On measuring the importance of income sources and population subgroups for income inequality

This paper points to flaws in Gini decompositions by income sources and population subgroups and to common pitfalls in the interpretation of decomposition results, focusing on methods within the framework of Rao (1969). We argue that within this framework Gini elasticities may provide the only meaningful way to examine the relevance of income sources or population subgroups for total income inequality. Moreover, we show that existing methods are unsuitable to decompose the trend in the Gini coefficient and provide a coherent method to decompose the Gini trend by income sources. We add to the recent trend of multi-decompositions by deriving Gini elasticities from a simultaneous decomposition by income sources and population subgroups

Three Essays in Financial Economics

This thesis consists of three chapters/papers. The first two are related to the literature on herd behavior in financial markets. The third chapter is on trade classification, a method to classify trades into the orders of liquidity demanders and providers, which is a necessary first step in many studies on financial and financial economics topics, including studies on herd behavior. Herd behavior by investors can be a significant threat to the functioning of financial markets. The distorting effects of herding range from informational inefficiency to increased stock price volatility, or even bubbles and crashes. Consequently, there exists one the one hand a large theoretical literature that shows analytically how herding arises even in rational markets, and a large empirical literature on the other that tests for the presence of herd behavior in financial markets. It has been noted, however, that these two strands of the herding literature are largely disconnected. While herd models do not provide empirical testable hypotheses, empirical works do not rigorously tie their proposed measures to the theoretical concept of herding. This thesis, particularly the first and second chapter, contributes towards closing the gap between the theoretical and empirical herding literature. The third chapter, while contributing to the empirical herding literature as well, is a more general contribution to the empirical toolkit of financial economists by proposing a new algorithm to classify transaction data into the orders of liquidity demanders and suppliers. Knowing the trade direction of the liquidity demanding, impatient side of a trade is key to many financial market research topics. Measures of informed trading, price efficiency and market quality all depend on the trade direction of the liquidity demander. To link this topic to the previous chapters, herding models, for example, assume that the information about an asset's value is conveyed by the impatient trader and that subsequent traders, therefore, try to learn from the action of the impatient side of the transaction. Yet, information on the trade direction of the impatient side of a trade is generally not available and the established methods to classify transactions into the orders of liquidity demanders and suppliers face certain difficulties in today's data environments due to the increased frequency of order submissions on financial exchanges. Hence, I propose a new algorithm that overcomes these difficulties and show its superiority over the established algorithms.Die vorliegende Arbeit besteht aus drei Kapiteln. Die ersten beiden Kapitel stehen im starken Bezug zur Literatur über Herdenverhalten. Das dritte behandelt das Thema der sogenannten ``trade classification'', einer Methode um Finanzmarkttransaktionen den jeweiligen liquiditätbereitstellenden und -nehmenden Ordern zuzuordnen. Diese Zuordnung ist ein notwendiger erster Schritt in vielen Studien über finanzwissenschaftliche oder finanz-ökonomische Themen. Herdenverhalten von Investoren kann eine signifikante Bedrohung für das Funktionieren von Finanzmärkten darstellen. Die disruptiven Effekte reichen von Preisineffizienz, im Sinne der Funktion der Informationsaggregation durch Preise, bis hin zu erhöhter Preisvolatilität und gar Preisblasen und -einstürze. Konsequenterweise existiert auf der einen Seite eine ausgiebige, theoretische Literatur, die zeigt, dass Herdenverhalten selbst in komplett rationalen Märkten entstehen kann, und eine empirische Literatur, auf der anderen Seite, die auf Herdenverhalten auf Finanzmärkten testet. Diese beiden Stränge der Literatur stehen jedoch in einem entkoppelten Verhältnis. Während theoretische Modelle wenige, empirisch überprüfbare Hypothesen bereit hält, sind empirische Messmethoden gleichermaßen nicht streng an das theoretische Konzept von Herdenverhalten gebunden. Die vorliegende Arbeit, insbesondere die ersten beiden Kapitel, trägt zum Zusammenbringen der theoretischen und empirischen Literatur über Herdenverhalten bei. Das dritte Kapitel, wenn gleich es ebenfalls zu der empirischen Literatur über Herdenverhalten beiträgt, ist ein mehr allgemeiner Beitrag zum empirischen Handwerkszeug von Ökonomen. Im dritten Kapitel schlage ich einen neuen Algorithmus zum Klassifizieren von Transaktionsdaten in die jeweiligen Order von Bereitstellern und Nehmern von Liquidität vor. Der Liquiditätsbegriff bezieht sich dabei auf die Möglichkeit beispielsweise Aktien zu großen Mengen kaufen oder verkaufen zu können, ohne einen starken Einfluss auf den Preis der Aktie auszuüben. Die Kenntnis der Handelsrichtung, also ob Käufer oder Verkäufer, des Liquiditätsnehmers ist grundlegend für viele Studien zu finanzwissenschaftlichen und ökonomischen Themen, wie beispielsweise informiertes Handeln auf Finanzmärkten hin zu ``Insider-Trading'', Preiseffizienz und Marktqualität. Um dieses Kapitel mit den vorangegangenen zu verknüpfen, in Modellen zu Herdenverhalten, beispielsweise, wird generell angenommen, dass Informationen über den fundamentalen Wert eines Assets durch die Handlung (Kaufen oder Verkaufen) des Liquiditätsnehmers transportiert wird, sodass aufeinander folgende Händler versuchen, von den Handlungen der vorangegangenen Liquiditätsnehmern etwas über den fundamentalen Wert des Assets zu lernen. Jedoch, eine Zuordnung von Transaktionen zu der Seite des Liquiditätsnehmers und -bereitstellers sind üblicherweise nicht von vorneherein in den Daten gegeben und etablierte Methoden, um diese Information aus den Daten zu filtern, sind heutzutage durch die erhöhte Aktivität an Finanzmärkten mit gewissen Schwierigkeiten konfrontiert, welche deren Klassifizierungsgüte beeinflusst. Daher schlage ich einen neuen Algorithmus vor, der diese Schwierigkeiten überwindet und zeige seine Überlegenheit gegenüber den etablierten Methoden auf

