Gold Price Research Articles

Financial and market risks of bitcoin adoption as legal tender: evidence from El Salvador

Bitcoin adoption as a legal tender threatens a financial crisis because of the lack of regulatory frameworks and systems for exchanging Bitcoin into local currencies. This study analyzes monthly data from 2010 to 2022 using a structural vector autoregressive model, estimating Bitcoin’s pass-through into remittance, money multiplier, the US Dollar index, and gold price. The results show that Bitcoin prices decrease the money multiplier and gold prices in both the short and long run, while remittances moderately increase in the long run. The implication of these results suggests the potential for international business opportunities to stimulate the credit, savings, and investment monetary policy channel. The results are robust to alternative SVAR identification strategies.

Asymmetric connectedness among BSE SENSEX, INR–USD exchange rate, gold price and crude oil price: fresh evidence from nonlinear ARDL

PurposeThis paper investigates the asymmetric connectedness among the Indian stock market, crude oil price, gold price and the USD–INR exchange rate.Design/methodology/approachWe construct a nonlinear autoregressive distributed lag model that contains the four aforementioned variables. We further used the pairwise Granger causality test to identify the direction of causality.FindingsThe results verify an asymmetrical long-run co-movement between the Indian stock market index, exchange rate, international crude oil prices and gold prices. In the long run, the performance of the Indian stock market is mainly affected by both positive as well as negative shocks in the INR–USD exchange rate, positive shocks in gold prices and positive shocks in crude oil prices. Further, a feedback mechanism is observed between positive shocks in the INR–USD exchange rate and Indian stock market performance.Originality/valueThe research paper revisited the linkage among the variables using a novel methodology proposed by Shin et al. (2014).

Interactive Relationship of Exchange Rates with Gold and Silver Prices in Domestic versus Global Markets

Interactive Relationship of Exchange Rates with Gold and Silver Prices in Domestic versus Global Markets

Application of the Average Based Fuzzy Time Series Lee Method for Forecasting World Gold Prices

Gold is a investment that investors are interested in because it has relatively low risk and gold investment is not affected by inflation. Gold prices always change from time to time, so it is necessary to forecast gold prices as a basis for investors in making decisions. The forecasting method used in the fuzzy time series lee method. The purpose of this research is determine the world prices and determine the accuracy of the gold price forecasting value ortained using fuzzy time series lee method. The results of this research are forecasting gold prices in the period November 20, 2023 of US$ 63,89/grams and relatively the level of forecasting accuracy based on MAPE value of 0,540091% included in the very good criteria in forecasting gold prices.

COMPARATIVE ANALYSIS OF TIME SERIES FORECASTING MODELS USING ARIMA AND NEURAL NETWORK AUTOREGRESSION METHODS

Gold price fluctuations have a significant impact because gold is a haven asset. When financial markets are volatile, investors tend to turn to safer instruments such as gold, so gold price forecasting becomes important in economic uncertainty. The novelty of this research is the comparative analysis of time series forecasting models using ARIMA and the NNAR methods to predict gold price movements specifically applied to gold price data with non-stationary and non-linear characteristics. The aim is to identify the strengths and limitations of ARIMA and NNAR on such data. ARIMA can only be applied to time series data that are already stationary or have been converted to stationary form through differentiation. However, ARIMA may struggle to capture complex non-linear patterns in non-stationary data. Instead, NNAR can handle non-stationary data more effectively by modeling the complex non-linear relationships between input and output variables. In the NNAR model, the lag values of the time series are used as input variables for the neural network. The dataset used is the closing price of gold with 1449 periods from January 2, 2018, to October 5, 2023. The augmented Dickey-Fuller test dataset obtained a p-value = 0.6746, meaning the data is not stationary. The ARIMA(1, 1, 1) model was selected as the gold price forecasting model and outperformed other candidate ARIMA models based on parameter identification and model diagnosis tests. Model performance is evaluated based on the RMSE and MAE values. In this study, the ARIMA(1, 1, 1) model obtained RMSE = 16.20431 and MAE = 11.13958. The NNAR(1, 10) model produces RMSE = 16.10002 and MAE = 11.09360. Based on the RMSE and MAE values, the NNAR(1, 10) model produces better accuracy than the ARIMA(1, 1, 1) model.

