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The price of oil is highly complex to predict as it is impacted by global demand and supply, geopolitical events, and market sentiment. The accuracy of such predictions, however, has far-reaching implications for supply chain performance, portfolio management, and expected stock market returns. This paper contributes to the oil price prediction literature by evaluating the predictive impact of the US President's communication on Twitter, while benchmarking various Natural Language Processing (NLP) techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Doc2Vec, Global Vectors for Word Representation (GloVe), and Bidirectional Encoder Representations from Transformers (BERT). These techniques are combined with a deep neural network Long Short-Term Memory (LSTM) architecture using a five-day lag for both the oil price and the textual Twitter data. The data was collected during the term of US President Donald Trump, resulting in 1449 days of crude oil price prediction and a total of 16,457 tweets. The study is validated for Brent and West Texas Intermediate blends, using the daily price of a barrel of crude oil as the target variable. The results confirm that including the US President's tweets significantly increases the predictive power of oil price prediction models, and that an LSTM architecture with BERT as NLP technique has the best performance. [ABSTRACT FROM AUTHOR]

We test the impact of globalization on the inflation process (global slack hypothesis) for 15 emerging economies over the period (1996Q1-2019Q4). The novelty of this article lies in testing the impact of globalization on the inflation process. We introduce the smooth transition regression (STR) model of the Phillips curve to capture the gradual impacts of globalization on inflation dynamics. The main results are as follows. (i) The asymmetries in inflation exist that are not captured by the linear Phillips curve. (ii) The domestic output gap remains a main driving force of inflation. (iii) Globalization based on the trade weighted average of the domestic output gap does not affect the inflation process compared to other variables like the trade openness, import prices, real exchange rate and international oil prices. Our results yield important implications that the central banks of the emerging economies can stabilize inflation and output gap in an effective way, exploiting the inflation-output gap relationship. When economic expansion of the trading partners increases, the process of inflation dynamics changes in two economies (Brazil, and China). [ABSTRACT FROM AUTHOR]

This study investigates the relationship between consumer sentiment (CONS), inflation expectations (INEX) and international energy prices, drawing on principles from behavioral. We focus on Brent crude oil price and Henry Hub natural gas prices as key indicators of energy market dynamics. Based on the monthly data from January 2003 to March 2023, three wavelet methods are applied to examine the time-frequency linkage, while the nonlinear distributed lag model (NARDL) is used to verify the asymmetric impact of two factors on energy prices. The results highlight a substantial connection between consumer sentiment, inflation expectations and international energy prices, with the former in the short term and the latter in the medium to long term. Especially, these correlations are particularly pronounced during the financial crisis and global health emergencies, such as the COVID-19 epidemic. Furthermore, we detect short-term asymmetric effects of consumer sentiment and inflation expectations on Brent crude oil price, with the negative shocks dominating. The positive effects of these factors on oil prices contribute to observed long-term asymmetry. In contrast, inflation expectations have short-term and long-run asymmetric effects on natural gas price, and both are dominated by reverse shocks, while the impact of consumer sentiment on natural gas prices appears to be less asymmetric. This study could enrich current theories on the interaction between the international energy market and serve as a supplement to current literature. [ABSTRACT FROM AUTHOR]

This study investigates the dynamic, multi-scale relationship between sentiment related to the COVID-19 pandemic and extreme returns in crude oil. The recently developed COVID-19 indices are employed to gauge pandemic sentiment. Utilizing daily data spanning from January 2020 to December 2021, Granger's linear and nonlinear causality tests reveal that indices nonlinearly influence extreme fluctuations in West Texas Intermediate and Brent crude oil prices. Interestingly, a reciprocal causation is also identified: extreme crude oil returns significantly affect the indices. Furthermore, the wavelet transform coherence analysis sheds light on the indices' ability to predict extreme crude oil price volatility across specific time-frequency domains, displaying diverse distributions and lead-lag patterns among the sub-indices. Our study underscores the efficacy of indices in anticipating extreme fluctuations in crude oil values during the COVID-19 pandemic, carrying important implications for investors, scholars, and policymakers. [ABSTRACT FROM AUTHOR]

This research incorporates crude oil price series with different time scales, China's dirty energy, new energy and green bond index into a system, so as to investigate the spillover effects during some major periods. The results indicate that the spillover effects of short-term oil price fluctuations are mild, and the green bond market is the least affected. While the long-term oil price fluctuations have profound effects on the aforementioned three markets, the spillovers are especially enhanced by extreme events. Our results are verified by appropriate robustness tests. [ABSTRACT FROM AUTHOR]

