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Verleger oil forecast forex

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The spread at this stage was mainly affected by the WTI price, and the correlation between the price difference and the oil price trend was not strong. After the United States lifted the ban on oil exports at the end of , the correlation between the price spread and the oil price trend increased significantly. After the lifting of the oil export ban, the WTI oil price was no longer solely affected by the internal supply and demand of the United States. During this period, Brent and WTI oil prices were more consistent, resulting in a narrower spread.

In addition, after , the consistency of them increased significantly. To compare the forecasting performance of our proposed approach with some other benchmark models from level forecasting and directional forecasting, three main evaluation criteria, i. Three indicators are defined as follows:. To provide statistical evidence of the forecasting performance of our proposed ensemble learning approach, three tests, i.

The DM test checks the null hypothesis of equal predictive accuracy. In this study, the Mean Squared Error is applied as DM loss function and each model is compared against a random walk. The PT test examines whether the directional movements of the real and forecast values are the same.

In other words, it checks how well rises and falls in the forecasted value follow the actual rises and falls of the time series. The null hypothesis is that the model under study has no power in forecasting the oil prices. In this paper, the number of MLP output layer neurons is 1, the number of iterations in the training stage is 10,, and the number of hidden layer neurons is determined by the trial and error method to be Similarly, the number of LSTM hidden layers and the number of delays were set as 5 and 4, respectively, and the number of output layer neurons was set as 1.

The structure of the LSTM neural network was trained by backpropagation algorithm BP , and the learning rate, batch size and the number of the epoch were set as 0. The convergence rate is controlled by the learning rate, which is a function of decreasing time. When the number and learning rate of epochs is set at and 0. When the parameter combination changes, once it converges, the experimental results tend to be stable. All models are implemented using Matlab B software.

According to the forecast results of Table 3 , we can find some interesting conclusions: 1 no matter in the sample inside or outside the sample prediction, this chapter proposed variable selection - machine learning approach to integration in the training set and test set level precision RMSE and MAPE and direction DS were better than that of the single variable precision model and the core factors extracted model.

It shows that the prediction accuracy of the variable selection-machine learning integrated model is significantly improved compared with that of the univariate model and the univariate model. Secondly, the number of core variables selected by BMA is neither the most nor the least among the three variable selection models, indicating that the number of core variables will also affect the prediction results.

According to Table 4 statistical test results can be seen that 1 the step ahead prediction samples, variable selection - machine learning integration model of DM test results are less than 7. This also means that the variable selection-machine learning method is the best direction prediction performance and also can be seen that the direction of the ARIMA predicts performance is the worst. In this paper, we proposed a variable selection and machine learning framework that combines the variable selection BMA and forecasting method LSTM to forecast the oil price and compared its forecasting performance with other primary and new variable selection methods elastic-net and spike and slab Lasso.

Specifically, our contributions are as follow:. Introduce the variable selection before forecasting. In this process, we compare three different methods and analyze core influencing factors based on the literature review from supply and demand, global economic development, financial market, and technology aspects. Testing the performance of the proposed variable selection and machine learning framework based on 3 variable selections and 8 individual forecasts. Comparing with the 8 individual forecasts without variable selection, the combinations forecasting reduces the errors.

The results showed that 1 the variable choice-machine learning integration method proposed in this chapter is superior to the univariate model and the model without core factor extraction in both training set and test set level accuracy RMSE, MAPE and direction symmetric DS. This indicates that the variable selection-based machine learning integrated research framework proposed in this chapter significantly improves the forecasting performance of oil prices.

In future research, we may introduce more independent variables with the help of internet search data, test our framework performance. Moreover, investor sentiment can be quantified in this process. In addition, different variable selection methods can be introduced more.

A monetary alternative. Article Google Scholar. An empirical evaluation of the verleger hypothesis. Working Papers in Economics 32 5 — Google Scholar. Energy Econ — Appl Energy — Cifarelli G, Paladino G Oil price dynamics and speculation: a multivariate financial approach.

Energy Econ 32 2 — Coleman L Explaining crude oil prices using fundamental measures. Energy Policy — J Bus Econ Stat 20 1 — Doroodian K, Boyd R The linkage between oil price shocks and economic growth with inflation in the presence of technological advances: a CGE model.