A Nonlinear Causality Analysis of the Relationship Between Gold Prices, Exchange Rates and Stock Market in Türkiye

With globalisation and rapidly developing technology, financial investment instruments have diversified, and investors have begun to look for ways to make the most of their investments. Consequently, the relationships between financial instruments have gained more and more importance. This study aims to determine the direction of the causality relationship between exchange rates, gold prices, and the BIST100 index by using monthly data covering the period of 2003:01-2022:06 in the Turkish economy. Diks & Panchenko’s (2006) non-linear causality test was used to determine the direction of causality, and the test shows a unidirectional causality relationship from BIST100 to exchange rates.

Research on Gold Price Prediction Based on LSTM Modeling

Gold plays a pivotal role in asset allocation, and the construction of gold price prediction models represents a complex yet rewarding task within the field of finance. The problem of international gold price is addressed in this paper forecasting by proposing a standard Long-Short Term Memory (LSTM) model and introducing bi-directional LSTM (Bi-LSTM) networks and multivariate analysis to compare the forecasting accuracy of the relevant models. This comparison is based on the daily gold price of the London Bullion Market Association (LBMA) for 2013-2022. This methodology takes into account various factors that influence the gold market and incorporates them as input sources into the Multi-factor LSTM model, thereby enhancing the interpretability of the LSTM model. Correlation analysis and the Granger test are employed to analyze these influencing factors. However, the results of this study suggest that while the Multi-factor LSTM model may be more accurate in predicting outliers, it tends to increase the number of small errors. On the other hand, the inclusion of Bi-LSTM networks can improve the overall accuracy of gold price prediction.

Artificial intelligence techniques recruitment for gold ore (Crude gold) price prediction

Artificial intelligence techniques recruitment for gold ore (Crude gold) price prediction

Gold Price Prediction Using Two-layer Decomposition and XGboost Optimized by the Whale Optimization Algorithm

Gold Price Prediction Using Two-layer Decomposition and XGboost Optimized by the Whale Optimization Algorithm

Natural resources and sustainable development: Evidence from the dynamic correlation between crude oil and gold market

Amidst the uncertainty in markets spurred by globalization and technological advancements, the volatility of the crude oil and gold markets, which are emblematic of natural resource markets (NRMs), profoundly impacts global economic sustainability. This study innovatively quantifies and contrasts the impact of macroeconomic uncertainty factors on the volatility and dynamic correlation of these pivotal NRMs by extending the GJR-GARCH-MIDAS-X and DCC-MIDAS-RC-X models grounded in global economic policy uncertainty (GEPU), trade policy uncertainty (TPU), monetary policy uncertainty (MPU), and geopolitical risk (GPR). Concurrently, employing principal component analysis (PCA) blends numerous uncertainty factors, heightening the models’ explanatory capacity and predictive precision. Empirical findings suggest that all uncertainty factors exert significant positive effects on both crude oil and gold volatility, albeit with heterogeneity. Furthermore, the correlation between crude oil and gold fluctuates over time under the influence of uncertainty factors. Additionally, this study revealed asymmetric effects on realized volatility between the two NRMs. This research not only provides novel theoretical insights and empirical evidence concerning the intricate dynamic interplay between the two NRMs, but also has significant ramifications for policymakers, market regulators, and investors in crafting effective strategies and decisions amidst market uncertainty.

The effect of cow manure amendment and the ratio of gold mine tailings on sweet potato gold phytomining

