Forecasting Gold Price Research Articles

Research on gold price prediction based on ARIMA model

Gold is one of the most prevalent currencies in the world and its price has a very strong influence in the global financial markets. Gold has safe-haven properties, which can have a significant impact on its demand and price, especially in times of social unrest or financial crisis. Now, the demand for gold by investors has increased dramatically. Therefore, being able to accurately predict the direction of the gold price can help investors to effectively develop investment strategies and risk management measures. The overall objective of this study is to forecast the price of gold futures for the next six months. In this study, the Kaggle website was searched to find the price of gold from 2020 to 2024 and finally the CLOSE price was chosen as the final predicted price. This paper uses the ARIMA model for gold price forecasting. By comparing the RSME size of each model, ARIMA (1, 1, 2) is finally chosen. From the prediction results the price of gold remains stable in the first half of the year and then increases significantly. From the results of the residual test, there is no autocorrelation, and then it is white noise.

Machine Learning Models for Gold Price Prediction: A Comparative Analysis and Evaluation

The prediction of gold prices is meaningful for multiple industries. Gold is a relatively stable store of value, and its price is closely linked to stocks, exchange rates, and monetary policy. Observing the trend of gold prices can promote the stable development of financial businesses. In this article, the daily opening and closing prices of gold over the past ten years were chosen as the data support. Seven different models were trained to predict prices on a yearly basis with a half-year interval. These predictions were then compared to actual data, and evaluations and analyses were conducted to identify the most suitable model for gold price forecasting. The machine learning models used included Ridge Regression, Linear Regression, Decision Tree Random Forest, Lasso Regression, Support Vector Machine, and K-Nearest Neighbors. In the evaluation process, Mean Squared Error (MSE) was used as the criterion to assess the accuracy of the model predictions.

Indonesia Gold Price Forecasting Using ARIMA Model (0,1,1) - GARCH (1,0)

A frequently employed time series model is the Autoregressive Integrated Moving Average (ARIMA) model. In highly volatile data, ARIMA models sometimes produce residual variances that are heteroscedasticity. One method that can overcome the problem of residual variance heteroscedasticity is the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) method. The purpose of this study is to obtain the ARIMA-GARCH model for daily gold price data in Indonesia for the period 1 January 2022 to 31 December 2022, and to obtain daily gold price forecasting results in Indonesia. The daily gold price forecasting model obtained for Indonesia is ARIMA (0,1,1) - GARCH (1,0) with a MAPE value of 0.5745% which shows that the model is very good because the MAPE value is less than 10%. The results of Indonesia's daily gold price forecast from January 1st, 2023 to January 3rd, 2023 remain stable.

An Enhanced Study on Gold Price Prognosis using Machine Learning

Machine learning has emerged as a prominent research area for predicting gold prices, utilizing historical data and algorithms. The field aims to uncover patterns, trends, and connections among various factors that influence gold prices, including economic indicators, geopolitical events, and supply and demand dynamics. By employing machine learning algorithms, predictive models can be constructed to provide valuable insights into potential patterns in gold price movements. This enables traders, investors, and other stakeholders to make informed decisions when it comes to gold investments. In our study, we delve into the realm of data science and machine learning techniques to forecast gold prices. We meticulously analyze historical gold price data, develop sophisticated forecasting models, and rigorously evaluate their performance. Through this process, we are able to identify meaningful patterns and correlations that significantly contribute to the prediction of future gold prices. One of the key aspects of our study is the assessment of the reliability and accuracy of various machine learning models specifically designed for gold price prediction. We examine different algorithms and approaches, comparing their effectiveness in capturing the underlying patterns in gold price movements. This evaluation provides us with important findings and insights, enabling us to determine the most suitable models for accurate gold price forecasting. However, it is crucial to acknowledge the limitations inherent in our study. The forecasting of gold prices is a complex task influenced by a multitude of factors, some of which may be unpredictable or subject to sudden changes. Therefore, our models may not capture all the nuances and intricacies of gold price dynamics. To address these limitations, we propose recommendations for future research, such as exploring novel data sources, incorporating additional variables, or improving the models' adaptability to changing market conditions. Machine learning plays a pivotal role in the field of gold price prediction. By leveraging historical data and employing sophisticated algorithms, we can uncover valuable insights and patterns that assist in forecasting future gold prices. Our study aims to contribute to this growing body of research by developing reliable models and providing important insights for traders, investors, and other stakeholders in the gold market

