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Essays on asset pricing with big and unconventional data
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Giannone, Danilo Antonino (2021) Essays on asset pricing with big and unconventional data. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b3766470
Abstract
This thesis comprises three papers on empirical asset pricing, where big and unconventional datasets are implemented through Artificial Intelligence, and recent advancements in econometrics. The first paper analyses intraday returns to investigate the time-varying characteristic of the systematic risk around unscheduled firm-level news. More precisely, I study firm-level news writing about secondary equity offering programs. I show that on the day that the press reports the news, the beta of the company drops by a statistically significant and economically important amount. I demonstrate that, through this variation, it is possible to explain more than a half of the abnormal return documented on the event day. The second paper leverages recent advancements in text analysis to derive a novel measure from the distribution of words in firm-level news. This proxy variable overcomes most of the limitations of classical text analysis and does not require to be trained on any quantitative variables. I show that this proxy captures the changes in the stock return volatility and in the systematic risk, and it explains the variations in market risk. Hence, I argue that this measure is a useful instrument to understand the time-varying uncertainty of the company, and by extension, of the market. Finally, the third paper investigates whether the New York Federal Reserve primary dealers are special marginal investors with very different characteristics relative to the non-primary broker-dealer sector. Using publicly available information, we identify the publicly traded ultimate parent company of each primary dealer and show that they are not special. Through recent advancement in econometrics, we also conclude that the factors derived from the primary dealer observations does not possess any pricing performance.
Item Type: | Thesis (PhD) | ||||
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Subjects: | H Social Sciences > HB Economic Theory H Social Sciences > HF Commerce H Social Sciences > HG Finance P Language and Literature > PN Literature (General) |
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Library of Congress Subject Headings (LCSH): | Capital assets pricing model, Financial statements, Stocks, Stockbrokers, Journalism, Commercial, Text data mining, Time-series analysis, Econometrics -- Computer programs | ||||
Official Date: | October 2021 | ||||
Dates: |
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Institution: | University of Warwick | ||||
Theses Department: | Warwick Business School | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Supervisor(s)/Advisor: | Robotti, Cesare | ||||
Format of File: | |||||
Extent: | x, 165 leaves : illustrations, charts | ||||
Language: | eng |
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