Price Variation Research Articles

Nexus between carbon, stock, and energy markets in New Zealand: An analysis of causal domains

In the context of Industry 4.0 and the urgent need for a decarbonization transition, the Emissions Trading Scheme (ETS) emerges as a vital and cost-effective tool for promoting global decarbonization. While the connections between the ETS, stock, and the energy markets are theoretically strong, the nature of their causality has been a subject of debate in existing research, especially in New Zealand (NZ). Therefore, we quantitatively explore the potential dynamic linkages within the “Carbon-Stock-Energy” system using more advanced techniques, such as nonparametric Granger causality-in-quantile and cross-quantilogram techniques. Our findings attest to a bidirectional causality between the ETS and the markets examined in the short run. The foreseeability of all lag lengths is, moreover, positive in a bullish market context. Thus, price movements in the NZ ETS could contribute to the volatility of price variations in the stock and the energy markets. Fundamentally, the outcomes of the freshly devised regime-dependent portfolio strategy call for strategic investors to switch their portfolios adaptively between short-term and long-term positions in the ETS. In short, the outcomes may give a deeper glimpse into the complex interconnections between the ETS, stock, and the energy markets in NZ.

Energy poverty in EU: Using regional climatic conditions and incidence of electricity prices to map vulnerability areas across 214 NUTS2 European regions

The rising political instabilities and the associated variations in energy prices have recently amplified the existing debate on energy poverty. Generally defined as the inability to afford enough energy services to live a comfortable life, energy poverty represents a serious issue affecting multidimensional elements of life. To address disparities and to achieve the 7th goal of the 2030 Agenda, EU Member States have been requested to track energy poverty within the National Energy and Climate Plans. The lack of a commonly adopted methodological approach makes however difficult to compare the energy poverty risks. To address this gap, this paper proposes, for the first time, an energy poverty vulnerability indicator that can be used to investigate the differences across NUTS2 European regions. By combining data on regional income, electricity prices and local climatic conditions, the proposed methodology uses a threshold approach to maps vulnerability risks for the years 2010 and 2020. Results show that in 2010 15% of the considered households classified into the highest vulnerability risk and by 2020, 40 additional regions moved to a higher category of risk. Mediterranean countries have been the most affected with extensive climatic and price incidence changes. The proposed indicator can support the definition of policies aiming to reduce regional disparities and improve the average European trends.

Statistical Analysis on Turmeric Production and Price Movement in Tamil Nadu

Since ancient times, agriculture has been one of the chief factors in the advancement of many nations. Also, it has been a huge contributor to the GDP (Gross Domestic Product). But the farmers and the small-scale retailers are still unaware of the demand and supply of the commodities in mandis. Ultimately, they are compelled to use the repetitive pattern of crops. Through knowledge of the intra-mandi prices for seasonal crops is necessary to gain the desired profit. Price trends in several Tamilnadu markets were investigated for Turmeric in order to forecast price volatility and problems that produce mandi-level projections, enhancing its usefulness. Given that medicinal crops have various medical and culinary uses, boosting their productivity could increase farmers' profits in comparison to typical pattern crops. So, the aim of this study is to evaluate the optimum preferred crop conditions for Turmeric with respect to profit. A data from the past decade was screened to observe the variation in prices of the crops along with production analysis. Also, it was validated statistically by various statistical measures like Standard Deviation, Mean and Coefficient of Variation. We examined intra-mandi prices and found that prices fluctuate over the whole peak season, with notable fluctuations in modal prices among several mandis. To demonstrate the variations in pricing reported for certain mandis on specific days, we've given a few instances. A low standard deviation implies that the commodities price is clustered around the mean, whereas a large standard deviation shows that the commodities price is more dispersed. Price volatility is the discrepancy between what is anticipated and what occurs. Keywords: Agriculture, Turmeric, Marginal farmers, Price Movement

Hedonic analysis of attributes and factors affecting small ruminant price in Benin, West Africa

