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Categorizing SEC 8-K Filings : = A Comparison of Machine Learning Classification Models.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Categorizing SEC 8-K Filings :/
Reminder of title:	A Comparison of Machine Learning Classification Models.
Author:	Montgomery, Meg.
Description:	1 online resource (75 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-03.
Contained By:	Masters Abstracts International85-03.
Subject:	Finance. -
Online resource:	click for full text (PQDT)
ISBN:	9798380361019

Categorizing SEC 8-K Filings : = A Comparison of Machine Learning Classification Models.
Montgomery, Meg.

Categorizing SEC 8-K Filings :A Comparison of Machine Learning Classification Models. - 1 online resource (75 pages)

Source: Masters Abstracts International, Volume: 85-03.

Thesis (M.S.)--College of Charleston, 2023.

Includes bibliographical references

In the United States, public companies are required to submit form 8-K filings to the SEC within four days of any material event occurring. The timely nature of these announcements makes them important resources for investors, industry professionals, and researchers alike. As part of an 8-K filing, one Item, Item 5.02, is of particular interest as it announces the arrival and departure of key executive officers and board members. Machine learning and classification algorithms have been applied most commonly to 10-K and 10-Q SEC filings, but 8-K filings have only received little research attention. Studying the contents of 8-K filings by applying and comparing advanced classification algorithms is therefore of utmost importance. This study uses a big dataset of 8-K textual filings and applies 11 classification models via two different experiments: (a) identifying the Item based on 8-K textual inputs, and (b) categorizing the type of executive departure event described in Item 5.02. The study compares four traditional classification algorithms combined with two feature extraction techniques, a recurrent neural network model (LSTM), and two transformer models (SmallBERT and GPT-2).

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

Mode of access: World Wide Web

ISBN: 9798380361019Subjects--Topical Terms:

559073
Finance.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

554714
Electronic books.

Categorizing SEC 8-K Filings : = A Comparison of Machine Learning Classification Models.
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