Colorado College Logo

  DigitalCC

Use AND (in capitals) to search multiple keywords.
Example: harmonica AND cobos

MAPPING SENTIMENT: A TEXTUAL ANALYSIS ON 10-K DOCUMENTS USING AN ARTIFICIAL NEURAL NETWORK

by Sin, Ryan

Abstract

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.

Note

The author has given permission for this work to be deposited in the Digital Archive of Colorado College.

Colorado College Honor Code upheld.

Includes bibliographical references.

Administrative Notes

The author has given permission for this work to be deposited in the Digital Archive of Colorado College.

Colorado College Honor Code upheld.

Copyright
Copyright restrictions apply.
Publisher
Colorado College Tutt Library
PID
coccc:27411
Digital Origin
born digital
Extent
15 pages
Thesis
Senior Thesis -- Colorado College
Thesis Advisor
Daniel Johnson
Department/Program
Economics and Business
Degree Name
Bachelor of Arts
Degree Type
bachelor
Degree Grantor
Colorado College Tutt Library
Date Issued
2017-05