E-Finance Lab e.V.
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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.
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.
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.
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.
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.
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.
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]
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
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.
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
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.
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.
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.
Interview mit Dr. Stefan Fenner, Managing Director CAPVERIANT GmbH
The customer determines the success or failure of the company : interview with Philipp Schmitt
(2021)
Start thinking in systems
(2021)
Start Thinking in Systems / Berthold Kracke
Buying into Fraud – German Retail Investors and the Wirecard Scandal / Konstantin Bräuer, Andreas Hackethal, Guido Lenz, Thomas Pauls
Insights from Explainable Interactive Machine Learning in the Age of COVID-19 / Oliver Hinz, Nicolas Pfeuffer, Wolfgang Stammer, Patrick Schramowski, Benjamin M. Abdel-Karim, Andreas Bucher, Christian Hügel, Gernot Rohde, Kristian Kersting
The Customer Determines the Success or Failure of the Company : interview with Philipp Schmitt
COVID-19 HAS AGAIN TIGHTENED ITS GRIP AROUND THE WORLD AND THE HEALTH SYSTEM. THIS ARTICLE GIVES AN INTRODUCTION TO EXPLAINABLE INTERACTIVE MACHINE LEARNING AND PROVIDES INSIGHTS ON HOW THIS METHOD MAY NOT ONLY HELP IN ENGINEERING MORE POWERFUL AI SYSTEMS, BUT ALSO HOW IT MAY HELP TO EASE THE BURDEN OF VIRAL STRAINS ON THE HEALTHCARE SYSTEM.
ON JUNE 18TH, WIRECARD’S SHARE PRICE PLUMMETED BY MORE THAN 60% FOLLOWING THE FIRM’S ADMISSION OF BEING SUBJECT TO “ENORMOUS FRAUD” AND BILLIONS OF EUROS MISSING. THIS REPORT DOCUMENTS GERMAN RETAIL INVESTORS’ RESPONSE AND FINDS THAT THE POPULARITY OF WIRECARD AMONG RETAIL INVESTORS LED TO SUBSTANTIAL LOSSES IN THEIR PORTFOLIOS. THESE LOSSES WERE EXACERBATED BY STRONG BUYING SENTIMENT AFTER THE ANNOUNCEMENT. THE FAILING STOCK WAS PURCHASED BY INVESTORS ALREADY ENGAGED IN IT AS WELL AS NON-EXPOSED CUSTOMERS.
NATURAL LANGUAGE (NL) IS A PROMISING ALTERNATIVE INTERFACE TO DATABASE MANAGEMENT SYSTEMS (DBMSs) BECAUSE IT ENABLES NON-TECHNICAL USERS TO FORMULATE COMPLEX QUESTIONS. RECENTLY, DEEP LEARNING HAS GAINED TRACTION FOR TRANSLATING NATURAL LANGUAGE TO SQL. HOWEVER, THE CORE PROBLEM WITH EXISTING DEEP LEARNING APPROACHES IS THAT THEY REQUIRE AN ENORMOUS AMOUNT OF MANUALLY CURATED TRAINING DATA IN ORDER TO PROVIDE ACCURATE TRANSLATIONS. WE PRESENT DBPAL THAT USES A NOVEL TRAINING PIPELINE TO LEARN NL2SQL INTERFACES WHICH SYNTHESIZES TRAINING DATA AND, THUS, DOES NOT RELY ON MANUALLY CURATED TRAINING DATA.
THE PROLIFERATION OF THE INTERNET HAS ENABLED PLATFORM INTERMEDIARIES TO CREATE TWO-SIDED MARKETS IN MANY INDUSTRIES. IN SUCH MARKETS, NETWORK EFFECTS OFTEN OCCUR WHICH CAN DIFFER FOR NEW AND EXISTING CUSTOMERS. THE AUTHORS DEVELOP AN INFLUX-OUTFLOW MODEL TO INVESTIGATE THE CONDITIONS UNDER WHICH THE ESTIMATION OF SAME-SIDE AND CROSS-SIDE NETWORK EFFECTS SHOULD DISTINGUISH BETWEEN ITS IMPACT ON THE NUMBER OF NEW CUSTOMERS (I.E., ACQUISITION) AND EXISTING CUSTOMERS (I.E., THEIR ACTIVITY).
Digitalizing Asset Management – The Way Forward / Alexander Lichtenberg
Economic Value of Data / Rene Laub, Klaus Miller, Bernd Skiera
DBPal: A Novel Lightweight NL2SQL Training Pipeline / Benjamin Hättasch, Nadja Geisler, Carsten Binnig
Why Open Innovation in B2B Needs a Push interview with Sven Siering
Closing the Gap between Physical and Electronic Trading / Michael König
Estimating Network Effects in Two-Sided Markets / Oliver Hinz, Thomas Otter, Bernd Skiera
Investor Attention and Algorithmic Decision Making in Financial Markets / Benjamin Clapham, Michael Siering, Peter Gomber
Why Getting Started with Data Science Is Scary, and a Necessity : interview with Kim Nilsson
Economic value of data
(2020)
FIRMS COLLECT A LARGE AMOUNT OF DATA BY ENGAGING HEAVILY IN THE COLLECTION AND STORAGE OF ONLINE USER ACTIVITY VIA VARIOUS USER TRACKING TECHNOLOGIES. RECENT POLICY INITIATIVES AIM AT RESTRICTING THIS PRACTICE TO PROTECT CONSUMER PRIVACY. WE STUDY EMPIRICALLY THE CONSEQUENCES OF SUCH RESTRICTIONS FOR ONLINE PUBLISHERS, SUCH AS NEWS WEBSITES, BECAUSE THEY STRONGLY RELY ON REVENUES THAT ARE GENERATED BASED ON USER DATA. WE FIND A PRICE DECREASE OF CA. 30% FOR ONLINE ADS WHEN NO DATA FROM USER TRACKING IS AVAILABLE. THE POTENTIAL REVENUE LOSS COULD BE MORE THAN EUR 14 BILLION IN THE EU AND MORE THAN USD 27 BILLION IN THE US.
ALGORITHMIC DECISION MAKING PLAYS AN IMPORTANT ROLE IN FINANCIAL MARKETS. ONE SOURCE OF INFORMATION FOR SUCH ALGORITHMS IS THE SENTIMENT OF SOCIAL MEDIA MESSAGES AND NEWS ARTICLES CONCERNING A LISTED COMPANY. YET, CURRENT TOOLS DO NOT DISTINGUISH BETWEEN POPULAR AND LESS POPULAR NEWS AND IT IS UNCLEAR WHETHER METHODOLOGIES BASED ON DATA ANALYTICS CAN BE APPLIED ON SMALL DATASETS OF LESS POPULAR COMPANIES. THEREFORE, WE ANALYZE WHETHER THE IMPACT OF MEDIA SENTIMENT ON FINANCIAL MARKETS IS INFLUENCED BY TWO LEVELS OF INVESTOR ATTENTION AND WHETHER THIS IMPACTS ALGORITHMIC DECISION MAKING.
The efl publishes the insights in the form of a periodic newsletter which appears two times a year. Besides a number of printed copies, the efl insights is distributed digitally via E-mail for reasons of saving natural resources. The main purpose of the newsletter is to provide latest efl research results to our audience. Therefore, the main part is the description of two research results on a managerial level – complemented by an editorial, an interview, and some short news.