Gold Stock Research Articles

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.

Aplikasi Kasir Online Berbasis QR-Code pada Toko Emas Karunia Cepu

Karunia gold shop, as one of the gold trading retail players, has several branches in Cepu City and its surroundings. In order to improve operational sales efficiency, this shop will adopt the latest technology by implementing a sales information system. The purpose of this study is to develop an online cashier system based on QR Code at the Karunia gold shop for purchase/sale and payment transactions. Jewelry transaction data from 2018 was IDR 150,594,000, in 2019 it was IDR 109,309,000, and 2020 it was IDR 120,683,000. The structural model system development method uses web-based PHP programming and a MySql database. The results of the black box testing of the new system obtained a functional value of 72.3%. The final result is an Online Cashier System with QR Code which has the potential to increase transaction speed, facilitate gold stock management, and provide a different experience to customers in making effective, efficient and better transactions.

The gold stock nexus: Assessing the causality dynamics based on advanced multiscale approaches

This paper relights the discourse surrounding the gold-stock nexus, delving into intrinsic cross-correlations and causalities through a trio of multiscale-based methodologies. Leveraging the adaptive characteristics of wavelet analysis, empirical mode decomposition, and swarm filtering, we construct bivariate bidirectional causality curves. Our analysis scrutinizes the dynamic lead-lag relationships by investigating intrinsic multiscale cross-correlations between gold-S&P 500 and gold-DJIA pairs. This comprehensive examination sheds light on the underlying mechanisms governing the gold and stock markets, especially during the turbulent 2020–2023 period, marked by the influence of the COVID-19 pandemic and the Russian war. The study also extends its purview to the relatively tranquil periods, encompassing over 15 years of daily observations in various sub-samples. Notably, our primary contribution lies in providing a profound analysis of the gold-stock nexus, utilizing advanced multiscale Granger causality estimators. Our findings from time-frequency cross-correlations reveal the presence of unidirectional and bidirectional causality, characterized by varying degrees of strength contingent on scales and pairs. The S&P 500 and DJIA indices emerge as leading indicators, reciprocally influencing gold prices, and vice versa. These initial outcomes are substantiated by results obtained through three Granger causality tests, affirming bidirectional causal relationships and highlighting the significance of heterogeneous interactions among the considered pairs. This study enhances our understanding of gold's role as a haven or hedge, especially during periods of financial turbulence, empowering market participants to formulate strategies founded on credible expectations. The incorporation of multiscale analyses, with their flexible properties, ensures a more comprehensive assessment, unveiling hidden information across time scales.

Stock Market Prediction Using Deep Reinforcement Learning

Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has introduced advanced predictive algorithms, reshaping investment strategies. Essential to this transformation is the profound reliance on historical data analysis, driving the automation of decisions, particularly in individual stock contexts. Recent strides in deep reinforcement learning algorithms have emerged as a focal point for researchers, offering promising avenues in stock market predictions. In contrast to prevailing models rooted in artificial neural network (ANN) and long short-term memory (LSTM) algorithms, this study introduces a pioneering approach. By integrating ANN, LSTM, and natural language processing (NLP) techniques with the deep Q network (DQN), this research crafts a novel architecture tailored specifically for stock market prediction. At its core, this innovative framework harnesses the wealth of historical stock data, with a keen focus on gold stocks. Augmented by the insightful analysis of social media data, including platforms such as S&P, Yahoo, NASDAQ, and various gold market-related channels, this study gains depth and comprehensiveness. The predictive prowess of the developed model is exemplified in its ability to forecast the opening stock value for the subsequent day, a feat validated across exhaustive datasets. Through rigorous comparative analysis against benchmark algorithms, the research spotlights the unparalleled accuracy and efficacy of the proposed combined algorithmic architecture. This study not only presents a compelling demonstration of predictive analytics but also engages in critical analysis, illuminating the intricate dynamics of the stock market. Ultimately, this research contributes valuable insights and sets new horizons in the realm of stock market predictions.

