BRAIN is a Research Company that creates alternative datasets and develops proprietary algorithms for investment strategies on financial markets.

BRAIN also supports Clients in developing, optimizing and validating their own proprietary models.

BRAIN

BRAIN platform includes a Natural Language Processing and a Machine Learning Infrastructure that enables Clients to integrate state of the art approaches in their strategies. All software is highly customizable to support the investment approach of our Clients.

The system is built to be open to inclusion of Alternative Data and capable to evaluate their relevance to the models. A dynamic approach allows to adapt to new datasets that typically may have a shorter history than conventional data

By selling its proprietary signals BRAIN is also a player in the Alternative Data ecosystem.

BRAIN also developed proprietary strategies for asset allocation using as input conventional and Alternative Data

BRAIN PRODUCTS

Dynamic Volatility Signal

BRAIN SENTIMENT
INDICATOR

The Brain Sentiment Indicator (BSI) measures the “mood” on approximately 7000 global stocks based on the analysis of financial news using Natural Language Processing techniques.
As a free trial we provide approximately fiour years of history for a subset of about 3000 stocks and upon request a 30 days access to the daily report corresponding to the full universe.

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Brain Sentiment Indicator

BRAIN MACHINE LEARNING 
STOCK RANKING

Brain Machine Learning proprietary platform is exploited to generate a daily stock ranking based on the predicted future returns of a universe of 1000 stocks on four time horizons: 2, 3, 5 and 10 days (other time horizons could be developed and tested upon request). The model implements a series of techniques to reduce the well-known overfitting problem for financial data.

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Brain Market Sentiment

BRAIN MARKET SENTIMENT

Brain Market Sentiment (BMS) provides a daily a score on the general mood of the market by automatically clustering by topic thousands of news from most popular financial media. The sentiment of each topic is calculated using Brain proprietary Natural Language Processing platform.. The BMS provides an aggregate score for the news topic sentiment of the current day. 

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Brain Thematic Baskets

RISK ON / RISK OFF SIGNALS

Risk ON / Risk OFF signals based on VIX statistical indicators (Dynamic Volatility Signal) and including measures of financial stress indicators and macro-economic environment (Brain Dynamic Allocation Indicator). Portfolio strategies based on these signals are implemented: the strategies toggle between two dynamic portfolios, each of which is monthly rebalanced.

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Clustering of Market Scenarios

CLUSTERING OF MARKET SCENARIOS

With Unsupervised Machine Learning techniques our algorithms identify non trivial patterns among a large number of financial and macroeconomic data to find days in the past which are “similar" to the current scenario. Investment models can be built by analyzing the performance of various financial instruments on the market clusters identified by the system.

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Stock Selection Model

BRAIN THEMATIC BASKETS

Algorithm-based selection among a large database of companies of a basket of stocks whose business is related to a specific theme (es. "nanotechnology"). The selection is performed by analyzing company public documents and web pages by leveraging on natural language processing and machine learning classifications and clustering techniques.

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Language Analysis of Earnings Calls

LANGUAGE ANALYSIS OF EARNINGS CALLS

Brain has developed a method to analyze the sentiment and various language complexity metrics of earnings calls transcripts. Each call is divided in the main parts (Management, Q&A) and actors (Managers, Analysts); for each piece the metrics are calculated and a correlation score is evaluated between such metrics (sentiment, language complexity) and the average future return of a company.

Brain Language Similarity Analysis

ANALYSIS OF LANGUAGE CHANGES IN 10-K(Q)S REPORTS

Brain has built a methodology to analyze the language changes in 10Ks and 10Qs reports with respect to the content of the previous year (quarter) report using various language aspects (e.g. positive language  or litigious language). The output is a ranking of stocks orderder by a score quantifying how much the language of the current report is similar to the language used in the report of the previous period.

BRAIN TECHNOLOGIES

Proprietary technologies  that constitutes the engine of Brain Products

Natural Language Processing

NATURAL LANGUAGE PROCESSING

Brain products and solutions leverage on Natural Language Processing techniques (NLP) to extract from structured and unstructured texts meaningful metrics such as sentiment, language complexity and topics. In the context of NLP we use various machine learning techniques to assess the relevance of a company document (e.g. text extracted from web site) with respect to a specific theme (es. “nanotechnology” or “robotics”) or to identify the relevant topics in documents.