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Correlated Trades and Herd Behavior in the Stock Market

Herd behavior is often viewed as a significant threat for the stability and effciency of financial markets. This paper sheds new light on the relevance of herd behavior for observed correlation of trades. We introduce numerical simulations of a herd model to derive theory-guided predictions regarding the impact of various aspects of uncertainty on herding intensity. We test the predictions using a novel data set including all real-time transactions of institutional investors in the German stock market. In light of the model simulations, empirical results strongly suggest that the observed correlation of trades is mainly due to the common reaction of investors to new public information and should not be misinterpreted as herd behavior

Herding in financial markets

Due to data limitations and the absence of testable, model-based predictions, theory and evidence on herd behavior are only loosely connected. This paper attempts to close this gap in the herding literature. From a theoretical perspective, we use numerical simulations of a herd model to derive new, theory-based predictions for aggregate herding intensity. From an empirical perspective, we employ high-frequency, investor-specific trading data to test the theory-implied impact of information risk and market stress on herding. Confirming model predictions, our results show that herding intensity increases with information risk. In contrast, herding measures estimated for the financial crisis period cannot be explained by the herd model. This suggests that the correlation of trades observed during the crisis is mainly due to the common reaction of investors to new public information and should not be misinterpreted as herd behavior

Information Risk, MarketStress and InstitutionalHerding in FinancialMarkets

This paper employs numerical simulations of the Park and Sabourian (2011) herd model to derive new theory-based predictions for how information risk and market stress influence aggregate herding intensity. We test these predictions empirically using a comprehensive data set of highfrequency and investor-specific trading data from the German stock market. Exploiting intra-day patterns of institutional trading behavior, we confirm that higher information risk increases both buy and sell herding. The model also explains why buy, not sell, herding is more pronounced during the financial crisis

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants