BrainBrain is a research company that develops proprietary signals and algorithms for investment strategies on financial markets using Statistical methods, Machine Learning and Natural Language Processing techniques. Brain also partners with Clients to support them in developing, optimizing and validating their own proprietary models.
The founders and the R&D team have a PhD in Physics and share research experience in model development and validation, statistics, artificial intelligence and optimisation problems.
Below we provide a summary of our alternative datasets, the related factsheets are included in the document section:
Brain Sentiment Indicator: The Brain Sentiment Indicator (BSI) uses proprietary Natural Language Processing strategies to monitor public financial news for over 10000 global stocks from more than 2,000 financial media sources.
Brain Market Sentiment: the Brain Market Sentiment indicator gives a score on the general mood of the market by automatically selecting and analyzing thousands of news articles every few minutes from the most popular financial media. The news coverage is global but with a larger focus on US markets.
Language Metrics on Company Filings: the Brain Language Metrics (BLM) on Company Filings dataset has the objective of monitoring several language metrics on 10-Ks and 10-Qs company reports for approximately the largest 1000 US stocks.
Brain Machine Learning Stock Ranking: Brain Machine Learning Stock Rankings are generated daily and based on the predicted future returns of a dynamic universe of the largest 1,000 US stocks across five time horizons: 2,3, 5, 10 and 21 days.
Risk Off / Risk On 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.
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 classification and clustering techniques.
