Crude Oil Price Prediction Using Particle Swarm Optimization and Classification Algorithms

The price of crude oil is tied to major economic activities in all nations of the world, as a change in the price of crude oil invariably affects the cost of other goods and services. This has made the prediction of crude oil price a top priority for researchers and scientists alike. In this paper we present an intelligent system that predicts the price of crude oil. This system is based on Support Vector Machines. Support Vector Machines are supervised learners founded upon the principle of statistical learning theory. Our system utilized as its input key economic indicators which affect the price of crude oil and has as its output the price of crude oil. Data for our system was obtained from the West Texas Intermediate (WTI) dataset spanning 24 years and experimental results obtained were very promising as it proved that support vector machines could be used with a high degree of accuracy in predicting crude oil price.

The crude oil is the major raw material for oil product production in the globalization era. Changes of price will affect country’s economic growth and development. The aim of the study is to examine the Crude Oil Price changes and its impact on the Indian GDP in the recent scenario. To do the same, the study has applied secondary data, statistical tools like chart, the table, graph, correlation and descriptive analysis to show the changes of crude oil price and country’s GDP during the study period. The study reveals that the annual percentage price of crude oil has been increased or decreased positively and negatively but the India’s GDP seems to move forward constantly between 5% to 8% over the study period and the Correlation test conformed that the percentage of crude oil price and percentage change of GDP has moderate positive correlation. OPEC is widely seen as the most influential player in oil price fluctuations in the during the study periods.

In 2020, the outbreak of COVID-19 caused a sharp drop in international crude oil prices. The outbreak of the Russian Ukrainian conflict in 2022 has also had a great impact on the fluctuation of international crude oil prices. So far as an important source of energy, the fluctuation of crude oil price is associated with the entire financial market. Therefore, this paper try to find how the air transport industry will going through the dynamic changes of its main source of energy——crude oil. Then the data of Brent oil price and air transportation industry index are selected from six months earlier before the Russian Ukrainian conflict broke out, used for constructing VAR model and ARMA-GARCH model with their logarithmic sequences. Through testing and observing the constructed Var model and ARMA-GARCH model, find the impact of international crude oil price changes on the yield and volatility of air transportation industry, so that the air transport industry can acquire the initiative of energy source.

Crude oil is an important energy commodity to mankind. The fluctuation of crude oil prices has affected many related sectors and stock market indices. Hence, forecasting the crude oil prices is essential to avoid the future prices of the non-renewable natural resources to raise sky-rocket. In this study, daily West Texas Intermediate (WTI) crude oil prices data is obtained from Energy Information Administration (EIA) from 2nd January 1986 to 30th September 2009. This study uses the Box-Jenkins methodology and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) approach in analyzing the crude oil prices. ARIMA(1,2,1) and GARCH(1,1) are found to be the appropriate models under model identification, parameter estimation, diagnostic checking and forecasting future prices. In this study, the analyses are done with the aid of EViews software where the potential of this software in forecasting daily crude oil prices time series data is explored. Finally, using several measures, comparison performances between ARIMA(1, 2, 1) and GARCH(1,1) models are made. GARCH(1,1) is found to be a better model than ARIMA(1, 2, 1) model. Based on the study, it is concluded that ARIMA(1,2,1) model is able to produce good forecast based on a description of history patterns in crude oil prices. However, the GARCH(1,1) is the better model for daily crude oil prices due to its ability to capture the volatility by the non-constant of conditional variance.

This study compares how crude oil and ethanol price changes are passed through to the wholesale prices of a conventional fuel (E10), which contains 10% ethanol, and a high-blend ethanol fuel (E85), which contains 51% to 83% ethanol. Daily observations from October 2017 to June 2019 were obtained from a large market in the United States that provided wholesale fuel prices and ethanol prices. The Error Correction Model (ECM) was applied to each fuel specification using Seemingly Unrelated Regressions (SURs) in order to improve the efficiency of the estimates. Comparable to prior research, the long-run pass-through coefficient for E10 with respect to crude oil was 1.13. In contrast, the E85 long-run pass-through coefficient with respect to crude oil was 0.74. Estimates for the short-run analysis indicated asymmetry in the transmission of crude oil price changes to E10, with crude price increases passing through at greater rates compared to crude price decreases. Symmetry was found in the transmission of ethanol price changes to E85, indicating the same response to rising and falling ethanol costs. Despite the differences in ethanol requirements, the relative prices of crude oil and ethanol are still important for both fuels.