General Assessment of the Impact of the War in Ukraine on the Shipping Industry Using Parametric Methods

The ongoing Ukrainian war has introduced significant uncertainty and crisis into the global economy, particularly in financial and stock markets. This study is a part of a larger research project that aims to assess the impact of the conflict between Russia and Ukraine on shipping companies, given the direct influence of rising gas and oil prices on the valuation of freight transport service providers. Economic sanctions were imposed on Russia, leading to the destabilization of the global economy. Industries such as the global shipping and tourism sectors experienced significant declines in share value. Investors began reallocating their portfolios, seeking safer and less risky investments, such as gold stocks. The riskiness of a particular stock can be assessed by various methods, including volatility measurement. This paper focuses on calculating and presenting the volatility value of the shares of A. P. Moller-Maersk, the world's largest operator of container lines and vessels. Additionally, the STOXX50 index, i.e. the Euro Zone stock market index, representing the overall European market, is used for comparison. Another risk measure discussed in this paper is Value at Risk (VaR), a quantitative method used to predict potential cash losses over a certain time period. The parametric method of calculating VaR was used, which assumes the normal distribution of stock value fluctuations. VaR was calculated using historical stock price data of A. P. Moller-Maersk. Findings indicate significant volatility and high-risk environment in the financial markets. The calculated VaR of 27.66% for a 30-day period with the 95% confidence level reflects the substantial potential losses associated with investing in Maersk shares during the crisis, surpassing typical risk levels. In conclusion, the war in Ukraine has disrupted the maritime industry, which was already recovering from the impact of the COVID-19 pandemic. The sanctions imposed on Russia and the war situation in Ukraine have created uncertainty and turbulence in financial markets, prompting investors to seek safer investment options. The study emphasizes the need for continued monitoring of the impact of the war on the global maritime industry. The devastating effects of the war on the sector have significant implications for the global economy, human well-being, and future research in the field.

Gold Is Old: Noble Metal in the Indian Economy Through Ages

Indians have had a fascination for possessing gold since ancient times. I document the sacredness of gold, ancient references to jewellery, gold extraction and minting technology, its function as money and store of value, and specie inflow due to trade surplus with the Occident up until the medieval era. India’s gargantuan stock of about 28,000 tonnes of gold is hoarding and not savings. Today, gold demand is causing leakage from the circular flow of gross domestic product and trade deficit. Policy suggestions are made that will re-channel idle excess stock of gold into financial markets, reduce the trade deficit and contribute to gross domestic product growth.

Model Exponential GARCH (EGARCH) Untuk Memprediksi Harga Saham PT Merdeka Copper Gold Tbk

The price of gold is determined by trading in the gold market and its derivatives. This study aims to determine the EGARCH model in predicting gold stock prices at PT Merdeka Copper Gold Tbk and to obtain the prediction results of the EGARCH model at PT Merdeka Copper Gold Tbk. There are two asymmetric GARCH response modeling techniques, namely the Threshold GARCH model (TGARCH) from Glosten, et al (1993) and Exponential GARCH (EGARCH) from Nelson (1991). The research results show that the ARIMA model (0,1,1) has the smallest AIC value of 6096.38. The GARCH model is obtained by GARCH (0,1), so it can be modeled using the EGARCH model. The EGARCH model obtained is EGARCH (1.7).

A STUDY ON FINANCIAL PLANNING INFLUENCE THE INVESTMENT DECISION OF NON-TEACHING WOMEN STAFFS IN COLLEGES WITH SPECIAL REFERENCE OF COIMBATORE CITY

The main objective of the research is to determine the financial planning and investment decision of non-teaching staff in colleges done in Coimbatore city. Sample size of study is 90 and use cluster sampling technique is used for data collection. The major finding is most of the respondents are willing to take moderate risk investment, they expect 8-10% of returns from their investment, 25.56% of respondents are invest in physical gold. From the study all the respondents have known the ETF gold stock Mutual fund but them don’t have willing to invest in long term investments. Keywords: Financial planning, Investment decision, Investment pattern

The social relations of gold: How a gendered asset serves social reproduction and finance in Pakistan

AbstractWomen's gold jewelry, particularly in the context of the Indian subcontinent, has been referred to as a barbaric relic, an uncivilized obsession, and a leakage from the economy. Yet women in the region continue to save almost exclusively in gold. In dismissing these savings as a leakage and a waste, we miss a critical opportunity to understand the social relations of gold, where both gold and land are gendered assets. Using the lens of transnational feminism, I draw upon two empirical studies from Pakistan to show that gold is for the most part the only investment women own and control. In the era of liberalization and fiscal discipline, as economic vulnerabilities deepen and Southern states are forced to cut back on welfare, impoverished women's meager gold stocks play a critical role in supporting social reproduction. Attracted by the nonfiat nature of gold, commercial and microfinance banks exploit these sociocultural and economic realities in the pawn market for women's gold. This has critical implications for women's agency, which is relational and fluid in the present context, resulting in its subversion by the gendered regimes of finance capitalism.

