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Analyzing the Impact of Multiple Stock Indices in Prediction of US Dollar Index

More, Mrunali (2020) Analyzing the Impact of Multiple Stock Indices in Prediction of US Dollar Index. Masters thesis, Dublin, National College of Ireland.

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Foreign currency exchange rate and stock prices are the most important factors which play a vital role in the country’s economic health. Forex rates are essential for the businesses during the international trading. Increase in the exchange currency rate is liable for a rise in demand for international trading; as a result, the rise in the profit and stock prices of the firm. Recent studies have proved that correlation exists between the stock prices and forex rates. This research study aims to forecast the US dollar index prices, considering values of four stock indices in the USA as the external factors. Time series models ARIMA and Facebook’s Prophet along with machine learning model Extreme Gradient Boosting and Long Short-Term Memory (LSTM) neural network is applied for the forecasting. Result of these techniques is evaluated using mean absolute percentage error, while the result of multivariate ARIMA is used as a benchmark. Comparing the result of all techniques, we can see that the Prophet model outperformed by achieving the lowest mean absolute percentage error rate following ARIMA and LSTM models. The predicted results are following the actual trend of prices but need to improve the volatility of the output. Based on the findings of this analysis, stock prices can be successfully used to predict US dollar index rates for the USA market.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 20 Jan 2021 17:45
Last Modified: 20 Jan 2021 17:45

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