Predicting Future Gold Rates using Machine Learning Approach

Abstract

Historically, gold was used for supporting trade transactions around the world besides other modes of payment. Various states maintained and enhanced their gold reserves and were recognized as wealthy and progressive states. In present times, precious metals like gold are held with central banks of all countries to guarantee re-payment of foreign debts, and also to control inflation. Moreover, it also reflects the financial strength of the country. Besides government agencies, various multi-national companies and individuals have also invested in gold reserves. In traditional events of Asian countries, gold is also presented as gifts/souvenirs and in marriages, gold ornaments are presented as Dowry in India, Pakistan and other countries. In addition to the demand and supply of the commodity in the market, the performance of the world’s leading economies also strongly influences gold rates. We predict future gold rates based on 22 market variables using machine learning techniques. Results show that we can predict the daily gold rates very accurately. Our prediction models will be beneficial for investors, and central banks to decide when to invest in this commodity.

Authors and Affiliations

Iftikhar ul Sami, Khurum Nazir Junejo

Keywords

Related Articles

An Agent Cellular Residential Mobility Model : From Functional and Conceptual View

Residential mobility is of great challenge to sustainable cities. Developing computer models based simulation could be a powerful tool to support informing urban decisions especially with the fact that half of the world’...

A Copula Statistic for Measuring Nonlinear Dependence with Application to Feature Selection in Machine Learning

Feature selection in machine learning aims to find out the best subset of variables from the input that reduces the computation requirement and improves the predictor performance. In this paper, a new index based on empi...

The use of Harmonic Balance in Wave Concept Iterative Method for Nonlinear Radio Frequency Circuit Simulation

This paper presents the birth of the new hybrid method for the non-linear Radio frequency circuits’ simulation. This method is based on the combination of the wave concept iterative procedure (WCIP) and the harmonic bala...

A Proposed Framework to Investigate the User Acceptance of Personal Health Records in Malaysia using UTAUT2 and PMT

Personal Health Records (PHRs) can be considered as one of the most important health technologies. PHRs enroll the patients directly to their health decision making through giving them the authority to control and share...

Performance Comparison between Merge and Quick Sort Algorithms in Data Structure

In computer science field, one of the basic operation is sorting. Many sorting operations use intermediate steps. Sorting is the procedure of ordering list of elements in ascending or descending with the help of key valu...

Download PDF file
  • EP ID EP258301
  • DOI 10.14569/IJACSA.2017.081213
  • Views 111
  • Downloads 0

How To Cite

Iftikhar ul Sami, Khurum Nazir Junejo (2017). Predicting Future Gold Rates using Machine Learning Approach. International Journal of Advanced Computer Science & Applications, 8(12), 92-99. https://europub.co.uk/articles/-A-258301