On March 24th, lux future lab held its fourth Pleased2MeetU breakfast to welcome SESAMm, a FinTech software company that harnesses social media and other textual data sources. The company presentation was made by Sylvain Forte and Pierre Rinaldi.
The goal of these sessions is to introduce newcomers to the lfl community.
Who we are
SESAMm is an innovative FinTech software startup founded in May 2014 in France. The company was created by three engineering and business school students with a common goal: to bring big data technologies to the trading industry. SESAMm opened an office in Luxembourg last month.
What we do
SESAMm develops and commercializes stock market forecasting tools based on social media and other textual data sources. These products are used by banks and hedge funds. The company provides financial indicators created by using big data methods and allowing new approaches to trading strategies.
Our team of engineers and PhD holders has extensive IT and mathematical expertise, from machine learning and distributed cloud computing to quantitative analysis. It allows us to integrate the best technologies from both worlds into our products.
What we think
As new technologies emerge, fundamental and technical analysis are becoming less relevant. Tomorrow’s trading technologies will be based on big data analytics and machine learning, but only few financial market players have realized this already.
We strongly believe that there is an urgent need for new trading technologies based on big data. We provide trading indicators that are ready-to-use, relevant and high performance to help develop the future of stock market analysis.
Where we’re headed
SESAMm has won five startup contests and the team is incubated both in France and Luxembourg. The company raised €730,000 to accelerate its R&D program and to develop new innovative indicators to offer its clients. This year, SESAMm plans on hiring seven top experts and to open an office in Paris.
The company aims at providing a wider range of trading indicators and strategies based on the analysis of textual data.