Forecasting of Daily Gold Price by Using Box-Jenkins Methodology

Rashid, A.; Iqbal, Muhammad; Qamar, Sadia; Akhtar, Noureen; Mahmood, Tahir; Hyder, Mehvish; Jamshed, Muhammad Tariq

doi:10.18488/journal.1/2016.6.11/1.11.614.624

Cited by 8 publications

(3 citation statements)

References 1 publication

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“…This result was in line with Mahto et al (2019), the results of his research related to commodity modeling using ARIMA also produced good modeling with MAPE 2,30%. Also based on Mahmood and Ali (2016), daily data modeling of gold prices with the ARIMA(1,1,0) model showed very good result with MAPE 0.82%. Figure 10 shows the overall comparison between actual data, model, and error of ANN 2-4-1 sigmoid architecture.…”

Section: Methodsmentioning

confidence: 98%

Model Effect of Copper Price on Freeport McMoran Stock Price

Riyanto¹,

Achsani

Santoso

2021

jabm

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The copper price in copper mining companies is an essential aspect in terms of profit, revenue, production targets, and hedging. This research aims to determine an alternative of copper price modeling and its causality relationship to Freeport McMoRan (FCX) stock price. The methods utilized in this research were Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), Genetic Algorithms (GA) and Granger Causality Test. Based on this research result, all modeling methods equally show excellent performance for modeling copper price. Another finding from this research is that the copper price positively affects the FCX stock price. Therefore, it can be concluded that the copper commodity price influences the value of a copper mining company. The results of this research can be utilized as a reference for company analysts as a part to estimate profit probability, estimate revenue, estimate production targets, and hedging strategies.

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Section: Methodsmentioning

confidence: 98%

Model Effect of Copper Price on Freeport McMoran Stock Price

Riyanto¹,

Achsani

Santoso

2021

jabm

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“…Dritsakis et al In 2018, conducted a study using the Box-Jenkins method for the problem of predicting unemployment rates in the United States [5]. Ali et al in 2016 used the Box-Jenkins method in the study to forecast gold prices [6]. Another study conducted by Adedia et al in 2018 using the Box-Jenkins method to predict miscarriage rates in Ghana [7].…”

Section: Related Workmentioning

confidence: 99%

Motorcycle Parts Sales Forecasting Using Auto-Regressive Integrated Moving Average Model

Maslim¹,

Ernawati²,

Arinanda³

2020

IJCTE

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In the business world, the need for the availability of goods is critical, especially in motorcycle workshops. The goods availability is related to problems with customer trust, loss of capital, and storage warehouse capacity. Therefore, the ability of decision makers to predict the number of sales in the coming period is essential to be able to determine the procurement of goods more precisely. There is a method called Auto-Regressive Integrated Moving Average (ARIMA). This method is one model that can be used to forecast sales based on sales time series data in previous periods. The forecasting implementation with the ARIMA model can be done using the Pmdarima 1.1.0 library for Python. The test in this study uses sales data of 62 motorcycle parts from January 2017 to February 2019. Forecasting is done to help decision-makers in determining the amount of procurement of goods to meet the sales of the next three periods.

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“…In a similar study using ARIMA, Ali et al (2016) surveyed the forecasting of the daily price movement of gold using the dataset of USD per ounce from Jan 02, 2014, to Jul 03, 2015. The research found ARIMA (1,1,0) and (0,1,1) to be close to each other in the prediction using MAE and MAPE are evaluation criteria.…”

Section: Introductionmentioning

confidence: 99%

Gold price prediction using the Box-Jenkins methodology

Babatunde¹,

Igboeli²

2020

UMIMRJ

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Information on the speculation and trading of gold abounds. Investors are attracted to moving their funds to gold as guaranteed storage of wealth, while traders capitalize on the dynamism of the market to build capital. The ups and downs in the price of gold and other precious metals can be predicted with proven mathematical and artificial intelligent algorithms. This study used machine learning algorithm in the price prediction of gold over a ten-year period. Autoregressive Integrated Moving Average (ARIMA) model was used in the experiment, while Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) evaluation metrics were used in the evaluation of the performance of the various ARIMA models. The results obtained in the study proved that ARIMA could achieve high prediction performance over the entire period of prediction. The best prediction outcome of 98.23% was obtained during the 52-week period.

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Forecasting of Daily Gold Price by Using Box-Jenkins Methodology

Cited by 8 publications

References 1 publication

Model Effect of Copper Price on Freeport McMoran Stock Price

Model Effect of Copper Price on Freeport McMoran Stock Price

Motorcycle Parts Sales Forecasting Using Auto-Regressive Integrated Moving Average Model

Gold price prediction using the Box-Jenkins methodology

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