Conference Program

Do Cryptocurrencies Have Fundamental Values

Yukun Liu, University of Rochester

Jinfei Sheng, University of California, Irvine

Wanyi Wang, University of California, Irvine

Competition and Product Quality: Fake Trading on Crypto Exchanges

Dan Amiram, Tel Aviv University

Evgeny Lyandres, Tel Aviv University

Daniel Rabetti, Tel Aviv University

Peer Co-Movement in Crypto Markets

Gustavo Schwenkler, Santa Clara University

Hannan Zheng, Boston University

The Cyber Risk Premium

Hao Jiang, Michigan State University

Naveen Khanna, Michigan State University

Qian Yang, Michigan State University

Deep Learning in Asset Pricing

Luyang Chen, Stanford University

Markus Pelger, Stanford University

Jason Zhu, Stanford University

Asset Pricing with Realistic Crises Dynamics

Goutham Gopalakrishna, EPFL

Assessing Human Information Processing in Lending Decisions: A Machine  Learning Approach

Miao Liu, University of Chicago

Real-time Price Discovery via Verbal Communication:
Method and Application to Fedspeak

Roberto Gomez-Cram, London Business School

Marco Grotteria, London Business School

The Conduits of Price Discovery: A Machine Learning  Approach

Amy Kwan, University of Sydney

Richard Philip, University of Sydney

Andriy Shkilko, Wilfrid Laurier University

The Serial Dependence of the Commodity Futures Returns: A Machine Learning Approach

Yufeng Han, University of North Carolina at Charlotte

Lingfei Kong, University of North Carolina at Charlotte

The Colour of Finance Words

Diego Garcia, University of Colorado Boulder

Xiaowen Hu, University of Colorado Boulder

Maximilian Rohrer, Norwegian School of Economics

AlphaPortfolio: Single-Step Construction through  Reinforcement Learning and  Economically Interpretable AI

Lin William Cong, Cornell University

Ke Tang, Tsinghua University

Jingyuan Wang, Beihang University

Yang Zhang, Beihang University

You Only Look Once: Real Time Volatility Regime Detection with Intraday Realized Features

Peng Cheng, J.P.Morgan

Marko Kolanovic, J.P.Morgan

How to Talk When a Machine is Listening:  Corporate Disclosure in the Age of AI

Sean Cao, Georgia State University

Wei Jiang, Columbia University

Baozhong Yang, Georgia State University

Alan Zhang, Georgia State University

The Impact of Tightly Contested Governance Proposals on Firms' Narrative  Disclosures: Evidence from a Regression-Discontinuity Design

Abhishek Ganguly, University of Oklahoma

Arup Ganguly, University of Mississippi

Lin Ge, University of Mississippi

Chad Zutter, University of Pittsburgh

Is Positive Sentiment in Corporate Annual Reports Informative?  Evidence from Deep Learning

Mehran Azimi, University of Alabama

Anup Agrawal, University of Alabama

Fundamental Analysis via Machine Learning

Kai Cao, HKUST

Haifeng You, HKUST

What Can Analysts Learn from Artificial Intelligence about Fundamental  Analysis?

Oliver Binz, INSEAD

Katherine Schipper, Duke University

Kevin Standridge, Duke University

Bankruptcy Prediction of Privately Held SMEs Using Feature  Selection Methods

Florentina Paraschiv, NUST

Markus Schmid, University of St. Gallen

Ranik Raaen Wahlstrom, NUST

Speakers:

Zhengling Qi, The George Washington University
Ganggang Xu, University of Miami
Yan Yu, University of Cincinnati

Uncovering Mutual Fund Skill with Machine Learning

Alan Zhang, Georgia State University

Artificial Intelligence and High-Skilled Work: Evidence from Analysts

Jillian Grennan, Duke University

Roni Michaely, University of Geneva 

Aggregating Artificially Intelligent Earnings Forecasts

Vidhi Chhaochharia, University of Miami

Alok Kumar, University of Miami

Swathi Murali, University of Miami

Ville Rantala, University of Miami

Machine Learning Classification Methods and Portfolio Allocation: An Examination of Market Efficiency

Yang Bai, University of Missouri

Kuntara Pukthuanthong, University of Missouri

Charting By Machines

Scott Murray, Georgia State University

Houping Xiao, Georgia State University

Yusen Xia, Georgia State University

Modeling Heterogeneity in Firm-level Return Predictability with Machine Learning

Theodoros Evgeniou, INSEAD

Ahmed Guecioueur, INSEAD

Rodolfo Prieto, INSEAD