Literature Review

The historical data proves that, gold was utilized as a form of currency in various countries, and the United States of America is not an exception. Even today, gold has managed to retain its value. Gold is used as a means to assess the country’s financial strength. India is among those countries which buys gold and the USA, South Africa, and Australia are those countries which sells gold. When compared to alternate investment options, gold investment is considered as the safe investment, by the small investors. As, this commodity has the capacity to bear the in-built investment risks. The financial conditions of a country helps the government to make governmental investments in gold, and interest rates. Because, the financial condition indicates the country’s economic strength. If the Global investors predict drastic decline in the gold rates, then they find other place to invest. In general, the gold spot rates are decided two times per day depending on the supply and demand in the gold market. Any form of fractional change in the gold price could turn out as a huge profit or loss for the investors and the government banks. Therefore, a daily forecast on rise and decline of the gold rates, is beneficial for the investors in deciding when to purchase or sell the commodity.

The purpose of this document is to set out the requirements for predicting the factors which mainly impacts the gold price fluctuation, in the Indian market.

The scope of the project is to utilize the data mining techniques to help in the prediction of fluctuating gold price.

The objectives of this project is to stress on the Data mining techniques like, classification, clustering, regression methods, decision tree, to predict the fluctuation in gold price in Indian market, for a certain period. Followed by, reading negative consequences, and to find and prove which other factors could affect the fluctuation of price. The evidence base will be presented, by undertaking a regular search of current reviews.

The risks for the project include inflation, monetary policy, economic data, supply and-demand and currency movements, as these factors fluctuates the gold price. 

According to [1], the authors have proved the importance of gold and its fluctuating price prediction. Gold is valued and is utilized as a means to assess the country’s financial strength. The daily forecast on rise and decline of the gold rates, is said to benefit the investors. So, a prediction models are developed in this research. This research predicts the future gold rates depending on 22 market variables, with the help of machine learning algorithms. The represented results denote that the daily gold rates are predicted accurately. The developed prediction models are said to beneficial the investors and the central banks, for deciding on time to invest in gold. It is observed that predicting the gold rate is not an easy task. This study is comprehensive to date, so the various countries and companies’ economic indicators are considered.  To the contrary we show that stock value of a major company has more influence on the gold rates than US economy. In future, we intend to improve our results by using ensemble learning, and deep learning.

Data Virtualization

As per [2], Develop a forecasting model for predicting and forecasting gold prices based on economic factors such as inflation, currency price movements and others. For investing the money, investors are putting their money into gold because gold plays an important role as a stabilizing influence for investment portfolios. Due to the increase in demand for gold in India, it is necessary to develop a model that reflects the structure and pattern of gold market and forecast movement of gold price. The most appropriate approach to the understanding of gold prices Support vector Regression and decision tree model. The experimental result will show the better performance from these two (Decision tree algorithm and support vector regression algorithm) algorithms.

It is stated in [3] that, the global gold market has recently attracted a lot of attention and the price of gold is relatively higher than its historical trend. For mining companies to mitigate risk and uncertainty in gold price fluctuations, make hedging, future investment and evaluation decisions, depend on forecasting future price trends. The first section of this paper reviews the world gold market and the historical trend of gold prices from January 1968 to December 2008. This is followed by an investigation into the relationship between gold price and other key influencing variables, such as oil price and global inflation over the last 40 years. The second section applies a modified econometric version of the long-term trend reverting jump and dip diffusion model for forecasting natural-resource commodity prices. This method addresses the deficiencies of previous models, such as jumps and dips as parameters and unit root test for long-term trends. The model proposes that historical data of mineral commodities have three terms to demonstrate fluctuation of prices: a long-term trend reversion component, a diffusion component and a jump or dip component. The model calculates each term individually to estimate future prices of mineral commodities. The study validates the model and estimates the gold price for the next 10 years, based on monthly historical data of nominal gold price.

According to [4], The time series of gold price in the Indian market and the global consumer price index for the period of January 1985 to June 2013 are analyzed in terms of the multiracial detruded fluctuation analysis (MF-DFA). Multifractal variables, such as the generalized Hurst exponent, the multifractal mass exponent, the singularity spectrum, are extracted for both the series. Special emphasis is given on the possible source(s) of correlations in these series. The multifractal results are fitted to the generalized binomial multifractal model consists of only two parameters. Our analysis show that the multifractal nature of the Indian gold market time series and the global consumer price index series is due to both the long-range temporal correlation and the fat-tailed probability density function of the values. Surprisingly, the series are well described by the two-parameter binomial multiracial model used.

