Joseph Simonian, Senior Investment Strategist, Acadian Asset Management
| October 09, 2019
Dr. Simonian addresses the types of questions that asset owners should be asking when speaking to managers who claim to use machine learning in their investment process, Watch the highlights of this presentation, you can also request access to the full video.
Dr. Simonian discusses the differences, advantages, and disadvantages of traditional econometrics vs. financial data science.
He is drawing on his own experience with and solutions to common pitfalls in financial data science research.
This presentation was held at the
RavenPack Research Symposium in New York on September 10, 2019
Please use your business email. If you don't have one, please email us at email@example.com.
We will process your personal data with the purpose of managing your personal account on
RavenPack and offering our services. You can exercise your rights of access, rectification,
erasure, restriction of processing, data portability and objection by emailing us at firstname.lastname@example.org. For more information, you can
Your request has been recorded and a team member will be in touch soon.
High inflation has returned in developed markets after decades of lying low. In our latest paper, we show how to build an inflation-based asset allocation strategy using sentiment data and we illustrate that sentiment-based strategies outperform models that depend merely on past observed inflation values.
This year's RavenPack Research Symposium brought two intense days of knowledge sharing in London and New York, from 25 top experts in natural language processing, quantitative investing and machine learning. Together, we explored how firms can leverage new language models to generate alpha, better manage risk and respond to calls for more sustainable investment practices.
Human capital is at the heart of value creation. Our latest research demonstrates how unprecedented workforce insights, sourced from over 200 million job postings, can generate more alpha.