Asset Management

Summary

  • Cutting edge targeted company sentiment analysis using advanced machine learning.

Description

  • We enable asset managers to quickly focus on those parts of their portfolio where action may be required.
    • A large amount of relevant data is available as unstructured text.
    • This data is increasingly time-consuming to assimilate.
    • It is not easy to read everything which is relevant to our portfolio.
    • We use natural language processing in a number of languages to continuously scan and analyze a number of sources - including news, regulatory reports, and social media - relevant for our client's portfolio and benchmark.
    • Gradually expanding to include fundamental, technical, and macroeconomic analysis.
  • Machine learning approaches we use include Deep Neural Networks, LSTM's, Generative adversarial networks, and Bi-directional encoder representations from transformers.


Team Members

Dr Denis de Montigny
UCL | Partner

Prof. Philip Treleaven
UCL | Partner

Eric Chamoun
UCL | MEng

Ahmed Elsharkawi
UCL | MEng

Ilaria Leoni
DTU | MSc

Jinge Wu
UCL | MSc

Udi Ibgui
UCL | MSc

Yuya Okuma
UCL | MSc

Cyrus Horban
UCL | MEng

Sander Da Mata Miranda
UCL | MEng

Yacoub Ahmed
UCL | MEng

Lovepreet Singh
UCL | MEng

Sohum Sen
UCL | MEng

Swapnil Agarwal
BITS | BSc

Aviraj Singh Bevli
IIT Kharagpur | BSc

Soumyajit Chakraborty
IIM | BSc

Shivam Shrivastava
IIT Kharagpur | BSc

Vansh Ambashta
NIT | BSc

Ritik Yadav
IIT Delhi | BSc

Thiago Medeiros
PUC Rio | MSc