The demand for small ruminants is increasing, but livestock production is not very market oriented. Understanding buyers' preferences allows breeders to orient their production and make more profit. This research aimed to determine the attributes of small ruminants and other specific factors that explain price variations of small ruminants in ordinary period. Twenty-one markets were selected using stratified sampling. Data were collected in these markets on September 2020 from 432 small ruminant transactions between sellers and buyers and analysed with the hedonic regression model. Results showed that the price of goats purchased for slaughter and breeding were respectively 9.4 % and 7.2 % higher than those bought for trade. At the same time, when sheep were purchased for breeding, consumption and catering, their prices were respectively 14.5 %, 10.3 % and 9.4 % higher than those purchased for trade. The attributes weight, age (2–4 years old), sex (female), coat colour (white), body condition (good), breed (sahelian breeds for sheep and djallonke breed for goats) and absence of horn determined significantly and positively the small ruminant's price. In addition to these attributes, the small ruminant prices varied according to type of market and seller, but the buyer had less control over these factors. Thus, it is important to improve attributes preferred by buyers through training for breeders on the control of small ruminant reproduction. These attributes should also be considered when developing strategies to improve the small ruminant sector to transform traditional livestock farming into market-oriented livestock farming. Facilitating access to information through the establishment of functional market information systems will enable breeders to move towards market-oriented livestock systems.

Early forecasting of corn yield and price variations using satellite vegetation products

Several approaches based on process-based, statistical or economic models have been developed and are currently used by market analysts and governmental agencies to forecast agricultural yields and commodity prices at the national and global scales. However, these models require input data that are often difficult to obtain before crop harvest and their predictions are not always reliable. Satellite data products provide easily accessible sources of information on vegetation characteristics, potentially relevant for predicting crop yield and commodity prices at a large scale, before harvest. This study presents and evaluates several methods for forecasting variations in US corn yield and global corn prices using regional vegetation satellite products. The spatial dimension of maps of vegetation indices derived from satellite products in the US Corn Belt is first reduced either by their spatial means or by principal components derived using the Empirical Orthogonal Function (EOF) method. Mean values and EOF principal components are then used as explanatory variables in generalized LASSO linear models to forecast corn price and yield changes at different time horizons, before harvest. A cross-validation method was implemented to assess the model performances using Area under the ROC curve (AUC), the Brier Skill Score (BSS) and the Matthew Correlation Coefficient (MCC). The results revealed that EOF principal components derived from satellite-based gross primary production (GPP) in July could be used to predict variations in US corn yields, with an AUC of 0.97 (95% CI 0.92-1), a BSS of 0.75 and an MCC of 0.83. The results also showed that the July spatial averages of satellite GPP data obtained in the US Corn Belt could be used to forecast the forthcoming increase or decrease of global corn prices as evidenced by an AUC reaching up to 0.91, a BSS up to 0.5, and an MCC up to 0.67. Finally, we compared our forecasts of yield and price variations to those derived from the WASDE forecasting method currently implemented in the US. This comparison showed that our approach was at least as accurate and often more accurate than WASDE. This study demonstrates how satellite data can be used to forecast variations in crop yields and corn prices several months before harvest.

Optimizing building material selection: A machine learning approach for efficient concrete compressive strength forecasting

Price changes in construction materials have a significant impact on building construction projects. Such price variations occur at random and at varying rates over time. A system that can estimate the magnitude and quantity of the change in material prices with reasonable accuracy is required. The primary goal is to create a machine-learning model that can predict the type of building material chosen based on environmental factors. The compressive strength of concrete is critical in defining its mechanical qualities. Long laboratory testing is needed to determine the compressive strength of concrete. The capacity of powerful machine learning algorithms to forecast concrete compressive strength speeds up these lengthy experimental methods while also lowering expenses. This study provides abilities to precisely anticipate and categorize numerous qualities and traits of distinct materials. The framework includes a broad dataset that details materials, composition, and performance characteristics. Machine learning algorithms such as logistic regression (LR), decision trees (DT), and random forests (RF) train models on the training data. The models are hyper-parameter tweaked and feature developed to achieve the most outstanding performance. The k-fold method is used throughout the training and assessment phase to guarantee robustness and reduce bias. The F1 score and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) curve are two performance measures used to measure how accurate and predictive the trained models are. The study findings provide insights into the qualities of the materials, facilitating improved material selection, quality assurance, and decision-making in the building sector. In the analyses, the best accuracy value was 99.92%, and the precision value was 88.83% using the LR algorithm. As a result, it was determined that the LR algorithm had the least execution 57.826 ms, and is thus the most suitable for use in concrete compressive strength estimation.

