The firm was founded in 2006 by Stefano Peron and Andrea Calesso. BrainBrane was born as a group of private traders who merge financial and statistical personal knowledge.
Afterward BrainBrane team started to develop software for algorithmic trading, statistical arbitrage and options pricing, using the state-of-art technology and often discovering new ways of trading.

FOUNDERS
Dr. Stefano Peron
Stefano Peron received Bachelor’s Degree in Economics from Padua University in 2006. His trading attitude brings him to started MSc Degree in Finance focused on statistic and quantitative finance.
In 2008 he received MSc Degree with honors in Finance from Padua University with a thesis on stochastic volatility models calibration.

Dr. Eng. Andrea Calesso
Andrea Calesso received Bachelor’s Degree in Telecommunication Engineering from Padua University in 2005. He started to work in BrainBrane for sharing his knowledge about stochastic processes and data mining. In 2008 he received MSc Degree in Telecommunication Engineering focused on network and signal processing.

WHAT WE DO
The central focus of the BrainBrane is the research of statistical arbitrage and other investment opportunities. Our investment strategies are based into two broad categories: quantitative strategies based on mathematical and computational models and cognitive strategies based on the analysis of human behavior and mind process.
Quantitative strategies are the central focus of our activities, and are used to identify underpriced and overpriced securities. The strategies are based on mathematical models embodied in computer software (java and c++).

In the other hand cognitive strategies are based on the detailed financial analysis which applies scientific research on human and social, cognitive and emotional factors to better understand economic decisions by consumers, borrowers, investors, and how they affect market prices, returns and the allocation of resources.

Our quantitative models and strategies are based on:
• the use of mathematical techniques to identify profit opportunities based on statistical mean reversion property;
• the application of proprietary models designed to measure value at risk;
• the use of quantitative techniques to hedge our portfolio and minimize the transaction costs;
• the utilization of proprietary technology to trade vanilla options dynamically and try to forecast volatility and correlation between assets.
In the course of identifying profit opportunities, BrainBrane analyzes a great amount of data in different frequency, from tick by tick data to month returns.
Our research also includes multifractal volatility models, the study of fat tail events and the application of options theory to trade along real markets distribution.
We collaborate with many phd and professor in Maths, Bioinformatic, Sociology, Signal processing and Informatic.