Summary

Applications of Natural Language Processing (NLP) and graph machine learning in finance have received tremendous attention within the last decade. An increasing number of areas from the applied finance community are successfully leveraging and blending tools from NLP, network analysis and graph machine learning, for tasks ranging from asset pricing, portfolio construction, and risk management, to understanding large scale supply chain networks, market crash and fraud detection. This workshop aims to illustrate the broad interplay between those techniques and analysis tools in the context of financial applications, showcasing a suite of problems of interest to both researchers and practitioners. The main goal of the proposed workshop is to bring the researchers and practitioners both from academia and industry together to brainstorm and interact on various sub-topics in the aforementioned theme; to understand the established as well as emerging novel ideas and different schools of thoughts; and, provide a place to achieve new interdisciplinary research.

The workshop will be in conjunction with the 3rd ACM Conference on AI in Finance which will be held in-person in New York, and also virtually.

Organizers

• Leman Akoglu (Dean’s Associate Professor of Information Systems, Carnegie Mellon University)

• Mihai Cucuringu (Associate Professor, University of Oxford and Turing Fellow)

• Xiaowen Dong (Associate Professor, University of Oxford)

• Dhagash Mehta (Head of Applied ML Research, BlackRock, Inc.)

• Saurabh Nagrecha (Applied ML Researcher, eBay)

• Stefan Zohren (Associate Professor, University of Oxford, Turing Fellow and Principal Quant at Man Group).

Call for Papers

We invite papers on NLP and Network Analysis with applications to the financial industry. Topics of interest include, but are not limited to, the following:

• • • Analysis of financial markets as complex networks

    • Network models for portfolio management and systemic risks

    • Financial news networks as multimodal message passing systems

    • Graph-based machine learning models for financial applications

    • Graphs as models of non-i.i.d. financial risks and phenomena

    • Applications of NLP in trading as well as portfolio constructions

    • Other applications of NLP and networks in finance

We also invite tutorials and introductory papers to bridge the gap between academia and the financial industry:

Overview of Industry Challenges

• Short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers. These papers should describe problems that can inspire new research directions in academia, and should serve to bridge the information gap between academia and the financial industry.

Algorithmic Tutorials

• Short tutorials from academic researchers that explain current solutions to challenges related to the technical areas mentioned above, not necessarily limited to the financial domain. These tutorials will serve as an introduction and enable financial industry practitioners to employ/adapt latest academic research to their use-cases.

Submission Guidelines:

All submissions must be PDFs formatted in the Standard ACM Conference Proceedings Template. Submissions are limited to 4-8 content pages, including all figures and tables but excluding references. All accepted papers will be presented as posters and some would be selected for oral presentations, depending on schedule constraints. Accepted papers will be posted on the workshop website.

Following the conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed.

Papers should be submitted on CMT3 by 16th September, 2022, 11:59 PM, anywhere in the world.

Submission URL: https://cmt3.research.microsoft.com/NLPNAFA2022

Key dates

Paper submission deadline is 16th September, 2022, 11:59 PM, anywhere in the world.

• Submission deadline: 16th September, 2022, 11:59 PM, anywhere in the world.

• Author notification: 18th September, 2022

• Workshop: 2nd November, 2022.