FinTech Research Team


Simon Alfano	Daniel Drummer	Stefan Feuerriegel Leiter Forschungsgruppe	Joscha Märkle-Huß	Nicolas Pröllochs	Prof. Dr. Dirk Neumann Lehrstuhlinhaber

Kontakt: tondumdummymyality@is.uni-dudummymmyfreiburg.de

Our Research

Fintech has become one of the fastest growing sectors in many economies. Fundamental shifts in the financial industry have become manifest over recent years. The University of Freiburg is at the forefront of conducting research in the field of this exponentially growing phenomenon.

Prof. Dr. Dirk Neumann and his team can recourse to more than 10 years of active research in the space of financial technology space (see below for a selection of previous projects).

Research Topics

Current research topics in the Fintech space include:

Peer-to-peer/market place lending
Blockchain technology
Payment analytics and big data

Organization of FinanceCom

The Chair for Information Systems Research has been selected to host the prestigious 2016 FinanceCom in Frankfurt. After very successful FinanceCom workshops in Sydney, Regensburg, Montreal, Paris, Frankfurt and Barcelona, FinanceCom 2016 will be returning to Frankfurt on 8th December 2016. Advancements in Information and Communication Technologies have paved the way to new business models, markets, networks, services, and players in the Fintech industry.

http://www.financecom2016.is.uni-freiburg.de

Industry Cooperation

The Chair of Information Systems is also involved in high profile cooperations with companies from the Fintech industry, nationally and internationally.

For more information please reach out to findumdummymytech@is.uni-dudummymmyfreiburg.de

Project: Dictionary Generation for Financial News

Aims

Understanding how companies frame the language of their news disclosures
Analyzing information processing of investors in financial news
Generating domain-specific dictionaries for sentiment analysis

Method

Data basis: Regulated ad hoc announcements language of German firms in English
Using stock market reactions as an objective measure to quantify news tone
Utilizing Bayesian variable selection methods to select decisive positive and negative key words

Results

Generated dictionaries using regularization methods outperform static dictionaries in terms of goodness-of-fit and predictive performance
Manually-selected word lists lead to misclassifications in a financial context
Investors expose the framing of positive into negative words when judging an investment

Kernel Density Estimation of generated dictionaries Top 5 positive and negative words

Pröllochs N, Feuerriegel S, Neumann D: Generating Domain-Specific Dictionaries Using Bayesian Learning, 23rd European Conference on Information Systems (ECIS 2015), Münster, Germany, May 26-29, 2015.

Pröllochs N, Feuerriegel S, Neumann D: Say it Right – How Managers Prettify Corporate Disclosures 2015 (Winter Conference on Business Intelligence (WCBI 2015), Snowbird, Utah, 12-14 March 2015.

Pröllochs N, Feuerriegel S, Neumann D: Is Human Information Processing Affected by Emotional and Cognitive Biases? Evidence from the Stock Market, Working Paper, University of Freiburg, 2015.

Project: Information Processing of Initial Public Offering Filings

Aims

Stakeholders in Initial Public Offerings (IPO) must understand how filings affect pricing
Additional focus on the textual content of pre-IPO news coverage
Hypothesis that news and filings receive different attention

Method

Data basis for U.S. IPOs: Form 424 filings and Thomson Reuters News Archive for Machine Readable News
Utilizing sentiment analysis to extract subjective information from filings and news
Linking sentiment and first-day returns to study news reception

Results

Degree of specific investor attention depends on the specific type of information
The more negative the pre-IPO news coverage, the higher the first-day returns
Risks are recognized in filings, whereas emphasis on company opportunities in news

News Reception around Initial Public Offerings

Conclusion: Information processing depends on the attention different information receives

Feuerriegel S, Schmitz J T, Pröllochs N, Neumann D: What Matters Most? How Tone in Initial Public Offering Filings and Pre-IPO News Influences Stock Market Returns What Matters Most? How Tone in Initial Public Offering Filings and Pre-IPO News Influences Stock Market Returns 2015 (2015 FMA European Conference, Venice, Italy, June 11-12, 2015.

Feuerriegel S, Schmitz J T, Neumann D: What Matters Most? How Tone in Initial Public Offering Filings and Pre-IPO News Influences Stock Market Returns, Working Paper, University of Freiburg, 2014.

Project: News Reception in Accordance with the Noise Trader Theory

Aims

Understand information processing of different investor types
Examine the effect of news sentiment on crude oil prices for different investor types in accordance with the Noise Trader Theory

Method

Data basis: Thomson Reuters News Archive for Machine Readable News
Data utilization: Apply sentiment analysis techniques to extract subjective information from news
Data analysis: Decompose the crude oil price with a Kalman filter into a fundamental price component and a noise residual, and regress a standardized news sentiment score on both decomposed oil price components

Results

News sentiment has a significant positive effect on the noise residual as suggested by the noise trader approach, but also on the fundamental price in contrast to the Noise Trader Theory

News reception with noise trader theory

The effect of new sentiment is stronger on the noise residual than on the fundamental price

Alfano S, Feuerriegel S, Neumann D: Is News Sentiment More than Just Noise?, 23rd European Conference on Information Systems (ECIS 2015), Münster, Germany, May 26-29, 2015.

Alfano S, Feuerriegel S, Neumann D: Do Pessimists Move Asset Prices? Evidence from Applying Prospect Theory to News Sentiment, Working Paper, University of Freiburg 2015.

Project: Asymmetric Information Processing of Financial News Sentiment

Aims

Understanding processing of information with different polarity (negative vs. positive) by different investor types (extracted via Kalman filtering)
Finding evidence of asymmetric information processing and the negativity bias

Method

Data basis: Thomson Reuters News Archive for Machine Readable News
Utilizing sentiment analysis to extract subjective information from news
Applying quantile regressions to study the differential effect of positive and negative sentiment (negativity bias) on different investor types

Results

Different investor types process information differently
Informed investors weigh news with a very positive or negative sentiment less, uninformed investors weigh negative information more than positive confirming the negativity bias