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ORCID2nd ICAIF 2021: Virtual Event
- Anisoara Calinescu, Lukasz Szpruch:
ICAIF'21: 2nd ACM International Conference on AI in Finance, Virtual Event, November 3 - 5, 2021. ACM 2021, ISBN 978-1-4503-9148-1 - Xiao-Yang Liu, Hongyang Yang, Jiechao Gao, Christina Dan Wang:
FinRL: deep reinforcement learning framework to automate trading in quantitative finance. 1:1-1:9 - Micah Goldblum, Avi Schwarzschild, Ankit B. Patel, Tom Goldstein:
Adversarial attacks on machine learning systems for high-frequency trading. 2:1-2:9 - Yinfei Li, Jiafeng Shou, Philip C. Treleaven, Jun Wang:
Graph neural network for merger and acquisition prediction. 3:1-3:8 - Kshama Dwarakanath, Svitlana S. Vyetrenko, Tucker Balch:
Profit equitably: an investigation of market maker's impact on equitable outcomes. 4:1-4:8 - Gabriele D'Acunto, Paolo Bajardi, Francesco Bonchi, Gianmarco De Francisci Morales:
The evolving causal structure of equity risk factors. 5:1-5:8 - Maria Eleni Athanasopoulou, Justina Deveikyte, Alan Mosca, Ilaria Peri, Alessandro Provetti
:
A hybrid model for forecasting short-term electricity demand. 6:1-6:6 - Leo Ardon, Nelson Vadori, Thomas Spooner, Mengda Xu, Jared Vann, Sumitra Ganesh:
Towards a fully rl-based market simulator. 7:1-7:9 - Marco Schreyer, Timur Sattarov, Damian Borth:
Multi-view contrastive self-supervised learning of accounting data representations for downstream audit tasks. 8:1-8:8 - Abhinav Nadh Thirupathi, Tuka Alhanai, Mohammad M. Ghassemi:
A machine learning approach to detect early signs of startup success. 9:1-9:8 - Siyi Wang, Xing Yan, Bangqi Zheng, Hu Wang, Wangli Xu, Nanbo Peng, Qi Wu:
Risk and return prediction for pricing portfolios of non-performing consumer credit. 10:1-10:9 - Lin Li:
An automated portfolio trading system with feature preprocessing and recurrent reinforcement learning. 11:1-11:8 - Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian:
Deep risk model: a deep learning solution for mining latent risk factors to improve covariance matrix estimation. 12:1-12:8 - Erik Altman:
Synthesizing credit card transactions. 13:1-13:9 - Nestoras Chalkidis, Rahul Savani:
Trading via selective classification. 14:1-14:9 - Christopher J. Cho, Timothy J. Norman:
Bit by bit: how to realistically simulate a crypto-exchange. 15:1-15:9 - Vipul Satone, Dhruv Desai, Dhagash Mehta:
Fund2Vec: mutual funds similarity using graph learning. 16:1-16:8 - Ricardo Barata, Miguel Leite, Ricardo Pacheco, Marco O. P. Sampaio, João Tiago Ascensão, Pedro Bizarro:
Active learning for imbalanced data under cold start. 17:1-17:9 - Shivshankar Reddy, Pranav Poduval, Anand Vir Singh Chauhan, Maneet Singh, Sangam Verma, Karamjit Singh, Tanmoy Bhowmik:
TeGraF: temporal and graph based fraudulent transaction detection framework. 18:1-18:8 - Oscar Fernández Vicente, Fernando Fernández Rebollo, Francisco Javier García-Polo:
Deep Q-learning market makers in a multi-agent simulated stock market. 19:1-19:9 - Katherine Mayo, Michael P. Wellman:
A strategic analysis of portfolio compression. 20:1-20:8 - Mahmoud Mahfouz, Tucker Balch, Manuela Veloso, Danilo P. Mandic:
Learning to classify and imitate trading agents in continuous double auction markets. 21:1-21:8 - Srijan Sood, Zhen Zeng, Naftali Cohen, Tucker Balch, Manuela Veloso:
Visual time series forecasting: an image-driven approach. 22:1-22:9 - Cynthia Pagliaro, Dhagash Mehta, Han-Tai Shiao, Shaofei Wang, Luwei Xiong:
Investor behavior modeling by analyzing financial advisor notes: a machine learning perspective. 23:1-23:8 - Buhong Liu, Maria Polukarov, Carmine Ventre, Lingbo Li, Leslie Kanthan:
Agent-based markets: equilibrium strategies and robustness. 24:1-24:8 - Gary Ang, Ee-Peng Lim:
Learning knowledge-enriched company embeddings for investment management. 25:1-25:9 - Panagiotis Kanellopoulos, Maria Kyropoulou, Hao Zhou:
Financial network games. 26:1-26:9 - Zheng Gong, Carmine Ventre, John G. O'Hara:
