Gold Price Research Articles

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.

Price processes in the global gold market

Recurring global crises, increasing uncertainty in the world economy, rising geopolitical risk and recently geoeconomic fragmentation have renewed and intensified interest in gold in the 21st century. The precious metal still retains an important role in the economy, most notably as a long-term store of value and efficient portfolio diversifier. One of the most remarkable changes on the global gold market refers to the role of central banks. Over several decades after the breakup of the Bretton Woods system, central banks were selling gold held in their reserves. However, following the global financial crisis central banks ceased to be a source of gold supply and became a significant component of global demand for gold. We demonstrate that gold remains valuable and expensive in terms of both currencies and other commodities. Moreover, its record prices are a result of rising geopolitical tensions and uncertainty as well as the resurrection of inflationary pressures. We argue that gold prices are also overwhelmingly influenced by the monetary policy of the Federal Reserve and the US dollar exchange rate. On the other hand, the rapid increase in the price of gold reflects rising income and private demand for gold in emerging market economies, particularly in China and India. The purpose of the article is to assess the value of gold using a wide range of metrics as well as to present the determinants of gold prices, with special attention given to demand and supply fundamentals and financial instruments based on gold. The research methods used include statistical-descriptive analysis, comparative analysis and literature study.

Analisis Pengaruh Harga Emas dan Minyak Mentah Periode Terhadap LQ45 Menggunakan Model ARDL

This research aims to analyze the influence of gold and crude oil prices on LQ45 using the ARDL approach. In the research carried out, researchers used the variables Gold Price and Oil Price. The data used is secondary data sourced from the websites www.yahoo.co.id and investing.com. Using purposive sampling technique and the research sample obtained was 240 time series data. The results of this research show that gold prices have a negative effect and crude oil prices have a positive effect in the long term on LQ45. The results of this research can be a reference material for investors in developing investment strategies, especially during a crisis.

Examining the factors influencing Stock Market in India

This study explores the factors that take hold on prices of stock which represent financial and real sector of our economy. These factors can be of any type like gold price, money supply, election, monetary policy, political change, investor sentiment, social media, unemployment, psychology, air quality, exchange rate, financial institutional investor, commodity channel index, consumer price index, domestic institutional investor, economic policy uncertainty, net profit margin, debt equity ratio, gross profit ratio and Morgan Stanley capital international in India. The study also reveals the relation between the factors with respect to change in stock prices. Study also determined whether there is any relation of income, education and investment with respect to stock market in India. Which factors affect the most to stock market in India? Study has done factor analysis of each factor with respect to Stock Market. The study reveals that there is relation among the factors affecting stock market in India. In thi study, ANOVA used to calculate the F value and it was founded that F value is greater than table or critic value. Hence null hypothesis is rejected.

Currency Exchange Rate Volatility as a Moderating Effect of the Gold and Coal Price Against Fund Age and Mutual Fund in Indonesia

Abstract Shifts in gold and coal prices have a crucial impact on the macro economy, especially investment growth. On a financial scale, there is still little discussion of the relationship between gold and coal prices and stock mutual funds. Also, the role of exchange rates and interest rates in moderating this relationship has not been examined. This research reviews the performance of exchange-traded funds (ETFs) in the scope of fund age and Indonesian stock mutual funds which are influenced by gold and coal prices with the moderating effect of interest rates and exchange rates. Core data was obtained from 20 companies as mutual fund owners officially registered with the Financial Services Authority (OJK) throughout 2019–2022. By operating Moderated Regression Analysis (MRA) into SmartPLS 4, there are interactions between prices and exchange rates, prices and interest rates, and prices and gold prices. As with the relationship between the age of ETF mutual funds traded on the Indonesian Stock Exchange (IDX) and the gold price, these two variables have a strong and positive correlation. This implies that relatively small changes in gold prices over the monitoring period may impact the fund age ETF. This positive association shows that an ETF investment can have its life extended by a relatively small increase in gold prices. This research indicates a strong causality, which improves the ETF’s performance on the IDX as fund age rises. Furthermore, the second model examines how fund age, gold, and coal have a direct impact on ETF mutual fund performance. It also looks closely at how indirectly coal, gold, and fund age affect ETF performance. The age of the fund, gold, coal, and exchange rates affect the performance of ETFs. While the effect is not statistically significant, interest rates, gold, coal, and fund age have a beneficial impact on ETF performance. Implications for policy makers to monitor and re-evaluate commodity price fluctuations using mining production regulation scenarios that are balanced with quality. Weaknesses of the current study such as the observation period need to be taken into consideration in future research.

