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A common element of market structure analysis is the spatial representation of firms’ competitive positions on maps. Such maps typically capture static snapshots in time. Yet, competitive positions tend to change. Embedded in such changes are firms’ trajectories, that is, the series of changes in firms’ positions over time relative to all other firms in a market. Identifying these trajectories contributes to market structure analysis by providing a forward-looking perspective on competition, revealing firms’ (re)positioning strategies and indicating strategy effectiveness. To unlock these insights, we propose EvoMap, a novel dynamic mapping framework that identifies firms’ trajectories from high-frequency and potentially noisy data. We validate EvoMap via extensive simulations and apply it empirically to study the trajectories of more than 1,000 publicly listed firms over 20 years. We find substantial changes in several firms’ positioning strategies, including Apple, Walmart, and Capital One. Because EvoMap accommodates a wide range of mapping methods, analysts can easily apply it in other empirical settings and to data from various sources.
Regulators worldwide have been implementing different privacy laws. They vary in their impact on the value for advertisers, publishers and users, but not much is known about these differences. This article focuses on three important privacy laws (i.e., General Data Protection Regulation [GDPR], California Consumer Privacy Act [CCPA] and Personal Information Protection Law [PIPL]) and compares their impact on the value for the three primary actors of the online advertising market, namely, advertisers, publishers and users. This article first compares these three privacy laws by developing a legal strictness score. It then uses the existing literature to derive the effects of the legal strictness of each privacy law on each actor’s value. Finally, it quantifies the three privacy laws’ impact on each actor’s value. The results show that GDPR and PIPL are similar and stricter than CCPA. Stricter privacy laws bring larger negative changes to the value for actors. As a result, both GDPR and PIPL decrease the actors’ value more substantially than CCPA. These value declines are the largest for publishers and are rather similar for users and advertisers. Scholars and practitioners can use our findings to explore ways to create value for multiple actors under various privacy laws.
For many services, consumers can choose among a range of optional tariffs that differ in their access and usage prices. Recent studies indicate that tariff-specific preferences may lead consumers to choose a tariff that does not minimize their expected billing rate. This study analyzes how tariff-specific preferences influence the responsiveness of consumers’ usage and tariff choice to changes in price. We show that consumer heterogeneity in tariff-specific preferences leads to heterogeneity in their sensitivity to price changes. Specifically, consumers with tariff-specific preferences are less sensitive to price increases of their preferred tariff than other consumers. Our results provide an additional reason why firms should offer multiple tariffs rather than a uniform nonlinear pricing plan to extract maximum consumer surplus.
Digitale Technologien begünstigen den Einsatz einer dynamischen Preisgestaltung, also von Preisen, die für ein prinzipiell gleiches Produkt unangekündigt variieren. Dabei werden in der öffentlichen Diskussion unterschiedliche Ausgestaltungsformen dynamischer Preise oftmals vermischt, was eine sinnvolle Analyse der Vor- und Nachteile der dynamischen Preisgestaltung erschwert. Das Ziel des Beitrags ist die Darstellung der ökonomischen Grundlagen und die Diskussion sowie Klassifikation der Ausgestaltungsmöglichkeiten der dynamischen Preisgestaltung. Darüber hinaus erfolgt eine Bewertung der Vor- und Nachteile der dynamischen Preisgestaltung aus Käufer- und Verkäufersicht. Abschließend werden Implikationen für die betriebswirtschaftliche Forschung diskutiert.
Generative AI is a game changer – also in the financial sector. Institutions and their IT service providers need to consider carefully: Which AI approach will enable them to implement optimal solutions for themselves and their customers in this highly regulated environment? How did Finanz Informatik, as the savings banks’ digitalization partner, proceed here?
The significance of data and Artificial Intelligence (AI) has a profound impact on all industries, presenting both challenges and opportunities. Given its power and relevance, AI has not gone unnoticed in the public affairs sector. The upcoming German federal election in 2025 brings discussions about AI to the forefront, raising questions about the extent to which data will drive the public affairs field and how it will be handled.