Монетарне золото як складова міжнародних резервів центральних банків

Introduction. International reserves of central banks (CBs) are used to support the exchange rate of national currencies and have an impact on the monetary policy. In the unified standards of the IMF, international reserves include only gold, and not all precious metals. The state can call on its reserves in case of emergencies, so they are often compared with the volume of imports. It was natural to expect changes in the structure of international reserves due to the coronavirus pandemic. Problem Statement. Monetary gold does not lose its weight as a stable component of reserves, despite the volatility of world prices and the comparative profitability of other components of these reserves. Countries differ in the dynamics and share of monetary gold in reserves, as well as the possibilities of mining and physical preservation of reserves. A serious problem is the adequate revaluation of precious metals, which affects the high volatility of international reserves of many countries. The purpose of the article is to assess the role of monetary gold as a component of international reserves for the implementation of monetary policy in conditions of systemic stress, such as the coronavirus pandemic. Methods. Comparative and correlational analysis was used to characterize changes in reserves; abstract-logical analysis - to generalize the reasons for differences in the levels of the gold reserve; statistical and economic analysis - to study the influence of these variability factors on gold. Results. The analysis made it possible to distinguish monetary gold from the general circle of precious metals, to structure non-monetary gold. Several types of risks related to the ownership of gold have been singled out, which the Central Bank should take into account when managing reserves: the threat of manipulation by large players on commodity and stock exchanges; high historical and expected price volatility; the probability of loss of gold during storage and transportation due to damage, robbery, counterfeiting, etc. Conclusions. Gold remains a valuable component of the country's currency reserves. At the same time, it should not be considered as an anti-inflation product for the short term, although it retains the status of a reliable asset. There is no significant correlation between the size of the gold reserve and its share in the reserves of the Central Bank. The hypothesis that a higher level of development of the country's economy corresponds to a smaller share of gold was not confirmed. In Ukraine, the National Bank should define, as a component of monetary policy, a separate strategy for monetary gold, taking into account the state of war and the prospects of post-war economic reconstruction. In the management of monetary gold reserves, central banks should take into account the risks inherent in it. At the same time, it can be successfully used to neutralize certain risks of general reserves and liquidity risk, soften fluctuations in the national currency rate and normalize the balance of payments. It is advisable to improve the toolkit of gold price forecasting and stress testing using hypothetical and historical scenarios.

A Novel Deterministic Probabilistic Forecasting Framework for Gold Price with a New Pandemic Index Based on Quantile Regression Deep Learning and Multi-Objective Optimization

The significance of precise gold price forecasting is accentuated by its financial attributes, mirroring global economic conditions, market uncertainties, and investor risk aversion. However, predicting the gold price is challenging due to its inherent volatility, influenced by multiple factors, such as COVID-19, financial crises, geopolitical issues, and fluctuations in other metals and energy prices. These complexities often lead to non-stationary time series, rendering traditional time series modeling methods inadequate. Our paper presents a multi-objective optimization algorithm that refines the interval prediction framework with quantile regression deep learning in response to this issue. This framework comprehensively responds to gold’s financial market dynamics and uncertainties with a screening process of various factors, including pandemic-related indices, geopolitical indices, the US dollar index, and prices of various commodities. The quantile regression deep-learning models optimized by multi-objective optimization algorithms deliver robust, interpretable, and highly accurate predictions for handling non-linear relationships and complex data structures and enhance the overall predictive performance. The results demonstrate that the QRBiLSTM model, optimized using the MOALO algorithm, delivers excellent forecasting performance. The composite indicator AIS reaches −15.6240 and −11.5581 at 90% and 95% confidence levels, respectively. This underscores the model’s high forecasting accuracy and its potential to provide valuable insights for assessing future trends in gold prices. The deterministic and probabilistic forecasting framework for gold prices captures the market dynamics with the new pandemic index and comprehensively sets a new benchmark for predictive modeling in volatile market commodities like gold.