AN EMPIRICAL STUDY ON IMPACT OF GOLD PRICE ON EXCHANGE RATE IN INDIA

Macroeconomic variables play a vital role in the economic performance of any country. They act as the barometer for measuring the success of any economy. The macroeconomic variables are interlinked with each other and in many instances influence each other’s performance. Hence it is of utmost importance to study the interrelationship among them, to construct a concept about contribution of each variable and as well as it’s impact on other variables in the economic development. The present study focuses on the interrelationship between two macroeconomic variables – Gold price in India & Exchange rate (USD/INR). Karl Pearson’s Correlation Coefficient is used to study the extent to which the movement of the two variables is associated. A linear regression model with Exchange rate as dependent variable & Gold price as independent variable is fitted. Further ANOVA analysis is done to evaluate the acceptability of the model from statistical perspective. The study concludes that the selected macroeconomic variables are strongly associated with each other & a tenable regression model is developed with a very high degree of Coefficient of determination. Further the validity of the developed model is also justified by the p value from ANOVA table. KEYWORDS: Karl Pearson’s Correlation Coefficient, Linear regression model, ANOVA

US Dollar Exchange Rate Elasticity of Gold Returns at Different Federal Fund Rate Zones

We examine the relationship between gold prices and the U.S. dollar exchange rate, arguing that their interactions are state-dependent and asymmetric under different market conditions. State dependency hinges on different short-term interest rate zones. To prove this point, we determine three distinct levels or zones of the effective federal funds rate using SETAR(2,p) tests. Subsequently, we perform conditional least square estimations of log changes in gold prices as a function of log changes in the nominal broad U.S. dollar exchange rate index for each of the obtained zones. Their relationship is consistently inverse, suggesting that gold and the U.S. dollar are risk-hedging substitutes for normal market periods. This also implies that gold is a safe-haven asset against the U.S. dollar exchange rate risk against a broad range of currencies. The substitution is weaker in the low-interest rate zone, more robust in the intermediate zone, and very pronounced in the high zone. We also perform a Markov switching test on the double-log function of gold prices and the exchange rate. The tests show a pronounced inverse relationship, i.e., substitution between assets, at normal market conditions. The relationship becomes significantly positive during episodes of financial distress, indicating complementarity between gold and U.S. dollar assets.

The Comparison Between Random Forest and LSTM Models Based on the Gold Price Prediction

The gold price forecast is crucial for financial institutions, investors, mining firms, and associated enterprises. Precise price prediction is especially important in the gold market because of its volatility and unpredictability; this has important consequences for strategy formulation and decision making. In order to give practitioners a better prediction technique to increase the accuracy of market trading, the aim of this study is to assess the predictive effectiveness of the two models in predicting the gold market. The study forecasts gold prices using the Random Forest and Long Short-Term Memory (LSTM) models, comparing their performances using five assessment metrics. The end findings prove that the Random Forest method outperforms the LSTM model in comparison. In particular, the Random Forest model predicts more accurately and with less inaccuracy. The study finds that, while the Random Forest model is more accurate, both the LSTM model and the Random Forest model are appropriate techniques for gold market projections. To increase the precision and consistency of the predictions, future studies will keep refining the LSTM model and look for causes for its subpar performance.

Impact of Commodity Price and Exchange Rate on IHSG with Geopolitics as Moderation

This research aims to determine the influence of world gold prices, world oil prices, world coal prices, and exchange rates on the Indonesian composite stock index. The tests of the ponder are Indonesia's world oil prices, world gold prices, world coal prices, trade rate, geopolitical chance record, and composite stock prices for Indonesia, data from www.investing.com and www.policyuncertainty.com January 2020 to December 2023. The method used to analyze the data in this research is multiple regression analysis and residual tests for moderator variables. Through F test analysis it is known that simultaneously all variables X have a significant influence on the Indonesian Composite Stock Price Index at the 5% level. Based on these results, the research hypothesis is accepted. Furthermore, through t-test analysis (subtest), the variables of world gold prices, world oil prices, world coal prices, and exchange rates have a partially significant influence on the Indonesian Composite Stock Price Index at a confidence level of 100. It turns out to be 95% (a = 5%). It can be concluded that changes in these variables influence changes in the Indonesian composite stock index. In addition, geopolitical variables cannot moderate the relationship between all variables X and Y. This shows that geopolitical factors do not play a significant role in this context. The variation in the Indonesian Composite Stock Price Index of 88.0 can be explained by combining all variables X. The remaining 12.0% can be explained by other factors not included in the estimation model.