Oil, Gold and International Stock Markets: Extreme Spillovers, Connectedness and its Determinants

Oil, Gold and International Stock Markets: Extreme Spillovers, Connectedness and its Determinants

Quantile Dependence and Portfolio Management between Oil, Gold, Silver and MENA Stock Markets

Quantile Dependence and Portfolio Management between Oil, Gold, Silver and MENA Stock Markets

Country risk and the interaction between gold price and gold stock index return volatilities: evidence from the South African market

Country risk and the interaction between gold price and gold stock index return volatilities: evidence from the South African market

The Prediction of Gold Price Movement by Comparing Naive Bayes, Support Vector Machine, and K-NN

Gold is a yellow precious metal that can be forged so it is easy to form with various forms of jewelry such as pendants, earrings, rings, bracelets and others, gold has a high value. Gold itself is an exchange rate used in ancient times before the existence of money as it is today. Gold also can be used as an investment that is profitable for the investor and it has less risks. Investment is a form of fund management to give benefit by putting fund in allocation that is predicted will give additional benetifs. Prediction of gold price movements or predictions of gold price in gold stock investment, this research uses 3 (three) algorithms that will be implemented in analysis and increase accuracy, in the discussion or research that was made using the Naïve Bayes algorithm, Support Vector Machine and K-Nearest Neighbor, the dataset is obtained from the website, namely www.finance.yahoo.com the data was then tested using Rapid miner tools so that the average value of the Support Vector Machine algorithm with an accuracy rate of 57.59%, precision 58 ,73% and recall 51,78%. The next is the Naïve Bayes algorithm so that it is known to have an accuracy rate of 55.59%, precision 54.55% and recall 51.70%. Based on the comparison of the three algorithms, it is known that the one with the best accuracy, precision, and recall is the K-NN algorithm with 61.90% accuracy, 60.98% precision, and 60.35% recall. Furthermore, the results of testing the K-Nearst Neighbor algorithm have good results compared to the 3 (three) other algorithm tests and the Naïve Bayes algorithm testing has a low level of accuracy, namely 55.59%, precision 54.55% and recall 51.70%. The research uses 3 algorithms, namely naive bayes, K-nearst neighbor and Support Vector Machine, because the three algorithms are well-established algorithms to be applied to research, especially in time series gold price research and are very good, especially for classification

Hedging UK stock portfolios with gold and oil: The impact of Brexit

The purpose of this paper is to examine dynamic co-movements and portfolio management strategies between UK stock indices (aggregate market index and sector indices) and each of gold and crude oil futures markets, during the Brexit referendum (2016) and the Brexit day (2021). Using multivariate GARCH models, the time-varying conditional correlations between the focal markets are extracted. Then, portfolio weights, hedge ratios, hedging effectiveness, and downside risk reductions are determined. The results reveal that, during the Brexit referendum period, gold and crude oil provide a diversification opportunity for the UK stock portfolios. Specifically, investors should give more weight to gold than stocks in their gold–stock portfolios, whereas they should allocate more funds into stocks than oil to minimize risk in their oil–stock portfolios. Based on hedging analysis results, crude oil appears more effective than gold in reducing stock portfolio exposure to downside risk and in improving the hedging effectiveness. Further analysis involving an event study considers the potential shift in the correlations between the UK stock market index (sector indices) and each of gold and crude oil during short-term event widows (one day, one day; five days, five days; and ten days, ten days) around the announcement day of the initial, transmission and actual Brexit periods or during each of these sub-periods. The results show that early news about Brexit led to a substantial change in the means of correlation coefficients in comparison to the tranquil period, where the initial UK stock price movements were driven by fears of a cyclical downturn following the referendum.