The Short-Term Price Elasticity, Temperature Elasticity, and Wind Speed Elasticity of Electricity: A Case Study from Norway

Energy production using hydropower has a 150-year history in Norway. High mountains, lots of rain, and a well-developed technology laid the foundation for low and stable electricity prices. The Norwegian electricity market is unique and different from any other country. Nearly all electricity produced (98.3 percent) comes from renewable energy sources and 75 percent of the energy used for heating is electricity. From autumn 2020, major changes have been observed in the electricity market in Norway. In 2021, Norway opened two transmission cables, one to Germany and one to England. Both cables have a capacity of 1400 MW. The average price per MWh was NOK 263 in southern Norway in the period 2013–2020, which more than quadrupled to NOK 1192 per MWh in the period 2021–2023. We have investigated how the market reacted to the large price increase. We found that price elasticity is low even when the price is very high. It is the temperature that controls the consumption. When it is cold—below zero degrees Celcius—the temperature elasticity is close to zero; the temperature elasticity is not constant. When the temperature is above zero, the temperature elasticity is about −0.7. Price variations or changes in wind speed only lead to minor adjustments in electricity consumption. It is the variations in temperature that result in the observable fluctuations in electricity consumption. Since Norway exports electricity to Sweden, Denmark, Finland, Germany, the Netherlands, and England, knowledge of the Norwegian electricity market is relevant for many market participants. The Norwegian electricity market differs from those in other countries. Therefore, there is a risk that conclusions drawn about the Norwegian electricity market based on research conducted in other countries may be incorrect or inaccurate. Our contribution with this case study is to deepen the knowledge of how the electricity market in Norway operates.

Seasonal Patterns in Cumin Arrivals and Prices in Indian Markets: A Post-COVID 19 Analysis

Cumin known as jeera in India, holds significant economic and cultural importance in the country's spice industry. My research paper explores the seasonal dynamics of cumin arrivals and prices in selected Indian markets, considering the profound impact of the COVID-19 pandemic. Utilizing data from the Agmarknet portal spanning from January, 2006 to December, 2021, this study employs rigorous statistical techniques to analyze the patterns. The study reveals distinctive characteristics in cumin arrivals and prices across different markets. Notably, markets like Unjha, Jamnagar, Gondal, Merta City and Mumbai witnessed significant seasonal price fluctuations in August (102.40), January (103.23), July (105.37), August (104.49) and October (108.17) respectively. Mumbai, characterized by stable supply and demand, exhibited moderate price variation (CV = 17.81%), whereas Mandore experienced substantial fluctuations (CV = 43.55%) due to seasonal factors exacerbated by the COVID-19 pandemic. This research provides invaluable insights into the altered seasonal patterns of cumin arrivals and prices following the pandemic, offering vital information for farmers, traders and policymakers. Informed decisions about cumin production, marketing and pricing can be made, aligning with the dynamic post-COVID-19 landscape of the spice market in India.

An Analysis of Vehicle-to-Grid in Sweden Using MATLAB/Simulink

With more electric vehicles introduced in society, there is a need for the further implementation of charging infrastructure. Innovation in electromobility may result in new charging and discharging strategies, including concepts such as smart charging and vehicle-to-grid. This article provides an overview of vehicle charging and discharging innovations with a cable connection. A MATLAB/Simulink model is developed to show the difference between an electric vehicle with and without the vehicle-to-grid capabilities for electricity grid prices estimated for Sweden for three different electric vehicle user profiles and four different electric vehicle models. The result includes the state-of-charge values and price estimations for the different vehicles charged with or without a bidirectional power flow to and from the electric grid. The results show that there is a greater difference in state-of-charge values over the day investigated for the electric vehicles with vehicle-to-grid capabilities than for vehicles without vehicle-to-grid capabilities. The results indicate potential economic revenues from using vehicle-to-grid if there is a significant variation in electricity prices during different hours. Therefore, the vehicle owner can potentially receive money from selling electricity to the grid while also supporting the electric grid. The study provides insights into utilizing vehicle-to-grid in society and taking steps towards its implementation.