Gold Price Fluctuation Forecasting Based on Newton and Lagrange Polynomial Interpolation

Gold is a highly valuable commodity and an investment opportunity for many people. However, thereare often significant fluctuations in gold prices that affect investment decisions. Various mathematicalforecasting methods have been developed to anticipate gold price fluctuations. This study uses historicaldaily data of gold prices during January-May 2023. The method used in this study is the Newton andLagrange polynomial interpolation method with several orders to analyze data and forecast gold pricefluctuations. The purpose of this study is to compare the performance and accuracy of the order levels ofthe Newton and Lagrange polynomial interpolation forecasting models. In this study, the test data pointsand orders are selected so that a range is formed that matches the amount of data available. The testorders used in this study include orders 2, 3, 5, 6, and 10. This study found that the 2nd order polynomialinterpolation method is more effective and accurate in forecasting gold price fluctuations compared tothe higher orders tested. This is indicated by the results of the calculation of MAE, RMSE, and MAPEvalues in 2nd order polynomial interpolation which are smaller than in 3rd, 5th, 6th, and 10th orderpolynomial interpolation. This suggests that a polynomial of 2nd order has been able to model andforecast gold price fluctuations well. However, it is important to remember that these conclusions arebased on the data and methods used in this study. Variability in forecasting results can occur dependingon the quality of the data, the time period used, and the interpolation method applied, among others.Therefore, further research and wider testing needs to be conducted to validate these conclusions.

Forecasting gold price using machine learning methodologies

This study investigates the potential of advanced Machine Learning (ML) methodologies to predict fluctuations in the price of gold. The study employs data from leading global stock indices, the S&P500 VIX volatility index, major commodity futures, and 10-year bond yields from the US, Germany, France, and Japan. Lagged values of these features up to 10 previous days are also used. Four machine learning models are used: Random Forest, Gradient Boosted Regression Trees (GBRT), and Extreme Gradient Boosting (XGBoost), to forecast future gold prices. The study finds that the most influential stocks indices for prediction are one-day lagged data of ASX, S&P500, TA35, IBEX, and AEX, as well as U.S. and Japan bonds yields and delayed data of gas and silver. Furthermore, the study's models identify that one-day lagged VIX score and our VIX dummy variable have a significant impact on gold price, indicating that economic uncertainty affects gold prices. The results suggest that incorporating various financial indicators and moving averages can be a powerful tool for predicting future gold prices. GBRT and XGBoost can be valuable models for making informed decisions about gold investments.

IMPLEMENTASI METODE FUZZY TIME SERIES PADA PERAMALAN HARGA EMAS DI INDONESIA

Changes in gold prices in Indonesia continue to occur from time to time with uncertain changes and can be said to be volatile. With the condition of the volatile data on changes in gold prices, predictions are needed that are appropriate or close to the existing data. Chen's fuzzy time series method can be used as a solution to this problem because it has a low error rate. The purpose of this study was to predict the price of gold in Indonesia in the coming period by implementing the Chen fuzzy time series method, these steps can be solved using R software. The data used in this study were daily gold price data in Indonesia for the period January 1, 2022, to June 30, 2022. The results obtained are gold price forecasts for the period July 1, 2022, amounting to Rp. 988,313 and the MAPE value of 0.60515%. The MAPE value indicates that Chen's fuzzy time series method was very well used to forecast Indonesias gold price data.

