From Alchemy to Analytics: Unleashing the Potential of Technical Analysis in Predicting Noble Metal Price Movement
Marcin Chlebus () and
Artur Nowak ()
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Marcin Chlebus: University of Warsaw, Faculty of Economic Sciences
Artur Nowak: University of Warsaw, Faculty of Economic Sciences
No 2023-13, Working Papers from Faculty of Economic Sciences, University of Warsaw
Abstract:
Algorithmic trading has been a central theme in numerous research papers, combining knowledge from the fields of Finance and Mathematics. This thesis aimed to apply basic Technical Analysis indicators for predicting price movement of three noble metals: Gold, Silver, and Platinum in a form of multi-class classification. That task was performed using four algorithms: Logistic Regression, k-Nearest Neighbors, Random Forest and XGBoost. The study incorporated feature filtering methods such as Kendall-tau filtering and PCA, as well as five different data frequencies: 1, 5, 10, 15 and 20 trading days. From a total of 40 potential models for each metal, the best one was selected and evaluated using data from period 2018-2022. The result revealed that models utilizing only Technical Analysis indicators were able to predict price movements to a significant extent, leading to investment strategies that outperformed the market in two out of three cases.
Keywords: precious metals; algotrading; machine learning; multiclass classification; logistic regression; nearest neighbors; random forest; xgboost (search for similar items in EconPapers)
JEL-codes: C38 C51 C52 C58 G17 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2023
New Economics Papers: this item is included in nep-big and nep-cmp
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https://www.wne.uw.edu.pl/download_file/2837/0 First version, 2023 (application/pdf)
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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2023-13
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