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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | The Predictive Power of News Sentiment in the High Frequency Foreign Exchange Asset Class |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 60 |
| Date | 2022 |
| Abstract Text | The objective of this thesis is to examine viable return-generating trading strategies in the high frequency foreign exchange space using shifts in news sentiment momentum as signals. More specifically, this thesis backtests various moving average crossover strategies of hourly Euro-specific news sentiment using the TRNA dataset with over 70’000 observations. The crossover events serve as entry (exit) signals to assume long (short) positions in the EUR/USD exchange rate. In line with the findings of prior literature, an OLS regression analysis reaffirms the existence of a predictive relationship between Euro-specific news sentiment and the EUR/USD exchange rate. A backtesting exercise evaluates both simple and exponential moving average crossover strategies for three different combinations of crossover periods (2-10-hour, 2-20-hour, and 2-50-hour) with varying levels of transaction costs. The moving average crossover approach reduces the number of transactions and consequently brings down transaction costs. The results show that both sets of strategies outperform the buy-and-hold benchmark strategy and yield consistent and positive returns for the 2-20-hour and the 2-50-hour crossover periods, even after taking transaction costs into account. |
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