Swiss Finance Institute @ EPFL

The Swiss Finance Institute @ EPFL has been created to foster research in finance and to develop a strong offering of programs in finance and financial engineering at the Ecole Polytechnique Fédérale de Lausanne. The focus is on the areas within finance that have a natural interaction with mathematics, statistics, engineering, and science, namely, mathematical finance, financial econometrics, and entrepreneurial finance.

The Swiss Finance Institute @ EPFL participates in two teaching programs, The Master in Financial Engineering at EPFL, which is a highly selective 2-year master program, and The PhD in Finance, which is organized jointly with the Swiss Finance Institute and the Universities of Geneva and Lausanne.

The Swiss Finance Institute @ EPFL benefits from the institutional support of the Swiss Finance Institute, a private foundation created in 2006 by Switzerland’s banking and finance community in cooperation with leading Swiss universities, and from Swissquote, who endowed the Swissquote Chair in Quantitative Finance.

Bank Risk-Taking and the Economy: Evidence from the Housing Boom and its Aftermath Real

The short-termism of lenders amplifies boom-bust credit cycles, leading in turn to real costs for the aggregate economy. During the U.S. housing credit boom, publicly-traded banks increased mortgage lending activity and relaxed standards much more than privately-held banks, and more so if they were run by short-term oriented CEOs. In the ensuing bust, counties with greater exposure to short-term oriented public banks experienced more severe downturns across a variety of outcomes, including economically large drops in aggregate employment, durable consumption, and retail sales. The findings hold for text-based measures of short-term focus and are robust to using an identification strategy that instruments for county mortgage lending with shocks that areplausibly unrelated to local economic conditions. In all, we provide micro-founded evidence that the ownership structure and short-term focus of depository institutions matter for the real economy.

By: Antonio FALATO, Federal Reserve Bank

Swissquote Conference 2018 on Machine Learning in Finance

Machine learning has come to play a prominent role in modern finance. Current applications range from text-based analysis of business reports to deep learning for credit risk and portfolio management. But the noise and behavioral elements inherent in financial data often require nonstandard machine learning solutions, possibly yet to be developed. The full potential of machine learning in finance is still to be explored.
The 9th annual Swissquote Conference will feature current research and insights on machine learning in finance provided by leading experts and scholars in the field. The event addresses academics and practitioners alike, and shall foster the interaction among individuals and across institutions

By: Hans Buehler, JP Morgan - Rama Cont, University of Oxford - Isabelle Flückiger, Accenture - Kay Giesecke, Stanford University - Gerard Hoberg, USC Marshall - Artur Sepp, Julius Baer - David L. Shrier, University of Oxford