Future Gold Prices Research Articles

Gold’s Value as an Investment

Abstract This paper presents an approach for pricing gold from investors’ perspective. The model is based on no-arbitrage principles with minimal structural assumptions. There is no need to specify investor preferences. When fitted to match 10-year real U.S. Treasury rates, the model can replicate the salient fluctuations in the time series of gold prices since 2007. The model is also able to capture key patterns of CME Comex gold futures prices. The model implies that the majority of the value of gold is due to its role as an investment asset. (JEL G12, G13)

The Fluctuation Characteristics and Risk Investigation of China's Gold Future Market

This paper deeply explores the fluctuation characteristics and risks of China's gold futures market. Through the collection of data on macroeconomic factors, stock index interest rates, and energy futures, the paper employs multiple linear regression models and intermediary variable models to quantitatively analyze the impact of these factors on the volatility of gold futures prices. The research finds that there is a significant positive correlation between the gold futures price and the growth of the U.S. GDP and the silver futures price index, while the impact of other macroeconomic indicators such as China's gold reserves and the inflation rate is not significant. Policy support and market regulation play a key role in the healthy development of the market, and the broad participation of market participants and the innovation of financial products have driven the diversification of the market. The intermediary effect analysis shows that the impact of macroeconomic factors on the gold futures price is partially indirect through the intermediary variable of stock index interest rates.

Textual analysis and gold futures price forecasting: Evidence from the Chinese market

This paper examines the predictive capacity of online news on the gold futures prices. The empirical results derived from the Chinese market demonstrate that the textual features extracted through natural language processing techniques contain complementary predictive content for gold futures prices, which enhance the 1-day ahead prediction accuracy across different machine learning methods and train-test sets.

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

Application of the Support Vector Regression Method with the Grid Search Algorithm to Predict Movement Gold Price

Gold is an investment with the smallest risk because it can be sold anytime and anywhere. In Indonesia, gold bullion as an investment product is known for its purity level of 99.99%, namely gold bullion produced by PT. Aneka Tambang (Antam) through its Precious Metals business unit. Apart from its pure production, Antam gold bullion is easier to resell anytime and anywhere because it has an official certificate from the international gold standardisation institution, namely LBMA (London Bullion Market Association), to more easily estimate the value of gold bullion when sold. To overcome this, predictions of future gold prices are needed. In this research, one of the prediction methods is Support Vector Regression with the Grid Search Algorithm. In this method this method will be used to predict the price of gold, which aims to predict and find out the price of gold one year in the future to produce a level accuracy (MAPE) of 5.43% and the prediction of gold prices increasing from 2023-June-01 to 2024-March-23 while experiencing a decline starting in 2024-March-24. Research by examining the relationship between variables, which emphasises data consisting of numbers so that it is analysed based on statistical procedures using the Support Vector Regression method with data sourced from the daily price of gold bullion through PT. Gallery 24 Pawnshops, North Sumatra. Where this method is very well used in predicting by choosing the best kernel used is the linear kernel because, from these three kernels, the best hyperparameters were obtained for predicting gold price movements using a linear kernel with a division for training and testing data of 60: 40. The MAPE value obtained was 5.43.

Fluctuation and forecasting of gold prices in Saudi Arabia’s market

PurposeThe purpose of this study is to analyze the fluctuations in gold prices within the Saudi Arabian market and to develop a reliable forecasting model to aid market participants and policymakers in making informed decisions.Design/methodology/approachIn this study, we employ a rigorous time series analysis methodology, including the ARIMA (Auto Regressive Integrated Moving Average) model, to analyze historical gold price data in the Saudi Arabian market. The approach involves identifying optimal model parameters and assessing forecast accuracy to provide actionable insights for market participants.FindingsThe study showcases that the autoregressive properties of past gold prices play a pivotal role in capturing the inherent serial correlation within the market, enabling the ARIMA model to effectively forecast future gold price movements with accuracy.Research limitations/implicationsOur study primarily focuses on quantitative analysis, whereas few qualitative parameters are not included. Future studies may benefit from incorporating qualitative factors and expert opinions to enhance the robustness of gold price predictions and capture the full spectrum of market dynamics.Social implicationsParticipants and policymakers may find this study helpful in navigating the complicated Saudi Arabian gold market. By understanding financial stability and investment decisions more thoroughly, individuals and institutions may be able to manage their portfolios more effectively.Originality/valueBy combining historical insights with advanced ARIMA modeling techniques, this research provides valuable insight into gold price dynamics in the Saudi Arabian market.

