Join the Conference Live Here
ICAIF 2020 proceedings are available at the ACM Digital Library.
ICAIF 2020 will be held as an online digital event. Registrants will be contacted by email for instructions on how to login and participate. A detailed schedule covering all sessions, times, locations and presenters is now available (current version is 2.0 as of October 13, 2020).
We are pleased to host two workshops as part of the ICAIF’20 program.
AI in Africa for Sustainable Economic Development (Wednesday October 14th, 8:00-12:30 US ET)
Artificial intelligence (AI), facilitated by easier data collection and improved computing resources, is shaping the dynamics of many sectors that are closely linked with achieving the Sustainable Development Goals. Many African countries have tremendous opportunities to use AI in a number of key sectors including finance, agriculture, health, infrastructure and food security. However, the lack of expertise and capacity, as well as impacts of the current Covid19 pandemic, pose significant challenges. This workshop aims to bring together researchers and industry practitioners to discuss the opportunities and challenges of applying AI to address sustainable financial and economic development in Africa.
Women in AI and Finance (Wednesday October 14th, 13:30-16:30 US ET)
The goal of the workshop is to bring together women at the intersection of AI and finance and create a forum where they can share their experience, ideas and vision. We encourage industry professionals, AI/ML researchers and practitioners to participate in the workshop and advance AI in Finance through collaboration. Hopefully, we can all learn from each other’s perspectives and make some new connections along the way.
Global Head, Quantitative Research & Development, Abu Dhabi Investment Authority
Marcos Lopez de Prado is Global Head – Quantitative Research and Development at the Abu Dhabi Investment Authority, one of the largest sovereign wealth funds. He is also Professor of Practice at Cornell University, where he teaches machine learning at the School of Engineering. Since 2011, Prof. López de Prado has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science), where he has conducted research on scientific supercomputing. In the year 1999 he received the National Award for Academic Excellence from the Government of Spain, and in 2019, The Journal of Portfolio Management named him “Quant of the Year.” Last December, the U.S. Congress invited him to testify on AI policy..
Managing Director, Goldman Sachs
Charles Elkan is a managing director and the global head of machine learning at Goldman Sachs. From 2014 to 2018, he was the first Amazon Fellow, leading a team of over 30 scientists and engineers in Seattle, Palo Alto, and New York doing research and development in applied machine learning for both e-commerce and cloud computing. Before joining Amazon, Dr. Elkan was a tenured professor of computer science at UCSD. His students have continued on to faculty positions at universities that include Carnegie Mellon, Columbia, Stanford, and the University of Washington, and to executive positions at Google and other companies.
Research Professor of Computer Science and Spatial Sciences and Principal Scientist at USC Information Sciences Institute
Dr. Yolanda Gil is Director of Knowledge Technologies at the Information Sciences Institute of the University of Southern California, and Research Professor in Computer Science and in Spatial Sciences. She is also Associate Director of Data Science at USC and Director of the Center for Knowledge-Powered Interdisciplinary Data Science. She received her M.S. and Ph. D. degrees in Computer Science from Carnegie Mellon University, with a focus on artificial intelligence. Dr. Gil collaborates with scientists in many domains on semantic workflows and metadata capture, social knowledge collection, computer-mediated collaboration, and automated discovery. She uses artificial intelligence techniques for data science, with particular interest in reuse of data analysis workflows and capturing of provenance. Her current focus is on using artificial intelligence for environmental resources, integrating climate, hydrology, agriculture, and socioeconomic models. She is a Fellow of the Association for Computing Machinery (ACM), and Past Chair of its Special Interest Group in Artificial Intelligence. She is also Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), and was elected as its 24th President in 2016.