Supervised Machine Learning

SUPERVISED MACHINE LEARNING

Brain has developed a set of Machine Learning and financial features engineering tools aiming at providing inference on the markets. Our models yield statistical predictions on targets such as assets returns; using ensemble machine learning models we can calculate probabilities associated to the spectrum of predictions. These tools can be used as building bricks for investment strategies or for proprietary and third parties’ portfolio models.                                                                                                     

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UNSUPERVISED
MACHINE LEARNING

Brain combines various clustering algorithms together with dimension reduction techniques to extract relevant features and to cluster various types of data sets, for example all company documents by topic or the past history of market days in order to extract meaningful information.                    

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PROPRIETARY BACKTESTING AND VALIDATION MODULE

Brain has developed a proprietary backtesting and validation approach that we use to test and optimize our models, so that our results are less dependent on the specific historical trajectory markets have undergone. The method can be used also to validate or to optimize third parties’ models.

SERVICES

Brain assists Investment Management firms in the development of their proprietary algorithms.

Leveraging on our competences and coding resources we aim at becoming a client’s partner in validating and optimizing pre-existing algorithms, as well as offering support to new developments.

We would like to support companies at transforming their expertise and investment approach into algorithms. This would add value to the company by deploying across the whole organization what typically is concentrated knowledge.

  • Partnership in model development
  • Algorithm validation and optimization
Brain Services

Brain assists Investment Management firms in the development of their proprietary algorithms.

TEAM

Matteo Campellone

Executive Chairman and Head of Research

Matteo Campellone holds a Ph.D. in Physics and a Master in Business Administration. Matteo’s past activities included Financial Modeling and Risk Management for financial institutions as well as Corporate Risk and Value Based Management for industrial companies. As a Theoretical Physicist he worked in the field of statistical mechanics of complex systems and of non-linear stochastic equations. Amongst other results, he put forward some new solutions for the finite size corrections to an universality class of Spin Glass models, and developed an approximation method to approach some non-linear stochastic equations.                                                                       

Francesco Cricchio

Chief Executive Officer and CTO

Francesco Cricchio obtained his Ph.D. in Computational Physics applied to Quantum Physics from Uppsala University in 2010. He is the author of several scientific publications on the prediction of material properties from computer simulations with focus on superconductors and magnetic compounds. In 2009 one of his publications has been awarded the cover of Physical Review Letters. He focused his career in solving complex computational problems in different sectors using a wide range of techniques, from density functional theory in the domain of solid state physics to the application of machine learning methods and advanced statistics in the industrial domain.

Simone Conradi

Machine Learning Specialist

Simone Conradi obtained a Ph.D. in Theoretical Physics focusing his research activities on Lattice Quantum Chromodynamics using methods of Computational Physics. He specialized in statistical physics and in thermodynamics of quantum field theories applied to the fundamental matter, achieving new insights about the confining properties of quarks and gluons at finite temperature and density. Moreover, he got a ten years long career in the railway industry, focusing in the development of human safety relevant systems and in the management of trains diagnostic data, from cloud architecture design to predictive models development.

Alessandro Sellerio

Specialist

Alessandro Sellerio obtained a Ph.D. in Physics focusing on jamming and vitrous phase transition in granular media, using theoretical models, simulations and experiments. He has extensive experience and a ten year long career in the fields of condensed matter physics, statistical physics and complex systems, during which he collaborated with a number of international research groups. Currently he is lecturer at University of Milan teaching python, machine learning and molecular dynamics for the masters course in  Computational Methods.                                                                                                                                                   

Michael Burnett

Board Member

Michael Burnett has an MBA in finance and strategy from London Business School and a Bachelor of Science from the University of Southern California where he attended on academic scholarship. Michael’s career has spanned technology and finance, working for companies such as Apple, Cisco and Yahoo! and working in investment banking where he closed more than 45 transactions with media and technology companies totaling more than $25 billions. Michael has been invited and guest lectured at New York University (Stern School of Management), SDA Bocconi and Università Cattolica.

Lucia Rota

Board Member

Lucia Rota is a Certificated Public Accountant in Torino, Partner of Studio Rota, President of Board of Director of ACR srl, auditing company, and statutory auditor of Fidersel Spa, Nomen Fiduciaria Spa and Cofin Srl. She holds a MBA degree in SDA Bocconi, Milan (2010). She is also registered as freelance journalist. She created InnerDesign, an online platform dedicated to the design world. She collaborates with Italian accelerators and Business Angels as external consultant. IED Moda Professor in Entrepreneurship, Communication and Marketing for the period 2013 – 2016.

Partners

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CityFALCON

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Eagle Alpha

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Neudata

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Crux Informatics

Contact Us

Milan, Italy

contacts@braincompany.co

Brain Copyright

contacts@braincompany.co

Disclaimer: the content of this web site is not to be intended as investment advice. The material is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory or other services by Brain. Brain makes no guarantees regarding the accuracy and completeness of the information expressed in this web site.