Trend Analysis for Daily Gold Price in Malaysia

The awareness of gold price trends is essential for promoting financial stability, wealth preservation, diversified investments, preserving cultural practices and economic stability. However, there is a noticeable absence of studies utilizing least square models to analyze the daily gold price trend (RM), highlighting a gap in research on this crucial financial aspect. This study aims to uncover trends in daily gold prices over time, examine the correlation between gold prices and time, and develop a least squares model for analyzing daily gold price (RM). The dataset consists of 1569 daily gold price observations in Malaysia from August 2018 to November 2022, obtained through the secondary data collection method from the Public Gold website. The study discovered a positive correlation between Gold Price (RM) and Time (Day) with Pearson Correlation Coefficient of 0.879. This implies that, over the specified time frame, there is a consistent upward trend in gold prices, a critical insight for investors and financial decisionmakers. Furthermore, the developed least squares model features an intercept of 182.801 and a slope of 0.074. This slope signifies that for each day's increase in time, the gold price is anticipated to rise by RM0.074. The daily gold price analysis not only provides valuable insights into gold price patterns but also offers a practical tool for making informed financial decisions and predicting future gold prices, thereby contributing to economic stability, and fostering responsible and sustainable gold practices.

The Impact of the Federal Reserve's Interest Rate Hike on Gold Futures Prices: The Dynamic Trend

This article aims to study how the increase in the Federal Reserve’s Interest Rate is going to impact the gold futures prices under a global economic recession period. Through collecting historical gold prices in chronological order from 2010, the data are then adapted into ARIMA model. It is to investigate the predicted correlation between the macro indicators and performance of the financial derivatives. The result includes that the policy change will have more dominant effects in the long run than short run. The real-world analysis should also incorporate multiple factors that range from qualitative to quantitative to facilitate a more precise prediction of future derivative’s price. The significance of the study is to suggest the time value of policy. Investors who hope to yield long-term return, should pick historical data, and learn the resultant change that happen only a few months after the policy announcement. The authorities should also take note of the intensity of the policy change, trying their best to achieve its economic goal at once, which would avoid rippling vicious effect and panic among the public.

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.

COMPARISON OF SUPPORT VECTOR REGRESSION AND RANDOM FOREST REGRESSION ALGORITHMS ON GOLD PRICE PREDICTIONS

This research was conducted to test how the Support Vector Regression and Random Forest Regression algorithms predict gold futures prices. The data used in this research was taken from the Investing.com website which will later be processed into a prediction model by comparing the SVR and RVR algorithms. The Support Vector Regression and Random Forest Regression algorithms will be tested to see the performance of each prediction model. The test results show that the Support Vector Regression model is superior in terms of accuracy with a value of 83%. However, the Random Forest Regression algorithm is superior with a smaller error rate, namely with an MSE value of 270.85 and an MAE value of 12.53. Keyword: Comparison, Prediction, Support Vector Regression, Random Forest Regression.

A Review of Gold Pricing: Real Interest Rate and U.S. Dollar Index

This paper explores key factors that could indicate the price of gold, with a specific focus on the real interest rate and the United States Dollar Index (USDX). By analyzing existing literature and historical events, this paper establishes the real interest rate as the primary driver of the price of gold and concludes that it could be used to predict future gold prices. As many existing studies purpose a negative relationship between the price of gold and USDX, this paper goes on to examine this relationship while considering the real interest rate. This paper purposes that the change in the U.S. real interest rate, relative to real interest rates in other countries, exhibits a positive correlation with the USDX. The real interest rate emerges as a key influencer for both the price of gold and USDX, potentially providing an explanatory framework for the relationship between the two. The finding of this paper is significant for researchers and investors seeking to predict future gold prices and study the relationship between the price of gold and USDX.

