Studying Banks with Python
Banking may not seem to be the most obvious area to use algorithms. But sometimes the models that researchers construct are too complex to be solved analytically. "Using algorithms gets you much further", says Silvio Petriconi, an economist who is specialized in banking and macro-financial theory. "It's one of those areas that are going to be more and more important to new generations of students. With the access to new data comes the challenge of developing more realistic models". Petriconi is currently writing a model with Elena Carletti of the Deparment of Finance and with Robert Marquez of the University of California in Davis. They are trying to understand the capital structure of banks in a world where equity is scarce and people need to be convinced to become equity investors.
"We began to study banks and firms together: how does the funding situation change for banks when equity of non-financial firms is doing particularly well, for instance? What does that imply for the level of banks' capital? There's no way to solve the full model other than by writing code. I'm using Python". Petriconi's work overlaps with applied microeconomics where, he says, using algorithms to solve problems is not fully accepted yet. "There is a cultural gap and a language problem. There is a lack of scholars who lend credibility to algorithms by demonstrating their appropriate use. We do not have the same level of training for our graduate students as in natural sciences". He should know: Petriconi used to be a physicist who did extensive research using computational techniques. He switched fields at age 24 and never came back. "Algorithms" he says "stand right in the middle of the division between the natural and social sciences".
Read the article by Emanuele Borgonovo on our lives with algorithms and how they enter the work of Bocconi researchers in various fields
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