Optimization of Backpropagation Using Harmony Search for Gold Price Forecasting

Gold is a precious metal often used for investment, due to its cash-in ease and yearly value increase. This indicates that price forecasting is used to determine the prospect of future gold prices. Strong gold price forecasting is highly desired by investors to make decisions. That is why technical indicators are very important used for forecasting. By using technical indicators the information obtained can be more informative than using pure gold prices. One of the commonly used methods is Backpropagation (BP). BP has been shown to have good performance in dealing with nonlinear problems. However, due to the random determination of the parameters of neurons in the hidden layer BP requires a number of neurons in the hidden layer to get optimal results. Therefore, this study aims to analyze the optimization of Backpropagation (BP) through the Harmony Search (HS) algorithm by evaluating the use of relevant technical indicators for forecasting gold prices. In the HS-BP model, this method is used to determine input variables and neurons in the hidden layer. HS with the principle of musicians with the aim of finding the best harmony. This technique is used based on the results of the fitness function. In this research, the fitness function used is Mean Square Error (MSE). HS aims to optimize BP in such a way that the forecasting system provides the lowest MSE and improves the forecasting performance of gold prices. Based on this research, the input variables used are Moving Average, Relative Strength Index, and Bollinger Bands. Next, the selected variables and neurons are applied to the BP algorithm. Where the implementation uses gold closing price data for January 2020-2021. The results showed that the proposed method has better results in forecasting accuracy and convergence error. HS-BP provides a better level of gold price forecasting than the regular BP model.

Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM

Gold price has always played an important role in the world economy and finance. In order to predict the gold price more accurately, this paper proposes a novel decomposition-ensemble model. Firstly, the original gold prices are decomposed into sublayers with different frequencies by the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). Secondly, the long short-term memory, convolutional neural networks, and convolutional block attention module (LSTM-CNN-CBAM) joint forecasting all sublayers. Finally, the prediction of the sublayers with different models is reconstructed as the final predicted results with the summation method. Among them, the proposed model could capture the essence of sequence effectively through ICEEMDAN algorithm, extract the long-term effect of the gold price by LSTM, mining the deep features of gold price data with CNN, and improving the feature extraction ability of the network through CBAM. It is proved by experiment that the cooperation among LSTM, CNN and CBAM can strengthen the modeling ability and improve the prediction accuracy. Moreover, the decomposition algorithm ICEEMDAN can further increase the forecast precision, and the prediction effect is better than other decomposition methods. Overall, the novel model ICEEMDAN-LSTM-CNN-CBAM (ILCC) could enhance the prediction accuracy and outperform other related comparative models.

GSTARIMA Model with Missing Value for Forecasting Gold Price

Gold is one of the investments that be a great demand. Selecting and applying the best GSTARIMA model for gold price forecasting was the aim of this study. However, the gold price data that has been obtained missing values. Missing value data has been imputed by the last data before the missing value and moving average techniques. The GSTAR (1) and GSTARI (1, 1) models have been combined with an imputation technique solved this problem. Based on the smallest RMSE value, the GSTARI (1, 1) model which has been combined with the imputation technique that used the last value was the best method because it produced the smallest RMSE when compared to other methods. Forecasting results shown that gold prices in the United States, United Kingdom, and Indonesia increased but gold prices in Turkey actually decreased. Forecasting gold prices in each of these countries become one of the references in investing in gold. Based on the results of gold price forecasting, gold prices changed but not significantly.

Forecasting gold price with the XGBoost algorithm and SHAP interaction values

Forecasting gold price with the XGBoost algorithm and SHAP interaction values

A Deep Learning-Based Method for Forecasting Gold Price with Respect to Pandemics.

