Die Schätzergebnisse erlauben eine Einteilung der Aktien in verschiedene Gruppen, abhängig davon, wie die Investoren einer Aktie auf ESG-bezogene Informationen reagieren, was wiederum mit der finanziellen Performance der Aktien zusammenzuhängen scheint. Das letzte Projekt widmet sich dann dem zweiten Aspekt der Doktorarbeit - der Frage, wie ein erweitertes Verständnis von hochdimensionalen Modellen neue Erkenntnisse für bekannte ökonometrische Modelle liefern kann. Das Projekt "Multiple structural breaks in cointegrating regressions: A model selection approach", welches in Ko-Autorenschaft mit Karsten Schweikert entstanden ist, zeigt die Vorteile des least absolute shrinkage and selection operator (lasso) als Instrument zur konsistenten Schätzung von Strukturbrüchen in kointegrierten Systemen. Wir zeigen zunächst für den Fall einer fixen Anzahl an Strukturbruchkandidaten, dass eine modifizierte Version des adaptive lasso Schätzers Strukturbrüche in der Konstanten und im Steigungsparameter einer kointegrierten Regression konsistent schätzt. Auch für den Fall einer divergierenden Anzahl an Strukturbruchkandidaten zeigen wir durch Simulationen, dass der Zeitpunkt und die Größe von Strukturbrüchen konsistent geschätzt werden können. Wir leiten außerdem die asymptotische Verteilung der Teststatistik eines neuen Kointegrationstest im Falle multipler Strukturbrüche her und zeigen, dass der von uns vorgeschlagene Test erstrebenswerte Eigenschaften aufweist. Zuletzt zeigen wir den Mehrwert unserer Methode für die Praxis, um beispielsweise die Effekte von Strukturbrüchen in der langfristigen Kaufkraftparität zu analysieren