Asymmetric impacts of coal prices, fintech, and financial stress on clean energy stocks

The emergence of fintech has revolutionized financial systems, alleviating financial stress through innovative solutions and diversification. This shift towards clean energy stocks has reduced demand for coal, impacting coal prices significantly and highlighting the pressing need to preserve mineral resources for sustainable, cleaner energy alternatives. Therefore, the study examines the interconnectedness between coal price, fintech, financial stress, and green energy stock using quantile-based regression. The findings reveal that the impact of coal price, fintech, and financial stress on the clean energy stock is heterogeneous across all levels of quantiles. Specifically, the findings indicate that variations in the coal price positively influence the clean energy stock index, yet this association displays asymmetry across quantiles. Fluctuations in the fintech index contribute to the susceptibility of renewable energy stock prices. The financial stress index exhibits a consistently adverse influence on clean energy stock across quantiles ranging from lower to higher orders. The study findings would guide stock market investors, ecological activists, and policymakers at administrative levels aiming to promote clean energy investment while controlling for the adverse outcomes from financial stress.

Costs of integrating hepatitis B screening and antiviral prophylaxis into routine antenatal care in Burkina Faso: Treat all versus targeted strategies.

Economic feasibility of eliminating mother-to-child transmission (MTCT) of hepatitis B virus (HBV) in highly endemic African countries remains uncertain. Prevention of MTCT (PMTCT) involves screening pregnant women for hepatitis B surface antigen (HBsAg), identifying those with high viral loads or hepatitis B e antigen (HBeAg), and administering tenofovir prophylaxis to high-risk women. We estimated the costs of integrating PMTCT services into antenatal care in Burkina Faso, based on four different strategies to select women for tenofovir prophylaxis: (1) HBV DNA (≥200 000 IU/mL), (2) HBeAg, (3) hepatitis B core-related antigen rapid diagnostic test (HBcrAg-RDT) and (4) all HBsAg-positive women. Using a micro-costing approach, we estimated the incremental economic cost of integrating each strategy into routine antenatal care in 2024, compared to neonatal vaccination alone. Sensitivity analyses explored variations in prevalence, service coverage, test and tenofovir prices. HBcrAg-RDT strategy was the least expensive, with a total economic cost of US$3959689, compared to HBV DNA (US$6128875), HBeAg (US$4135233), and treat-all (US$4141206). The cost per pregnant woman receiving tenofovir prophylaxis varied from US$61.88 (Treat-all) to US$1071.05 (HBV DNA). The Treat-All strategy had the lowest marginal cost due to a higher number of women on tenofovir (66928) compared to HBV DNA (5722), HBeAg (10020), and HBcrAg-RDT (7234). In sensitivity analyses, the treat-all strategy became less expensive when the tenofovir price decreased. HBcrAg-RDT minimizes resource use and costs, representing 0.61% of Burkina Faso's 2022 health budget. This study highlights the potential economic feasibility of these strategies and provides valuable resources for conducting cost-effectiveness analyses.

Extremely stablecoins

In this research, I explore the factors driving extreme fluctuations in stablecoin prices concerning financial and cryptocurrency market indices. Through quantile models, I reveal the directional predictability of diverse financial variables, capturing extreme price variations and return distribution fluctuations. The outcomes highlight asymmetric pricing reactions and the impact of stablecoin stability mechanisms. The varying effects observed in stablecoin and cryptocurrency behavior imply a broader market link and potential fragility. Notably, intermediary constraints and stablecoin liquidity exhibit predictive power for subsequent price shifts in both tails, providing insights into the complex dynamics within cryptocurrency and stablecoin markets.

Identification robust inference for the risk premium in term structure models

We propose identification robust statistics for testing hypotheses on the risk premia in dynamic affine term structure models. We do so using the moment equation specification proposed in Adrian et al. (2013). Statistical inference based on their three-stage estimator requires knowledge of the risk factors’ quality and can be misleading when the β’s are weak, which results when sampling errors are of comparable order of magnitude as the risk factor loadings. We extend the subset (factor) Anderson–Rubin test from Guggenberger et al. (2012) to models with multiple dynamic factors and time-varying risk prices. It provides a computationally tractable manner to conduct identification robust tests on a few risk premia when a larger number is present. We use it to analyze potential identification issues arising in the data from Adrian et al. (2013) for which we show that some factors, though potentially weak, may drive the time variation of risk prices, and weak identification issues are more prominent in multi-factor models.