Customer loyalty is a critical measure for success, showing if a firm's product is received well by its customers. To understand its development over time, two fundamental questions must be answered: (I) How will current customers' loyalty develop, and (II) will new customers' loyalty differ from current customers' loyalty? The authors empirically answer these questions based on a data set including ~500 B2B web technologies with jointly ~325 million customers spanning over 24 years. They show that loyalty hardly develops and, if so, it rather decreases than increases. The loyalty of current customers rarely changes and, if so, rather increases than decreases. New customers are most likely less loyal than current customers. These results show that by failing to account for these underlying developments, stakeholders, in most cases, draw the wrong conclusions about product value measured via customer lifetime value.
Existing table retrieval approaches estimate each table’s relevance for a particular information need and return a ranking of the most relevant tables. This approach is not ideal since the returned tables often include irrelevant data and the required information may be scattered across multiple tables. To address these issues, we propose the idea of fine-grained structured table retrieval and present our vision of R2D2, a system which slices tables into small tiles that are later composed into a structured result that is tailored to the user-provided information need. An initial evaluation of our approach demonstrates how our idea can improve table retrieval and relevant downstream tasks such as table question answering.
In a year marked by challenging market dynamics, the importance of ESG investments remains unwavering. But the wave of ESG regulations and requests generates a demand for more scalable ways to collect and analyze ESG data. The rise of AI could mark a turning point in an industry heavily burdened by reporting requirements, and unlock the true potential of ESG for businesses and investors alike.
Nowadays, firms lack information to derive the share of wallet, a vital metric that identifies how much additional spending a firm could capture from each customer. However, decoding Blockchain data enables observing all transactions of each wallet, respectively customer, on the Ethereum NFT market. To shed light on the share of wallet, we analyzed 22.7 million transactions from over 1.3 million customers across eight competing firms on the Ethereum NFT market.
This study explores high-frequency cross-asset lead-lag relationships for various market microstructure dimensions. Utilizing data from stocks, futures, and exchange traded products, the findings uncover significant lead-lag patterns, particularly among fundamentally related instruments. Our results demonstrate that knowledge about lead-lag relationships can be leveraged for forecasting short-term changes in financial markets.
Yes, they are. The securities services industry is at a tipping point of its digital transformation and will now see industry solutions to scale. We identify three fundamental drivers being adopted more broadly: cloud migration, data, and digitization. This triage also drives the scaling of Clearstream’s digital infrastructure D7, one of the leading digital infrastructures globally.
WE PRESENT OUR VISION OF OMNISCIENTDB, A NOVEL DATABASE THAT LEVERAGES THE IMPLICITLY STORED KNOWLEDGE IN LARGE LANGUAGE MODELS TO AUGMENT DATA SETS FOR ANALYTICAL QUERIES OR MACHINE LEARNING TASKS. OMNISCIENTDB EMPOWERS USERS TO AUGMENT DATA SETS BY MEANS OF SIMPLE SQL QUERIES AND THUS HAS THE POTENTIAL TO DRAMATICALLY REDUCE THE MANUAL OVERHEAD ASSOCIATED WITH DATA INTEGRATION. IT USES AUTOMATIC PROMPT ENGINEERING TO CONSTRUCT APPROPRIATE PROMPTS FOR GIVEN SQL QUERIES AND PASSES THEM TO A LARGE LANGUAGE MODEL LIKE GPT-3 TO CONTRIBUTE ADDITIONAL DATA, AUGMENTING THE EXPLICITLY STORED DATA. OUR INITIAL EVALUATION DEMONSTRATES THE GENERAL FEASIBILITY OF OUR VISION, EXPLORES DIFFERENT PROMPTING TECHNIQUES IN GREATER DETAIL, AND POINTS TOWARDS FUTURE RESEARCH.