Result of Research

The data is gathering from website, which contains the years and the gold value in the respective year (in Indian Currency).

Data virtualization of provided data set is illustrated as below. The provided data set has five variables such as gold prices, year, gold growth, gold inflation and gold quantity. These are visualized in below.

The above figures illustrate the data virtualization process, to represent the value of gold in the Indian market. The data virtualization is done by using the Tableau.

Throughout the research methodology, my efforts need to be on the prediction of new data. Machine Learning accurately suggests computers with the ability of learning from existing data, which can be useful for evidence acquisition.  I am going to practise multiple regression analysis for the exploration issue. I will join all the vital information and make it one document and will utilize SPSS, Python for exploratory and for visual effects I am going to use Tableau. I think the I am going to face bit issues finding the data sets and arranging them in to usual format, however not many with the correct data which I will require to attempt the exploration. What’s more, the other test can view programming as it could challenge for me by not getting expected results. No need for traveling for my work and every one of the materials can be collected from online sources. In SPSS statistics, we are using the two analyses to predicting the predicting the factor which mainly impacts the gold price fluctuation, in the Indian market. They are,

• Descriptive Statistics
• Linear Regression

The outcome of this study would be able to declare the significant influences that are affecting, at the same time fewer affecting factors of gold price in India, so that the Indian gold market will be stabilized, considering my results of this research. Gold is often considered as a safe option by investors and moreover the demand and price of gold goes up during political chaos as compared to peaceful times. It going to change the views of the gold investors in India as well as customers about the price fluctuation.

Descriptive Statistics

The descriptive Statistics is performed, where the mean for Gold Growth is 3007277.87, gold inflation is 30440.73, gold quantity is 2498.60 and a gold price is 45069.45999999999000. And, where the Standard deviation for Gold Growth is 1336036.218, gold inflation is 13413.055, gold quantity is 654.543and gold prices is 27859.752692441474000. The dependent variables (DV) are Year. The Independent variables (IV) is Gold quantity, Gold growth, gold inflation and gold prices.

Linear Regression

In linear regression, the dependent variable (DV) is Gold quantity. The Independent variables (IV) is Gold growth, gold inflation and gold prices. The Linear regression analysis is used to determine the gold inflation based on gold quantity. It is illustrated as below.

The histogram is illustrated in the below figure, where the mean is 10.32E-15, standard deviation is 0.886 and N is 15.

The Normal P-P Plot of regression Standardized residual dependent variable, Gold Quantity is plotted as shown in the below graph.

Gold is being favoured because of frail money related markets. Gold is contrarily identified with stocks, bonds and land. Money market has been demonstrating unpredictable conduct and this has again reflected in gold costs. Gold is as yet favoured as the others are not as sheltered as ventures as gold. Financing costs and Inflation Due to rising swelling on the ascent and RBI climbing loan costs routinely, gold costs have turned out to be shaky. Rising swelling has expanded gold costs while rising financing costs lead to a fall in gold costs. The gold has still ascended as the impact of swelling has been generous when contrasted with financing costs. Rising white collar class In India working class is rising which prompted an expansion sought after of gold. This prompts expanding gold costs. Worldwide Production Costs From numerous years the pattern of gold is in every case high as different elements impact the dimension of interest. It is a characteristic mineral, the gold isn’t sustainable. In this manner the measure of gold mined every year will diminish yet the measure of interest for valuable metals is expanding each year. The shortage in the long haul lead to gold costs will additionally rise [5].

Gold has dependably been viewed as a decent fence against swelling. Rising swelling rates ordinarily acknowledges gold costs. Customary hypothesis infers that the general cost of customer products and of such genuine resources as land and gold ought not to be forever influenced by the rate of expansion. An adjustment in the general rate of expansion should, in harmony, because an equivalent change in the rate of swelling at every benefit cost while computing the cost of gold there are two swelling rates. One is Gold interior swelling rate, which is change in its generation from its mines. Other is financial swelling. The cost of gold over the medium to long haul is controlled by its swelling rate in respect to that of the cash you need to quantify it with. With most fiat cash expansion rates, running considerably higher than gold’s swelling rate it is anything but difficult to perceive any reason why the gold cost will keep on expanding after some time, and why it has reliably expanded after some time. This isn’t going to change paying little mind to momentary instability.

