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数字金融Workshop:Algorithm Aversion: Theory and Evidence from Robo-Advice
发布日期：2023-05-30 12:00 来源：
时间：5月30日 周二 北京时间 下午 14:00-15:30
地点/Venue：
线下：北京大学国家发展研究院承泽园校区131教室（校外人员线下参加，请扫描邮件下方二维码填写信息，申报入院）
线上：Zoom 会议
https://us06web.zoom.us/j/82359576409?pwd=VHBUQzJJTVV5U2ZHU2o1MWlkZlFhQT09
会议号：823 5957 6409
密码：998306
主讲人/Speaker: Alberto Rossi
主持人/Host：胡佳胤 Jiayin Hu
摘要/Abstract:
Automation can lower costs and democratize access to many consumer services, but human discomfort with automation can pose barriers to technology adoption. We build a structural model of psychological “algorithm aversion,” which features ongoing disutility of dealing with an algorithm, pessimism about the algorithm's ability, and uncertainty about the algorithm's performance; all three components can be assuaged by human interaction. We estimate model parameters using unique data from a “hybrid” robo-advising service in which portfolio management is automated, but clients are randomly matched with human advisors who provide different standards of support. Algorithm aversion is mainly driven by ongoing disutility and uncertainty, and human advice is especially important in retaining investors in robo-advice during market downturns.
主讲人介绍/Biography:
Alberto Rossi is a Professor of Finance at the McDonough School of Business, Georgetown University. He is also the Director of the AI, Analytics, and Future of Work Initiative at Georgetown and an Academic Fellow of the Luohan Academy. His research interests include FinTech, Household Finance, Machine Learning, and Asset Pricing. His recent work studies how robo-advisors can help individuals make better financial decisions and how to predict stock market returns using machine learning algorithms. He has worked extensively in analyzing big data, has collaborated with major brokerage houses, FinTech firms, and asset managers around the world.
Professor Rossi’s work has been published in leading academic journals such as the Journal of Finance, the Review of Financial Studies, the Journal of Financial Economics and Management Science.
Before McDonough, he was an Associate Professor with tenure at the R.H. Smith School of Business, University of Maryland. He also worked as an economist at the Board of Governors of the Federal Reserve System in Washington DC. He received his Ph.D. in Economics from the University of California, San Diego.
线下参会请扫描下方二维码报名
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首页
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数字金融Workshop:Algorithm Aversion: Theory and Evidence from Robo-Advice
发布日期：2023-05-30 12:00 来源：
时间：5月30日 周二 北京时间 下午 14:00-15:30
地点/Venue：
线下：北京大学国家发展研究院承泽园校区131教室（校外人员线下参加，请扫描邮件下方二维码填写信息，申报入院）
线上：Zoom 会议
https://us06web.zoom.us/j/82359576409?pwd=VHBUQzJJTVV5U2ZHU2o1MWlkZlFhQT09
会议号：823 5957 6409
密码：998306
主讲人/Speaker: Alberto Rossi
主持人/Host：胡佳胤 Jiayin Hu
摘要/Abstract:
Automation can lower costs and democratize access to many consumer services, but human discomfort with automation can pose barriers to technology adoption. We build a structural model of psychological “algorithm aversion,” which features ongoing disutility of dealing with an algorithm, pessimism about the algorithm's ability, and uncertainty about the algorithm's performance; all three components can be assuaged by human interaction. We estimate model parameters using unique data from a “hybrid” robo-advising service in which portfolio management is automated, but clients are randomly matched with human advisors who provide different standards of support. Algorithm aversion is mainly driven by ongoing disutility and uncertainty, and human advice is especially important in retaining investors in robo-advice during market downturns.
主讲人介绍/Biography:
Alberto Rossi is a Professor of Finance at the McDonough School of Business, Georgetown University. He is also the Director of the AI, Analytics, and Future of Work Initiative at Georgetown and an Academic Fellow of the Luohan Academy. His research interests include FinTech, Household Finance, Machine Learning, and Asset Pricing. His recent work studies how robo-advisors can help individuals make better financial decisions and how to predict stock market returns using machine learning algorithms. He has worked extensively in analyzing big data, has collaborated with major brokerage houses, FinTech firms, and asset managers around the world.
Professor Rossi’s work has been published in leading academic journals such as the Journal of Finance, the Review of Financial Studies, the Journal of Financial Economics and Management Science.
Before McDonough, he was an Associate Professor with tenure at the R.H. Smith School of Business, University of Maryland. He also worked as an economist at the Board of Governors of the Federal Reserve System in Washington DC. He received his Ph.D. in Economics from the University of California, San Diego.
线下参会请扫描下方二维码报名
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