scholarly journals Leadership Sentiment and Price Fluctuations

2021 ◽  
Vol 1 (2) ◽  
pp. 1-36
Author(s):  
Tim Herzhoff ◽  
Thomas Teichert
Keyword(s):  
Crude Oil ◽  
Sentiment Analysis ◽  
Global Economy ◽  
Learning Algorithm ◽  
Oil Price ◽  
Price Fluctuations ◽  
West Texas ◽  
Crude Oil Prices ◽  
Threshold Price ◽  
Speech Transcripts

The relationship between prices and leadership mood was examined using the example of crude oil prices and the sentiment of the CEOs of 5 leading oil production companies between 2014 and 2019. The crude oil market was chosen due to recent price fluctuations and upcoming challenges, and for its significant for the global economy. This study uses an empirical approach based on a mood analysis of the CEO's natural language. 160 speech transcripts were analyzed using a leading aspect-based sentiment analysis machine learning algorithm to obtain sentiment data. The relation between sentiment and oil price was tested using linear regression. The results of this study show in detail that the sentiment correlates positively in times of low prices and negatively in times of high prices. The average threshold price calculated using this method was 63 USD per barrel of West Texas Intermediate (WTI) crude oil in the observed period. This corresponds to analysts who estimated the ideal oil price for 2018 at USD 60 to 70. Finally, the restrictions and prospects are discussed. Findings of this study could aid investors decision making and advance the use of sentiment analysis in economic sciences.

scholarly journals Relationship between International Crude Oil Price and Indian Stock Market (NSE)

2011 ◽  
pp. 63-73
Author(s):  
Rajendra Mahunta
Keyword(s):  
Crude Oil ◽  
Global Economy ◽  
Stock Exchange ◽  
Oil Prices ◽  
Oil Price ◽  
Raw Material ◽  
Crude Oil Price ◽  
New Era ◽  
Crude Oil Prices

In this new era of economic growth, the exceptional increase in the crude oil prices is one of the significant developments that affect the global economy. Crude oil is an important raw material used for manufacturing sectors, so that increase in the price of oil is bound to warn the economy with inflationary inclination. The study examine the long-term relationships between CNX NIFTY FIFTY index of National Stock Exchange and crude price by using various econometric test. The surge in crude oil prices during recent years has generated a lot of interest in the relationship between oil price and equity markets. The study covers the period between 01.01.2010 and 31.12.2014 and was performed with data consisting of 1245 days. The empirical results show there was a cointegrated long-term relationship between CNX index and crude price. Granger causality results reveal that there is unidirectional causality exists and crude oil price causes NSE (CNX) but NSE (CNX) does not cause oil price.

scholarly journals Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3603 ◽  
Author(s):  
Taiyong Li ◽  
Yingrui Zhou ◽  
Xinsheng Li ◽  
Jiang Wu ◽  
Ting He
Keyword(s):  
Crude Oil ◽  
Ridge Regression ◽  
Global Economy ◽  
Evaluation Criteria ◽  
Oil Prices ◽  
Ensemble Empirical Mode Decomposition ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Crude Oil Prices

As one of the leading types of energy, crude oil plays a crucial role in the global economy. Understanding the movement of crude oil prices is very attractive for producers, consumers and even researchers. However, due to its complex features of nonlinearity and nonstationarity, it is a very challenging task to accurately forecasting crude oil prices. Inspired by the well-known framework “decomposition and ensemble” in signal processing and/or time series forecasting, we propose a new approach that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), differential evolution (DE) and several types of ridge regression (RR), namely, ICEEMDAN-DE-RR, for more accurate crude oil price forecasting in this paper. The proposed approach consists of three steps. First, we use the ICEEMDAN to decompose the complex daily crude oil price series into several relatively simple components. Second, ridge regression or kernel ridge regression is employed to forecast each decomposed component. To enhance the accuracy of ridge regression, DE is used to jointly optimize the regularization item, the weights and parameters of each single kernel for each component. Finally, the predicted results of all components are aggregated as the final predicted results. The publicly available West Texas Intermediate (WTI) daily crude oil spot prices are used to validate the performance of the proposed approach. The experimental results indicate that the proposed approach can achieve better performance than some state-of-the-art approaches in terms of several evaluation criteria, demonstrating that the proposed ICEEMDAN-DE-RR is very promising for daily crude oil price forecasting.

