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Using publicly available earnings call transcripts, we attempt to fine-tune pre-trained Masked Language Models using modest hand-annotated datasets, in order to perform sentiment analysis on Executive's utterances. By segmenting those sentiment readings by time and industry, and through the development and application of a proprietary "negative …

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jvaidya87/Earnings-Call-NLP-Sentiment-Analysis-and-TS-Forecasting

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Earnings-Call-NLP-Sentiment-Analysis-and-TS-Forecasting

Using publicly available earnings call transcripts, we attempt to fine-tune pre-trained Masked Language Models using modest hand-annotated datasets, in order to perform sentiment analysis on Executive's utterances. By segmenting those sentiment readings by time and industry, and through the development and application of a proprietary "negative sentiment tripwire", we generate a rich NLP-based feature set upon which to train tree-based & transformer-based classification models for time-series classification and prediction of the next month's S&P500 returns

The current version of this notebook uses the following packages/versions:

torch == 1.8.1
tensorflow == 2.8.0
nltk == 3.6.2

Original Data

Original Transcript data soourced from the WRDS CapitalIQ Transcripts database; the transcripts data was then merged with various company-specific databases to generate information on company origin (only U.S. domiciled companies were included in this analysis), indsutry sector and various other identifiers

For more information

Please contact the authors, Jay Vaidya ([email protected]) and Ruchi Kumar ([email protected])

About

Using publicly available earnings call transcripts, we attempt to fine-tune pre-trained Masked Language Models using modest hand-annotated datasets, in order to perform sentiment analysis on Executive's utterances. By segmenting those sentiment readings by time and industry, and through the development and application of a proprietary "negative …

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