personal-coding/Stock-Earnings-Call-Transcript-Natural-Language-Processing
Natural Language Processing on Stocks' Earnings Call Transcripts: An Investment Strategy Backtest Based on S&P Global Papers.
This project helps quantitative traders and investors evaluate potential investment strategies by applying natural language processing to companies' earnings call transcripts. It takes raw earnings call text and historical stock prices as input, then calculates sentiment or topic-based scores to generate and backtest stock selection strategies. This is for investment professionals or quantitative researchers interested in validating or replicating NLP-driven trading signals.
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Use this if you want to explore or replicate an investment strategy based on analyzing the sentiment or specific topics within S&P 500 companies' earnings call transcripts.
Not ideal if you're looking for a ready-to-deploy, profitable trading algorithm, as the backtesting results presented here do not show significant outperformance.
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Python
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Last pushed
Aug 30, 2023
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