Gold Prices Prediction Using Random Forest

Abstract: Gold has historically served as one of the precious materials used to fund trading purchases. Gold-reserve holding nations are thought to being prosperous nations. The amount of gold held by the country's reserves reveals the state of its finances. Together with the government organizations, several other persons and companies have bought gold reserves. The value of gold prices are influenced by the performance of the world's leading economies. The change in gold's price has piqued the curiosity of more investors, who are now considering gold investments. Because the gold market is unpredictable, dealing with gold carries a high degree of risk, and its price varies greatly. It is possible to employ a variety of studies to investigate the connection between economic issues and gold prices. The project “Gold price prediction” aims to predict the price of gold using machine learning methodology gold price time series data was gathered from the Kaggle website.70% of the data served as the training set for Randomforest regressor model that were fitted. The statistical index R- squared error was then used to get the accuracy. Based on the R-squared error result, a prediction of future gold prices is made. Here we have taken the number of estimators to get the good accuracy

PENGARUH INSTRUMEN DERIVATIF MINYAK MENTAH, INDEKS DOLAR AS, INDEKS SAHAM UNGGULAN, SUKU BUNGA FED DAN INFLASI AS TERHADAP HARGA FUTURES EMAS: ANALISIS PERIODE 2012-2021

Gold futures have gained significant attention from investors in recent years, as demonstrated by the increasing trading volumes on the Jakarta Futures Exchange. Gold futures as a popular derivative instrument offers attractive returns and the potential to optimize portfolio allocation, making it one of the most preferred asset classes for investors. This study aims to analyze the influence of key factors such as the US dollar index, S&P 500 stock index, US inflation, FED funds rate, and crude oil futures on gold futures prices. The aim of the study is to provide investors, financial practitioners, and academics with a better understanding of the factors that can influence gold futures prices. This study utilized Error Correction Model as the research method, which effectively addresses the challenges associated with non-stationary time series data and enables the identification of short-term and long-term relationships among variables. The results show that the US dollar index has an inverse relationship with gold futures prices, influencing both the short and long-term. In the long run, an increase in the S&P 500 stock index and US inflation is anticipated to drive up gold futures prices. However, the impact of FED fund rates on gold futures prices is insignificant, primarily due to their low volatility. In addition, crude oil futures, as an alternative asset, have an adverse impact on gold futures prices over the long term.

Dynamic Asset Planning Model via Polynomial - ARIMA Prediction and Risk Analysis via Improved Volatility & EWAM

This paper investigates the trading strategies for gold and bitcoin. Based on past data, future gold and bitcoin prices are given by single-step forecasting, and optimal investment strategies are also proposed. In this paper, a polynomial-ARIMA model is developed using quadratic polynomial fitting and ARIMA algorithm depending on the size of the data. This forecasting model was used for single-step forecasting of gold and bitcoin prices. After that, a GA-based dynamic programming model was proposed based on the forecasting results and the results were obtained by it.

Gold and Bitcoin Price Prediction based on KNN, XGBoost and LightGBM Model

In the past few decades, there has been an increasing demand for assets trading with help of machine learning. Contemporarily, the cryptocurrency and gold market has become prosperous with extremely dramatical fluctuations. This paper aims to study the trading price laws based on machine learning scenarios of Bitcoin and Gold to predict the price of the two currencies. To be specific, this study gives an inside view of the application of a method combined three algorithms (i.e., KNN, XGBoost and LightGBM) to predict the future Gold and Bitcoin price browser based on past data from 2017 to 2022. According to the analysis, the study shows the difference of three models, the accuracy of the combined algorithms and proves the related metrics to predict the price of the Gold and Bitcoin. Overall, these results give a guideline for the investor to make sensible decisions about Bitcoin and Gold price and shed light on guiding further exploration of price forecasting in terms of machine learning approaches.

