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Meeting Industrial Demand for Skills in Information Security Education

Project Description

The lack of qualified specialists is a big problem in the IT industry due to the rapid digitization of modern soci-ety. This problem is particularly difficult in the field of information security where the fast development pace is further accelerated by the "arm's race" between the attack and defense. As a result, the set of skills need-ed for successful careers in the IT security industry evolves at a rapid pace, which presents challenges for academic institutions responsible for the adequate education of the future workforce.

The goal of the project was to develop the methodology for objective analysis of the skill gap in information security and to deploy it for the German speaking countries in Europe. The main idea for the methodology to be developed was to provide a systematic comparison of job ads (as a representation of the skills needed in industry) and academic curricula (as a representation of the skills delivered by academic programs). To avoid the linguistic issues of a strongly multilingual job market and academic environments, the project focus was limited to German speaking countries, and hence the project consortium had one partner for each major German speaking country in Europe (University of Liechtenstein focusing on Switzerland).

The project output comprises the new tools for job ad and curricula analysis as well as the findings obtained from deployment of these tools to skill gaps analysis in information security in German speaking countries. Several categories of information security skills have been identified with strong gaps between the demand in industry and the supply in academia. e.g., software and application security, security management, require-ments engineering, compliance and certification. Based on the results of our analysis, recommendations were provided for future curricula development in cybersecurity so as to decrease the identified skill gaps. The tools developed for the project can be used for periodic re-assessment of skill gaps and are available as open source under https://tinyurl.com/EMIDSISE


Measures of cross-sectional dispersion in international stock returns

Project Description

Time-series volatility is a long standing and well established measure of risk for both individual stocks and the market as such. However, the fact that volatility is time variant is not the sole set of available information. Especially during periods of high time-series volatility, the level of idiosyncratic risk can vary significantly in the cross-section of stock returns. This fact is well-known and implicitly embedded in many style investing approaches. So far cross-sectional volatility of returns (return dispersion) has grown in importance on behalf of both academics and practitioners regarding explanatory power in terms of empirical asset pricing and forecasting. The advantage of cross-sectional risk measures over classical option-implied or sample-dependent historical volatility measures is that they are simple to derive, are model free and can be calculated for any frequency without the drawbacks of other volatility measures (liquid derivative markets, loss of observations, …).

The purpose of this project is twofold: First of all, we aim to construct a database including a variety of cross-sectional market factors based on the longest available timeseries of international stock returns for academic and practical application in financial economics and portfolio management, such as asset pricing.

Secondly, we evaluate these factors in the context of explaining and forecasting asset prices (cf., Fama & French 2012, 2014; Welch & Goyal, 2007) and test them as measures of market opportunity in the context of investment management (Cremers and Petajisto, 2009).

Factors (all equally- and value-weighted):
  • Return dispersion (cross-sectional volatility)
  • Alpha and beta dispersion (for Academic and Practitioners)
  • Non-market dispersion
  • Cross-sectional covariance
  • Cross-sectional skewness
  • Cross-sectional kurtosis

Keywords

Cross-sectional volatility Return dispersion Alpha dispersion Beta dispersion Cross-sectional skewness Cross-sectional kurtosis Cross-sectional covariance Non-market dispersion International stock returns

Material neu

Project Description

Im Entwursstudio (15 Wochen) werden mit BAchelorstudierenden des 2. und 3. Jahres mögliche Holzverbindungen eines neu entwickelten Holzwerkstoffes erarbeitet.

Project Participants

Employee
Dipl.-Ing. Dr. techn. Carmen Rist-Stadelmann
- Project Manager
Senior Lecturer - Craft and Structure
Project Manager
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Employee
Prof. em. Dipl. Arch. ETH Urs Meister
- Project Collaborator
Project Collaborator

MASTIS - Establishing Modern Master-level Studies in Information Systems

Project Description

The main aim of the project is to improve Master Program in Information Systems according to the needs of the modern society; to bring the universities closer to changes in global labour market and world education sphere; to enable them to stay responsive to employers' needs; to give students an idea of various job profiles in the Information System domain; to ensure employability throughout graduates' professional and soft skills. Specific objectives of the project include improvement of Master Program in Information System according to the requirements of business; modernization of the current Degree Profile (DP) & curricula in Information System. Degree Profile and Curricula revision will be implemented in accordance with the newest standards of Higher education and the compatibility with the National qualification frameworks; development of innovative academic environment for Master Program on Information System as a platform for training/retraining, PhD, Long Life Learning; provision/modernization of labs infrastructure for Information System.
MASTIS will enable the Partner Country & EU Universities to modernize Information System education based on the student-oriented principals, strong university-enterprise cooperation and modern approaches to the education. It will give the Partner Country Universities an opportunity to prepare competitive specialists for Ukrainian, Montenegrin and global labour market.

