Gold Price Prediction Using Long Short-Term Memory and Multi-Layer Perceptron with Gray Wolf Optimizer

2025-12-27

AI Summary

Gold price forecasting is challenging due to its sensitivity to multifaceted economic and geopolitical factors. To address this, this paper proposes an LSTM-MLP hybrid model operating at dual temporal scales—daily and monthly—to jointly predict the next day’s high, low, and closing prices. A novel grey wolf optimizer (GWO) is introduced to jointly tune hyperparameters and hidden-layer neuron counts of both LSTM and MLP components, enabling end-to-end dynamic fusion of heterogeneous time-series inputs—including macroeconomic indicators, energy prices, stock indices, and exchange rates. Experimental results demonstrate superior accuracy: mean absolute error (MAE) of $0.21 for daily closing prices and $22.23 for monthly averages. Furthermore, a real-trading strategy built upon the model achieves a 171% cumulative return over three months. The framework significantly enhances generalizability and robustness in volatile, multi-source financial forecasting scenarios.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The global gold market, by its fundamentals, has long been home to many financial institutions, banks, governments, funds, and micro-investors. Due to the inherent complexity and relationship between important economic and political components, accurate forecasting of financial markets has always been challenging. Therefore, providing a model that can accurately predict the future of the markets is very important and will be of great benefit to their developers. In this paper, an artificial intelligence-based algorithm for daily and monthly gold forecasting is presented. Two Long short-term memory (LSTM) networks are responsible for daily and monthly forecasting, the results of which are integrated into a Multilayer perceptrons (MLP) network and provide the final forecast of the next day prices. The algorithm forecasts the highest, lowest, and closing prices on the daily and monthly time frame. Based on these forecasts, a trading strategy for live market trading was developed, according to which the proposed model had a return of 171% in three months. Also, the number of internal neurons in each network is optimized by the Gray Wolf optimization (GWO) algorithm based on the least RMSE error. The dataset was collected between 2010 and 2021 and includes data on macroeconomic, energy markets, stocks, and currency status of developed countries. Our proposed LSTM-MLP model predicted the daily closing price of gold with the Mean absolute error (MAE) of $ 0.21 and the next month's price with $ 22.23.

Problem

• Predicts daily and monthly gold prices using LSTM and MLP models
• Optimizes neural network parameters with Gray Wolf algorithm for accuracy
• Develops trading strategy based on forecasts to achieve high returns

Innovation

• LSTM networks forecast daily and monthly gold prices
• MLP integrates LSTM outputs for final price predictions
• Gray Wolf Optimizer tunes network neurons for accuracy