Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Correlating factors of U.S. presidential speeches with stock market movements - a machine learning approach
Thesis (MCom)--Stellenbosch University, 2023.
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | en_ZA |
| Published: |
Stellenbosch : Stellenbosch University
2023
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613760998342656 |
|---|---|
| access_status_str | Open Access |
| author | Rees, Pablo |
| author2 | Van Lill, Dawie |
| author_browse | Rees, Pablo Van Lill, Dawie |
| author_facet | Van Lill, Dawie Rees, Pablo |
| author_sort | Rees, Pablo |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MCom)--Stellenbosch University, 2023. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/127233 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:41:16.700Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/127233 Correlating factors of U.S. presidential speeches with stock market movements - a machine learning approach Rees, Pablo Van Lill, Dawie Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Economics. Machine Learning Natural language processing (Computer science) Finance -- Data processing Financial services industry -- Technological innovations UCTD Thesis (MCom)--Stellenbosch University, 2023. ENGLISH SUMMARY: The literature relating textual to stock market data is deep, but the relationship between speeches given by political figures and stock markets is relatively undefined. This research begins to rectify this by exploring the relationship between U.S. presidential speeches and daily price movements in the S&P 500 index. It was possible to explore this relationship by using natural language processing techniques, econometric time-series analysis, and machine learning models. It was found that models including presidential speech data can achieve prediction accuracy of about 60% over an S&P 500 index price movement proxy. This is an increase of about 0.3% (0.599 vs 0.601) over the models that did not include the presidential speech data (without losing ground in either recall or precision). Notably, this result was drawn from 71 years of data at a daily resolution. Thus, it is concluded that presidential speeches hold predictive power over stock market movements and that this relationship can be used to improve the power of predictive models. AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar Masters 2023-02-23T10:48:01Z 2023-05-18T07:11:08Z 2023-02-23T10:48:01Z 2023-05-18T07:11:08Z 2023-03 Thesis http://hdl.handle.net/10019.1/127233 en_ZA Stellenbosch University 55 pages : illustrations (some color), includes annexures application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Machine Learning Natural language processing (Computer science) Finance -- Data processing Financial services industry -- Technological innovations UCTD Rees, Pablo Correlating factors of U.S. presidential speeches with stock market movements - a machine learning approach |
| title | Correlating factors of U.S. presidential speeches with stock market movements - a machine learning approach
|
| title_full | Correlating factors of U.S. presidential speeches with stock market movements - a machine learning approach
|
| title_fullStr | Correlating factors of U.S. presidential speeches with stock market movements - a machine learning approach
|
| title_full_unstemmed | Correlating factors of U.S. presidential speeches with stock market movements - a machine learning approach
|
| title_short | Correlating factors of U.S. presidential speeches with stock market movements - a machine learning approach
|
| title_sort | correlating factors of u s presidential speeches with stock market movements a machine learning approach |
| topic | Machine Learning Natural language processing (Computer science) Finance -- Data processing Financial services industry -- Technological innovations UCTD |
| url | http://hdl.handle.net/10019.1/127233 |
| work_keys_str_mv | AT reespablo correlatingfactorsofuspresidentialspeecheswithstockmarketmovementsamachinelearningapproach |