Correlating factors of U.S. presidential speeches with stock market movements - a machine learning approach

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Stellenbosch : Stellenbosch University
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
Thesis (MCom)--Stellenbosch University, 2023.