Artisanal and small-scale gold mining has commonly used the mercury amalgamation method for gold ore extraction. The processing waste or tailings often contain gold and other metals. This study examined the effects of cow manure amendment and the ratio of gold mine tailings on sweet potato (Ipomoea batatas L.) gold phytomining. The pot experiment was conducted in the Faculty of Agriculture greenhouse at Padjadjaran University in Indonesia. The sweet potato MZ119 clone (an orange-fleshed sweet potato) was planted on a polybag (40 x 40 cm) consisting of 10 kg of planting media, which was a mixture of gold mine tailing and soil. The design used was a completely randomized design factorial with three replications. The first factor was ratios of gold mine tailing consisting of three levels, namely 30%, 50%, 70%, and 100% (w/w). The second factor was doses of organic manure, namely without cow manure, 250 g pot-1, 500 g pot-1, and 750 g pot-1. The results demonstrated that the application of cow dung and the gold mine tailings ratio (w/w) had a significant (p = 0.05) effect on plants' dry weight biomass, gold accumulation, and gold phytomining by sweet potato. The highest gold mass yield of 0.066 mg gold plant-1 was achieved by a 50% (w/w) gold mine tailing ratio and 750 g pot-1 cow manure treatment.

Golden Horizons: Investigating Shariah-Compliant Gold Investment Awareness Among Youth

Gold has long been regarded as a reliable medium of exchange and a store of value, owing to its inherent traits and functions associated with money. In the context of Malaysia, gold investment emerges as a lucrative and secure asset category for investors. They have the option to either physically possess and safeguard gold or opt for a more modern approach by establishing a gold investment account. Over recent years, there has been a noticeable growth in awareness among the public and seasoned gold investors in Malaysia. However, this upward trend has not been as pronounced among the younger demographic. This research aims to investigate the extent of awareness regarding Shariah-compliant gold investment among the young Muslim population in Terengganu, Malaysia. Utilizing a quantitative research methodology, data was collected through a survey administered by researchers via a Google Form, disseminated across various social media platforms. The study successfully obtained data from a sample of 120 respondents. The findings of this study reveal that, on the whole, the young Muslim generation in Terengganu exhibits a high level of awareness regarding Shariah-compliant gold investment. The significance of this research lies in shedding light on the dynamics of gold investment awareness, particularly within the context of Shariah compliance, among the youth in a specific region of Malaysia. The results provide valuable insights for financial institutions, policymakers, and educators seeking to tailor their strategies to enhance financial literacy and promote Shariah-compliant investment practices among the younger population. As Malaysia continues to evolve as a financial hub, understanding and addressing the investment preferences and awareness levels of its diverse demographic groups become crucial for fostering a robust and inclusive financial landscape. Since this study was conducted with small sample size and focusing in Terengganu only, it is strongly recommended to expand the research to other states in Malaysia.

Should you buy gold stocks or paper gold?

We analyze which type of gold investment is better for investors by studying the determinants of returns on gold mining stocks (gold stocks) and gold exchange-traded commodities (gold ETCs) over the period from March 2012 to February 2024. We find that gold ETCs outperform gold stocks in both returns and risk, and respond significantly more positively to economic policy uncertainty (EPU), offering investors more favorable safe-haven properties. Gold stocks are also more sensitive to oil price increases than are gold ETCs, and their relative price performance is negatively related to both oil price changes and Gold VIX. We conclude that investing in gold stocks is less suitable for investors who buy gold as a safe-haven asset.

Macroeconomic Determinants of the Gap between Domestic and International Gold Prices in Vietnam

This research investigates the factors influencing the disparity between domestic gold prices (SJC gold and 9999 gold) and global gold prices in Vietnam from January 2011 to December 2023. Using the Autoregressive Distributed Lag (ARDL) model, we identify key macroeconomic determinants impacting this price spread, including interest rates, oil prices, and consumer demand. Our findings reveal that higher interest rates are associated with larger price spreads, suggesting that monetary policy plays a significant role in the domestic gold market. Oil prices exhibit a negative relationship with the price spread for 9999 gold, indicating that rising oil prices tend to align domestic gold prices more closely with global trends. Consumer demand notably affects the SJC_Global price spread but not the 9999_Global spread, reflecting the different market dynamics of finished goods versus raw materials. Additionally, the error correction terms in both models indicate a robust mechanism for long-term equilibrium, demonstrating the Vietnamese gold market’s resilience. These insights highlight the need for policymakers to reconsider existing regulations, such as Decree 24, to ensure a stable and efficient gold market. The study concludes that while the Vietnamese gold market exhibits sustainable practices, regulatory revisions are necessary to support both market growth and community development. Future research should consider additional macroeconomic variables and the impact of regulatory changes over time to further understand the dynamics of gold price disparities in Vietnam.

