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Article title
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Abstract
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Keywords
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Relevance to practice
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1. Introduction
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2. Literature review
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2.1. Prediction of earnings changes
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2.2. 10-K narrative disclosures: information signal vs. noise
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2.3. Textual analysis and LDA
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3. Methodology
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3.1. Sample and variables
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3.2. Topic modeling on 10-K filings
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3.3. Machine learning predictive models
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3.3.1. Dataset structure
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3.3.2. Algorithms and parameter optimization
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3.3.3. Evaluation metrics
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4. Results
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4.1. Predictive power of narratives in 10-K filings
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4.2. Topics in 10-K filings predictive of earnings changes
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4.3. Sections in 10-K filings predictive of earnings changes
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5. Discussion and conclusion
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5.1. Implications for practitioners
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5.2. Limitations
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References
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