scholarly journals Wheat Futures Prices Prediction in China: A Hybrid Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yunpeng Sun ◽  
Jin Guo ◽  
Shan Shan ◽  
Yousaf Ali Khan
Keyword(s):  
Artificial Intelligence ◽  
Statistical Significance ◽  
Arima Model ◽  
Hybrid Approach ◽  
Futures Market ◽  
Point Estimation ◽  
Futures Prices ◽  
Out Of Sample ◽  
Futures Price ◽  
Hidden Layer

Stocks markets play their financial roles of price shocks and hedging just when they are proficient. The imperative highlights of productive market are that one cannot make extraordinary profit from the stocks markets. This research investigates whether China wheat futures price can be predicted by employing artificial intelligence neural network. This would add to our knowledge whether wheat futures market is resourceful and would enable traders, sellers, and investors to improve cost-effective trading strategy. We utilize the traditional financial model to forecast the wheat futures price and acquire out of sample point estimates. We additionally assess the robustness of our outcomes by applying several alternative forecasting techniques such as artificial intelligence with one hidden layer and autoregressive integrated moving average (ARIMA) model. Furthermore, the statistical significance of our point estimation was further tested through the Mariano and Diebold test. Considering random walk forecast as the bench mark, we used a number of economic indicators, trader’s expectation towards futures prices, and lagged value of futures price of wheat in order to forecast the evaluation of wheat futures price. The computable significance of out of sample estimations recommends that our ANN with one hidden layer has the best anticipating presentation among all the models considered in this exploration and has the estimating power in foreseeing wheat futures returns. Furthermore, this investigation discovers that the futures price of wheat can be predicted, and the wheat futures market of China is not productive.

scholarly journals Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Lu Zhang ◽  
Junbiao Zhang ◽  
Tao Xiong ◽  
Chiao Su
Keyword(s):  
Statistical Significance ◽  
Hybrid Approach ◽  
Futures Market ◽  
Futures Contracts ◽  
Support Vector ◽  
Futures Prices ◽  
Swarm Optimization ◽  
Modeling Process ◽  
Forecasting Performance ◽  
Daily Interval

This paper examines the interval forecasting of carbon futures prices in one of the most important carbon futures market. Specifically, the purpose of this study is to present a novel hybrid approach, which is composed of multioutput support vector regression (MSVR) and particle swarm optimization (PSO), in the task of forecasting the highest and lowest prices of carbon futures on the next trading day. Furthermore, we set out to investigate if considering some potential predictors, which have strong influence on carbon futures prices, in modeling process is useful for achieving better prediction performance. Aiming at testing its effectiveness, we benchmark the forecasting performance of our approach against four competitors. The daily interval prices of carbon futures contracts traded in the Intercontinental Futures Exchange from August 12, 2010, to November 13, 2014, are used as the experiment dataset. The statistical significance of the interval forecasts is examined. The proposed hybrid approach is found to demonstrate the higher forecasting performance relative to all other competitors. Our application offers practitioners a promising set of results with interval forecasting in carbon futures market.

The Unbiasedness and Hedging Effectiveness in KOSPI200 Futures Market

2007 ◽  
Vol 15 (1) ◽  
pp. 73-100
Author(s):  
Seok Kyu Kang
Keyword(s):  
Risk Management ◽  
Risk Premium ◽  
Futures Market ◽  
Spot Price ◽  
Sample Period ◽  
Futures Prices ◽  
Spot Prices ◽  
Hedging Effectiveness ◽  
Futures Price ◽  
Rolling Windows

This study is to examine the unblasedness hypothesis and hedging effectiveness in KOSPI20() futures market. The unbiasedness and efficiency hypothesis is carried out using a cointegration methodology. And hedging effectiveness is measured by comparing hedging performance of the naive hedge model, OLS hedge model. and constant correlation bivariate GARCH (1. 1) hedge model based on rolling windows. The sample period covers from May. 3. 1996 to December. 8, 2005. The empirical results are summarized as follows: First, there exists the cOintegrating relationship between realized spot prices and futures prices of the 10 day. 22 day. 44 day. and 59 day prior to maturity. Second. futures prices of backward the 10 day. 22 day. 44 day from maturity provide unbiased forecasts of the realized spot prices. The KOSPI200 futures price is likely to predict accurately future KOSPI200 spot prices without the trader having to pay a risk premium for the privilege of trading the contract. Third. for shorter maturity. the futures price appears to be the best forecaster of spot price. Forth, bivariate GARCH hedging effectiveness outperforms the naive and OLS hedging effectiveness. The implications of these findings show that KOSPI200 futures market behaves as unbiased predictor of future spot price and risk management instrument of KOSPI200 spot portfolio.

