國立虎尾科技大學 |

Three Essays in Financial Economics.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Three Essays in Financial Economics./
作者:	Bybee, Joseph Leland.
面頁冊數:	1 online resource (278 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-01, Section: A.
Contained By:	Dissertations Abstracts International86-01A.
標題:	Finance. -
電子資源:	click for full text (PQDT)
ISBN:	9798383350072

Three Essays in Financial Economics.
Bybee, Joseph Leland.

Three Essays in Financial Economics. - 1 online resource (278 pages)

Source: Dissertations Abstracts International, Volume: 86-01, Section: A.

Thesis (Ph.D.)--Yale University, 2024.

Includes bibliographical references

The first is titled "The Ghost in the Machine: Generating Beliefs with Large Language Models". I introduce a methodology to generate economic expectations by applying large language models to historical news. Leveraging this methodology, I make three key contributions. (1) I show generated expectations closely match existing survey measures and capture many of the same deviations from full-information rational expectations. (2) I use my method to generate 120 years of economic expectations from which I construct a measure of economic sentiment capturing systematic errors in generated expectations. (3) I then employ this measure to investigate behavioral theories of bubbles. Using a sample of industry-level run-ups over the past 100 years, I find that an industry's exposure to economic sentiment is associated with a higher probability of a crash and lower future returns. Additionally, I find a higher degree of feedback between returns and sentiment during run-ups that crash, consistent with return extrapolation as a key mechanism behind bubbles.The second is titled "Narrative Asset Pricing: Interpretable Systematic Risk Factors from News Text". We estimate a narrative factor pricing model from news text of The Wall Street Journal. Our empirical method integrates topic modeling (LDA), latent factor analysis (IPCA), and variable selection (group lasso). Narrative factors achieve higher out-of-sample Sharpe ratios and smaller pricing errors than standard characteristic-based factor models and predict future investment opportunities in a manner consistent with the ICAPM. We derive an interpretation of the estimated risk factors from narratives in the underlying article text. The third is titled "Business News and Business Cycles". We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text of 800,000 Wall Street Journal articles for 1984-2017, we estimate a topic model that summarizes business news into interpretable topical themes and quantifies the proportion of news attention allocated to each theme over time. News attention closely tracks a wide range of economic activities and explains 25% of aggregate stock market returns. A text-augmented VAR demonstrates the large incremental role of news text in modeling macroeconomic dynamics. We use this model to retrieve the narratives that underlie business cycle fluctuations.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383350072Subjects--Topical Terms:

559073
Finance.
Subjects--Index Terms:

Latent factor analysisIndex Terms--Genre/Form:

554714
Electronic books.

Three Essays in Financial Economics.
LDR:03748ntm a22003857 4500 001 1147555
005 20240909103811.5
006 m o d
007 cr bn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798383350072
035 $a (MiAaPQ)AAI30997238
035 $a AAI30997238
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Bybee, Joseph Leland. $3 1473313
245 1 0 $a Three Essays in Financial Economics.
264 0 $c 2024
300 $a 1 online resource (278 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-01, Section: A.
500 $a Advisor: Kelly, Bryan;Barberis, Nicholas.
502 $a Thesis (Ph.D.)--Yale University, 2024.
504 $a Includes bibliographical references
520 $a The first is titled "The Ghost in the Machine: Generating Beliefs with Large Language Models". I introduce a methodology to generate economic expectations by applying large language models to historical news. Leveraging this methodology, I make three key contributions. (1) I show generated expectations closely match existing survey measures and capture many of the same deviations from full-information rational expectations. (2) I use my method to generate 120 years of economic expectations from which I construct a measure of economic sentiment capturing systematic errors in generated expectations. (3) I then employ this measure to investigate behavioral theories of bubbles. Using a sample of industry-level run-ups over the past 100 years, I find that an industry's exposure to economic sentiment is associated with a higher probability of a crash and lower future returns. Additionally, I find a higher degree of feedback between returns and sentiment during run-ups that crash, consistent with return extrapolation as a key mechanism behind bubbles.The second is titled "Narrative Asset Pricing: Interpretable Systematic Risk Factors from News Text". We estimate a narrative factor pricing model from news text of The Wall Street Journal. Our empirical method integrates topic modeling (LDA), latent factor analysis (IPCA), and variable selection (group lasso). Narrative factors achieve higher out-of-sample Sharpe ratios and smaller pricing errors than standard characteristic-based factor models and predict future investment opportunities in a manner consistent with the ICAPM. We derive an interpretation of the estimated risk factors from narratives in the underlying article text. The third is titled "Business News and Business Cycles". We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text of 800,000 Wall Street Journal articles for 1984-2017, we estimate a topic model that summarizes business news into interpretable topical themes and quantifies the proportion of news attention allocated to each theme over time. News attention closely tracks a wide range of economic activities and explains 25% of aggregate stock market returns. A text-augmented VAR demonstrates the large incremental role of news text in modeling macroeconomic dynamics. We use this model to retrieve the narratives that underlie business cycle fluctuations.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Finance. $3 559073
653 $a Latent factor analysis
653 $a Economic expectations
653 $a Large language models
653 $a Economic sentiment
655 7 $a Electronic books. $2 local $3 554714
690 $a 0508
690 $a 0511
690 $a 0501
710 2 $a Yale University. $b Management. $3 1464632
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Dissertations Abstracts International $g 86-01A.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30997238 $z click for full text (PQDT)