Gold Investment Decisions to Anticipate Various Economic Problems in the Future (Study of Generation Z Mothers in Makassar City.Indonesia

Gold Investment Decisions to Anticipate Various Economic Problems in the Future (Study of Generation Z Mothers in Makassar City.Indonesia

Research on Efficiency and Multifractality of Gold Market under Major Events

(1) Background: As a safe-haven asset, gold attracts a large number of investors during major events. Uncertainties caused by global economic and market conditions lead to increased purchases of gold by investors, which in turn affects the efficiency of the gold market. (2) Methods: This article focuses on Shanghai gold and explores the potential impacts of different major events on the effectiveness and risk of China’s gold market using the Multifractal Detrended Fluctuation Analysis (MFDFA) method. (3) Results: the Chinese gold spot market is anti-persistence and exhibits significant multifractal characteristics, suggesting that the gold spot market possesses predictability and has significant volatility and high risk. Furthermore, the study conducts stage analysis based on different major events, and the results are as follows. The empirical results show that the gold market exhibits anti-persistence and multifractality features for three major events, i.e., the US–China trade war, the COVID-19 pandemic, and the Russia–Ukraine War. (4) Conclusions: for the COVID-19 pandemic, the intensity of anti-persistence is the highest. In addition, it is also found that the stronger the anti-persistence in the gold markets under a given event, the greater the corresponding risk. Finally, the article provides relevant decision-making suggestions for investors and risk managers.

Volatilitas Nilai Tukar dan Harga Komoditas Global selama Krisis Laut Merah

Purpose: This study aims to determine the most suitable model for assessing the volatility of currency exchange rates for Indonesia, Malaysia, and Singapore, as well as global commodity prices, specifically gold and crude oil. Methodology: The research was conducted by performing various tests using the SPSS version 25 application, followed by a volatility analysis employing the GARCH method with the E-Views version 9 application. Results: The study reveals that the Red Sea crisis significantly impacts global investment volatility. The EGARCH(1,1) model best describes the volatility of USD/IDR, USD/SGD, and crude oil prices, while the GJR-GARCH(1,1) model is most suitable for USD/MYR, and the GARCH(1,1) model fits gold price volatility. Limitations: This study is limited by its focus on only three countries (Indonesia, Malaysia, and Singapore) and two commodities (gold and crude oil). Additionally, the analysis only covers the period following the first attack in the Red Sea crisis. Contribution: This research provides valuable insights for decision-makers and investors by highlighting the impact of global events on market volatility.

Regime-Specific Spillover Effects Between Financial Stress, GCC Stock Markets, Brent Crude Oil, and the Gold Market

Regime-Specific Spillover Effects Between Financial Stress, GCC Stock Markets, Brent Crude Oil, and the Gold Market

A Prediction Model of Gold Price Change Against US Dollar Based on BP Neural Network

A Prediction Model of Gold Price Change Against US Dollar Based on BP Neural Network

Perbandingan Minat Gadai Emas di Bank Syariah dan Pegadaian Syariah pada Masyarakat Kota Jambi

In an interview conducted with Mr. Abdi, he stated that in the midst of the current economic crisis, pawning gold was indeed a profitable business because many people carried out this practice. The price of gold on the market experienced a significant and rapid increase, reaching around Rp. 1,131,823 per gram. The aim is to analyze the comparison of the estimated value of gold at Islamic banks and Islamic pawnshops and to analyze the similarities and differences in gold pawning transactions at Islamic banks and Islamic pawnshops. This research uses qualitative methods which aim to understand the phenomena experienced by research subjects by collecting data in the form of words and not numbers. The results in this research are a realistic picture, information based on facts obtained by researchers in the field. Researchers conducted a survey at the research location using interviews, documentation and observation of research subjects designated as informants in this research. So researchers can draw the conclusion that there is a difference in public interest in pawning gold at Bank Syariah Indonesia and Pegadaian Syariah. Input for Sharia Banks and Pegadaian Syariah in improving the quality of sharia gold pawn products, both operational systems and services. So that people can continue to get the best service from Sharia Banks and Sharia Pegadaian.

Comparison of Fuzzy Grey Markov Model (1,1) and Fuzzy Grey Markov Model (2,1) in Forecasting Gold Prices in Indonesia

Currently, gold investment is considered promising despite the ever-changing price of gold. However, obtaining optimal profits is a challenge for investors. Therefore, a proper forecasting method is needed to forecast the gold price so investors can know the best transaction time. This study used two forecasting methods: the Fuzzy Grey Markov Model (1,1) and a new, never-before-used approach, the Fuzzy Grey Markov Model (2,1). The Fuzzy Grey Markov Model (2,1) approach is interesting because it can be considered for forecast data that shows varying increases and decreases, such as the gold price data used in this study. Both methods are combined models that utilize fuzzy logic to handle uncertainty in data; the Grey model forms a forecasting model, and the Markov chain determines the state transition probability matrix. Next, the error rates of the two methods are compared based on the Mean Absolute Percentage Error (MAPE) value to obtain the best forecasting method. As a result of this study, the Fuzzy Grey Markov Model (1,1) was chosen as the best forecasting method with a MAPE value of 0.28%.