The efficient hedging frontier with deep neural networks. 27:1-27:8 - Hao Ni, Lukasz Szpruch, Marc Sabate Vidales, Baoren Xiao, Magnus Wiese, Shujian Liao:
Sig-wasserstein GANs for time series generation. 28:1-28:8 - Tiffany Tuor, Joshua Lockhart, Daniele Magazzeni:
Asynchronous collaborative learning across data silos. 29:1-29:8 - Selim Amrouni, Aymeric Moulin, Jared Vann, Svitlana Vyetrenko, Tucker Balch, Manuela Veloso:
ABIDES-gym: gym environments for multi-agent discrete event simulation and application to financial markets. 30:1-30:9 - Christoph Siebenbrunner, Michael Sigmund:
Learning regulator influence on internal risk weights. 31:1-31:8 - Wei Xiong, Rama Cont:
Interactions of market making algorithms: a study on perceived collusion. 32:1-32:9 - Muchen Zhao, Vadim Linetsky:
High frequency automated market making algorithms with adverse selection risk control via reinforcement learning. 33:1-33:9 - Elias Zavitsanos, Dimitris Mavroeidis, Konstantinos Bougiatiotis, Eirini Spyropoulou, Lefteris Loukas, Georgios Paliouras:
Financial misstatement detection: a realistic evaluation. 34:1-34:9 - Gilad Asharov, Tucker Balch, Antigoni Polychroniadou:
Privacy-preserving portfolio pricing. 35:1-35:8 - Parisa Hassanzadeh, Danial Dervovic, Samuel Assefa, Prashant Reddy, Manuela Veloso:
Tradeoffs in streaming binary classification under limited inspection resources. 36:1-36:9 - Edoardo Vittori, Amarildo Likmeta, Marcello Restelli:
Monte carlo tree search for trading and hedging. 37:1-37:9 - Antonio Riva, Lorenzo Bisi, Pierre Liotet, Luca Sabbioni, Edoardo Vittori, Marco Pinciroli, Michele Trapletti, Marcello Restelli:
Learning FX trading strategies with FQI and persistent actions. 38:1-38:9 - Zhen Zeng, Tucker Balch, Manuela Veloso:
Deep video prediction for time series forecasting. 39:1-39:7 - Francisco Caio Lima Paiva, Leonardo Kanashiro Felizardo, Reinaldo Augusto da Costa Bianchi, Anna Helena Reali Costa:
Intelligent trading systems: a sentiment-aware reinforcement learning approach. 40:1-40:9 - Blake Martin, Mithun Chakraborty, Sindhu Kutty:
Timing is money: the impact of arrival order in beta-bernoulli prediction markets. 41:1-41:9 - Yada Zhu, Wenyu Chen, Yang Zhang, Tian Gao, Jianbo Li:
Probabilistic framework for modeling event shocks to financial time series. 42:1-42:8 - Eren Kurshan, Jiahao Chen, Victor Storchan, Hongda Shen:
On the current and emerging challenges of developing fair and ethical AI solutions in financial services. 43:1-43:8 - Qiong Wu, Christopher G. Brinton, Zheng Zhang, Andrea Pizzoferrato, Zhenming Liu, Mihai Cucuringu:
Equity2Vec: end-to-end deep learning framework for cross-sectional asset pricing. 44:1-44:9 - Katherine Mayo, Shaily Fozdar, Michael P. Wellman:
An agent-based model of strategic adoption of real-time payments. 45:1-45:9 - Andrea Coletta, Matteo Prata, Michele Conti, Emanuele Mercanti, Novella Bartolini, Aymeric Moulin, Svitlana Vyetrenko, Tucker Balch:
Towards realistic market simulations: a generative adversarial networks approach. 46:1-46:9 - Fuli Feng, Xilin Rui, Wenjie Wang, Yixin Cao, Tat-Seng Chua:
Pre-training and evaluation of numeracy-oriented language model. 47:1-47:8 - Zechu Li, Xiao-Yang Liu, Jiahao Zheng, Zhaoran Wang, Anwar Walid, Jian Guo:
FinRL-podracer: high performance and scalable deep reinforcement learning for quantitative finance. 48:1-48:9 - Megan Shearer, David Byrd, Tucker Hybinette Balch, Michael P. Wellman:
Stability effects of arbitrage in exchange traded funds: an agent-based model. 49:1-49:9 - Mao Guan, Xiao-Yang Liu:
Explainable deep reinforcement learning for portfolio management: an empirical approach. 50:1-50:9 - Fuli Feng, Xiang Wang, Xiangnan He, Ritchie Ng, Tat-Seng Chua:
Time horizon-aware modeling of financial texts for stock price prediction. 51:1-51:8 - Debasmita Das, Yatin Katyal, Ram Ganesh, Rohit Bhattacharya, Rajesh Kumar Ranjan:
AuthSHAP: authentication vulnerability detection on tabular data in black box setting. 52:1-52:8
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