The spread of COVID-19 has had a devastating impact on the world economy, international trade relations, and globalization. As this pandemic advances and new potential pandemics are on the horizon, a precise analysis of recent fluctuations of trade becomes necessary for international decisions and controlling the world in a similar crisis. The COVID-19 pandemic made a new pattern of trade in the world and affected how businesses work and trade with each other. It means that every potential pandemic or any unprecedented event in the world can change the market rules. This research develops a novel model to have a proper estimation of the stock market values with respect to the COVID-19 dataset using long short-term memory networks (LSTM). The goal of this study is to establish a model that can predict near future regarding the variable set of features. The nature of the features in each pandemic is completely different; therefore, prediction results for a pandemic by a specific model cannot be applied to other pandemics. Hence, recognizing and extracting the features which affect the pandemic is pivotal. In this study, we develop a framework that provides a better understanding of the features and feature selection process. Although the global impacts of COVID-19 are complicated, we are trying to show how additional features like COVID-19 cases can help to forecast in a real-world scenario, rather than relying solely on the history of tickers, which is used conventionally for prediction. This study is based on a preliminary analysis of features such as COVID-19 cases and other market tickers for enhancing forecasting models’ performance against fluctuations in the market. Our predictors are based on the market value data and COVID-19 pandemic daily time-series data (i.e. the number of new cases). In this study, we selected Gold price as a base for our forecasting task which can be replaced by any other markets. We have applied Convolutional Neural Networks (CNN) LSTM, vector sequence output LSTM, Bidirectional LSTM, and encoder–decoder LSTM on the dataset. The results of the vector sequence output LSTM achieved an MSE of 6.0e-4, 8.0e-4, and 2.0e-3 on the validation set respectfully for 1 day, 2 days, and 30 days predictions in advance which are outperforming other proposed method in the literature.

Modeling and Forecasting Gold Prices

The aim of this paper is to explore the reasons of gold price volatility. It analyses the information function of the gold future market by open interest contracts as speculation effect, and further fundamental factors including inflation, Chinese yuan per dollar, Japanese yen per dollar, dollar per euro, interest rate, oil price, and stock price, in the short-run. The study proceeds to build a Dynamic OLS model for long-run equilibrium to produce reliable gold price forecasts using the following variables: gold demand, gold supply, inflation, USD/SDR exchange rate, speculation, interest rate, oil price, and stock prices. Findings prove that in the short-run, changes in gold price does granger cause changes in open interest, and changes in Japanese yen per dollar does granger cause changes in gold price. However, in the long-run, the results prove that gold demand, gold supply, USD/SDR exchange rate, inflation, speculation, interest rate, and oil price are associated in a long-run relationship.

Deep belief network for gold price forecasting

Fluctuations in gold price have historically attracted the attention of governments, institutions and individuals alike. Accurate forecasting of gold price can effectively capture the price change trend and reduce the impact of gold market fluctuations. However, this is challenging work due to the multi-factors and nonlinear feature of the gold market. In this paper, a deep belief network (DBN) model, composed of restricted Boltzmann machines (RBM) for pre-training and a layer of supervised back-propagation (BP) for fine-tuning, is proposed for gold price forecasting. Several highly related variables of gold price were used as inputs to build this DBN model for forecasting over the years 1984–2019. The obtained results were compared with those of the traditional BP neural network model, Genetic algorithm optimization for BP neural network model (GA-BP) and the mathematical linear model of Autoregressive Integrated Moving Average (ARIMA). The empirical results show that the proposed DBN model has an outstanding performance in prediction and direction, with the lowest Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE), and the highest directional statistic (Dstat). Overall, the novel DBN has improved accuracy compared to the traditional neural network and linear models, distinguishing itself as a very promising methodology for gold price forecasting.

Forecasting Short Run Gold Spot Price in India Using Mixed Frequency Models

Consumers, as well as investors, rely on bullion price forecasts for making sound investment decisions in the spot as well as futures markets. Enhanced accuracy of forecasts bodes well for the efficiency of markets as it facilitates rational expectations by agents in the economy which in turn may improve policy transmission. The usual time series forecasting methods require the dependent variables and independent variables to be of the same frequency e.g. to forecast monthly gold price, we require independent variables to be available at monthly frequency even if they are available at a higher frequency(daily or hourly). This is accomplished using aggregation of data which leads to loss of information. Such high-frequency data contains critical signals on the evolution of the dependent variable which can be leveraged to improve the forecast accuracy. In this work, I used a mixed frequency forecasting model for forecasting gold price in India using crude oil prices, stock index and USD-INR exchange rate as high-frequency predictors. I further demonstrate that the forecast accuracy achieved using the Mixed Data Sampling(MIDAS) regression outperforms the forecasts achieved using ARIMA and ARIMAX models.

