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