The study quantifies the impact of crude oil price changes on the production, sales turnover and raw material cost of select industries in India where crude oil is a major direct or indirect raw material. The results show that there exists a relationship between crude oil price changes and production of chemicals, coke, and refined petroleum products, a significant impact is observed on the sales turnover of plastic, oil refinery, and automobiles industries. The raw material cost of fertilizers, food processing, and paints industries show a high correlation with crude oil. Energy is the undercurrent that drives economic activity in the world. Fortunes of countries have changed because of crude oil. Its importance to the global economy is unmatched, proven by the fact that oil is responsible for 2.5% of the world GDP. If provided a conducive business environment, companies around the world would like to tap opportunities a billion plus people in India present. India is heavily dependent on crude oil imports thus volatility in crude oil prices is a cause for concern.Int. J. Soc. Sci. Manage. Vol-3, issue-2: 87-92

Oil is one of the most precious source of energy for the world and has an important role in the global economy. Therefore, the long-term prediction of the crude oil price is an important issue in economy and industry especially in recent years. The purpose of this paper is introducing a new Particle Swarm Optimization (PSO) algorithm to forecast the oil prices. Indeed, the PSO is a population-based optimization method inspired by the flocking behavior of birds. Its original version suffers from tripping in local minima. Here, the PSO is enhanced utilizing a convergence operator, an adaptive inertia weight and linear acceleration coefficients. The numerical results of mathematical test functions, obtained by the proposed algorithm and other variants of the PSO elucidate that this new approach operates competently in terms of the convergence speed, global optimality and solution accuracy. Furthermore, the effective variables on the long-term crude oil price are regarded and utilized as input data to the algorithm. The objective function of the optimization process considered in this research study is the summation of the square of the difference between the actual and the predicted oil prices. Finally, the long-term crude oil prices are accurately forecasted by the proposed strategy which proves its reliability and competence.

This research examines the relationship between regional conflicts and uncertainty in global crude oil prices. The study of the impact of uncertainty on crude oil prices has garnered significant attention from both investors and policymakers, as fluctuations in crude oil prices have a significant impact on the economy and financial markets. In general, regional conflicts and uncertainty can contribute to changes in crude oil prices. The research problem is framed by the question: How do the uncertainty index and regional conflicts affect the occurrence of bubbles in oil prices? This leads to the hypothesis that uncertainty measures and regional conflicts significantly influence the occurrence of both positive and negative bubbles in crude oil prices. The research employs an inductive approach and a descriptive data analysis method. Among the most important findings of this research are that measures of uncertainty and regional conflicts greatly affect the occurrence of positive and negative bubbles in crude oil prices.

Crude oil is a valuable resource for boosting global economic growth. In this regard, this study examined the effect of crude oil price fluctuations on Tanzania’s economic growth from 1989 to 2022. Empirically this study utilized time-series data extracted from World Bank in particular, GDP per capital from World Development Indicators (WDI) as proxy of economic growth and Statistical Review of World Energy for Crude Oil prices. In this study, Renaissance growth theory, Autoregressive distributed lag model (ARDL), Dickey and Fuller tests, The Johansen test for cointegration, Breusch- Godfrey test for Serial correlation LM, Breusch-Pagan heteroscedasticity test, Jarque-Bera normality test, Error Correction Model (ECM), Granger Causality test, CUSUM and CUSUMSQ curves tests were used to analyse the data. This study revealed that crude oil price changes have positive and significant impact on Tanzania’s economic growth. A dollar increase in crude oil prices brings about 0.232791unit increase Tanzania’s economic upturn in the short run. The ARDL results also shows that error correction model (ECM) of -0.1000 (p= 0.0025) is significant with its value, suggesting a moderate speed of convergence to equilibrium after shock. Thus, this study recommends to policy maker and the government to ensure that they use fiscal policies that will reduce the adverse impact of the world crude oil prices hikes, finding other sources of energy and promoting research and development to explore and harness the oil fossils to produce oil and reduce oil importations and increase oil exportations to further increase economic growth.

This study analyzes the impact of changes in oil prices on consumer inflation in Turkey. We compare the effects of the changes in crude oil and gasoline prices on the consumer prices. These effects differ symmetrically and asymmetrically for the period 2009:01-2020:04. For this purpose, inflationary effects are estimated using linear and non-linear ARDL models. According to the findings, changes in both oil and fuel prices have asymmetric effects on inflation in the short run. Both models explain the changes in consumer inflation with the increases in oil prices in the long run. The result indicates that the decreases in oil prices are not taken into account in pricing decisions.