ANN based method for improving gold price forecasting accuracy through modified gradient descent methods

Forecast of prices of financial assets including gold is of considerable importance for planning the economy. For centuries, people have been holding gold for many important reasons such as smoothening inflation fluctuations, protection from an economic crisis, sound investment etc.. Forecasting of gold prices is therefore an ever important exercise undertaken both by individuals and groups. Various local, global, political, psychological and economic factors make such a forecast a complex problem. Data analysts have been increasingly applying Artificial Intelligence (AI) techniques to make such forecasts. In the present work an inter comparison of gold price forecasting in Indian market is first done by employing a few classical Artificial Neural Network (ANN) techniques, namely Gradient Descent Method (GDM), Resilient Backpropagation method (RP), Scaled Conjugate Gradient method (SCG), Levenberg-Marquardt method (LM), Bayesian Regularization method (BR), One Step Secant method (OSS) and BFGS Quasi Newton method (BFG). Improvement in forecasting accuracy is achieved by proposing and developing a few modified GDM algorithms that incorporate different optimization functions by replacing the standard quadratic error function of classical GDM. Various optimization functions investigated in the present work are Mean median error function (MMD), Cauchy error function (CCY), Minkowski error function (MKW), Log cosh error function (LCH) and Negative logarithmic likelihood function (NLG). Modified algorithms incorporating these optimization functions are referred to here by GDM_MMD, GDM_CCY, GDM_KWK, GDM_LCH and GDM_NLG respectively. Gold price forecasting is then done by employing these algorithms and the results are analysed. The results of our study suggest that the forecasting efficiency improves considerably on applying the modified methods proposed by us.

A deep residual compensation extreme learning machine and applications

AbstractThe extreme learning machine (ELM) is a type of machine learning algorithm for training a single hidden layer feedforward neural network. Randomly initializing the weight between the input layer and the hidden layer and the threshold of each hidden layer neuron, the weight matrix of the hidden layer can be calculated by the least squares method. The efficient learning ability in ELM makes it widely applicable in classification, regression, and more. However, owing to some unutilized information in the residual, there are relatively huge prediction errors involving ELM. In this paper, a deep residual compensation extreme learning machine model (DRC‐ELM) of multilayer structures applied to regression is presented. The first layer is the basic ELM layer, which helps in obtaining an approximation of the objective function by learning the characteristics of the sample. The other layers are the residual compensation layers in which the learned residual is corrected layer by layer to the predicted value obtained in the previous layer by constructing a feature mapping between the input layer and the output of the upper layer. This model is applied to two practical problems: gold price forecasting and airfoil self‐noise prediction. We used the DRC‐ELM with 50, 100, and 200 residual compensation layers respectively for experiments, which show that DRC‐ELM does better in generalization and robustness than classical ELM, improved ELM models such as GA‐RELM and OS‐ELM, and other traditional machine learning algorithms such as support vector machine (SVM) and back‐propagation neural network (BPNN).

Forecasting Gold Price using Data Mining Techniques by Considering New Factors

Gold price forecast is of great importance. Many models were presented by researchers to forecast gold price. It seems that although different models could forecast gold price under different conditions, the new factors affecting gold price forecast have a significant importance and effect on the increase of forecast accuracy. In this paper, different factors were studied in comparison to the previous studies on gold price forecast. In terms of time span, the collected data were divided into three groups of daily, monthly and annually. The conducted tests using new factors indicate accuracy improvement up to 2% in neural networks methods, 7/3% in time series method and 5/6% in linear regression method.