It is specialized in artificial intelligence for investment professionals and leverages sentiment and emotion, such as fear or surprise, extracted from Big Data. Above all, it gathers insights from billions of press articles, blogs, or social media, to generate actionable insights for quantitative and fundamental investment strategies.
The company provides pre-processed datasets, flexible Natural Language Processing tools, and Machine Learning platforms to generate insights and integrate them into clients’ alpha research processes. As a result, it has a track record of working with tier 1 quantitative hedge funds, asset managers, and banks all around the world.
“Our work with Bloomberg will give their clients access to brand new data specifically tailored for the financial community” said Sylvain Forté, Co-Founder and CEO of SESAMm. “This represents a fantastic opportunity for SESAMm to reveal how sentiment and emotions data enable models to generate alpha and rapidly adjust to changing market conditions. This type of data is particularly important for investors in these times of high volatility.”
SESAMm provides exclusive metrics and insights related to emotions (such as anger, fear, surprise, joy, sadness) in multiple languages, for all asset classes. This wide spectrum ensures global coverage and helps discover regional trends by sector or industry. Consequently, its datasets are used in several applications, from long-short equity and stock picking to macro investment or volatility forecasting.
The entire dataset uses SESAMm’s data lake including more than 2 million data sources and more than 10 billion articles – increasing by an average of 6 million per day. In addition, the data is created with the company’s proprietary NLP and ML algorithms and provided to clients through flexible endpoints. All data is anonymized, ensuring no personal or identifiable data is included in it.
Launched in 2018, Bloomberg Enterprise Access Point is a website that allows Bloomberg Data License clients to discover and acquire reference, pricing, regulatory, historical, and alternative datasets.