Forging new paths – the Bundesbank’s transformation journey : interview with Karmela Holtgreve
(2023)
Iinterview with Karmela Holtgreve [Director General Strategy and Innovation, Deutsche Bundesbank]
WE STUDY REDISTRIBUTIVE EFFECTS OF INFLATION USING A RANDOMIZED INFORMATION EXPERIMENT ON BANK CLIENTS. ON AVERAGE, INDIVIDUALS ARE WELL INFORMED ABOUT CURRENT INFLATION AND ARE CONCERNED ABOUT ITS IMPACT ON WEALTH. YET, MOST INDIVIDUALS ARE NOT AWARE OF HOW INFLATION ERODES NOMINAL POSITIONS. ONCE THEY RECEIVE INFORMATION ON THIS EROSION CHANNEL, THEY UPDATE PERCEPTIONS AND EXPECTATIONS ABOUT OWN NET NOMINAL POSITIONS. LEARNING ABOUT THE INFLATION-INDUCED EROSION OF NOMINAL POSITIONS CAUSALLY AFFECTS CHOICES IN HYPOTHETICAL REAL-ESTATE TRANSACTIONS AND ACTUAL CONSUMPTION. THE FINDINGS SUGGEST THAT HOUSEHOLD WEALTH MEDIATES THE SENSITIVITY OF CONSUMPTION TO INFLATION ONCE HOUSEHOLDS ARE AWARE OF THE BALANCE-SHEET EFFECTS OF INFLATION.
Regulatory impact analysis (RIA) serves to evaluate whether regulatory actions fulfill the desired goals. Although there are different frameworks for conducting RIA, they are only applicable to regulations whose impact can be measured with structured data. Yet, a significant and increasing number of regulations require firms to comply by communicating textual data to consumers and supervisors. Therefore, we develop a methodological framework for RIA in case of unstructured data based on textual analysis and apply it to a recent financial market regulation: MiFID II.
Firms, researchers, and policy makers often want to measure consumption and especially how events, promotions, or policies affect it. Measuring consumption reactions is often hard. Firms lack access to competitors’ sales data and regularly do not share their own with outsiders. Large samples of smartphone location data could solve this problem. This article describes a research project using smartphone location data to estimate consumption reactions to political conflict during the Trump presidency.
What does your personality reveal about your financial behavior? Evidence from a FinTech experiment
(2022)
We co-operate with a German financial account aggregator (FAA) and conduct a personality survey with 1,700 app users. We combine the survey results with their anonymized transaction data and investigate links between personality traits and spending behavior. Observing many lottery windfalls in our dataset and treating these incidents as real-life experiments, we ask: what do individuals do with unexpected income changes? Our findings suggest that highly extraverted individuals tend to overspend in response to lottery windfalls.
Business practitioners increasingly use Artificial Intelligence (AI) applications to assist customers in making decisions due to their higher prediction quality. Yet, customers are frequently reluctant to rely on advice generated from machines, especially when their decision is at stake. Our study proposes a solution, which is to bring a human expert in the loop of machine advice. We empirically test whether customers are more accepting expert-AI collaborative advice than expert or AI advice.