Professor and National Center Chair, Computer and Information Science at the University of Pennsylvania
Michael Kearns is a professor in the Computer and Information Science department at the University of Pennsylvania, where he also has appointments in the Economics
department and Wharton School. He is founder and co-director of Penn’s Warren Center for Network and Data Sciences, and he is Senior Advisor in Morgan Stanley’s Center of Excellence for Machine Learning and AI. Kearns has long experience as a quant on Wall Street and as a consultant in the technology industry. His research interests are in machine learning, algorithmic game theory and quantitative trading. Kearns and his Penn colleague Aaron Roth are co-authors of the general-audience book “The Ethical Algorithm: The Science of Socially Aware Algorithm Design” (Oxford University Press, 2019).
Vice President, Market Regulation, FINRA
Susan is vice president of the Market Manipulation Group at the Financial Industry Regulatory Authority (FINRA). She holds a J.D. from Western Michigan University.
Professor and Director, Data Science Institute, Columbia University
Jeannette M. Wing is Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University. She came to Columbia in July 2017 from Microsoft, where she served as Corporate Vice President of Microsoft Research, overseeing a global network of research labs. She is widely recognized for her intellectual leadership in computer science, particularly in trustworthy computing. Jeannette’s seminal essay, titled “Computational Thinking,” was published more than a decade ago and is credited with helping to establish the centrality of computer science to problem-solving in fields where previously it had not been embraced.
Panel announcements to follow.
|A Hybrid Learning Approach to Detecting Regime Switches in Financial Markets||Peter Akioyamen (Western University)*; Yi Zhou Tang (Western University); Hussien Hussien (Western University)|
|A Multi-Faceted Approach to Large Scale Financial Forecasting||Urjitkumar Patel (S&P Global)*; Antony Papadimitriou (S&P Global); Lisa Kim (S&P Global); Azadeh Nematzadeh (S&P Global); Grace Bang (S&P Global); Xiaomo Liu (S&P Global)|
|A Tabular Sarsa-Based Stock Market Agent||Renato A Oliveira (Federal University of Minas Gerais State)*; Heitor Ramos Filho (UFMG); Daniel Dalip (CEFET-MG); Adriano C. M. Pereira (UFMG)|
|Algorithms in Future Capital Markets: A Survey on AI, ML and Associated Algorithms in Capital Markets||Adriano S Koshiyama (University College London)*; Nick Firoozye (University College London); Philip Treleaven (University College London)|
|An Analysis of political turmoil effects on stock prices – a case study of US-China trade friction –||Yukari Shirota (Gakushuin University)*; Kenji Yamaguchi (Ochanomizu University); Akane Murakami (Gakushuin University); Michiya Morita (Gakushuin University)|
|Analysis of the impact of maker-taker fees on the stock market using agent-based simulation||Isao Yagi (Kanagawa Institute of Technology)*; Mahiro Hoshino (Kanagawa Institute of Technology); 孝信 水田 (スパークス・アセット・マネジメント株式会社)|
|Choosing News Topics to Explain Stock Market Returns||Paul Glasserman (Columbia University); Kriste Krstovski (Columbia University); Paul Laliberte (Columbia University); Harry Mamaysky (Columbia Business School)*|
|Classifying High-Frequency FX Rate Movements with Technical Indicators and Inception Model||Zheng Gong (Unviersity of Essex)*; Carmine Ventre (King’s College London); John O’Hara (University of Essex)|
