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Three Essays on Financial Text Analysis /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Three Essays on Financial Text Analysis // Jiexin Zheng.
作者:	Zheng, Jiexin,
面頁冊數:	1 electronic resource (160 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
Contained By:	Dissertations Abstracts International86-05B.
標題:	Logic. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31718389
ISBN:	9798346543152

Three Essays on Financial Text Analysis /
Zheng, Jiexin,

Three Essays on Financial Text Analysis /Jiexin Zheng. - 1 electronic resource (160 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: B.

Processing financial text in an effective and efficient manner is a challenging task due to its complexity, large volume, and the strategic behaviors of communicators. Both the computational power of AI and the generalizability of human knowledge are desired for the effective processing of financial text. This three-study thesis focus on exploring human-AI augmentations methodologies for extracting valuable information from financial texts. In the first study, an algorithm is proposed that leverages both a human-defined sentiment word list and the word embedding approach to quantify text sentiment over time. The algorithm is then applied to investigate the evolutionary effects of sentiment words in financial text, shedding light on the strategic communication of managers and its influence on sentiment analysis. The second study introduces an augmentation approach that combines human knowledge with artificial intelligence for sentiment extraction from corporate disclosures. By incorporating a human-defined sentiment word list into a word embedding model, a weighting scheme is constructed to enhance the effectiveness of common sentiment measures. This study highlights the value of human knowledge in mitigating managers' strategic manipulation and emphasizes the complementarity between human knowledge and AI in processing financial text. The third study proposes a novel readability measure, the Language Predictability Score (\uD835\uDC3F\uD835\uDC43 \uD835\uDC46), aimed at assessing the processing costs associated with comprehending corporate disclosure text. Large language models are employed to simulate the language ability of market participants, and these models are then used to estimate the processing cost of a large sample of management discussion and analysis (MD&As) from annual reports. Empirical evidence suggests that the constructed \uD835\uDC3F\uD835\uDC43 \uD835\uDC46 measure can effectively reflect the processing difficulty experienced by market participants. This study demonstrates the effectiveness of capturing novel information in financial text from a human perspective while leveraging AI to enhance efficiency. Overall, these studies collectively contribute to the advancement of methodologies for processing financial text.

English

ISBN: 9798346543152Subjects--Topical Terms:

558909
Logic.

Three Essays on Financial Text Analysis /
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