The impact of economy policy uncertainty and oil price shocks on G20 banks’ stock performance: Wavelet coherence and non-parametric causality in quantiles approach

This study employs nonparametric causality-in-quantiles and wavelet coherence techniques to examine the impact of economic policy uncertainty and oil price variations on bank stocks in twelve prominent global economies. The results reveal that the effects of both economic policy uncertainty and oil prices on bank stock values vary significantly across countries and over time. Notably, during stress periods, we observe an inverse relationship between economic policy uncertainty and bank stocks in multiple countries, namely, Brazil, Canada, France, India, Russia, and the USA, with Japan exhibiting a particularly strong and long-term adverse correlation. Similarly, the influence of oil prices is primarily observed during crisis periods, but it demonstrates a substantial co-movement with bank stocks across the sample countries except Brazil. Our empirical analysis holds valuable implications for policymakers, bankers, investors, and portfolio managers.

Pengaruh Right Issue Terhadap Harga Saham Pada Perbankan Yang Terdaftar Di Bei Periode 2021-2023

A right issue is a business activity that has the potential to impact the quantity and value of shares available on the market and contains crucial information that shareholders may utilize to increase their earnings in the future. Since the market may respond to the rights issue announcement, the information contained in the announcement becomes significant. A right issue is a current share issuance in which the company grants its existing shareholders the right to purchase new shares. Each existing shareholder is given the option to purchase a number of new shares from the company at a predetermined price and time. The purpose of this research is to examining variations in the stock price before and after the right issue. This study was carried out on financial institutions that are listed on the Indonesia Stock Exchange between 2021-2023 period. Event research is often used to test the information content of an event, such as the notification of the rights problem. The sampling method using the purposive sampling method. 26 banking companiess were chosen as examples based on the predefined criteria. Secondary data from Yahoo Finance is the kind of data that was used. 2021-2023, daily stock price data from Indonesia Stock Exchange website . The Wilcoxon Signed Rank Test is used in the data analysis procedure by using the Eviews 12 program. The outcomes demonstrated that the outcomes yielded values of 0,0986; 0,4429; 0,1925; 0,0999. As a result, the analysis suggests that the price of the shares did not significantly change before or after the rights issuance.

Are Financial Ratios Able to Predict Bank Stock Prices During the COVID-19 Pandemic?

Fundamental analysis, such as financial ratios, is important to ensure capital markets move efficiently. This analysis represents 69% of the variation in stock prices in the capital market. However, fundamental analysis has not been widely carried out because expertise is needed to analyze qualitative and quantitative data from company characteristics that underlie the movement of a stock. In 2020 – 2022, bank performance experienced fluctuations due to the Covid-19 pandemic which caused bank share prices to also fluctuate. On the other hand, banks have an important role as intermediary institutions or intermediaries to support the funding needs of the real sector or the business world. However, previous research on fundamental analysis largely excluded companies in the financial sector. Therefore, this article aims to find out whether financial ratios are able to predict bank share prices during the Covid-19 pandemic. There are five financial ratios used in this research, namely loan loss provision ratio, total capital ratio, profit margin ratio, current ratio, and solvency ratio. The population in this study are financial companies listed on the Indonesia Stock Exchange for the period 2020 - 2022. The sample was selected using a purposive sampling method and obtained 38 companies and 114 observations. The results of multiple regression analysis show that the loan loss provision ratio has a significant negative effect and the total capital ratio has a significant positive effect on bank share prices. Meanwhile, the net profit margin ratio, current ratio and solvency ratio do not have a significant effect on bank share prices. This research has implications for encouraging banks to improve disclosure policies and encouraging investors to hone fundamental analysis skills to reduce mispricing and make markets more efficient.

Stock Market Price Prediction and Forecasting Using Stacked LSTM

Stock price movement is non-linear and complex. Several research works have been carried out to predict stock prices. Traditional approaches such as Linear Regression and Support Vector Regression were used but accuracy was not adequate. Researchers have tried to improve stock price prediction using ARIMA. Due to very high variations in stock prices, deep learning techniques are applied due to its proven accuracy in various analytics fields. Artificial Neural Network was deployed to predict stock prices but as stock prices are time-series based, recurrent neural network was applied to further improve prediction accuracy. Therefore, data scientists and analysts found that Deep Learning outperformed Machine Learning which is also proofed by all the collected research papers, and it is the most suitable methodologies to apply to the stock market forecasting domain. This paper explores different stacked LSTM Models for non-stationary financial time series in stock price prediction. This study is to predict stock market prices to make more acquaint and precise investment decisions. The experimental result will show that, this method can get quite accurate result, particularly effective in stock prediction. The proposed LSTM model will be designed to overcome gradient explosion, gradient vanishing, and save long-term memory. Firstly, The Model will have a comparative analysis between different LSTM models and integrating the model based on the result obtained from the analysis. The results will suggest that the developed stacked LSTM produces better predictive power and generalization. Key Words: stock market prediction, stacked LSTM, Deep Learning, Data pre-processing techniques, Data normalization, Neural Networks, Data Visualization, Training and Testing Set, Time series analysis, Predictive models, Future Forecasting.