|Conditional Mutual Information-Based Contrastive Loss for Financial Time Series Forecasting||Hanwei Wu (KTH Royal Institute of Technology)*; Ather Gattami (RISE SICS); Markus Flierl (KTH Royal Institute of Technology)|
|Connecting The Dots: Forecasting and Explaining Short-Term Market Volatility||Jie Yuan (ISU)*; Zhu (Drew) Zhang (ISU)|
|CryptoCredit: Securely Training Fair Models||Leo de Castro (MIT)*; Jiahao Chen (JP Morgan Chase); Antigoni Polychroniadou (JP Morgan Chase)|
|Dealing with Transaction Costs in Portfolio Optimization: Online Gradient Descent with Momentum||Edoardo Vittori (Politecnico di Milano)*; Martino Bernasconi de Luca (Politecnico di Milano); Francesco Trovò (Politecnico di Milano); Marcello Restelli (Politecnico di Milano)|
|Deep Ensemble Reinforcement Learning for Automated Stock Trading||Hongyang Yang (Columbia University); Xiao-Yang Liu (Columbia University)*; Shan Zhong (Columbia University); Anwar Walid (Bell Laboratories)|
|Deep Q-Network based Adaptive Alert Threshold Selection Policy for Payment Fraud Systems in Retail Banking||Hongda Shen (University of Alabama in Huntsville)*; Eren Kursun (Columbia University)|
|Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications||David Byrd (Ga Tech)*; Antigoni Polychroniadou (J.P. Morgan AI Research)|
|Dynamic Prediction Length for Time Series with Sequence to Sequence Network||Diego Klabjan (Northwestern University)*; Mark Harmon (Northwestern)|
|Explainable Clustering and Application to Wealth Management Compliance||Enguerrand Horel (Stanford University)*; Kay Giesecke (Stanford University); Victor Storchan (J.P. Morgan); Naren Chittar (J.P. Morgan)|
|Fast Direct Calibration of the G2++ Interest Rate Derivatives Pricing Model||Luca Sabbioni (Politecnico di Milano)*; Andrea Prampolini (Banca IMI); Marcello Restelli (Politecnico di Milano)|
|Financial Table Extraction in Image Documents||William Watson (S&P Global)*; Bo Liu (Nvidia)|
|Foreign Exchange Trading: A Risk-Averse Batch Reinforcement Learning Approach||Lorenzo Bisi (Politecnico di Milano)*; Pierre Liotet (Politecnico di Milano); Luca Sabbioni (Politecnico di Milano); Gianmarco Reho (Politecnico di Milano); Nico Montali (Politecnico di Milano); Cristiana Corno (Advanced Global Solutions); Marcello Restelli (Politecnico di Milano)|
|Generating synthetic data in finance: opportunities, challenges and pitfalls||Samuel Assefa (J.P.Morgan)*; Danial Dervovic (JPMorgan Chase & Co.); Tucker Balch (JPMorgan Chase & Co.); Mahmoud Mahfouz (J.P. Morgan); Robert Tillman (J.P. Morgan Chase & Co); Prashant Reddy (JP Morgan); Manuela Veloso (JP Morgan)|
|Get Real: Realism Metrics for Robust Limit Order Book Market Simulations||Svitlana Vyetrenko (J. P. Morgan Chase)*; David Byrd (Ga Tech); Danial Dervovic (JPMorgan Chase & Co.); Tucker Balch (JP Morgan); Mahmoud Mahfouz (J.P. Morgan); Nicholas Petosa (Georgia Institute of Technology)|
|Graphical Models for Financial Time Series and Portfolio Selection||Ni Zhan (carnegie mellon university)*; Yijia Sun (Carnegie Mellon University); Aman Jakhar (carnegie mellon university); He Liu (carnegie mellon university)|
|Improved Predictive Deep Temporal Neural Networks with Trend Filtering||Youngjin Park (UNIST); Deokjun Eom (KAIST); Jaesik Choi (KAIST)*|
|Index Tracking with Differentiable Asset Selection||Yu Zheng (Southwestern University of Finance and Economics); Yunpeng Li (University of Surrey); Qiuhua Xu (Southwestern University of Finance and Economics); Timothy Hospedales (Edinburgh University); Yongxin Yang (University of Surrey)*|
|Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks||Marco Schreyer (University of St. Gallen)*; Timur Sattarov (Deutsche Bundesbank); Anita Gierbl (University of St. Gallen); Bernd Reimer (PricewaterhouseCoopers WPG); Damian Borth (University of St. Gallen)|
|Learning-Based Trading Strategies in the Face of Market Manipulation||Xintong Wang (University of Michigan)*; Chris Hoang (University of Michigan); Michael Wellman (University of Michigan)|
|Machine Learning Fund Categorizations||Dhagash Mehta (The Vanguard Group)*; Dhruv Desai (The Vanguard Group); Jithin Pradeep (The Vanguard Group)|
|Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity||Joana Lorenz (NOVA-IMS); Maria Ines P P Silva (Feedzai)*; David Aparicio (Feedzai); Joao Ascesao (Feedzai); Pedro Bizarro (Feedzai)|
|Market Volatility Prediction Based on Long- and Short-Term Memory Retrieval Architecture||Jie Yuan (ISU)*; Zhu (Drew) Zhang (ISU)|
|Mixed Membership Recurrent Neural Networks for Modeling Customer Purchases||Ghazal Fazelnia (Columbia University); Mark Ibrahim (Capital One); Ceena Modarres (Capital One); Kevin Wu (Capital One); John Paisley (Columbia University)*|
|Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation||Michael Karpe (University of California, Berkeley)*; Jin Fang (University of California, Berkeley); Zhongyao Ma (University of California, Berkeley); Chen Wang ( University of California, Berkeley)|
|Navigating the Dynamics of Financial Embeddings over Time||Antonia Gogoglou (Capital One)*; C. Bayan Bruss (Capital One); Alan O Salimov (Capital One); Brian Nguyen (Capital One); Jonathan Rider (Capital One)|
|Optimal, Truthful, and Private Securities Lending||Emily R Diana (University of Pennsylvania)*; Michael Kearns (University of Pennsylvania); Seth V Neel (University of Pennsylvania); Aaron Roth (University of Pennsylvania)|
|Option Hedging with Risk Averse Reinforcement Learning||Edoardo Vittori (Politecnico di Milano)*; Michele Trapletti (Banca IMI); Marcello Restelli (Politecnico di Milano)|
|Paying down metadata debt: learning the representation of concepts using topic models||Jiahao Chen (JPMorgan Chase & Co.)*; Manuela Veloso (JP Morgan)|
|Power-law Mixtures of Bayesian Forests for Value Added Tax Audit Case Selection||Christos Kleanthous (Cyprus University of Technology)*; Theodoros Christophides (Cyprus University of Technology); Sotirios Chatzis (Cyprus University of Technology)|
|Predicting the Behavior of Dealers in Over-The-Counter Corporate Bond Markets||Yusen Lin (University of Maryland)*; Jinming Xue (University of Maryland); Louiqa Raschid (University of Maryland)|
|Quantifying ESG Alpha in Scholar Big Data: An Automated Machine Learning Approach||Qian Chen (Columbia University); Xiao-Yang Liu (Columbia University)*|
|Recommending Missing and Suspicious Links in Multiplex Financial Networks||Robert E Tillman (JPMorgan AI Research)*; Prashant Reddy (JP Morgan); Manuela Veloso (JP Morgan)|
|Risk-Sensitive Reinforcement Learning: a Martingale Approach to Reward Uncertainty||Nelson Vadori (JPMorgan)*; Sumitra Ganesh (JPMorgan); Prashant Reddy (JP Morgan); Manuela Veloso (JP Morgan)|
|SecretMatch: Inventory Matching from Fully Homomorphic Encryption||Ben Diamond (JPMorgan Chase)*; Antigoni Polychroniadou (JP Morgan Chase); Tucker Balch (JP Morgan)|
|Sig-SDEs model for quantitative finance||Imanol Perez Arribas (University of Oxford)*; Cristopher Salvi (University of Oxford); Lukasz Szpruch (University of Edinburgh)|
|Simulating and Classifying Behavior in Adversarial Environments Based on Action-State Traces: An Application to Money Laundering||Daniel Borrajo (JPMC AI Research and Universidad Carlos III de Madrid)*; Manuela Veloso (JP Morgan); Sameena Shah (JPMorgan Chase & Co.)|