Energy Policy 31 10 — Drachal K Forecasting spot oil price in a dynamic model averaging framework-have the determinants changed over time? J Stat Softw 33 1 :1— Ann Inst Stat Math 53 1 — Biometrika 82 4 — Hamilton JD a Understanding crude oil prices. Energy J 30 2 — Hamilton JD b Causes and consequences of the oil shock of no. National Bureau of economic research. Hansen PR A test for superior predictive ability.

J Bus Econ Stat 23 4 — Adv Neural Inf Proces Syst— Eur J Oper Res 2 — Ji Q, Fan Y Evolution of the world crude oil market integration: a graph theory analysis. Kilian L Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. Am Econ Rev 99 3 — Kilian L Explaining fluctuations in gasoline prices: a joint model of the global crude oil market and the US retail gasoline market.

Energy J— Kilian L, Hicks B Did unexpectedly strong economic growth cause the oil price shock of —? J Forecast 32 5 — Kilian L, Murphy DP The role of inventories and speculative trading in the global market for crude oil. J Appl Econ 29 3 — J Appl Econ 28 2 — Leamer EE Specification searches. Wiley, New York. Environ Model Softw 23 10—11 — Merlise A Bayesian model averaging and model search strategies. Bayesian Statistics Econ Model — Murat A, Tokat E Forecasting oil price movements with crack spread futures.

Energy Econ 31 1 — Appl Energy 87 10 — Energy Policy 38 7 — J Bus Econ Stat 10 4 — J Am Stat Assoc 92 — Reboredo JC Modelling oil price and exchange rate co-movements. J Policy Model 34 3 — J Am Stat Assoc — Sadorsky P Oil price shocks and stock market activity. Energy Econ 21 5 — Energy Econ 26 3 — Renew Sust Energ Rev 16 2 — Tour Manag — Valgaev O, Kupzog F, Schmeck H Adequacy of neural networks for wide-scale day-ahead load forecasts on buildings and distribution systems using smart meter data.

Energy Informatics 3 1 :1— Inf Sci — Wang Q, Sun X Crude oil price: demand, supply, economic activity, economic policy uncertainty and wars—from the perspective of structural equation modelling SEM. Energy — Wang Y, Liu L, Wu C Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models. Available at SSRN Int J Forecast 32 1 :1—9. Xiong T, Bao Y, Hu Z Beyond one-step-ahead forecasting: evaluation of alternative multi-step-ahead forecasting models for crude oil prices.

Energy Econ 40 2 — Energy Econ 30 5 — Yu L, Wang Z, Tang L A decomposition-ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting. Int J Forecast 35 1 — Energy Econ 30 3 — J Empir Financ — Evidence from stock, crude oil and natural gas markets. Zhang YJ, Wei YM The dynamic influence of advanced stock market risk on international crude oil returns: an empirical analysis. Quantitative Finance 11 7 — Zou H, Hastie T Regularization and variable selection via the elastic net.

Download references. Academy Conference Asia You can also search for this author in PubMed Google Scholar. All co-authors have read and approved the final manuscript. Correspondence to Hongbo Duan. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Neural networks training characteristics. Table A. Neural networks design and training characteristics. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.

If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Reprints and Permissions. Lu, Q. Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model. Energy Inform 4, 47 Download citation. Published : 24 September Anyone you share the following link with will be able to read this content:.

Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all SpringerOpen articles Search. Download PDF. Volume 4 Supplement 2. Abstract In recent years, the crude oil market has entered a new period of development and the core influence factors of crude oil have also been a change. Introduction Since , the international crude oil price has experienced the most significant volatility since the financial crisis.

Supply and demand As the fundamental factor, supply and demand have been the main factors affecting oil prices. Financial factor In addition to commodity attributes, crude oil also has financial attributes. Technology factor The Crack spread is defined as the price difference between crude oil and its refined oil, reflecting the supply and demand relationship between the crude oil market and its refined product market Wang et al.

Forecast method Except for the influence factors, researchers are also very concerned about the forecast methods for improving forecast accuracy. Full size image. Table 1 Initially selected feature variables and their explanations Full size table. Theoretical background As the crude oil market is very complex and has various uncertain determinants, we must select the core influence factors first before establishing forecasting models. An unrolled recurrent neural network.

