E-finance-lab at the House of Finance : about us

The financial services industry is believed to be on the verge of a dramatic [r]evolution. A substantial redesign of its value chains aimed at reducing costs, providing more efficient and flexible services and enabling new products and revenue streams is imminent. But there seems to be no clear migration path nor goal which can cast light on the question where the finance industry and its various players will be and should be in a decade from now. The mission of the E-Finance Lab is the development and application of research methodologies in the financial industry that promote and assess how business strategies and structures are shared and supported by strategies and structures of information systems. Important challenges include the design of smart production infrastructures, the development and evaluation of advantageous sourcing strategies and smart selling concepts to enable new revenue streams for financial service providers in the future. Overall, our goal is to contribute methods and views to the realignment of the E-Finance value chain. ..

Algorithmic trading engines versus human traders – do they behave different in securities markets?

After exchanges and alternative trading venues have introduced electronic execution mechanisms worldwide, the focus of the securities trading industry shifted to the use of fully electronic trading engines by banks, brokers and their institutional customers. These Algorithmic Trading engines enable order submissions without human intervention based on quantitative models applying historical and real-time market data. Although there is a widespread discussion on the pros and cons of Algorithmic Trading and on its impact on market volatility and market quality, little is known on how algorithms actually place their orders in the market and whether and in which respect this differs form other order submissions. Based on a dataset that – for the first time – includes a specific flag to enable the identification of orders submitted by Algorithmic Trading engines, the paper investigates the extent of Algorithmic Trading activity and specifically their order placement strategies in comparison to human traders in the Xetra trading system. It is shown that Algorithmic Trading has become a relevant part of overall market activity and that Algorithmic Trading engines fundamentally differ from human traders in their order submission, modification and deletion behavior as they exploit real-time market data and latest market movements

Liquidity dynamics in an electronic open limit order book: An event study approach

We analyze the dynamics of liquidity in Xetra, an electronic open limit order book. We use the Exchange Liquidity Measure (XLM), a measure of the cost of a roundtrip trade of given size V. This measure captures the price and the quantity dimension of liquidity. We present descriptive statistics, analyze the cross-sectional determinants of the XLM measure and document its intraday pattern. Our main contribution is an analysis of the dynamics of the XLM measure around liquidity shocks. We use intraday event study methodology to analyze how a shock affects the XLM measure. We consider two sets of liquidity shocks, large transactions (which are endogenous events because they originate in the market) and Bloomberg ticker news items (which are exogenous events because they originate outside of the market). We find that resiliency after large transactions is high, i.e., liquidity quickly reverts to normal levels. We further document that large trades take place at times when liquidity is unusually high. We interpret this as evidence that large transactions are timed. The Bloomberg ticker news items do not have a discernible effect on liquidity. --liquidity,limit order book,resiliency

Geld rast um die Welt : der Wertpapierhandel im 21. Jahrhundert

Kaum eine andere Industrie wurde in den vergangenen beiden Jahrzehnten so massiv durch den Einzug der Informationstechnologie geprägt wie der Wertpapierhandel. Traditionelle Geschäftsmodelle haben sich grundlegend verändert. Das klassische Börsenparkett ist nahezu vollständig elektronischen Handelsplattformen gewichen

Online Brokerage: Transforming Markets from Professional to Retail Trading

In this paper, we discuss different concepts for transforming professional trading mechanisms for the increasing market of private investors. Different perspectives of this fundamental change in capital markets are discussed. The driver of these changes is IT: technological progress provides new ways for private investors to participate actively in the capital markets in order to take advantage – and risk – in the area, where traditionally only professionals could realize their chances. Starting from these observations, one of the most important success factors for Online-brokers is discussed, namely that of the integration of the various phases within the “market transaction process“. Some already existing concepts in this area – going along with new forms of intermediation – are evaluated on the basis of the requirements of the different groups mentioned above. Beyond that, the integration level within the market processes serves as a clear framework for further development of IT support of trading activities in Onlinebrokerage. The concept of an Internet based electronic trading system is presented as a promising way to transform professional trading activities to the needs of private investors in the future

An Order-Channel Management Framework for Institutional Investors

Efficient Order-Channel Management, i.e. the process of information gathering, evaluation, decision and control regarding the setup of the overall trading infrastructure and the actual order routing implementation plays a crucial role for trading success as well as the competitiveness of Institutional Investors. This article introduces a framework intended to support Institutional Investors in establishing an individual Order-Channel Management (OCM). For this overall goal, OCM is decomposed into its strategic and operational constituents and the involved key entities, parameters, processes and their interdependencies are outlined. Based on the identified properties, a framework is derived that aims at identifying a suitable mapping from order characteristics to execution venues.

Who Is the Next “Wolf of Wall Street”? Detection of Financial Intermediary Misconduct

Financial intermediaries are essential for investors’ participation in financial markets. Because of their position within the financial system, intermediaries who commit misconduct not only harm investors but also undermine trust in the financial system, which ultimately has a significant negative impact on the economy as a whole. Building upon information manipulation theory and warranting theory and making use of self-disclosed data with different levels of external verification, we propose different classifiers to automatically detect financial intermediary misconduct. In particular, we focus on self-disclosed information by financial intermediaries on the business network LinkedIn. We match user profiles with regulator-disclosed information and use these data for classifier training and evaluation. We find that self-disclosed information provides valuable input for detecting financial intermediary misconduct. In terms of external verification, our classifiers achieve the best predictive performance when also taking regulator-confirmed information into account. These results are supported by an economic evaluation. Our findings are highly relevant for both investors and regulators seeking to identify financial intermediary misconduct and thus contribute to the societal challenge of building and ensuring trust in the financial system

A Taxonomy of Violations in Digital Asset Markets

Numerous frauds, market manipulations and other violations have recently shaken investor confidence in digital asset markets and digital assets themselves. Yet, investor confidence and market integrity are key requirements for the continued success of crypto and other digital assets. In order to facilitate the integrity of digital asset markets and avoid integrity incidents in the future, a systematic overview of violations and their main characteristics is needed to develop appropriate countermeasures. Therefore, we develop a taxonomy of violations in digital asset markets and evaluate the taxonomy based on real-world cases. Our results show that many types of market manipulation in traditional financial markets can also be observed in digital asset markets. However, there are new and additional violations in digital asset markets. We also find that many violations depend on specific capabilities of the violator, certain trading conditions, and asset-specific characteristics