語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
到查詢結果
[ subject:"Computer engineering." ]
切換:
標籤
|
MARC模式
|
ISBD
Categorizing SEC 8-K Filings: A Comparison of Machine Learning Classification Models.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Categorizing SEC 8-K Filings: A Comparison of Machine Learning Classification Models.
作者:
Montgomery, Meg.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, 2023
面頁冊數:
75 p.
附註:
Source: Masters Abstracts International, Volume: 85-03.
附註:
Advisor: Junglas, Iris.
Contained By:
Masters Abstracts International85-03.
標題:
Finance.
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30631576
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.
- Ann Arbor : ProQuest Dissertations & Theses, 2023 - 75 p.
Source: Masters Abstracts International, Volume: 85-03.
Thesis (M.S.)--College of Charleston, 2023.
This item must not be sold to any third party vendors.
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).
ISBN: 9798380361019Subjects--Topical Terms:
183252
Finance.
Subjects--Index Terms:
Machine learning
Categorizing SEC 8-K Filings: A Comparison of Machine Learning Classification Models.
LDR
:02336nmm a2200373 4500
001
655832
005
20240414211943.5
006
m o d
007
cr#unu||||||||
008
240620s2023 ||||||||||||||||| ||eng d
020
$a
9798380361019
035
$a
(MiAaPQ)AAI30631576
035
$a
AAI30631576
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Montgomery, Meg.
$3
966975
245
1 0
$a
Categorizing SEC 8-K Filings: A Comparison of Machine Learning Classification Models.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2023
300
$a
75 p.
500
$a
Source: Masters Abstracts International, Volume: 85-03.
500
$a
Advisor: Junglas, Iris.
502
$a
Thesis (M.S.)--College of Charleston, 2023.
506
$a
This item must not be sold to any third party vendors.
520
$a
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).
590
$a
School code: 1000.
650
4
$a
Finance.
$3
183252
650
4
$a
Computer engineering.
$3
212944
653
$a
Machine learning
653
$a
Executive officers
653
$a
Algorithms
653
$a
Neural network
690
$a
0508
690
$a
0464
710
2
$a
College of Charleston.
$b
Data Science and Analytics.
$3
966976
773
0
$t
Masters Abstracts International
$g
85-03.
790
$a
1000
791
$a
M.S.
792
$a
2023
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30631576
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000236847
電子館藏
1圖書
學位論文
TH 2023
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30631576
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入