The architecture of an LSTM memory cell. The repeating module in an LSTM contains four interacting layers. Empirical study In this section, we compare the variable selection-LSTM integrated learning approach to the predictive performance of some benchmark models. Variable selection We can see from Table 2 , the elastic-net selects the most number 18 variables , followed by the SSL method 11 variables , and the BMA method includes the least number 8 variables.

Table 3 Forecasting performance Full size table. Conclusions and future work In this paper, we proposed a variable selection and machine learning framework that combines the variable selection BMA and forecasting method LSTM to forecast the oil price and compared its forecasting performance with other primary and new variable selection methods elastic-net and spike and slab Lasso.

Specifically, our contributions are as follow: Introduce the variable selection before forecasting. Availability of data and materials Not applicable. About this supplement This article has been published as part of Energy Informatics Volume 4, Supplement 2 Proceedings of the Energy Informatics. View author publications. Ethics declarations Ethics approval and consent to participate Not applicable.

Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Supplementary Information. Additional file 1. About this article. Cite this article Lu, Q. Copy to clipboard. Sign in. Accessibility help Skip to navigation Skip to content Skip to footer. Become an FT subscriber to read: Beware the algorithms driving up oil prices Leverage our market expertise Expert insights, analysis and smart data help you cut through the noise to spot trends, risks and opportunities.

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As part of the use of technical analysis, we are ready to offer readers a forecast of oil prices for today. As a rule, the forecast for oil for today does not lose its relevance for tomorrow due to the analysis of the four-hour schedule of BRENT. The forecast of oil prices for today is made by a practicing trader and analyst of our resource. Due to the accumulated experience in the surveys, you can find out the latest forecast of oil, as well as get acquainted with the options for the movement of quotations in case of triggering of alternative options.

Brent oil Forecast June 17, 35 0. Brent continue to move as part of growth and a bullish channel. At the…. Brent oil Forecast June 15, 43 0. Brent oil Forecast June 13, 28 0. Help us improve our free forecast service with share! Crude Oil CL Price Prediction per barrel , Forecast for next months and years Below you will find the price predictions for , , , , , Short-term and long-term CL Crude Oil price predictions may be different due to the different analyzed time series.

Tweet Share. Log in with Or sign up with Walletinvestor. Oil price will cross 70 soon What is last target of crude oil today? Gujar 3 years ago. What will be High low of crude oil on monday 21st ? What is Monday crude oil opening? Question Box: How will Crude Oil price increase?

Will CL price go up? Will Crude Oil price fall? Will CL price drop? Will CL price rise? Is Crude Oil price going up? Is Crude Oil a profitable investment? Is CL price going to drop? When will CL price fall? When will CL price go down? When will Crude Oil price drop? Investors are responsible for their own investment.

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If later the price breaks the range to the downside, the market may resume moving within the downtrend with the short-term target at 1. Later, the market may form one more ascending structure to return to 1. After that, the instrument may resume moving within the downtrend with the target at 1.

If the price breaks this range to the upside, the market may continue the correction up to 1. Later, the market may form a new descending structure with the target at 1. Later, the market may resume growing to reach 1. Later, the market may resume falling to break 1. Ichimoku Cloud Analysis The instrument is currently moving inside Ichimoku Cloud, thus indicating a sideways tendency. Early in May, there was a similar test of the cloud, which resulted in a further downtrend Later, the m Today, the pair may correct with the target at 1.

Later, the market may then start a new decline towards 1. Later, the market may correct to correct towards 1. Later, the market may correct to return to 1. Later, the market may grow to return to 1. If later the price breaks this range to the downside, the market may form a new descending structure with the target at 1.

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Get My Guide. Crude oil is one of the most in-demand commodities, with the two most popularly traded grades of oil being Brent Crude and West Texas Intermediate WTI. Get information on key pivot points, support and resistance and crude oil news. S2 S3 R1 R2 R3 Pivot Points P S1 Daily Classical Pivot Points. Last Updated: Jun 20, Where to for WTI?

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Oil Technical Analysis for June 16, 2022 by FXEmpire

As veteran oil analyst Philip Verleger points out in a recent report, the amount of call contracts with strike prices above $ a barrel. Are Product Spreads Useful for Forecasting Oil Prices? An Empirical Evaluation of the Verleger Hypothesis *. October ; Macroeconomic. The paper which investigates the relationship between crude oil futures prices and the no-change forecasts most thoroughly is probably Alquist and Kilian ().