www.mate.polimi.it/fintech
June 10-11, 2021 – Online Conference

Big Data and Machine Learning are driving a significant transformation in the financial industry. Amazing examples include: robo-advisory; predicting frauds in payment systems; development of sophisticated algorithmic trading strategies; systemic risk assessment; rating of companies/financial products using a huge amount of information; development of chatbots for customers; nowcasting of financial time series; digital marketing; instant pricing of insurance products.

The transformation concerns the academia and the financial industry. The goal of the conference is to bring together academicians with different backgrounds (economists, finance experts, data scientists, econometricians) and representatives of the financial industry (banks, asset management, insurance companies) working in this field.

Papers on all areas dealing with Machine Learning and Big Data in finance (including Natural Language Processing and Artificial Intelligence techniques) are welcomed. The conference targets papers with different angles (methodological and applications to finance).

Invited speakers:

• Tomaso Aste (University College London)
• Emanuele Borgonovo (Università Bocconi)
• Tucker Balch (JP Morgan AI research)
• Juri Marcucci (Bank of Italy)
• Georgios Sermpinis (Adam Smith Business School, University of Glasgow)

For information: www.mate.polimi.it/fintech

Niklas Wagner (Passau University)

Give Me a Break: Is the Equity Premium a Trading Break Premium?

May 12, 2020 – 12.30

Abstract

This paper addresses the relation between market risk and expected market returns under periodic trading breaks. We propose a model where asset prices are driven by a diffusion process that operates during the trading day and a separate process that captures overnight price changes. Our empirical analysis shows that both components are important in explaining the equity market risk premium.
Trading breaks entail a lack of market functionality and liquidity and our results reveal that investors ask for a premium to hold the market portfolio overnight. Including additional state variables into the model, we find that uncertainty risk and illiquidity risk are both priced as well.

Pasquale Cirillo (TU Delft)

The distortions of finance

May 5, 2020 – 12.30

Abstract

Finance is a world of distortions. Many tools we use, many findings we know are actually the result of a distortion.

Take the well-known Black-Scholes model: the probability to be in the money at maturity under P and Q is a distortion. And the price of a European call? Another distortion.

Consider risk management, think about the expected shortfall, and—guess what?—a distortion.

And if you think that copulas are immune, you are wrong, plenty of distortions there.

Model risk is often represented in terms of distortions.

So, let’s talk about distortions, and in particular about the special class of Lorenz transforms.

Sospeso per indicazioni ministeriali

Rosario Mantegna (Palermo University)

Trading networks in a stock exchange: the case of LSE and of the Nordic Stock Exchange with a focus on high-frequency trading

Canceled

 Abstract

We study the heterogeneity of financial actors trading in a fully electronic stock market. The investigated stock markets are the Nordic Stock exchange and the London Stock Exchange. By using network concepts and network filtering methods we detect specialization of market members. Specifically, we detect over-expressed and under-expressed trading interactions of some market members that are statistically robust and that are stable over long period of times. These networked structures are specific to each market, time period, and specific financial asset. By analyzing trading networks both for buyers-sellers and aggressors-counterparties we interpret some network structures as related to the diffusion and implementation of high-frequency trading.

Andrea Tarelli – Università Cattolica (Milano)

Bail-in vs bail-out: Bank resolution and liability structure
(joint work with Luca Leanza and Alessandro Sbuelz)

February 18, 2020 – 12.00 

Abstract
What is the joint impact of different resolution regimes and capital requirements on the optimal liability structure of a bank holding insured deposits and issuing non-bailinable debt and bail-inable Tier1-capital debt? We address this novel question and find that: 1) a credible bail-in resolution regime rules out extreme leverage and creates value by postponing default; 2) a positive probability of bail-out destroys credibility with dramatic effects on financial risk-taking, to the point of reversing the classical positive link between optimal leverage and growth prospects; and 3) a strict enforcement of the Basel III CET1 capital requirement strongly mitigates the impact of a non-credible resolution regime.