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Predicting distresses using deep learning of text segments in annual reports
Rastin Matin
*
,
Casper Hansen
,
Christian Hansen
, Pia Mølgaard
*
Corresponding author af dette arbejde
6
Citationer (Scopus)
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Keyphrases
Deep Learning
100%
Audit Report
100%
Text Segmentation
100%
Financial Variables
50%
Corporate Distress
50%
Distress Prediction
50%
Financial Institutions
25%
Convolutional Recurrent Neural Network
25%
Prediction Accuracy
25%
Descriptive Representation
25%
Need to Evaluate
25%
Joint Model
25%
Corporate Firms
25%
Concatenated
25%
Distress Models
25%
Large Firms
25%
Performance Prediction
25%
State-of-the-art Models
25%
Default Risk
25%
Unstructured Textual Data
25%
Existing State
25%
Unstructured Data
25%
Computer Science
Unstructured Data
100%
Descriptive Representation
100%
Text Segment
100%
Deep Learning Method
100%
Utmost Importance
100%
Prediction Performance
100%
Recurrent Neural Network
100%
Economics, Econometrics and Finance
Auditor's Report
100%