Oil Price Predictors: Machine Learning Approach

An, Jun-Won; Mikhaylov, Alexey; Moiseev, Nikita

doi:10.32479/ijeep.7597

Cited by 95 publications

(34 citation statements)

References 21 publications

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“…The concept of market efficiency comes down to information (or price) on efficiency, but was subsequently identified as other aspects of it, especially the operators of the diet market efficiency and performance evaluation by the transistor automatic computer costs (primarily in terms of the non-market risk impact). Russian banks are characterized by low efficiency, a kind of "market curvature", which implies the inadequate display of information in price voltage (An & Dorofeev, 2019;An, Mikhaylov, & Moiseev, 2019).…”

Section: Resultsmentioning

confidence: 99%

Retraction: Confidence level and credit risk analysis in Russian banks

Lavrushin

Sokolinskaya

2020

Banks and Bank Systems

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The aim of the paper is to analyze the confidence level and credit risks in Russian banks after the 2014 sanction in terms of the economic behavior theory. The paper describes the banking system of Russia using analytical methods according to the polled data of the Financial University and the Bank of Russia. The survey included 106 responses of professors and researchers of the Financial University ranged by level of influence: 7 – very high; 6 – high; 5 – little higher, 4 – middle, 3 – little low, 2 – low, and 1 – very low. Statistics on Distance to Default and Probability of Default are used from Thomson Reuters. The survey proves that the problem of credit risk management in the Russian banking system is not so strong. It is confirmed that the confidence level is the solution to the problem of low level of institutional investors’ (mutual funds, pension institutions, insurance companies) capacity due to the lack of money coming into the financial system. The main conclusion is that in Russia there are no proper incentives for potential recipients of investment to attract investment resources in the domestic stock market, and the number of banks is steady decreasing. The results showed that the credit risk of Russian banks is higher than one year ago.

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Section: Resultsmentioning

confidence: 99%

Retraction: Confidence level and credit risk analysis in Russian banks

Lavrushin

Sokolinskaya

2020

Banks and Bank Systems

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“…This idea can be expanded to include EVs, as seen by the negative spillover effect between TSLA and USO -the decline in oil's prices has been accommodated by growth of the car company's stock. As has been mentioned, despite a lack of correlation between the studied ETFs, TSLA possess a considerable spillover effect with both of the assets (An et al, 2019).…”

Section: Discussionmentioning

confidence: 80%

Renewable Energy Transition: Evidence From Spillover Effects in Exchange-Traded Funds

Lisin

Senjyu

2021

IJEEP

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The article examines the spillover effect of the transition to renewable energy on different financial markets: specifically, the stock market. The research paper analyzes the prices and dynamics of oil-related assets and renewable funds, as well as reviews literature on the topic in order to evaluate the most important factors of development in these industries. The article utilizes methods of correlation analysis, analysis of variance, regression analysis and standards of deviation and error with the intention to test null hypotheses from previous research. The study uses empirical data spanning the majority of the last decade, in which a decline in the oil market can be observed. This is juxtaposed by the rapid and intense development of renewable energy -solar energy in particular. As concluded in the article, there is a strong spillover effect of this growth onto other markets, emphasizing the investments flowing into renewable energy. Moreover, the ambitious prospects of renewable energy development and it being a strong competitor to fossil fuels and, specifically, oil is drawn as a result of the conducted research.

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“…As a result of the study, it is observed that the estimated values are distributed at an acceptable level [8]. An et al [9] proposed a regression-based machine learning approach to estimate the oil price. The developed model can determine prices by including some indexes.…”

Section: Introductionmentioning

confidence: 93%

“…After the accuracy of the model validates, oil prices in 2019-2022 were estimated. According to the estimation results, it has been seen that the oil price will show a slight upward trend and will generally be stable [9]. Khashman and Nwulu [10] have developed a smart system that predicts crude oil price via Support Vector Machines (SVM).…”

Section: Introductionmentioning

confidence: 99%

LSTM ve Facebook Prophet Kullanarak Brent Ham Petrol Trendinin Tahmini

Güleryüz¹,

Özden²

2020

European Journal of Science and Technology

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Crude oil and petroleum products are among the critical inputs of industrial production and have an essential role in logistics and transportation. Hence, sudden increases and decreases in oil prices cause particular problems in global economies and thus, they have a direct or indirect effect on economies. Furthermore, due to crises in developing economies, trade disputes between major economies, and the dynamic nature of the oil price effect on demand and supply for oil and petroleum products, and time to time volatility in the oil price are very severe. The uncertainty in oil prices can leave both consumers and producers with heavy potential losses. Due to this rapid variability, predicting oil prices has global importance. In this study, to increase the accuracy and stability, the Long-Short Term Memory (LSTM) and Facebook's Prophet (FBPr) were applied to foresee future tendencies in Brent oil prices considering their previous prices. Comparing the two models made using the 32-year data set between June 1988 and June 2020 weekly for oil prices, and the model with the best fit was determined. The dataset was split into two sets: training and test sets-the twenty-five years are used for the training set and the seven years are used to validate forecasting accuracy. The coefficient of determination (R 2) for the LSTM and FBPr models was found as 0.92, 0.89 in the training stage, and 0.89, 0.62 in the testing stage, respectively. According to the results obtained, the LSTM model has superior results to predict the trend of oil prices.

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Oil Price Predictors: Machine Learning Approach

Cited by 95 publications

References 21 publications

Retraction: Confidence level and credit risk analysis in Russian banks

Retraction: Confidence level and credit risk analysis in Russian banks

Renewable Energy Transition: Evidence From Spillover Effects in Exchange-Traded Funds

LSTM ve Facebook Prophet Kullanarak Brent Ham Petrol Trendinin Tahmini

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