scholarly journals PENGARUH NILAI TUKAR DAN HARGA MINYAK MENTAH DUNIA TERHADAP RETURN SAHAM PT. INDOMOBIL SUKSES INTERNASIONAL TBK. DAN PT. ASTRA INTERNASIONAL TBK. TAHUN 2006-2016

2017 ◽  
Vol 1 (2) ◽  
pp. 61
Author(s):  
Arif Fadlilah ◽  
Sri Hermuningsih
Keyword(s):  
Exchange Rates ◽  
Crude Oil ◽  
Stock Prices ◽  
Stock Return ◽  
T Test ◽  
Oil Price ◽  
Crude Oils ◽  
Crude Oil Price ◽  
F Test ◽  
Crude Oil Prices

This research is meant to find out the influence of exchange rates and crude oil price either simultaneous or partial to the stock return at PT. Indomobil Sukses Internasional Tbk. and PT Astra Internasional Tbk. The data which is applied in this research is the automotive companies’ stock prices, Rupiah exchange rates, and crude oil price from 2006 to 2016. The multiple linear regressions are applied as the analysis technique by carrying out F test and t test. Based on the F test it is found that simultaneously the rupiah exchange rates and crude oil prices have influence to the stock return. Based on the t test it is found that partially the rupiah exchange rates have no influence to PT. Indomobil Sukses Internasional Tbk stock return but have influence to PT. Astra Internasional Tbk stock return and crude oils prices have influence to stock return. t test indicates the dominant influence to the stock return PT. Indomobil Sukses International Tbk is crude oils variable and stock return PT. Astra International Tbk is exchange rates variable

scholarly journals Investigation of Global Crude Oil Price Shocks verses Exchange Rates Nexus: Evidenced from Tanzanian Shillings

2020 ◽  
pp. 16-29
Author(s):  
Suma Mwankemwa ◽  
Isack Kibona ◽  
Aziza M. Said
Keyword(s):  
Exchange Rates ◽  
Crude Oil ◽  
Oil Prices ◽  
Oil Price ◽  
Series Data ◽  
Oil Price Shocks ◽  
Crude Oil Price ◽  
Price Shocks ◽  
Crude Oil Prices ◽  
Negative Effect

This study investigated the nexus of crude oil price shocks and exchange rates of Tanzanian shillings (TSh) as an oil importing country. Using weekly series data for the period 01/01/2005 to 31/12/2015, Vector Autoregressive (VAR) model was employed to test the relationship of crude oil prices and Tanzanian exchange rates. In addition, Granger Causality was tested to check the causality of these two variables. The findings of this study show that oil prices granger causes the exchange rate of TSh while exchange rates of TSh cannot Granger cause the oil prices. Also, the impulse response functions revealed that crude oil price shocks initially had a significant negative effect on TSh, however, there was a slightly negative effect on crude oil starting from TSh as a granger causer. VAR results showed that all the coefficients of TSh do not significantly influence crude oil prices. Crude oil price coefficients had a negative significance towards explaining the variability of Tanzanian shillings’ exchange rates (TZS).This revealed that a change in oil prices would precede changes in TSh movements.

scholarly journals PENGARUH HARGA MINYAK DUNIA DAN SUKU BUNGA BI TERHADAP HARGA SAHAM PERTAMBANGAN JANUARI PERIODE 2006-JULI 2008

2016 ◽  
Vol 7 (1) ◽  
pp. 9
Author(s):  
Anisa Iswandari ◽  
Nazaruddin Malik
Keyword(s):  
Crude Oil ◽  
Stock Price ◽  
Positive Influence ◽  
T Test ◽  
Oil Price ◽  
Crude Oil Price ◽  
F Test ◽  
Analysis Instrument ◽  
Independent Variable ◽  
Variable Influence

The purpose of this research has done to knowing crude oil price dan BI rate to stock price on mining. The hypothesis are guess that crude oil price influence to stock mining and BI rate influence to stock mining. Analysis instrument to knowing influence of crude oil price and BI rate is use multiple linier regression analysis. To know what is the reach of independent variable influence to dependent variable use a hypothesis testing with a partial test (t test), simultant test (f test) and to knowing how the independent variable representative to dependent variable use a godness of fit (R2). The results of hypothesis analysis shows that crude oil price and BI rate have a significant influence to stock mining. Proof from the results shows that F test 259,5605 > F table 3,32.  Partial test shows that crude oil price positive influence to mining which t test > t table (9,446>2,045) and BI rate not influence to mining which t test < t table (-12,008<2,045) with level a significant 5%.