scholarly journals Forecasting Performance Of Natural Gas Futures Market: An Assessment Of Recent Data

2011 ◽  
Vol 4 (11) ◽  
Author(s):  
Salah Abosedra ◽  
Khaled Elkhal ◽  
Faisal Al-Khateeb
Keyword(s):  
Natural Gas ◽  
Futures Market ◽  
Policy Implications ◽  
Energy Market ◽  
Futures Prices ◽  
Font Size ◽  
Spot Prices ◽  
Forecasting Performance ◽  
Futures Price ◽  
The U.S

<p class="MsoNormal" style="text-align: justify; margin: 0in 34.2pt 0pt 1in;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">Natural gas has assumed increasing importance in the global energy market. This study evaluates the forecasting performance of futures prices of natural gas in the large market of the U.S. at various time horizons. The results indicate that futures prices are unbiased predictors at the 1-, 6-, and 12- month horizons, but not at the 3- and 9- month horizons. The results further suggest that futures prices of natural gas, although biased at some intervals, significantly outperform na&iuml;ve forecasts in predicting future movements of spot prices. In addition, the information content of the 1-month ahead futures price proves especially useful as a forecasting device. Policy implications are also discussed.<span style="mso-bidi-font-style: italic; mso-bidi-font-weight: bold;"></span></span></span></p>

Optimal Hedge Ratio and Hedging Effectiveness in Stock Futures Market: Evidence from National Stock Exchange, India

2019 ◽  
Vol 118 (3) ◽  
pp. 137-152
Author(s):  
A. Shanthi ◽  
R. Thamilselvan
Keyword(s):  
Banking Sector ◽  
Stock Exchange ◽  
Research Work ◽  
Futures Market ◽  
Least Square ◽  
Time Varying ◽  
Hedge Ratio ◽  
Hedging Effectiveness ◽  
Out Of Sample ◽  
Optimal Hedge

The major objective of the study is to examine the performance of optimal hedge ratio and hedging effectiveness in stock futures market in National Stock Exchange, India by estimating the following econometric models like Ordinary Least Square (OLS), Vector Error Correction Model (VECM) and time varying Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) model by evaluating in sample observation and out of sample observations for the period spanning from 1st January 2011 till 31st March 2018 by accommodating sixteen stock futures retrieved through www.nseindia.com by considering banking sector of Indian economy. The findings of the study indicate both the in sample and out of sample hedging performances suggest the various strategies obtained through the time varying optimal hedge ratio, which minimizes the conditional variance performs better than the employed alterative models for most of the underlying stock futures contracts in select banking sectors in India. Moreover, the study also envisage about the model selection criteria is most important for appropriate hedge ratio through risk averse investors. Finally, the research work is also in line with the previous attempts Myers (1991), Baillie and Myers (1991) and Park and Switzer (1995a, 1995b) made in the US markets

Relationship between Coffee Prices in Spot and Futures Markets-An Empirical Analysis

2020 ◽  
pp. 180-188
Keyword(s):  
New York ◽  
Correlation Coefficients ◽  
Strong Relationship ◽  
Futures Market ◽  
Regression Coefficients ◽  
Futures Prices ◽  
Market Prices ◽  
Spot Prices ◽  
Correlation And Regression Analysis ◽  
Highly Correlated

The present study explored the relationship between spot and futures coffee prices. The Correlation and Regression analysis were carried out based on monthly observations of International Coffee Organization (ICO) indicator prices of the four groups (Colombian Milds, Other Milds, Brazilian Naturals, and Robustas) representing Spot markets and the averages of 2nd and 3rd positions of the Intercontinental Exchange (ICE) New York for Arabica and ICE Europe for Robusta representing the Futures market for the period 1990 to 2019. The study also used the monthly average prices paid to coffee growers in India from 1990 to 2019. The estimated correlation coefficients indicated both the Futures prices and Spot prices of coffee are highly correlated. Further, estimated regression coefficients revealed a very strong relationship between Futures prices and Spot prices for all four ICO group indicator prices. Hence, the ICE New York (Arabica) and ICE Europe (Robusta) coffee futures prices are very closely related to Spot prices. The estimated regression coefficients between Futures prices and the price paid to coffee growers in India confirmed the positive relationship, but the dispersion of more prices over the trend line indicates a lesser degree of correlation between the price paid to growers at India and Futures market prices during the study period.

scholarly journals Improving MACD Technical Analysis by Optimizing Parameters and Modifying Trading Rules: Evidence from the Japanese Nikkei 225 Futures Market