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

Volatility integration of gold and crude oil prices with the interest rates in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shailesh Rastogi ◽  
Adesh Doifode ◽  
Jagjeevan Kanoujiya ◽  
Satyendra Pratap Singh
Keyword(s):  
Interest Rate ◽  
Crude Oil ◽  
Interest Rates ◽  
Global Economy ◽  
Spillover Effect ◽  
Price Volatility ◽  
Volatility Spillover ◽  
Content Type ◽  
Crude Oil Prices ◽  
The Impact

PurposeCrude oil, gold and interest rates are some of the key indicators of the health of domestic as well as global economy. The purpose of the study is to find the shock volatility and price volatility effects of gold and crude oil market on interest rates in India.Design/methodology/approachThis study finds the mutual and directional association of the volatility of gold, crude oil and interest rates in India. The bi-variate GARCH models (Diagonal VEC GARCH and BEKK GARCH) are applied on the sample data of gold price, crude oil price and yield (interest rate) gathered from November 30, 2015 to November 16, 2020 (weekly basis) to investigate the volatility association including the volatility spillover effect in the three markets.FindingsThe main findings of the study focus on having a long-term conditional correlation between gold and interest rates, but there is no evidence of volatility spillover from gold and crude oil on the interest rates. The findings of the study are of great importance especially to the policymakers, as they state that the fluctuations in prices of gold and crude oil do not adversely impact the interest rates in India. Therefore, the fluctuations in prices of gold and crude may generally impact the economy, but it has nothing to do with interest rate in particular. This implies that domestic and foreign investments in the country will not be affected by gold and crude oil that are largely driven by interest rates in the country.Practical implicationsGold and crude oil are two very important commodities that have their importance not only for domestic affairs but also for international business. They veritably influence the economy including forex exchange for any nation. In addition to this, the researchers believe the findings will provide insights to policymakers, stakeholders and investors.Originality/valueGold and crude oil undoubtedly influence the exchange rates but their impact on the interest rates in an economy is not definite and remains ambiguous owing to the mixed findings of the studies. The lack of studies related to the impact of gold and crude oil on the interest rates, despite them being essentials for the health of any economy is the main motivation of this study. This study is novel as it investigates the volatility impact of crude oil and gold on interest rates and contributes to the existing literature with its findings.

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 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

An empirical model of daily highs and lows of West Texas Intermediate crude oil prices

2010 ◽  
Vol 32 (6) ◽  
pp. 1499-1506 ◽  
Author(s):  
Angela W.W. He ◽  
Jerry T.K. Kwok ◽  
Alan T.K. Wan
Keyword(s):  
Crude Oil ◽  
Empirical Model ◽  
Oil Prices ◽  
West Texas ◽  
Crude Oil Prices

scholarly journals The Nexus between Stock Returns of Oil Companies and Oil Price Fluctuations after Heavy Oil Upgrading: Toward Theoretical Progress

Economies ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 71 ◽  
Author(s):  
Sedighi ◽  
Mohammadi ◽  
Fard ◽  
Sedighi
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Stock Returns ◽  
Petroleum Industry ◽  
Oil Industry ◽  
Oil Price ◽  
Investment Policies ◽  
Price Fluctuations ◽  
Oil Companies ◽  
Petroleum Companies

This study attempts to discover the nexus between crude oil price fluctuation after heavy oil upgrading and stock returns of petroleum companies in the U.S. Stock Exchange for the years 2008 to 2018. One of the methods of upgrading heavy crude oil is to extract asphaltene from crude oil. Considering the Asphaltene Removal (AR) as a factor in the nexus between oil price and the stock market is an innovation in the literature of energy finance. Asphaltenes cause many problems in the petroleum industry, which increases the cost of oil production and reduces the financial efficiency of oil companies. The AR is certainly one of the significant matters of the oil industry and can affect the price of oil. Therefore, changes in the price of oil can influence the price of oil company stocks. Hence, changes in stock prices will certainly affect the stock returns of oil companies. In an effort to solve this puzzle, the four financial models were employed to explore the nexus between oil price fluctuations and stock returns. The analysis of the results demonstrated that the oil price fluctuations caused by the removal of asphaltenes influence the stock returns of petroleum companies. Eventually, the theoretical hypothesis was confirmed by considering the USA as a case study. The outcomes of this investigation are a theoretical progression in areas related to the petroleum industry and the stock market that could lead to the adoption of new investment policies in the petroleum industry including investing in new procedures to manage and decrease the costs and time of the AR process, which would result in the advancement of petroleum companies. In fact, we have introduced a modern investment strategy in the oil industry aimed at reducing oil production costs, improving financial statements and increasing the stock returns of petroleum companies. Eventually, we will present new investment policies in the oil industry that can lead to economic growth and development of financial markets especially stock market, derivatives market, futures exchange, commodities exchange, as well as bond market.

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