|Social media data reveals signal for public consumer perceptions||Neeti Pokhriyal (Dartmouth College)*; Abenezer Dara (Dartmouth College); Benjamin Valentino (Dartmouth College); Soroush Vosoughi (Dartmouth College)|
|Subgraph Anomaly Detection in Financial Transaction Networks||Yulong Pei (TU Eindhoven)*; Fang Lyu (TU Eindhoven); Werner van Ipenburg (Cooperatieve Rabobank U.A.); Mykola Pechenizkiy (TU Eindhoven)|
|SURF: Improving classifiers in production by learning from busy and noisy end users||Joshua Lockhart (JP Morgan); Samuel Assefa (JP Morgan); Ayham Alajdad (JP Morgan); Andrew Alexander (JP Morgan); Tucker Balch (JP Morgan); Manuela Veloso (JP Morgan)*|
|Towards Self-Regulating AI: Challenges and Opportunities of AI Model Governance in Financial Services||Eren Kursun (Columbia University); Hongda Shen (University of Alabama in Huntsville)*; Jiahao Chen (JPMorgan Chase & Co.)|
|Trading via Image Classification||Naftali Cohen (JP Morgan)*; Tucker Balch (JP Morgan); Manuela Veloso (JP Morgan)|
|Understanding Distributional Ambiguity via Non-robust Chance Constraint||Shumin Ma (City University of Hong Kong); Cheuk Hang Leung (City University of Hong Kong); Qi Wu (City University of Hong Kong)*; Wei Liu (Tencent); Nanbo Peng|
|Unsupervised-learning financial reconciliation: a robust, accurate approach inspired by machine translation||Peter A Chew (Galisteo Consulting Group Inc)*; Peter Chew (Galisteo Consulting Group Inc)|
|Utilization of Deep Learning to Mine Insights from Earning Calls for Stock Price Movement Predictions||Zhiqiang Ma (S&P Global)*; chong wang (S&P Global ); Grace Bang (S&P Global); Xiaomo Liu (S&P Global)|
|What can be learned from satisfaction assessments?||Naftali Cohen (JP Morgan)*; Prashant Reddy (JP Morgan); Simran Lamba (JP Morgan)|
|Extended Abstract: A Deep Learning Framework for Pricing Financial Instruments||Qiong WU (College of William and Mary); Zheng Zhang (College of William and Mary); Zhenming LIU (College of William & Mary)*; Andrea Pizzoferrato (Queen Mary University of London); Chun Wang (Tsinghua University); Mihai Cucuringu (University of Oxford and The Alan Turing Institute)|
|Extended Abstract: Adaptive Reduced Rank Regression||Qiong WU (College of William and Mary)*; Felix Wong (IR); Yanhua Li (“Worcester Polytechnic Institute, USA”); Zhenming LIU (College of William & Mary); Varun Kanade (University of Oxford)|
|Extended Abstract: Adversarial Attacks on Machine Learning Systems for High-Frequency Trading||Micah Goldblum (University of Maryland)*; Avi Schwarzschild (University of Maryland); Ankit B Patel (Rice University); Tom Goldstein (University of Maryland, College Park)|
|Extended Abstract: Directed Criteria Citation Recommendation and Ranking Through Link Prediction||William Watson (S&P Global)*; Lawrence Yong (S&P Global)|
|Extended Abstract: Financial Data Validation by Data Scientists versus Accountants||Stephanie Rosenthal (Carnegie Mellon University)*; Tingting (Rachel) Chung (College of William & Mary)|
|Extended Abstract: Machine learning to compute implied volatility from European/American options||Shuaiqiang Liu (Delft University of Technology)*|
|Extended Abstract: On embedding stocks||Qiong WU (College of William and Mary); Zheng Zhang (College of William and Mary); Yanhua Li (“Worcester Polytechnic Institute, USA”); Zhenming LIU (College of William & Mary)*; Mihai Cucuringu (University of Oxford and The Alan Turing Institute)|