An Empirical Investigation of Factors Affecting Stock Prices in Vietnam

2014 ◽  
pp. 74-89 ◽  
Author(s):  
Vinh Vo Xuan
Keyword(s):  
Exchange Rates ◽  
Crude Oil ◽  
Foreign Exchange ◽  
Stock Prices ◽  
Global Financial Crisis ◽  
Oil Prices ◽  
Foreign Exchange Rates ◽  
Factors Affecting ◽  
Crude Oil Prices ◽  
Before And After

This paper investigates factors affecting Vietnam’s stock prices including US stock prices, foreign exchange rates, gold prices and crude oil prices. Using the daily data from 2005 to 2012, the results indicate that Vietnam’s stock prices are influenced by crude oil prices. In addition, Vietnam’s stock prices are also affected significantly by US stock prices, and foreign exchange rates over the period before the 2008 Global Financial Crisis. There is evidence that Vietnam’s stock prices are highly correlated with US stock prices, foreign exchange rates and gold prices for the same period. Furthermore, Vietnam’s stock prices were cointegrated with US stock prices both before and after the crisis, and with foreign exchange rates, gold prices and crude oil prices only during and after the crisis.

Crude Oil Price and Food Security Related Variables in Malaysia

2016 ◽  
Vol 4 (2) ◽  
pp. 17-21
Author(s):  
Shri Dewi Applanaidu ◽  
Mukhriz Izraf Azman Aziz
Keyword(s):  
Food Security ◽  
Crude Oil ◽  
Price Index ◽  
Price Volatility ◽  
Food Price ◽  
Oil Price ◽  
Crude Oil Price ◽  
Price Changes ◽  
Oil Price Volatility ◽  
Crude Oil Prices

Objective - This study analyzes the dynamic relationship between crude oil price and food security related variables (crude palm oil price, exchange rate, food import, food price index, food production index, income per capita and government development expenditure) in Malaysia using a Vector Auto Regressive (VAR) model. Methodology/Technique - The data covered the period of 1980-2014. Impulse response functions (IRFs) was applied to examine what will be the results of crude oil price changes to the variables in the model. To explore the impact of variation in crude oil prices on the selected food security related variables forecast error variance decomposition (VDC) was employed. Findings - Findings from IRFs suggest there are positive effects of oil price changes on food import and food price index. The VDC analyses suggest that crude oil price changes have relatively largest impact on real crude palm oil price, food import and food price index. This study would suggest to revisiting the formulation of food price policy by including appropriate weight of crude oil price volatility. In terms of crude oil palm price determination, the volatility of crude oil prices should be taken into account. Overdependence on food imports also needs to be reduced. Novelty - As the largest response of crude oil price volatility on related food security variables food vouchers can be implemented. Food vouchers have advantages compared to direct cash transfers since it can be targeted and can be restricted to certain types of products and group of people. Hence, it can act as a better aid compared cash transfers. Type of Paper - Empirical Keywords: Crude oil price, Food security related variables, IRF, VAR, VDC

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

scholarly journals A Research on Markov Model and Hidden Markov Model in Crude Oil Price Forecasting for PETRONAS Malaysia

2019 ◽  
Vol 8 (12S2) ◽  
pp. 121-125 ◽  
Keyword(s):  
Markov Model ◽  
Crude Oil ◽  
Hidden Markov Model ◽  
Structural Equation ◽  
Hidden Markov ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Accurate Forecast

Crude oil price forecasting is an essential component of sustainable development of many countries as crude oil is an unavoidable product that exists on earth. In this paper, a model based on a hidden Markov model and Markov model for crude oil price forecasting was developed, and their relative performance was compared. Path analysis of Structural Equation Modelling was employed to model the effects of forecasted prices and the actual crude oil price to get the most accurate forecast. The key variables used to develop the models were monthly crude oil prices s from PETRONAS Malaysia. It was found that the hidden Markov model was more accurate than the Markov model in forecasting the crude oil price. The findings of this study show that the hidden Markov model is a potentially promising method of crude oil price forecasting that merit further study.

