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Chibaya, Colin Ochara, N. M. Nemavhola, Andisani 2021 2022-08-24T19:01:56Z 2022-08-24T19:01:56Z 2022-07-15 Nemavhola, A. (2021) Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates. University of Venda. South Africa.<http://hdl.handle.net/11602/2256>. http://hdl.handle.net/11602/2256 Nemavhola A. Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates. []. , 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11602/2256 Nemavhola, A. (2022). <i>Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates</i>. (). . Retrieved from http://hdl.handle.net/11602/2256 Nemavhola, Andisani. <i>"Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates."</i> ., , 2022. http://hdl.handle.net/11602/2256 TY - Dissertation AU - Nemavhola, Andisani AB - The global foreign currency exchange (Forex) market is regarded as one of the most important financial markets in the world, with daily transactions exceeding $4 trillion. In financial market research, forecasting currency rates is a crucial problem. Forex is notorious for being very volatile and difficult to forecast. In this study, we investigated the use of deep learning approaches in forex forecasting and compared the success of the Long Short-Term Memory (LSTM) model to the performance of AutoRegressive Integrated Moving Average (ARIMA) and Support vector regression (SVR) when predicting forex rates of US Dollar (USD) pair with South African Rand (ZAR) using daily timeframe data obtained from the Metatrader trading platform. The LSTM outperformed the SVR and ARIMA models according to MSE data. The LSTM is typically good for predicting USDZAR speeds, although being surpassed by the ARIMA model when the Mean Absolute Error (MAE) was assessed. DA - 2022-07-15 DB - ResearchSpace DP - Univen KW - AutoRegressive Integrated Moving Average KW - Long Short-Term Memory KW - Mean Absolute Error KW - Mean Squared Error KW - Support Vector Regression LK - https://univendspace.univen.ac.za PY - 2022 T1 - Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates TI - Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates UR - http://hdl.handle.net/11602/2256 ER - MCom Department of Business Information Systems The global foreign currency exchange (Forex) market is regarded as one of the most important financial markets in the world, with daily transactions exceeding $4 trillion. In financial market research, forecasting currency rates is a crucial problem. Forex is notorious for being very volatile and difficult to forecast. In this study, we investigated the use of deep learning approaches in forex forecasting and compared the success of the Long Short-Term Memory (LSTM) model to the performance of AutoRegressive Integrated Moving Average (ARIMA) and Support vector regression (SVR) when predicting forex rates of US Dollar (USD) pair with South African Rand (ZAR) using daily timeframe data obtained from the Metatrader trading platform. The LSTM outperformed the SVR and ARIMA models according to MSE data. The LSTM is typically good for predicting USDZAR speeds, although being surpassed by the ARIMA model when the Mean Absolute Error (MAE) was assessed. NRF 1 online resource (xii, 90 leaves) : color illustrations en University of Venda AutoRegressive Integrated Moving Average UCTD Long Short-Term Memory Mean Absolute Error Mean Squared Error Support Vector Regression 332.456 Foreign exchange market Foreign exchange Foreign exchange rates Application of Deep Neural Networks in Forecasting Foreign Currency Exchange rates Dissertation open.access