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  • Thumbnail for MAPPING SENTIMENT: A TEXTUAL ANALYSIS ON 10-K DOCUMENTS USING AN ARTIFICIAL NEURAL NETWORK
    MAPPING SENTIMENT: A TEXTUAL ANALYSIS ON 10-K DOCUMENTS USING AN ARTIFICIAL NEURAL NETWORK by Sin, Ryan

    Using nearly 8000 10-K documents published in 2016 and 2017, we generate contextual vectors through artificial neural networks and test whether the language of 10-K documents, without any detailed numeric indicators of financial performance, correlate with earnings per share and other financials of the S&P 500. We find significant correlation between earnings per share and contextual vectors, concluding that semantic analysis is a valuable tool that has great potential in financial analysis.

  • Thumbnail for Mapping Sentiment: a Textual Analysis on 10-K Documents Using an Artificial Neural Network
    Mapping Sentiment: a Textual Analysis on 10-K Documents Using an Artificial Neural Network by Sin, Ryan

    Using nearly 8000 10-K documents published in 2016 and 2017, we generate contextual vectors through artificial neural networks and test whether the language of 10-K documents, without any detailed numeric indicators of financial performance, correlate with earnings per share and other financials of the S&P 500. We find significant correlation between earnings per share and contextual vectors, concluding that semantic analysis is a valuable tool that has great potential in financial analysis.