Unveiling the Dynamics: Exploring the Relationship between Emerging Stock Market Prices and Macroeconomic Factors through ARDL Analysis

This study investigates the interaction between stock prices and prices in other markets using a panel ARDL model. Analyzing data from January 2004 to December 2022 for 19 emerging market countries, the study explores the impacts of gold prices, exchange rates, inflation rates, and interest rates on stock prices. The results indicate a long-term negative relationship, except for gold, between the variables. The effects are generally insignificant in the short run, except for gold's negative impact. The global financial crisis of 2008 negatively affected emerging market stock markets in both the short and long term. In the short run, the COVID-19 pandemic negatively impacted stock market returns, which turned positive in the long run. This study highlights the importance of sound monetary policies focused on price stability and fiscal policies aimed at reducing government dominance in financial markets to foster long-term growth in stock markets.

The The Influence of Financial Literacy, Risk Perception, Herding, Loss Aversion, Anchoring, And Income on Gold Investment Decisions at PT. Pegadaian (Persero)

The investment interest in Indonesia is rising in line with the society's awareness on the benefit of investment, especially gold investment. Investors act rationally or irrationally in making investment decisions. The aim of this research is to study the effect of financial literacy, risk perception, herding, loss aversion, anchoring and income to the decision of gold investment at PT. Pegadaian. This is a quantity experiment with the customer of PT. Pegadaian who used the gold investment as a population and sample of this research. Using the accidental sampling technique, there are 100 respondents. Data collection using a direct and online questionnaire. The Structural equation model partial least square method with the SmartPLS 3.0 software used to analyze the data collected. The results show that financial literacy affects investment decisions. Respondents have a fairly high level of education and moderately high association of financial literacy. While the risk perception, herding, loss aversion, anchoring and income are not affecting the investment decisions.

A study of the impact of cryptocurrency price volatility on the stock and gold markets

This paper selects data from January 18, 2018, to May 6, 2024, as a sample to study the impact of cryptocurrency price volatility on the stock and gold markets. The empirical study finds that the price volatility of the cryptocurrency market causes the price of the stock market to fluctuate in the same direction, and the price volatility of the cryptocurrency market causes the price of the gold market to fluctuate in the opposite direction.

PENGARUH VARIABEL MAKROEKONOMI TERHADAP DANA PIHAK KETIGA PADA BANK KONVENSIONAL DI INDONESIA

This study aims to determine the effect of macroeconomic variables on the collection of third party funds at conventional banks in Indonesia. The data used is monthly time series data from January 2017 to December 2022. The method used in this study is the Vector Autoregression (VAR)/Vector Error Correction Model (VECM) method. However, in choosing between the two methods, stationarity test must be done first. The variables used include inflation, BI rate, crude oil price, and gold price. The results of the study show that the inflation variable has a positive influence, while the BI rate, crude oil price, and gold price variables have a negative influence on third party funds of conventional banks in Indonesia. This research is expected to be applied to banking policy makers with the aim of being insightful to be able to adjust policies when there is instability in macroeconomic variables and for economic policy regulators in determining policies that can stabilize the banking system. In addition, it is used as knowledge for customers to make better decisions regarding investment portfolios with the aim of identifying risks based on macroeconomic conditions.

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.

Analysis of the factors affecting the price of gold based on multiple linear regression

Gold has a variety of properties, in addition to the commodity properties, gold also has a monetary function. There are many investment values, so the price of gold is also much attention by investors. Many factors may affect the price of gold. In this paper, relevant data from December 2011 through December 2018 have been selected from a number of sources in order to provide as comprehensive a picture as possible of the various factors that may affect the price of gold. An Ordinary Least Squares regression model was used for the analysis. It was found that U.S. stock market conditions, gold mining conditions, U.S. dollar exchange rate, and crude oil-related indices have different correlation results from various perspectives of the index analysis. Besides, there is a significant positive relationship between the price of other precious metals and Gold Price. Meanwhile, The U.S. Economic Related Index has a significant negative impact relationship with Gold Price.