Big Data and Securities Markets
Itay Goldstein, Chester S. Spatt, and Mao Ye, Organizers
December 3-4, 2020
Supported by the National Science Foundation, in conjunction with the Review of Financial Studies
| Thursday, December 3 | ||||
| Meeting format: Authors, 20 minutes; discussants, 15 minutes; general discussion, 10 minutes. | ||||
| 10:55 am | Welcome | |||
| 11:00 am |
Who Benefits from Robo-advising? Evidence from Machine Learning
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| 11:45 am |
Man vs. Machine Learning: The Term Structure of Earnings Expectations and Conditional Biases
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| 12:30 pm | Break | |||
| 1:00 pm |
How to Talk When a Machine is Listening: Corporate Disclosure in the Age of AI
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| 1:45 pm |
Life Cycles of Firm Disclosures
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| 2:30 pm | Break | |||
| 3:00 pm |
The Value of Differing Points of View: Evidence from Financial Analysts' Geographic Diversity
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| 3:45 pm |
Stock-Specific Price Discovery from ETFs
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| 4:30 pm | Adjourn | |||
| Friday, December 4 | ||||
| 11:00 am |
Does Big Data Improve Financial Forecasting? The Horizon Effect
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| 11:45 am |
Imprecise and Informative: Lessons from Market Reactions to Imprecise Disclosure
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| 12:30 pm | Break | |||
| 1:00 pm |
True Cost of Immediacy
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| 1:45 pm |
Vestigial Tails? Floor Brokers at the Close in Modern Electronic Markets
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| 2:30 pm | Break | |||
| 3:00 pm |
The Anatomy of Trading Algorithms
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| 3:45 pm |
The Good, the Bad, and the Ugly: How Algorithmic Traders Impact Institutional Trading Costs
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| 4:30 pm | Adjourn | |||