MATLAB w obliczeniach finansowych

Webinarium poświęcone jest wykorzystaniu środowiska MATLAB do obliczeń w szeroko pojętym sektorze usług finansowych. Podczas spotkania zostanie omówione, w jaki sposób MATLAB może znacząco skrócić czas potrzebny do przeprowadzania analizy danych, tworzenia własnych modeli finansowych i wdrażania czy dostosowywania algorytmów na potrzeby zarządzajacych portfelami inwestycyjnymi, aktuariuszy czy traderów. Przedstawiony będzie również przykład dotyczący empirycznej predykcji niewypłacalności.

Webinarium będzie prowadzone w języku angielskim.

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REJESTRACJA

ABSTRAKTY i PRELEGENCI

Getting Started with MATLAB for Computational Finance
Technological and regulatory changes create pressure for the financial services sector to evolve. Companies are responding to trends such as cloud computing, artificial intelligence, and climate change. Financial professionals and Educators worldwide use MATLAB and other MathWorks tools to rapidly develop financial models to deal with current and future business needs and deploy customized algorithms to decision makers such as investment managers, actuaries, and traders.   In this session you’ll find out how MATLAB can help you significantly reduce the time it takes to analyze financial data and develop custom financial models. Highlights: A quick overview of the MATLAB environment Modeling Risks on a portfolio Pricing derivatives AI for Fraud forecasting
	Francesca Perino is Principal Application Engineer at MathWorks. She focuses on enabling MathWorks customers to be successful in their adoption and use of MATLAB platform to solve their numerical modelling and AI challenges with cutting-edge technology, tools and methods. She has expertise in data ingestion and processing, software design and application development in MATLAB, big data for enterprise-scale predictive analytics. Before MathWorks, she spent few years working as research engineer and software developer. She holds a M.Sc. in Physics specializing in numerical methods and statistics in atmospheric science from Torino University in Italy.

Empirical Prediction of Default with Corporate Strategy
Probability of default (PoD) is an evergreen topic in finance and economics. In recent years, progress has been achieved mainly through increased sophistication in big-data analysis and extension of the scope of research beyond purely financial parameters. Our research team use a double stochastic Poisson process forward intensity model with multi-period prediction to estimate PoD for over 15,000 (mostly) private companies. Subsequently, the results are cross-validated with the results of an extensive questionnaire on corporate strategy of 300 randomly selected firms. We confirmed empirically some intuitions around corporate survival related to marketing or employees’ loyalty and alignment with corporate goals. We also observed some non-linear relations, for example, between default risk and company age. Our study falsified the key hypotheses related to impact of corporate strategy on the chance of bankruptcy which have been formulated in the literature.
	Bogusław Bławat holds a PhD in economics from the Polish Academy of Sciences. He teaches Financial Markets at Kozminski University (Warsaw, Poland), and he is also with Polish Agency for Audit Oversight where he controls the audit of banks’ financial statements. In the past, he headed the Institute for Market Research, Consumption and Business Cycles (now the Polish Economic Institute). His research interests include modeling the microstructure of financial markets and the probability of default.