ETF Markets’ Prediction & Assets Management Platform Using Probabilistic Autoregressive Recurrent Networks

Waleed Mahmoud Solıman; Zhiyuan Chen; Colin Johnson; Sabrina Wong

doi:10.55549/epstem.1372067

Conference Paper

ETF Markets’ Prediction & Assets Management Platform Using Probabilistic Autoregressive Recurrent Networks

Year 2023, Volume: 23, 485 - 494, 30.09.2023

Waleed Mahmoud Solıman Zhiyuan Chen Colin Johnson Sabrina Wong

https://doi.org/10.55549/epstem.1372067

Abstract

The significance of macroeconomic policy changes on ETF markets and financial markets cannot be disre-garded. This study endeavors to predict the future trend of these markets by incorporating a group of selected economic indicators sourced from various ETF markets and utilizing probabilistic autoregressive recurrent net-works (DeepAR). The choice of economic indicators was made based on the advice of a domain expert and the results of correlation estimation. These indicators were then divided into two categories: "US" indicators, which depict the impact of US policies such as the federal reserve fund rate and quantitative easing on the global markets, and "region-specific" indicators. The findings of the study indicate that the inclusion of "US" indicators enhances the prediction accuracy and that the DeepAR model outperforms the LSTM and GRU models. Fur-thermore, a web platform has been developed to apply the DeepAR models, which enables the user to predict the trend of an ETF ticker for the next 15 time-steps using the most recent data. The platform also possesses the capability to automatically generate fresh datasets from corresponding RESTful API sources in case the current data becomes outdated.

Keywords

Macroeconomic policy , ETF markets , Financial markets , DeepAR , Economic indicators , Machine learning

References

Arbel, N. (2018, December 21). How LSTM networks solve the problem of vanishing gradients. https://medium.datadriveninvestor.com/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577
C3 AI. (2022). Root Mean Square Error (RMSE). https://c3.ai/glossary/data-science/root-mean-square-error-rmse/
Chen, J. (2022, October 22). What is an exchange-traded fund (ETF)? Investopedia. https://www.investopedia.com/terms/e/etf.asp

Year 2023, Volume: 23, 485 - 494, 30.09.2023

Waleed Mahmoud Solıman Zhiyuan Chen Colin Johnson Sabrina Wong

https://doi.org/10.55549/epstem.1372067

Abstract

References

Arbel, N. (2018, December 21). How LSTM networks solve the problem of vanishing gradients. https://medium.datadriveninvestor.com/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577
C3 AI. (2022). Root Mean Square Error (RMSE). https://c3.ai/glossary/data-science/root-mean-square-error-rmse/
Chen, J. (2022, October 22). What is an exchange-traded fund (ETF)? Investopedia. https://www.investopedia.com/terms/e/etf.asp

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Details

Primary Language	English
Subjects	Environmental and Sustainable Processes
Journal Section	Articles
Authors	Waleed Mahmoud Solıman Zhiyuan Chen Colin Johnson Sabrina Wong
Early Pub Date	October 6, 2023
Publication Date	September 30, 2023
Published in Issue	Year 2023 Volume: 23

Cite

APA	Solıman, W. M., Chen, Z., Johnson, C., Wong, S. (2023). ETF Markets’ Prediction & Assets Management Platform Using Probabilistic Autoregressive Recurrent Networks. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 23, 485-494. https://doi.org/10.55549/epstem.1372067