Key Points-in-Time Identification of Gold Futures Market: A Complex Network Approach

Important nodes can determine the internal structure of complex networks and reveal the internal relationships of real-world systems, and identifying key nodes in complex networks is one of the important research areas of complex network science. As the king of commodities, changes in the price of gold significantly impact the economic development of various countries. Especially in the early stages of the outbreak of war between Russia and Ukraine, the price of gold futures has been greatly impacted, and the systemic risks are gradually spreading. In this paper, a gold future price series is mapped into a visibility graph (VG), the characteristics of the gold price time series and key points-in-time, have been explored from the perspective of complex network. First, according to the data structure characteristics of gold futures, this paper converts the closing prices of gold futures of the New York Mercantile Exchange into a complex network through the VG model. Then, by using the complex network model to further delve into the price of gold futures, it is found that the degree distribution of the gold futures network follows a power-law distribution, and has obvious scale-free characteristics. Finally, this paper uses the visual network node shrinking algorithm and the technique for order preference by similarity to ideal solution (TOPSIS) analysis method to identify the key nodes of the gold futures visual map to find the key time nodes in the timeline of gold futures market. Analysis of the key time nodes of this market by four methods reveals that the repetition rate of the key time nodes in the methods’ top 10 ranking is as high as 82.5%, indicating that the results obtained in this paper are robust. This study introduces a new model to describe the characteristics of gold futures price series, one which can find key time nodes in gold futures prices and provide potential help for predicting gold futures prices.

DEPLOYMENT OF RANDOM FOREST ALGORITHM TO PROPHESIZE THE EXACT PRICE OF GOLD

Historically, gold, unlike other payment channels, was employed to support trading acquisitions worldwide. We forecast future gold prices based on twenty-two market variables using a machine learning technique. In order to analyze these data, one machine learning approach, random forest regression, was applied. Numerous states have kept and increased their gold reserves while being progressive and rich. In reality, central banks throughout the world keep precious metals like gold on hand to ensure foreign debt service in addition to stabilizing inflation. The primary goal of this research is to anticipate the increase and fall in routine gold rates, which will assist investors in deciding whether to purchase or sell gold. Logistics forecasting is critical to the fiscal performance of an organization. The secondary market is derived by examining the dataset including the previous year's gold price. There is concern that these high prices will not be sustainable and will fall despite the fact that several research has been conducted to examine the relationship between the exchange rate volatility and various economic factors. We employed machine learning through python to forecast financial indicators. Keywords: Gold, machine learning, random forest, python, gold price, ETF, dataset

What Causes Future Gold Price Volatility in Indonesia?

The purpose of this study is to determine the factors that influence the future volatility of gold commodities in Indonesia. Methods of quantitative research as well as secondary data are utilized in this study. The years 2011 to 2018 are used in this study example. On the official website, there is a total of 32 data collected quarterly. SPSS version 24 is the software that is being utilized. The findings, the exchange rate is the only factor that has impact on future gold commodities. Other factors, including as the consumer price index and the stock market index, have no such impact. Research can be used to determine the reasons of price volatility from macroeconomic influences on future gold commodities

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.

Correlation between COVID-19 cases and gold price fluctuation

ABSTRACT Covid-19 has damaged the global economy, as the series of lockdowns had negative impacts on the global economy. Investors around the world invested heavily in gold, and as a result, steep increase occurred. This study was intended to determine the relationship between COVID-19 infection rates and the price of gold, using different statistical analyses. Johansen cointegration test revealed that cointegration existed from a lag of 0 to 21 days, yet no relationship was observed from day 22 onwards. Also, Granger causality test revealed that gold price had a significant response to increasing COVID-19 cases. Conversely, gold price did not have Granger causes on the COVID-19 infection rates. Furthermore, the Vector Error Correction Model (VECM) result indicated that a 1% increase in COVID-19 cases led to a 0.0166% increase in gold price. Considering the exponential growth in COVID-19 cases of 17.38% average growth per day during the period from 1 January 2020 to 31 March 2022, 0.2885% increase in gold price per day has been attributed from COVID-19 cases. The sequential methodology presented in this paper is useful to potential gold investors in understanding the responsiveness of gold price to global pandemics and forecasting future gold prices during such global crises.