Startup “Markets Flare” Funded by »vor.GRÜNDEN 2024« Project

Following the successful pitch by the startup founders, the »vor.GRÜNDEN 2024« project submitted by the University of Klagenfurt was approved by the KWF Board of Trustees. The funding covers the costs for the pre-commercial development of three startups over a period of nine months. The startup „Markets Flare“ by Andrii Ivanov and Amirreza Badpa is hosted by the Department of Economics / QED with Prof. Martin Wagner providing scientific supervision. The aim is developing an app / web solution to retrieve and process complex financial data using taylor-made learning algorithms. For more information see the description below. Congratulations to our young entrepreneurs!

Executive Summary

Markets Flare is a solution that gives real time access to complex financial data with a particular focus on a user-friendly interface, thereby providing access to financial data also novices. Markets Flare is based on recent advances in artificial intelligence and machine learning that allow it to transform raw data into insightful, easy-to-understand, and manageable information. The underlying basis of the Markets Flare decision support system is the iterative application of tailor-made learning algorithms to financial information retrieval and processing. The focus on a user-friendly interface supports not only professionals in strategy development, but also helps novices to retrieve financial information without the steep learning curve traditionally associated with using financial information systems. The retrieved information is seamlessly combined with numerous financial models that are provided in the Reporting Assistant tool; one of the six integral tools of Markets Flare described below. Capitalizing on the scale advantages generated by applying machine learning algorithms to big data, Markets Flare is a cost-effective integrated suite of information retrieval and processing tools to support the entire financial decision-making cycle, from news coverage and analysis (including alerting) to financial data visualizations, data analysis to financial planning and reporting. Furthermore, Markets Flare is designed for collaboration across flexible teams.

Innovation

Markets Flare: Innovative Financial Analytics Platform

Market Screener: Harnessing Artificial Intelligence algorithms for news filtering and sentiment analysis, it provides real-time insights into market trends, facilitating informed decision-making and enhances portfolio management.

Portfolio Assistant: Seamlessly integrated with the Market Screener, it tracks financial assets, identifies patterns in market fluctuations, and delivers timely updates on news impact, ensuring users stay well-informed about their investment decisions.

Research Tool: Utilizing public data, it streamlines company research by aggregating relevant information from various sources, minimizes time spent on research, and facilitates informed decision-making. Reporting Assistant: Offers pre-built Excel templates of popular financial models, automating the modeling process, reducing errors, and enhancing efficiency in financial analysis.

Workspace Tool: Facilitates team collaboration through a centralized workspace, enabling efficient communication, document sharing, and task management for enhanced productivity.

Event Tracker: Proactively monitors the internet for significant events related to users’ assets, providing insights into upcoming events’ potential impact on stock prices, and letting users make informed investment decisions.

6th Vienna Workshop on High Dimensional Time Series in Macroeconomics and Finance

On May 16 and 17, 2024, the 6th Vienna Workshop on High Dimensional Time Series in Macroeconomics and Finance co-organized by Martin Wagner was held at the Institute for Advanced Studies Vienna.

The Quantitative Economics Division of the Department of Economics contributed three presentations:

R. Kawka, M. Vetter and M. Wagner: “Robust Inference for the Long Run”
S. Veldhuis and M. Wagner: “Integrated Modified OLS Estimation and Inference in Systems of I(2) Cointegrating Regressions”
D. Bauer, A. Konstantopoulos, M. Wagner and Ch. Zwatz: “On (Static) Linear Transformations of VAR Models”

8. Mikro-Workshop

The 8th Micro-Workshop takes place on Thursday, May 16, 2024 in room N.0.43 from 12:00-14:30.

The presentation is held by  Dr. Daniel Rehsmann MSc. BSc. The title of the presentation is „Choose your auction: Mechanism design for a bidder“.

All guests are welcome!

A Machine Learning Approach to Smart Electricity Management

The project „A Machine Learning Approach to Smart Electricity Management: From Disaggregation to Policy Impact Evaluation” – submitted by Wilfried Elmenreich and Martin Wagner – has been awarded a predoc position following the second call in the „Ada Lovelace Programme“ by the Universisty of Klagenfurt. The project is truly interdisciplinary, located at the interface of energy systems analysis, machine learning and AI on the one hand and policy impact analysis on the other hand to estimate the economic and ecelogical potential of smart meter data and dynamic pricing.