Forecasting gold price using artificial neural networks : a comparative study with linear regression and strategy performance analysis

การพยากรณ์ราคาทองคำโดยใช้โครงข่ายประสาทเทียม : การเปรียบเทียบกับการถดถอยเชิงเส้นและการวิเคราะห์ประสิทธิภาพของกลยุทธ์

Name: Gasidis Taeng-on

keyword: Gold price forecasting

; Artificial neural networks (ANNs)

; Trading strategy

; Threshold-based strategy

; Moving averages

Abstract: This study investigates the application of Artificial Neural Networks (ANNs) and Linear Regression models for short-term gold price forecasting. Using technical indicators such as the 7-day and 30-day moving averages (MA7 and MA30), along with daily returns, both models were trained to predict next-day gold prices. The ANN architecture was developed through a trial-and-error process, while the regression model served as a benchmark. Model performance was evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), along with a rule-based trading strategy that incorporated predicted returns. The ANN model had a higher RMSE (305.65) than the Linear Regression model (30.82) and underperformed in trading, resulting in a cumulative loss of –52.91%. In contrast, the regression-based strategy delivered a profit of 150.56%, based on the total net return from the same trading rules.

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Rujira Chaysiri

Role: advisor

Created: 2025

Modified: 2025-11-24

Issued: 2025-11-24

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2024.513

eng

DegreeName: Master of Engineering

Level: Master's Degree

Descipline: Logistics and Supply Chain Systems Engineering

Grantor: Thammasat University

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