Major results of the project:
  • Degree Profile & curriculum for Master Program on Information System;
  • Teaching materials for Master Program on Information System;
  • Degree Profile & curricula revision mechanism;
  • Joint Double Diploma Master programme;
  • IT environment in Partner Country High Education Institutions;
  • Web portal for stakeholders;
  • Network of EU-Partner Country universities & employers.

Keywords

Business Informatics Learning Master

Machine Learning for Facilitating Software Testing

Project Description

As today's technology progresses, customer needs and the functionality of the software change. One of the biggest causes of software issues, like applications not being delivered on time, is that the testing of software is not clearly determined when creating software test plans.
The testing stage is a process that reveals whether the product meets the requirements criteria and the software has been developed as expected. Test engineers face many challenges every day and spend a lot of time finding a suitable solutions.

Project Participants

Machine Learning in Financial Economics: An Investment Perspective

Project Description

With increasing computing power, advanced algorithms and growing data resources, machine learning methods are increasingly applied in various scientific domains. Deviating from other research fields, economic as well as financial data suffer from low signal-to-noise ratios, which significantly hinders the meaningful application of these advanced methods. This dissertation deals with the development and practical application of noise-robust algorithms in the research domain of financial economics. In particular, we focus on current problems in asset pricing, portfolio management and international finance research. The combination of both research areas (financial economics and data science) enables the discovery and practical exploitation of learnable and generalizable patterns in large, noisy data sets.

Participating Institutions

Machine Learning enabled Asset Allocation

Project Description

With increased computing power, more advanced algorithms, and growing data resources, machine learning is widely used in different scientific areas. Machine learning is particularly useful in prediction and clustering tasks and enriches the econometrician's toolbox. Financial machine learning diverges from classical machine learning because financial (market) data suffers from low signal-to-noise ratios, which complicates signal extraction. The high amount of noise also hinders classical asset allocation tasks. Traditional Markowitz mean-variance optimization is unable to outperform a simple 1/N portfolio's return out of sample, which is mainly caused by noise in the data and difficulties in matrix inversion. Machine learning algorithms have the potential to provide financial researchers with new insights. Machine learning based hierarchical clustering already showed promising evidence in this direction. The target of this doctoral thesis will be the further investigation of machine learning's potential in advanced asset allocation. Both clustering and prediction techniques will be applied to evaluate machine learning's strenghts in contrast to classic econometrics.

Market liquidity dynamics

Project Description

In several segments of financial markets, trading books shrink, passive investment strategies become more important and the execution risk shifts from dealers to investors. As a result, liquidity has gained relevance for academia, regulators, investors, market makers and issuers. However, academic research on liquidity is still at an early stage for measurement and data availability problems.

This dissertation therefore analyses liquidity dynamics of international stock and bond markets. It is made up of four articles: The first one shows the importance of liquidity for stock price patterns. The second publication analyzes the general drivers of liquidity for fixed-income instruments. It demonstrates that in addition to a bond's size, age and risk properties liquidity is influenced by primary market activity, demand and allocation during the initial offering and by seasonality. While the third publication analyses seasonality in bond liquidity in greater detail, the fourth one concentrates on primary market effects.

Keywords

market microstructure market liquidity covered bonds

Participating Institutions

Project Participants

Employee
Prof. Dr. Michael Hanke
- Supervisor
Professor - Finance Dean - Liechtenstein Business School
Supervisor
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Employee
Dr. rer. oec. Michael Weigerding
- PhD-Student
PhD-Student
Univ.-Prof. Mag. Dr. Stefan Pichler
- Co-Supervisor
Co-Supervisor

Market liquidity dynamics

Project Description

In several segments of financial markets, trading books shrink, passive investment strategies become more important and the execution risk shifts from dealers to investors. As a result, liquidity has gained relevance for academia, regulators, investors, market makers and issuers. However, academic research on liquidity is still at an early stage for measurement and data availability problems.

This dissertation therefore analyses liquidity dynamics of international stock and bond markets. It is made up of four articles: The first one shows the importance of liquidity for stock price patterns. The second publication analyzes the general drivers of liquidity for fixed-income instruments. It demonstrates that in addition to a bond's size, age and risk properties liquidity is influenced by primary market activity, IPO characteristics and seasonality. While the third publication analyses seasonality in bond liquidity in greater detail, the fourth one concentrates on primary market effects.

Keywords

market microstructure market liquidity covered bonds

Project Participants

Market Entry Strategies for Liechtenstein Life Insurance Undertakings as an Asset Management Product in Germany and Austria

Project Description

The aim of this project is the evaluation of market entry strategies for Liechtenstein life insurance undertakings as an asset management product in Germany and Austria. Legal aspects as well as fiscal effects are analysed for German and Austrian customers. Furthermore, these aspects are summarized in a sales presentation. Based on this work and according to interviews with several banks and asset managers in Germany and Austria a market entry strategy is developed.

Project Participants

Employee
Prof. em. Dr. Marco J. Menichetti
- Principal Investigator
Professor Emeritus - Liechtenstein Business School
Principal Investigator
Dr. Marcel Vaschauner MBA
- Project Collaborator
Project Collaborator
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