AI/Machine learning approach to identifying potential statistical arbitrage opportunities with FX and Bitcoin Markets
| dc.contributor.advisor | Georg, Co-Pierre | |
| dc.contributor.author | Ntsaluba, Kuselo Ntsika | |
| dc.date.accessioned | 2020-02-20T09:42:27Z | |
| dc.date.available | 2020-02-20T09:42:27Z | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2020-02-14T08:12:15Z | |
| dc.description.abstract | In this study, a methodology is presented where a hybrid system combining an evolutionary algorithm with artificial neural networks (ANNs) is designed to make weekly directional change forecasts on the USD by inferring a prediction using closing spot rates of three currency pairs: EUR/USD, GBP/USD and CHF/USD. The forecasts made by the genetically trained ANN are compared to those made by a new variation of the simple moving average (MA) trading strategy, tailored to the methodology, as well as a random model. The same process is then repeated for the three major cryptocurrencies namely: BTC/USD, ETH/USD and XRP/USD. The overall prediction accuracy, uptrend and downtrend prediction accuracy is analyzed for all three methods within the fiat currency as well as the cryptocurrency contexts. The best models are then evaluated in terms of their ability to convert predictive accuracy to a profitable investment given an initial investment. The best model was found to be the hybrid model on the basis of overall prediction accuracy and accrued returns. | |
| dc.identifier.apacitation | Ntsaluba, K. N. (2019). <i>AI/Machine learning approach to identifying potential statistical arbitrage opportunities with FX and Bitcoin Markets</i>. (). ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management. Retrieved from http://hdl.handle.net/11427/31185 | en_ZA |
| dc.identifier.chicagocitation | Ntsaluba, Kuselo Ntsika. <i>"AI/Machine learning approach to identifying potential statistical arbitrage opportunities with FX and Bitcoin Markets."</i> ., ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2019. http://hdl.handle.net/11427/31185 | en_ZA |
| dc.identifier.citation | Ntsaluba, K. 2019. AI/Machine learning approach to identifying potential statistical arbitrage opportunities with FX and Bitcoin Markets. | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Ntsaluba, Kuselo Ntsika AB - In this study, a methodology is presented where a hybrid system combining an evolutionary algorithm with artificial neural networks (ANNs) is designed to make weekly directional change forecasts on the USD by inferring a prediction using closing spot rates of three currency pairs: EUR/USD, GBP/USD and CHF/USD. The forecasts made by the genetically trained ANN are compared to those made by a new variation of the simple moving average (MA) trading strategy, tailored to the methodology, as well as a random model. The same process is then repeated for the three major cryptocurrencies namely: BTC/USD, ETH/USD and XRP/USD. The overall prediction accuracy, uptrend and downtrend prediction accuracy is analyzed for all three methods within the fiat currency as well as the cryptocurrency contexts. The best models are then evaluated in terms of their ability to convert predictive accuracy to a profitable investment given an initial investment. The best model was found to be the hybrid model on the basis of overall prediction accuracy and accrued returns. DA - 2019 DB - OpenUCT DP - University of Cape Town KW - Financial Technology LK - https://open.uct.ac.za PY - 2019 T1 - AI/Machine learning approach to identifying potential statistical arbitrage opportunities with FX and Bitcoin Markets TI - AI/Machine learning approach to identifying potential statistical arbitrage opportunities with FX and Bitcoin Markets UR - http://hdl.handle.net/11427/31185 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/31185 | |
| dc.identifier.vancouvercitation | Ntsaluba KN. AI/Machine learning approach to identifying potential statistical arbitrage opportunities with FX and Bitcoin Markets. []. ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2019 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/31185 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | African Institute of Financial Markets and Risk Management | |
| dc.publisher.faculty | Faculty of Commerce | |
| dc.subject | Financial Technology | |
| dc.title | AI/Machine learning approach to identifying potential statistical arbitrage opportunities with FX and Bitcoin Markets | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MPhil |