scholarly journals Data Size Requirement for Forecasting Daily Crude Oil Price with Neural Networks

2019 ◽  
Vol 66 (3) ◽  
pp. 363-388
Author(s):  
Serkan Aras ◽  
Manel Hamdi
Keyword(s):  
Neural Networks ◽  
Crude Oil ◽  
Significant Interaction ◽  
Statistical Tests ◽  
Oil Price ◽  
Training Data ◽  
Forecasting Model ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Selection Of

When the literature regarding applications of neural networks is investigated, it appears that a substantial issue is what size the training data should be when modelling a time series through neural networks. The aim of this paper is to determine the size of training data to be used to construct a forecasting model via a multiple-breakpoint test and compare its performance with two general methods, namely, using all available data and using just two years of data. Furthermore, the importance of the selection of the final neural network model is investigated in detail. The results obtained from daily crude oil prices indicate that the data from the last structural change lead to simpler architectures of neural networks and have an advantage in reaching more accurate forecasts in terms of MAE value. In addition, the statistical tests show that there is a statistically significant interaction between data size and stopping rule.

scholarly journals Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Daniel Štifanić ◽  
Jelena Musulin ◽  
Adrijana Miočević ◽  
Sandi Baressi Šegota ◽  
Roman Šubić ◽  
...  
Keyword(s):  
Crude Oil ◽  
Stock Prices ◽  
Short Term Memory ◽  
Oil Price ◽  
Stationary Wavelet Transform ◽  
Crude Oil Price ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
The Impact

COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&P 500, and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM + WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.

The optimal thermal causal path analysis on the relationship between international crude oil price and stock market

Kybernetes ◽  
2018 ◽  
Vol 47 (6) ◽  
pp. 1242-1261 ◽  
Author(s):  
Can Zhong Yao ◽  
Peng Cheng Kuang ◽  
Ji Nan Lin
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Stock Markets ◽  
Oil Prices ◽  
Oil Price ◽  
Chinese Stock Market ◽  
Crude Oil Price ◽  
Content Type ◽  
Crude Oil Prices ◽  
Causal Path

Purpose The purpose of this study is to reveal the lead–lag structure between international crude oil price and stock markets. Design/methodology/approach The methods used for this study are as follows: empirical mode decomposition; shift-window-based Pearson coefficient and thermal causal path method. Findings The fluctuation characteristic of Chinese stock market before 2010 is very similar to international crude oil prices. After 2010, their fluctuation patterns are significantly different from each other. The two stock markets significantly led international crude oil prices, revealing varying lead–lag orders among stock markets. During 2000 and 2004, the stock markets significantly led international crude oil prices but they are less distinct from the lead–lag orders. After 2004, the effects changed so that the leading effect of Shanghai composite index remains no longer significant, and after 2012, S&P index just significantly lagged behind the international crude oil prices. Originality/value China and the US stock markets develop different pattens to handle the crude oil prices fluctuation after finance crisis in 1998.

Common Trends and Common Cycles in Oil Price and Real Exchange Rate

2014 ◽  
Vol 14 (2) ◽  
pp. 249-263 ◽  
Author(s):  
Hem C. Basnet ◽  
Puneet Vatsa ◽  
Subhash Sharma
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Crude Oil ◽  
Real Exchange Rates ◽  
Oil Prices ◽  
Oil Price ◽  
Cycle Decomposition ◽  
The Real ◽  
Crude Oil Prices ◽  
Common Cycles

This study explores the long- and short-run movement between oil prices and the real exchange rates of two large oil-exporting countries – Canada and Norway. Cointegration and serial correlation common features tests are jointly used to identify the long-term common trend and short-term common cycles. Our test results find that oil prices and the real exchange rates of the Canadian Dollar and the Norwegian Krone have two shared trends and one shared cycle. The trend–cycle decomposition shows a great deal of positive comovement among the trend and cyclical components. The two currencies show economic dynamics very similar to crude oil prices. They do not exhibit any qualitative differences in the trajectory of the trend and cycles when controlling for different crude oil prices. Our results indicate that oil price fluctuations play significant role in explaining the exchange rate movements of oil-exporting countries.

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