This thesis consists of three empirical studies on what drives stock market dynamics. The first empirical study explores the effect of crude oil price changes on the stock market returns of oil-exporting countries and oil-importing countries as well as those of a number of global stock indices. Using the Ordinary Least Squares (OLS) approach as well as the more robust Quantile Regression (QR) approach to explore the relationship between crude oil and stock market dynamics. The empirical findings suggest that the QR approach provides further insights compared to the OLS approach. For instance, the QR approach is able to identify specific quantiles where a significant relation exists. In particular crude oil price increases tend to have a negative impact on the stock market returns for some oil-exporting countries (such as Mexico, Iraq, Ecuador, and Venezuela) and a positive effect for other oil-exporting countries (such as Brazil and Algeria). However, the OLS approach suggests that these relationships are insignificant at the level of the mean. Overall, the empirical findings confirm that the QR approach can reveal more information about the relationship between crude oil price changes and stock market return across different quantiles of their distribution.The second study explores the extent to which implied volatility extracted from commodity markets and developed stock markets can predict the implied volatility of stock markets in BRICS countries. Using daily data from 2011 to 2016 and employing the newly developed Bayesian Graphical Vector Autoregressive (BGVAR) model of Ahelegbey et al. (2016) which does not suffer from over-parameterization and the identification problems associated with traditional VAR frameworks, this study finds that implied volatilities extracted from global and regional stock markets have a significant predictive power over the implied volatilities in BRICS stock markets. However, the predictive power of implied volatility from commodity markets are significant only in the case of South Africa.The third empirical study analyses the relationship between illiquidity and stock market returns in the G7 and BRICS countries. More specifically, this study explore the extent to which the Amihud (2002) illiquidity measure can improve the explanatory power of three commonly used asset pricing models, namely the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model and the Carhart four-factor model. The empirical analysis is based on 15 years of monthly data on the returns of seven stock portfolios: 100 largest companies (Largest100), small value (S/V), small neutral (S/N), small growth (S/G) stocks, big value (B/V) stocks, big neutral (B/N) stocks, and big growth (B/G) stocks. The findings suggest that incorporating illiquidity as an additional factor results in a significant improvement in the explanatory power of these asset pricing models across several of the sample countries (8 countries in the case of the CAPM and Carhart four-factor model, and 6 countries in the case of the Fama-French three-factor model). For example, in the US adding illiquidity to the CAPM leads to an increase of the goodness of fit by 2.6% in the B/V portfolio, and for the Fama-French three-factor model the goodness of fit increases by up to 3% in all portfolios Moreover, the goodness of fit increases in all portfolios in the US by adding illiquidity to the Carhart four-factor model, with an up to 36% increase in the B/N portfolio.

This paper aims to simulate and evaluate the impacts of increases and decreases in oil price on industrial sectors in China. We develop an oil-economy computable general equilibrium (OE-CGE) model with crude oil as an important factor in production. The transmission mechanism of crude oil price swings to various industrial sectors is described in the model. We calibrate parameters in the model parameters using input-output data. In addition, we simulate the rise and fall of oil prices in the model and assess the impact of crude oil prices on various industrial sectors. The results show that crude oil price changes have the greatest impact on the output and consumption of crude oil and gas extraction products sector, crude oil refined coke products, and processed nuclear fuel products sector. The investment of public utilities sector is the most sensitive to changes in crude oil price. When the price of crude oil changes, its investment drops significantly. Crude oil price stability is extremely important for investment and output stability in all sectors.

Causes of oil price increase have been a subject of much interest and numerous studies given the importance of oil as the main source of energy of the world. In the last two decades, the price volatility of crude oil has always remained a controversy. Literature has two views. The first view argues that the changes in oil prices are due to supply and demand. The other claims that financial variables (speculation and futures markets) are vital in crude oil price changes. The advent of new variables that serve as paradigm for oil price volatility creates the need to understand the intricacies of the oil market. This paper investigated the factors that determine the prices of crude oil together with the degree to which prices have been affected by the global health pandemic the Corona Virus Disease (COVID-19) in 2020 by employing a multiple regression method in its assessment. With oil price as the dependent variable, all other variables were analyzed to assess the dependency of the crude oil prices on these independent variables. Refinery capacity, Organization of Petroleum Exporting Countries (OPEC) production and oil trade movements were found to be the main affecting factors in this work whilst the cases and deaths arising from the COVID-19 confirmed cases and recorded deaths in the year 2020 proved insignificant as a determining crude oil factor

This study investigated the prediction of crude oil price based on energy product prices using genetically optimized Neural Network (GANN). It was found from experimental evidence that the international crude oil price can be predicted based on energy product prices. The comparison of the prediction performance accuracy of the propose GANN with Support Vector Machine (SVM), Vector Autoregression (VAR), and Feed Forward NN (FFNN) suggested that the propose GANN was more accurate than the SVM, VAR, and FFNN in the prediction accuracy and time computational complexity. The propose GANN was able to improve the performance accuracy of the comparison algorithms. Our approach can easily be modified for the prediction of similar commodities.

This research analyzes the influence of Crude Oil Price (COP) together with Crude Oil Price Volatility (COPV) and Crude Oil Production (COPR) on Nigerian economic performance through their impact on Real Gross Domestic Product (RGDP) and Gross National Product (GNP). The research utilizes FDI as a control parameter to measure how external investment affects the results. The study analyzes the long-term relationships between variables using the Autoregressive Distributed Lag (ARDL) Bounds Test for Cointegration and it assesses short-term patterns through the Error Correction Model (ECM). Economic data shows that crude oil price presents the strongest positive relationship with Nigerian economic growth which has substantial effects on RGDP (0.1809 p=0.0120) alongside GNP (0.8197 p=0.0066). Crude oil production shows minimal effects on national income and GNP (0.6446, p=0.3260) while price volatility produces just a weak impact on economic performance. The statistical tests establish that FDI has no significant impact on growth because foreign capital fails to generate sustainable development outcomes. Analysis of the error correction term reveals that economic shocks get corrected in 62.53% of cases for RGDP segments and 56.16% for GNP segments each year thus showing moderate adjustment speed. The research shows that Nigeria depends primarily on crude oil price changes rather than production quantity or price stability which emphasize the need for diversification. Policy recommendations include improved financial policies, enhanced production efficiency, and oil price stabilization programs