2021 ◽  
Vol 14 (1) ◽  
pp. 37
Author(s):  
Byung-Kook Kang
Keyword(s):  
Moving Average ◽  
Public Information ◽  
Technical Analysis ◽  
Weak Form ◽  
Futures Market ◽  
Traditional Model ◽  
Analysis Tool ◽  
Futures Prices ◽  
Improved Performance ◽  
Parameter Values

Much research has examined performance or market efficiency by using the moving average convergence divergence (MACD) technical analysis tool. However, most tests fail to verify efficiency with the traditional parameter settings of 12, 26, and 9 days. This study confirms that applying the traditional model to Japan’s Nikkei 225 futures prices produces negative performance over the period of 2011–2019. Yet, it also finds that the MACD tool can earn significant positive returns when it uses optimized parameter values. This suggests that the Japanese market is not weak-form efficient in the sense that futures prices do not reflect all public information. Hence, the three parameters values of the MACD tool should be optimized for each market and this should take precedence over finding other strategies to reduce false trade signals. This study also tests which models are able to improve profitability by applying additional criteria to avoid false trade signals. From simulations using 19,456 different MACD models, we find that the number of models with improved performance resulting from these strategies is far greater for models with optimized parameter values than for models with non-optimized values. This approach has not been discussed in the existing literature.

scholarly journals New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production

Fuels ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 286-303
Author(s):  
Vuong Van Pham ◽  
Ebrahim Fathi ◽  
Fatemeh Belyadi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Shale Gas ◽  
Field Data ◽  
Marcellus Shale ◽  
Hybrid Approach ◽  
Gas Production ◽  
Bayesian Optimization ◽  
Real Field ◽  
Engineering Problems

The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.

scholarly journals Analysis and Prediction of “AI + Education” Attention Based on Baidu Index—Taking Guizhou Province as an Example

2021 ◽  
Vol 13 (5) ◽  
pp. 120
Author(s):  
Yulin Zhao ◽  
Junke Li ◽  
Jiang-E Wang
Keyword(s):  
Artificial Intelligence ◽  
Southwest China ◽  
Arima Model ◽  
Modern Technology ◽  
Guizhou Province ◽  
Western Region ◽  
Elastic Coefficient ◽  
The Sustainable Development ◽  
Intelligence Education ◽  
Coefficient Method

Studying the attention of “artificial intelligence + education” in ethnic areas is of great significance for China for promoting the integrated development of new educational modes and modern technology in the western region. Guizhou province is an area inhabited by ethnic minorities, located in the heart of Southwest China. The development of its intelligent education has strong enlightenment for the whole country and the region. Therefore, this paper selects the Baidu Index of “artificial intelligence (AI) + education” in Guizhou province from 2013 to 2020, analyzes the spatial–temporal characteristics of its network attention by using the elastic coefficient method, and builds the ARIMA model on this basis to predict future development. The results show that the public’s attention to “AI + education” differs significantly in time and space. Then, according to the prediction results, this paper puts forward relevant suggestions for the country to promote the sustainable development of education in western ethnic areas.

scholarly journals Livestock Gross Margin–Dairy: An Assessment of Its Effectiveness as a Risk Management Tool and Its Potential to Induce Supply Expansion

2014 ◽  
Vol 46 (2) ◽  
pp. 245-256
Author(s):  
Kenneth H. Burdine ◽  
Yoko Kusunose ◽  
Leigh J. Maynard ◽  
Don P. Blayney ◽  
Roberto Mosheim
Keyword(s):  
Risk Management ◽  
Management Tool ◽  
Futures Prices ◽  
Gross Margin ◽  
Insurance Program ◽  
Multiple Regions ◽  
Estimated Risk ◽  
Futures Price ◽  
Supply Analysis ◽  
Risk Reducing

An evaluation of the risk-reducing effectiveness of the Livestock Gross Margin–Dairy (LGM-Dairy) insurance program, using historical futures price data, predicts economically significant reductions in downside margin risk (24–41%) across multiple regions. Supply analysis based on the estimated risk reduction shows a small supply response, assuming minimal subsidization. A decomposition of the simulated indemnities into milk price and feed price components shows comovements in futures prices moderating the frequency and levels of indemnities.

Hybrid Fluid Flow Simulation Combining Full Physics Simulation and Artificial Intelligence

2021 ◽  
Author(s):  
Mokhles Mezghani ◽  
Mustafa AlIbrahim ◽  
Majdi Baddourah
Keyword(s):  
Artificial Intelligence ◽  
Reservoir Simulation ◽  
History Matching ◽  
Hybrid Approach ◽  
Flow Simulation ◽  
Latin Hypercube Sampling ◽  
Simulation Models ◽  
Fine Scale ◽  
Simulation Results ◽  
Optimization Loop

Abstract Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model. The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model. The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time. CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.

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