Research Article
Gold Returns Prediction
@ARTICLE{10.4108/eetsis.3323, author={Ahmet Yavuz and S\'{y}leyman Eken}, title={Gold Returns Prediction}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={10}, number={5}, publisher={EAI}, journal_a={SIS}, year={2023}, month={7}, keywords={gold returns, Machine learning, sentiment analysis, text analysis, Deep learning}, doi={10.4108/eetsis.3323} }- Ahmet Yavuz
Süleyman Eken
Year: 2023
Gold Returns Prediction
SIS
EAI
DOI: 10.4108/eetsis.3323
Abstract
INTRODUCTION: Major events such as economic crises, inflation, geopolitical tensions, and interest rates can have a significant impact on the price and returns of gold. OBJECTIVES: In this work, we focus on gold return prediction in five major events that occurred in Turkey. METHODS: We work on two data, one of which is text-based and the other is financial data. In the financial part, many algorithms are tested and it is found that Extra Trees Regressor gives the best results in most metrics. In text-based part, we first create a new dataset and then implement sentiment analysis and topic modelling. RESULTS: Working on data with two different modes (numeric and text) offers different perspectives. CONCLUSION: The use of sentiment analysis alone to forecast gold returns is not advised, it should be noted. To produce a more precise and trustworthy estimate of gold returns, additional fundamental and technical elements including interest rates, inflation, geopolitical concerns, and supply and demand should also be taken into account.
Copyright © 2023 Yavuz et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