Stability of gold standard and its selected consequences: a comment

Kvasnicˇka (2007) attempts to demonstrate that the gold standard may be an unlikely alternative for small economies to revert to. The crux of his argument deals with supply considerations that could prove fatal to a fledgling currency. An influx of gold from the world’s existing gold stock, or its new production could prove to be a destabilising supply-side factor undermining the usability, and hence, adoption, of such a monetary system. We find fault with this for two main theoretical reasons. First, a distinction must be made between gold stock, and monetary gold. Second, the mechanism that creates monetary gold works as a natural break on unrestrained supply growth. Several additional clarifications are necessary as well, among them the role money supply plays in the trade cycle, and the casual relationship that exists between supply and price. Finally, an insightful comparison used linking monetary gold in a small economy to the use of cigarettes in a Second World War P.O.W. camp must be slightly altered to make a true comparison. Kvasnicˇka does a great service to free-marketers by providing a theoretical attack on the choice of gold as a medium of exchange, however, in light of this paper’s theoretical conclusions, the free-market defence of commodity money endures.
 JEL Classification: B53 – Austrian Economics, E42 – Monetary Systems, E51 – Money Supply.

Stability of Gold Standard and Its Selected Consequences

The gold standard was quite stable in the past: the price level changes were relatively small, and trade cycles mild. However, its past stability does not guarantee stability nowadays. We show that the stability of the gold standard to shocks stemming from the world gold market depends critically on the size of the monetary stock of gold relative to the extent of these shocks. Every change decreasing the relative size of the monetary stock of gold lowers its stability. We discuss some consequences of the thesis too: first, any system economizing on its gold reserves (e.g. the mature fractional reserve free banking system of the Scottish type) may undermine the stability of the gold standard. Second, an attempt to reestablish the gold standard may have to include a collective action of many countries of a great economic power. If a single small country tried to resume to the gold, its monetary stock of gold would probably be tiny relative to the world gold market shocks, and the economy could be destabilized by these shocks. It makes the return of the gold standard rather improbable in the near future.
 Key words: Gold Standard, Stability, Inflation, Trade Cycle.
 JEL Classification: E31, E39, E42, N10.

Stability of gold standard and its selected consequences: A reply to prof. Howden

Since I am naturally biased in favor of the gold standard, I was glad when I found that Prof. Howden (2008) exerted effort to uproot the thesis presented in my paper «Stability of Gold
 ˆ
 Standard and Its Selected Consequences» (Kvasnicka, 2007) that the price level could be very unstable under the gold standard if the overall monetary stock of gold is relatively small, and one of its consequences, that this instability makes an independent restitution of the gold standard in a small country unlikely. No one would be happier than me if he succeeded. However, I will claim here that his critique failed. To show why, I will first summarize major points of my former paper and then comment on Prof. Howden’s critique. Since Prof. Howden concentrates only on the feasibility of the restitution of the gold standard in the present world, I will not defend all the propositions made in my former paper, but will confine myself to the question whether a small country adopting the gold standard independently in the present world would or would not suffer from the price level instability and trade cycles caused by such a gold standard.

Gold Price, Inflation, and Dollar Exchange Rate: The Case of Gold Investment in Indonesia

This paper aims to investigate the relationship between gold price, inflation and the dollar exchange rate on gold stock price of PT Aneka Tambang Indonesia. The ARDL time series model is used in the study involving PT Aneka Tambang Indonesia from December 2016 to November 2019. The findings show the price of gold doesn’t have a significant effect on the gold stock price while dollar exchange rate and inflation have a negative effect on the short and long term of the gold stock price of PT Aneka Tambang Indonesia.

Country risk and the interaction between gold price and gold stock index return volatilities: evidence from the South African market

Country risk and the interaction between gold price and gold stock index return volatilities: evidence from the South African market

Re-examining the real option characteristics of gold for gold mining companies

This paper re-examines the static and time-varying asymmetries in quantile-specific relationship between gold returns and gold mining companies' stock returns. In doing so, we use novel cross-quantilogram approach that is built on quantiles of both distributions and control for the effect of crude oil price changes, overall stock market movements, stock market volatility and economic policy uncertainty. We find that the gold returns negatively predict the gold stock returns around the median quantile. Whereas the gold returns positively depend on the gold mining companies’ stock returns in the low and high quantiles. We therefore, support for an embedded real option in gold for gold mining companies which is time-varying and occurs in very short-run. This finding is useful for devising portfolio allocation and diversification strategies.