Impact of Industry-Specific Risk Factors on Stock Returns of the Malaysian Oil and Gas Industry in a Structural Break Environment

This study examines the impact of industry-specific risk factors such as oil price, gas price, and exchange rate on stock returns of Malaysian oil and gas firms in a structural break environment by employing the break least square approach of Bai and Perron (1998, 2003). Existing studies fall short of providing such empirical evidence. The results document evidence of structural breaks in the relationship between industry risk factors and the stock returns of the oil and gas industry. Industry-specific risk factors are shown to significantly affect the stock returns of oil and gas industry sub-sectors alongside market-based risk factors. The results reveal that the beta values of oil price, gas price, and exchange rate vary across sub-periods hence confirming that exposure of oil and gas stocks to industry risk factors varies over time and across sub-periods. The effects of oil, gas, and exchange rate risk factors also differ across the sub-industry, with impacts and directions largely dependent on the core business activities of the oil and gas sub-industries. The empirical results offer implications for asset managers and investors.

DEVELOPMENT Pengembangan Sistem Brooder Berbahan Dasar Uap Bensin sebagai Upaya Menekan Biaya Operasional Budi Daya Ayam Broiler

Broiler chicken is one of popular alternative source of protein for local people in Indonesia due to the low price and good quality meat. However, broiler breeders unable obtain enough income because face high operating costs due to the animal feed and high consumption of LPG gas in the Day Old Chick (DOC) brooder process. During 15 days brooder DOC process, at least 36 kg/day of LPG gas is consumed for 3000 chicks. Therefore, the purpose of this community service activity is to reduce brooder DOC operational costs for broilers through research, development and implement an alternative heaters using gasoline vapor vaporization. Community service activities are carried out with six stages of activity, namely 1. Real identification of the brooder process in the field 2. Factor analysis using a causes and effects table 3. Improvement of brooder management procedures 4. Conduct an alternative low cost brooder design 5. Investigation of the fabricated brooder sistem and 6. Analysis of the results of the change impact test. The results of the high operational cost DOC process investigation are tight less of the brooder DOC curtain cage, uncontrolled the intensity of the flames, and unfixed of LPG price. The new procedure proposed is fixing the brooder DOC curtain cage, new setting intensity of the flames in day and night, and using an alternative low cost brooder design to avoid unfixed LPG gas price. The proposed procedure is able to reduce brooder operational costs by up to 50%.

Innovation and Technology needs for the oil and gas industry in Peru

Peru experienced the oil rush due to the discovery of reserves, but since the Camisea project was found in the Cuzco region, oil has been in decline, Peru is rated too low competitiveness rates below countries such as Ecuador and Colombia. The hydrocarbons sector accounted for 54% of the total energy tender in 2012. The oil and gas industry has advanced technologically. Today, the technological challenges are focused on taking advantage of unconventional resources having a strong focus on process safety, environmental management, and fuel quality. In a new era of high volatility in the oil and gas price, technology is crucial to maximizing the value; among them, the Modernization Project of the Talara Refinery and its process is announced. Peru is perfecting technologies to take advantage of gas and oil that are difficult to access so that its role in the Peruvian economy is fundamental.

Opportunities and Challenges in Wind Energy

Wind energy increasingly plays a crucial role in replacing existing systems based on fossil fuels and nuclear generators. However, skepticism about the technological feasibility, economy of scale, operational profitability prevails. Our paper aims to fill this gap by analyzing the affordability of wind energy and by exploring the optimization issue incurring during the integration process of the micro electricity grid. Focusing on Locational Marginal Pricing (LMP) and Midwest Independent System Operator (MISO) data between January 2014 and December 2020, we find wind power generation helps decrease the system energy price after control the effect of gas price. Further, our model captures the important seasonal and uncertain characteristics of power generation and consumption over the years. We also identify and discuss the bottleneck issues of the MISO power grids. These findings provide insights into affordability and technologic integration for long-term strategic planning on green energy including pricing, seasonality, dispatch, and reliability issues.

Opportunities and Challenges in Wind Energy

Wind energy increasingly plays a crucial role in replacing existing systems based on fossil fuels and nuclear generators. However, skepticism about the technological feasibility, economy of scale, and operational profitability prevails. The paper aims to fill this gap by analyzing the affordability of wind energy and by exploring the optimization issue incurring during the integration process of the micro electricity grid. Focusing on locational marginal pricing (LMP) and Midwest independent system operator (MISO) data between January 2014 and December 2020, the authors find wind power generation helps decrease the system energy price after control the effect of gas price. Further, the model captures the important seasonal and uncertain characteristics of power generation and consumption over the years. They also identify and discuss the bottleneck issues of the MISO power grids. These findings provide insights into affordability and technologic integration for long-term strategic planning on green energy including pricing, seasonality, dispatch, and reliability issues.

Natural Gas Price Prediction Using Machine Learning

Natural gas varies with season. In addition, natural gas supply, demand, storage, and imports are important indicators related to natural gas price. There are plenty of methods for analyzing and forecasting natural gas prices and machine learning is increasingly used. Machine learning algorithms can learn from historical relationships and trends in the data and make data-driven predictions or decisions. Here a new model for predicting price for natural gas by using Machine Learning concepts. Here some algorithms have been used to build the proposed model: Random Forest Regression, Linear Regression, Decision Tree, Multilinear Regression. By using the algorithm, a Flask model has been implemented and tested. The results have been discussed and a full comparison between algorithms was conducted. Random forest Regression was selected as best algorithm based on accuracy.