Analysing COVID-19′s impact: Gold, oil, and stock markets in African oil-exporting economies

This article employs quantile regressions to investigate the link between oil prices, gold prices, and stock market returns in five African oil-exporting nations: Nigeria, Ghana, Egypt, Algeria, and Tunisia. The analysis utilises daily data spanning from January 1, 2017, through September 30, 2021. Before the onset of COVID-19, oil played a pivotal role as a hedge in Tunisia, Egypt, Algeria, and Ghana, providing stability in both normal and bullish market conditions. Furthermore, it functioned as a diversifier in Egypt and Ghana during bearish market phases. However, as the pandemic unfolded, oil took on the characteristics of a safe haven in Egypt, Ghana, Nigeria, Algeria, and Tunisia, particularly notable during bearish and normal market conditions. Gold, prior to the pandemic, served as a hedge in Egypt, Ghana, Nigeria, and Tunisia, while also acting as a diversifier across both bearish and bullish market conditions in these economies. Conversely, during the pandemic, gold predominantly functioned as a diversifier in most oil-exporting African economies, with significant hedge properties observed specifically in Algeria and Ghana. The practical implications of these findings are significant for various stakeholders, including oil exploration and production firms, businesses in the transport and hospitality sectors, policymakers, and investors with stocks sensitive to oil and gold prices.

Home Ownership Dynamics: Analysis of The Impact of Gold, Bonds and Stocks on Property Investment Moderated by Loan Interest Rates

The purpose of this study is to examine how shares, bonds, and gold affect real estate investment, which is influenced by loan interest rates. In this study, the data type used is quantitative. Secondary data in the form of residential property price index data, bonds, stocks, loan interest rates and gold prices were sourced from Bank Indonesia. On a monthly basis, sampling employed a time series data method. Multiple linear regression is the data analysis technique employed in this study, which makes use of the E-views software. The study's findings indicate that gold has little bearing on real estate investments. Bonds have an impact on real estate investing. This demonstrates how interest rates or the inclination to invest in real estate can be impacted by an increase in bonds. Increase in shares can influence the interest or tendency to invest in property. Loan interest rates cannot moderate the influence of gold on property investment. Loan interest rates can moderate the effect of bonds on property investment. The results of this research show that there is a negative correlation, meaning that loan interest rates can weaken the influence of bonds on property investment. Loan interest rates can moderate the influence of shares on property investment. The results of this research show that there is a negative correlation, meaning that loan interest rates can weaken the influence of shares on property investment.

GOLD PRICE PREDICTION IN INDONESIA BASED ON INTEREST RATE USING DISTRIBUTED LAG ALMON TRANSFORMATION

Gold is valued for its safety and profitability, driven by steady price changes and influenced by interest rates. Accurately predicting gold prices is very important to make the right investment decisions. This study aims to build a gold price model in Indonesia using the Almon transformation lag distribution and see gold price predictions based on the model that has been built. We used the data on gold prices and interest rates from January 2016 to December 2023. Based on the results of the analysis, the best Almon transformation model used in this study is the Almon model with a maximum lag length of 16 and the second polynomial degree. The prediction results have a MAPE of 16.49%, which shows that the Almon model can predict gold prices well for one year. This study contributes to the understanding of gold price dynamics amid economic variations. However, limitations in the model assumptions should be considered.

Islamic and conventional stocks: Impact of Russia-Ukraine saga on global commodities

Purpose – This study intends to scrutinize the relationship between oil and gold prices on Indonesia’s Islamic stock market before and during the war between Russia and Ukraine, and compare the nature of sharia and conventional stock during the period of study.Methodology – This study uses daily price data of oil price, gold, Islamic index, and LQ45 index. The period spans from 2020 to 2022, which is split into two sub-periods: pre-war and during the war period. Structural vector autoregression (SVAR) was used for data analysis.Findings – This study provided three main findings. First, the Islamic index was found to be more exogenous than conventional stock, implying the safe haven properties of Islamic stock. Second, oil prices have a negative and significant impact on both conventional and Islamic stocks. However, Islamic stocks are less affected by oil price shocks than conventional stocks. Lastly, Gold is a safe haven asset for both Islamic and conventional stocks.Implications – This study is expected to be beneficial for Islamic investors making investment decisions, particularly in the war period, which is still ongoing and more specific for taking advantage of diversification opportunities.Originality – Despite a huge number of studies that investigate the Islamic stock – oil price nexus, this study uncovers the impact of the most recent geopolitical tension during the Russia-Ukraine War.