The Society of Quantitative Analysts (SQA) is a not-for-profit organization that focuses on education and communication to support members of the quantitative investment practitioner community. We seek to encourage the dissemination of leading-edge ideas and innovations relevant to the work of the quantitative investment practitioner. The knowledge of such ideas and innovations can assist portfolio and risk managers, strategists, analysts, traders, regulators, asset owners such as pension sponsors and foundations in performing their functions and responding to the ever-quickening pace of change. The Society welcomes the participation of academics and students. The SQA provides forums for the presentation of theory and practice by practitioners, academics and regulators with an emphasis on the new and controversial. The Society holds monthly meetings in New York City from September through June. Monthly meetings consist of one-hour talks on a specific topic. The Society also organizes a half-day seminar in the fall on a topic of current interest and a full-day "Fuzzy Day" seminar in the spring on an exporatory topic.
Data Science is blossoming in the financial industry and literature. More and more financial firms are introducing machine learning systems to forecast markets and trade. Academics are astounded by “unprecedented out-of-sample return prediction” ability of ML and are setting а “new standard for accuracy in measuring risk premia.” They find that “in designing and pricing securities, constructing portfolios, and risk management… deep learning can detect and exploit interactions in the data that are, at least currently, invisible to any existing financial economic theory.” At the same time, “rapid empirical success in this field currently outstrips mathematical understanding.”
Join us to learn from leading academics and practitioners about Data Science applications in finance and to understand what’s behind these techniques and why they work so well.
 Shihao Gu, Bryan Kelly and Dacheng Xiu ”Empirical Asset Pricing via Machine Learning.” Chicago Booth Research Paper No. 18-04
 J. B. Heaton, N. G. Polson and J. H. Witte “Deep Learning in Finance.” arXiv:[masked]v3 [cs.LG] 14 Jan 2018
 Sanjeev Arora “Mathematics of Machine Learning: An introduction.” https://www.cs.princeton.edu/~arora/
You can register at www.sqa-us.org
First 100 people to register get $170 discount.