ETFs Prove Their Worth in Turbulent Times / Eric Leupold, Managing Director / Head of Cash Market, Deutsche Börse AG
Is Human-AI Advice Better than Human or AI Advice? / Cathy Liu Yang, Kevin Bauer, Xitong Li, Oliver Hinz
What Does Your Personality Reveal about Your Financial Behavior? Evidence from a FinTech Experiment / Andreas Hackethal, Fabian Nemeczek, Jan Radermacher
“MiCA” – Regulating the European Markets in Crypto-Assets / Dr. Stefan Berger, Member of the European Parliament, Committee on Economic and Monetary Affairs
News of the efl
MANY PEOPLE CLAIM THAT FIRMS NEED TO EMBRACE DIGITAL TECHNOLOGIES. YET, WE KNOW LITTLE ABOUT DIGITAL EMBRACEMENT, ITS ANTECEDENTS, AND ECONOMIC CONSEQUENCES. THIS ARTICLE PROPOSES A TEXTUAL APPROACH TO MEASURE DIGITAL EMBRACEMENT AND APPLIES IT IN AN EMPIRICAL STUDY COVERING 2,278 PUBLICLY LISTED U.S. FIRMS OVER 17 YEARS. THE RESULTS OUTLINE A VAST HETEROGENEITY IN FIRMS’ DIGITAL EMBRACEMENT IN AND ACROSS INDUSTRIES. REMARKABLY, A HIGHER DIGITAL EMBRACEMENT PREDICTS HIGHER FINANCIAL PERFORMANCE.
RECENTLY, A NEW CLASS OF SYSTEMS FOR SHARED AND COLLABORATIVE DATA MANAGEMENT HAS GAINED MORE AND MORE TRACTION. IN CONTRAST TO CLASSICAL DATA BASE MANAGEMENT SYSTEMS (DBMS), SYSTEMS FOR SHARED DATA NEED TO PROVIDE ADDITIONAL GUARANTEES TO ENSURE THE INTEGRITY OF DATA AND TRANSACTION EXECUTION. IN THIS PAPER, WE PRESENT TRUSTDBLE, A NEW DBMS THAT EXTENDS THE ACID PROPERTIES (I.E., ATOMICITY, CONSISTENCY, ISOLATION, DURABILITY) USED BY CLASSICAL DBMSS WITH A NEW VERIFIABILITY COMPONENT TO ADDRESS THESE NEW REQUIREMENTS.
Jacob Hetzel : But This Time It’s Different – the Rise of the Retail Investor
Carsten Binnig, Muhammad El-Hindi, Simon Karrer, Benedikt Völker : TRUSTDBLE: Towards a New Class of DBMSs for Data Sharing
Simeng Han, Alexander Hillert, Bernd Skiera : Digital Embracement of Firms: Measurement, Antecedents, and Financial Consequences
Interview with Christina Sell : The Role of ESG Data in the Sustainable Transformation of the Real Economy
Interview mit Dr. Stefan Fenner, Managing Director CAPVERIANT GmbH
PHISHING E-MAILS CONTINUE TO POSE A TOP THREAT TO AN ORGANIZATION’S INFORMATION SECURITY. DESPITE TECHNICAL ADVANCES, THE BURDEN OF DETECTING AND DEALING WITH THEM ULTIMATELY REMAINS ON THE SHOULDERS OF THE INDIVIDUAL EMPLOYEE. THIS ARTICLE PRESENTS RESULTS OF A MULTI-METHOD PHISHING EXPERIMENT INCLUDING THE USE OF AN EYE-TRACKING DEVICE TO ASSESS EMPLOYEES’ ACTUAL AWARENESS OF PHISHING AND INFLUENCING FACTORS. PRACTICAL IMPLICATIONS FOR SECURITY TRAININGS ARE ALSO DISCUSSED.
GAMESTOP, A COMPANY THAT WAS PRESUMED DEAD DUE TO SHRINKING PROFITS OFITS BRICK-AND-MORTAR BUSINESS MODEL, HIT THE HEADLINES BECAUSE OF ASHORT SQUEEZE OF ITS STOCK PRICE. THE POPULAR OPINION REPORTED BY MAIN-STREAM MEDIA SUGGESTED THAT THE GAMESTOP FRENZY WAS EXCLUSIVE TO YOUNGAND INEXPERIENCED INVESTORS GATHERING ON THE SOCIAL MEDIA PLATFORMREDDIT. IN CONTRAST, OUR RESULTS INDICATE THAT ALSO MORE EXPERIENCEDRETAIL INVESTORS IN GERMANY PARTICIPATED.