It might be reasoned that the normal yearly development is 12.27 percent which shows that interest in gold is a compelling speculation road in the hand of financial specialists. The ongoing patterns of the gold cost have lead to gold’s “place of refuge” venture alternative. Speculator deleveraging and capital departure from the Euro has constrained the US dollar higher, hosing gold’s affectability to fundamental hazard and hampering its execution since September 2011. Regardless of whether, with task curve arriving at an end in the following couple of months, US business information demonstrating a stagnating US work market and Europe’s lawmakers still a long way from an exhaustive answer for the locale’s issues, the probability of another round of US quantitative facilitating is expanding. Be that as it may, high effect danger of an all out Euro emergency could likewise be an impetus at the gold cost to break higher. The flexibility of gold amid late unpredictability in the products advertises represents the quality of the worldwide gold market and its novel interest drivers. Customer trust in India has been thumped by the constancy of high local expansion rates. Expansion of relatively 9.5 percent, as estimated by the Wholesale Price Index (WPI), antagonistically influenced adornments request, through its effect on both discretionary cash flow levels and general purchaser pitch [6].

Conclusion

Any country’s financial conditions help the government to work on governmental investments in gold, and interest rates. The fractional change in the gold price can result in huge profit or loss for the investors and the government banks. Hence, for the investors, daily forecast related to the rise and decline of gold rates is necessary, as it helps to decide when to purchase or sell the commodity. The project concludes that there exists certain factors which significantly influences the gold price in India, for stabilizing the Indian gold market. The objectives of this project is met, as the data mining techniques like, classification, clustering, regression methods, decision tree, are utilized for predicting the fluctuation of gold price in the Indian market, for a certain period. Then, the reading negative consequences, and finds the other factors which could affect the fluctuation of price. The evidence base is presented. This report also projects the role played by the commodity (gold) in the Indian Market. Gold is always believed to be the safe option by the investors. Moreover, the demand and price of gold can increase during the political chaos, when compared to the peaceful times, which changes the gold investors and customers’ views on India, about the price fluctuation. This project predict the gold prices, accurately. It is observed that this project includes risks like, inflation, monetary policy, economic data, supply and-demand and currency movements, as these factors fluctuates the gold price. Additionally, it is understood that, the historical data has proofs which shows that the commodity-gold was used as a form of currency in several countries. The country’s financial strength is assessed by, gold. This commodity is sold and purchased by some countries, whereas India is among those countries which buys gold. It is agreed that, in contrast to various alternative investment options, the gold investment is a safe investment, for the investors, as gold has the capacity to bear the in-built investment risks.

References

D. Hankerson, G. Harris and P. Johnson, Introduction to information theory and data compression. Boca Raton, Fla.: Chapman & Hall/CRC Press, 2010.

D. Dr. Sindhu, “A study on impact of select factors on the price of Gold”, IOSR Journal of Business and Management, vol. 8, no. 4, pp. 84-93, 2013.

K. K.S.Nemavathi and D. Dr. V.R Nedunchezhian, “A Study on Impact of Price Behaviour of Commodity Gold and Gold ETF”, International Journal of Scientific Research, vol. 2, no. 8, pp. 240-241, 2012.

S. Sinha and D. Dutta, “An Assessment of Impact of Domestic Price of Gold on NAV of Selected Gold Exchange Traded Funds”, Adhyayan: A Journal of Management Sciences, vol. 6, no. 1, 2016.

A. Erdo?du, “The Most Significant Factors Influencing the Price of Gold: An Empirical Analysis of the US Market”, Economics World, vol. 5, no. 5, 2017.

L. Gaspareniene, R. Remeikiene, A. Sadeckas and R. Ginevicius, “Gold Investment Incentives: An Empirical Identification of the Main Gold Price Determinants and Prognostication of Gold Price Future Trends”, Economics & Sociology, vol. 11, no. 3, pp. 248-264, 2018.