Finance

The finance field draws on computational econometrics, data mining and analytics, computational intelligence and machine learning, and nonlinear stochastic dynamic models of financial and economic data in a non-Gaussian environment and under extreme events. Research objectives range from the analysis of traditional markets to innovative microfinance, including: the study and modeling of financial assets, the development of statistical algorithmic arbitrage strategies, the assessment and mitigation of systemic, systematic, and operational risks in financial markets, the use of quantitative methods for portfolio management and hedging solutions, and the analysis of the impact of technology on asset pricing and behavioral finance. In the field of microfinance, particular attention is paid to studies on crowdfunding through the application of advanced statistical techniques to analyze investor behavior, evaluate campaign success, identify critical success factors, and analyze crowdfunding dynamics. Quantitative methods are used to test theories and develop models for the study of financial systems at all levels, with a particular focus on agent-based computational models and computational experiments on artificial markets. The goal is to determine the regularities and emergent properties of financial systems resulting from the behaviors and interactions of heterogeneous, rational, and bounded economic agents, both in traditional markets and alternative finance platforms. To this end, a large-scale agent-based financial simulator (Genoa Artificial Stock Market) has been developed. The simulator serves as a computational laboratory for conducting what-if analyses and computational experiments, allowing for the exploration of complex scenarios that include both the dynamics of established markets and the emerging dynamics of digital finance and crowdfunding.

Laboratories involved
Management Engineering – DOGE (RADRL Head: Prof. Silvano Cincotti)

Representative Publications

  • Testa, S., Troise, C., Cincotti, S., & Camilleri, M. A. (2024). The role of electronic waste management solutions and message framing in influencing consumer behaviours: Exploring the crowdfunding context. Business Strategy and the Environment, 33(2), 917-929.
     
  • Atawna, T., Testa, S., & Cincotti, S. (2024). The Impact of Geography on the Success of Prosocial Crowdfunding. International Journal of Electronic Commerce, 28(3), 332-357.
     
  • Ponta, L., Trinh, M., Raberto, M., Scalas, E., & Cincotti, S. (2019). Modeling non-stationarities in high-frequency financial time series. Physica A: statistical mechanics and its applications, 521, 173-196.
     
  • Ponta, L., Pastore, S., & Cincotti, S. (2018). Static and dynamic factors in an information-based multi-asset artificial stock market. Physica A: Statistical Mechanics and its applications, 492, 814-823.
     
  • Raberto, M., Cincotti, S., Focardi, S. M., & Marchesi, M. (2001). Agent-based simulation of a financial market. Physica A: Statistical Mechanics and its Applications, 299(1-2), 319-327.
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