The research activity of the group has the aim of using quantitative methods, both analytical and empirical, to investigate several aspects of financial markets at different time scales. The group has active collaborations with universities and research centers, banks, investment firms, IT companies, and market regulators.
Main research areas
- High frequency finance and market microstructure. The research is devoted to the mathematical modeling and empirical characterization of financial time series at high (transaction by transaction) and ultra-high (offers to buy and sell, limit order book) frequency. The areas of interest are liquidity modeling, price formation mechanisms, and optimal order execution. Finally the group investigates possible metrics of financial market instability at high frequency and the role of market structure on the high frequency properties of prices.
- Value investing and market efficiency. The group is working on an empirically based model of returns dynamics focusing on their evolution over a time horizon of fifteen - twenty years. Aim of the approach is trying to answer Robert Shiller's question about returns of a stock index being predictable. We have collected empirical support to Shiller’s test of the appropriateness of prices in the stock market based on the Cyclically Adjusted Price Earning ratio, showing that it is a powerful predictor of future long run performances of the market. Other make use of Shannon entropy of symbolic time series to test efficient market hypothesis and more generally as measure of informational efficiency.
- Dependency between financial variables, correlation structures, and networks. By using techniques from multivariate statistics, data mining, and the theory of complex networks, we investigate and model the dependencies between financial variables, such as stock returns, Credit Default Swap returns, and trading activity of investors or brokerage firms. The objectives are to identify and model risk factors of an asset portfolio, to build more efficient estimators of covariance matrix for optimal portfolio allocation, and to build taxonomies of investors in order to study their mutual interaction.
- Systemic risk. The group is involved in researches on the mechanisms that might lead financial markets (or the whole economy) to an excessive risk of systemic events. This is done by using mathematical and computational models and empirical analyses. The considered entities are banks or investment firms, which invest in assets and are connected through credit networks. The mechanisms investigated as possible responsible of systemic risk are excessive leverage, positive feedback loops that amplifies small perturbations, and an excessive homogeneity among portfolios.