Growth and Instability Analysis of Pepper with Reference to Production and Price

Black pepper, the most traded spice in the world is native to South India. Though Pepper is popularly known as black gold, it does not provide the value of gold to its cultivators because of several persisting problems. Market imperfections, illegal imports, ineffective implementation of minimum import duty, lack of institutional support etc are some of the problems faced by pepper growers. Production of pepper has become unremunerative due to depressed prices in the domestic and/or global markets coupled with increasing input costs. In this context, the study has focused on trends in area, production and yield of pepper, the seasonal effect of price and also the impact of the production of pepper on its prices to diagnose reasons for price fluctuations. The main objective of the study is to examine trends in area, production and productivity in major pepper-producing states and monthly price variations of pepper in India. The study relies on time series data. The data on area, production, productivity and monthly average price of pepper have been collected from the Spices Board of India. To study the behaviour of prices of pepper in different months, a seasonal effect has been constructed. To study the interrelationship between the price of pepper with its production and different months, a correlation technique has been employed. Regression analysis is being undertaken to know the present and future price trends of pepper. The time-series analysis of the monthly average prices of pepper is attempted from 2018-19 to 2022-23. The study revealed that there is a strong positive correlation between the prices of pepper and different months. The regression coefficient reveals that month significantly contributes to the growth and instability in pepper prices in the market. The study also suggests that, if the government could provide enough institutional support to stabilise the market prices, the pepper growers would certainly consider their crop as black gold.

Price Model for Transit-Oriented Developments in Kuala Lumpur, Malaysia

Abstract The idea of Transit Oriented Development (TOD) was to foster urban development around railway networks and has been strategically built and applied since the late 19th and early 20th centuries. Previous studies reported positive, negative, or irrelevant impact of TOD on prices of surrounding real estate. The study aims to evaluate the impact of TOD on property prices in Kuala Lumpur. It utilizes secondary data obtained from the National Property Information Centre (NAPIC), Malaysia, after data cleaning, Nine Thousand Five Hundred and Forty-Nine (9549) Housing Transactions between the periods 2009 and 2018 were used. The research design was quantitative, and the Hedonic Price Model (log-log model) was used for data analysis. The model revealed a multiple correlation coefficient (R) of 0.891 and an adjusted R2 value of 0.794, indicating that 79.4% of the house price variation is explained by the model. The F value of 996.921, which is statistically significant, indicates that the predictors significantly combine to predict the price of TOD areas in Kuala Lumpur. The coefficient for LnTrainsta is 0.056, indicating that there was a positive relationship between residential house prices and TOD in Kuala Lumpur; this explains that house prices increase by 5.6% for every 100 meter closer to the rail transit station.

INTELLIGENCE ANALYTICAL SUBSYSTEM OF CORPORATE MANAGEMENT (ON THE EXAMPLE OF AN OIL & GAS COMPANY)

The changes that have taken place in the Ukrainian economy in recent years due to the consistent impact of global macroeconomic, epidemiological and military factors of the current deep crisis - actualized for enterprises and corporations a complex adaptive reorganization of their analytical subsystem of their management [1, 2].
 In addition, in order to be consistently effective in today's global business environment (liberalization of markets, their globalization, increased competition, decreasing consumer loyalty, constant variation in oil and gas prices, etc.), oil and gas companies must have an innovative data and knowledge environment that allows seamless and effective joint leveraging knowledge inside the corporation and across the value chain [3]. The application of knowledge-based decision support systems as an important tool of innovative management policy enables effective and sustainable management of a complex of integrated processes of an oil and gas production company (mitigating the impact of crisis factors) [4].
 The scientific and practical results presented in the article regarding the methodology and design of innovative analytical subsystem of the corporation management information systems should be taken into account when reengineering corporate information systems not only in the oil and gas industry in Ukraine [5-6]. 
 Moreover, the obtained results are relevant and applicable not only for local companies, but also for international corporations on emerging markets in the context of global and regional macroeconomic and edipemical crisis phenomena.