Gold Returns Prediction
Assessment based on Major Events
DOI:
https://doi.org/10.4108/eetsis.3323Keywords:
gold returns, Machine learning, sentiment analysis, text analysis, Deep learningAbstract
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.
References
Akgul, H.G.: Fake news as a tool of populism in turkey: The pastor andrew brunson case. Polish Political Science Review 7(2), 32–51 (2019)
Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 international conference on engineering and technology (ICET). pp. 1–6. IEEE (2017)
Benlagha, N., El Omari, S.: Connectedness of stock markets with gold and oil: New evidence from covid-19 pandemic. Finance Research Letters 46, 102373 (2022)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifers. In: Proceedings of the ffth annual workshop on Computational learning theory. pp. 144–152 (1992)
Chen, Y.C., Huang, W.C.: Constructing a stock-price forecast cnn model with gold and crude oil indicators. Applied Soft Computing 112, 107760 (2021)
Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Ding, X., Wang, M.: The effectiveness of foreign exchange intervention: Empirical evidence from vietnam. The Journal of Asian Finance, Economics and Business 9(2), 37–47 (2022)
Ekinci, E., Omurca, S.I.: Net-lda: a novel topic modeling method based on semantic document similarity. Turkish Journal of Electrical Engineering & Computer Sciences 28(4), 2244–2260 (2020)
Gkillas, K., Gupta, R., Pierdzioch, C.: Forecasting realized gold volatility: Is there a role of geopolitical risks? Finance Research Letters 35, 101280 (2020)
Hochreiter, S., Schmidhuber, J.: Lstm can solve hard long time lag problems. Advances in neural information processing systems 9 (1996)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization.Nature 401(6755), 788–791 (1999)
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Malliaris, A.G., Malliaris, M.: What drives gold returns? a decision tree analysis. Finance Research Letters 13, 45–53 (2015)
Ramchoun, H., Ghanou, Y., Ettaouil, M., Janati Idrissi, M.A.: Multilayer perceptron: Architecture optimization and training (2016)
Risse, M.: Combining wavelet decomposition with machine learning to forecast gold returns. International Journal of Forecasting 35(2), 601–615 (2019)
Van Houdt, G., Mosquera, C., Napoles, G.: A review on the long short-term memory model. Artifcial Intelligence Review 53, 5929–5955 (2020)
Vapnik, V.N.: Adaptive and learning systems for signal processing communications, and control. Statistical learning theory (1998)
Yavuz, M.H., Balci, B.: Turkey’s July 15th Coup: What Happened and Why. University of Utah Press Salt Lake City (2018)
Bentes, Sonia R.: Forecasting volatility in gold returns under the GARCH, IGARCH and FIGARCH frameworks: New evidence. Physica A: Statistical Mechanics and its Applications 438, 355-364 (2015)
Chai, Jian, et al.: Structural analysis and forecast of gold price returns. Journal of Management Science and Engineering 6.2, 135-145 (2021)
Plakandaras, Vasilios, and Qiang Ji.: Intrinsic decompositions in gold forecasting. Journal of Commodity Markets 28, 100245 (2022)
Jiang, Weiwei.: Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications 184, 115537 (2021)
Lu, Wenjie, et al.: A CNN-BiLSTM-AM method for stock price prediction. Neural Computing and Applications 33, 4741-4753 (2021)
Livieris, Ioannis E., Emmanuel Pintelas, and Panagiotis Pintelas.: A CNN–LSTM model for gold price time-series forecasting. Neural computing and applications 32, 17351-17360 (2020)
Wang, Qinan, and Weiwei Jiang.: Beat Wash-Sale Tax with Multigraph Convolutional Neural Networks Based Trading Strategy. Security and Communication Networks 2022 (2022)
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