Computational Finance

Computational Finance is a comprehensive field that combines financial theory and advanced computing to better analyze and understand financial markets and investment instruments.

Modern financial markets are increasingly complex and volatile, requiring a more sophisticated approach to data analysis. Traditional methods of financial analysis, which depend on simple mathematical models and investors’ intuition, are often insufficient to deal with this complexity and at the speed that the market practices today.

At FICO-ITA we use mathematical modeling techniques, machine learning, data science and other advanced technologies to analyze large amounts of financial data and develop investment strategies. This allows investors and managers to make more informed and accurate decisions about how to invest their money.

Some examples of research areas in Computational Finance include analyzing financial time series, detecting market patterns, optimizing portfolios, and creating risk and volatility models. These techniques can be applied to a wide range of financial instruments including stocks, bonds, currencies and commodities.

One of its main advantages is the ability to analyze large amounts of data, finding complex patterns that are difficult to identify manually, and allowing investors to identify differentiated investment opportunities and manage risks more effectively. Additionally, computing technology can help reduce costs and increase efficiency, allowing investors to maximize their returns and automate much of the operations.

To learn more about how our research on Computational Finance, contact us.