This study explores the time-varying effects of crude oil prices (OP) and gold prices (GP) on the Turkish stock market using a weekly data series from November 26, 1989 to July 10, 2022. For this purpose, we develop a new hybrid technique, the rolling window-based nonparametric quantile causality test, which allows the investigation of time-varying causality at various quantiles. The results reveal that (i) under all market conditions, there is time-varying causality from crude OP and GP to Turkish stock market returns (SMR) and volatility. (ii) The causal effects of both crude OP and GP on stock market volatility are larger than their causal effects on SMR. (iii) The crude OP have a greater impact on SMR than the GP, while the GP has a greater impact on stock market volatility than the crude OP. (iv) Both crude OP and GP have the strongest (least) causal impact on SMR and volatility under normal (bullish) market conditions. (v) Crude OP and GP have a greater impact on stock market volatility than on stock returns under all market conditions. Overall, our results highlight that OP and GP have a strong impact on the Turkish stock market, and this impact varies by returns and volatility. Therefore, financial investors should consider the volatility of crude OP and GP in the Turkish stock market. [ABSTRACT FROM AUTHOR]

The paper investigates the intricate relationship between oil and stock markets in the context of different oil price shocks and a country's energy profile. Focusing on five major oil-producing and consuming countries, the analysis reveals a significant, time-varying dimension in the oil-stock relationship. It highlights the importance of distinguishing between various types of oil shocks and a nation's reliance on oil revenues. The paper finds that countries heavily reliant on oil revenues exhibit robust responses to oil supply and demand shocks. In contrast, oil producers show more moderate reactions due to lower oil revenue dependence. This evidences the importance of recognizing the multifaceted impacts of oil shocks on different economies for making well-informed decisions in asset allocation, portfolio management, and policymaking. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to evaluate the resource curse hypothesis in an oil-dependent economy, Saudi Arabia, through examining the impact of oil price volatility on foreign ownership among Saudi listed firms. Design/methodology/approach: The study analyzes a unique data set of firm-level data on foreign ownership for the period 2009–2015. A multivariate regression model was applied to analyze the relationships under study. Findings: The analysis reveals a negative association between oil price volatility and foreign ownership in firms with high leverage and low stock volatility. Research limitations/implications: Policymakers are encouraged to develop policies to control shocks in the supply and demand of oil and enforce economic diversification. Investors can better understand the dynamics of an oil-based economy stock market based on the investment behavior of foreign investors and their response to oil price shocks. Originality/value: This study adds to the literature by analyzing the relationship understudy in an oil-rich and oil-dependent emerging economy, where its critical economic parameters are influenced by oil price volatility and it has the largest and the most liquid stock exchange in the MENA region. [ABSTRACT FROM AUTHOR]

New Zealand relies on imported fossil fuels for about 38% of its primary energy. The country's energy demand is expected to grow due to population and economic growth, which will put more pressure on the energy system. Besides, resource scarcity, energy price volatility, and environmental challenges have made energy security a major concern for New Zealand and other countries. Given the lack of significant research on the effects of energy security factors in New Zealand, this study aims to shed light on the primary determinants of energy security using the dynamic autoregressive distributed lag method based on time series data from 1978 to 2021. The study found that a long-run link exists between energy security and energy intensity (energy efficiency), renewable energy use, fossil fuel consumption, and global oil prices. Real GDP, renewable energy consumption, and energy security were found to improve energy security, while fossil fuel consumption and world oil prices had a negative impact. The study also revealed a one-way causality from real GDP, fossil fuel consumption, and renewable energy use to energy security. In contrast, the relationship between energy intensity and energy security is bidirectional. Simulation results showed that global crude oil prices have a lower impact on energy security compared to other variables and are most responsive to a 5% shock in fossil fuel consumption, followed by economic growth. [ABSTRACT FROM AUTHOR]

Crude oil is an essential energy source that affects international trade, transportation, and manufacturing, highlighting its importance to the economy. Its future price prediction affects consumer prices and the energy markets, and it shapes the development of sustainable energy. It is essential for financial planning, economic stability, and investment decisions. However, reaching a reliable future prediction is an open issue because of its high volatility. Furthermore, many state-of-the-art methods utilize signal decomposition techniques, which can lead to increased prediction time. In this paper, a model called K-means-dense-sparse-dense long short-term memory (K-means-DSD-LSTM) is proposed, which has three main training phrases for crude oil price forecasting. In the first phase, the DSD-LSTM model is trained. Afterwards, the training part of the data is clustered using the K-means algorithm. Finally, a copy of the trained DSD-LSTM model is fine-tuned for each obtained cluster. It helps the models predict that cluster better while they are generalizing the whole dataset quite well, which diminishes overfitting. The proposed model is evaluated on two famous crude oil benchmarks: West Texas Intermediate (WTI) and Brent. Empirical evaluations demonstrated the superiority of the DSD-LSTM model over the K-means-LSTM model. Furthermore, the K-means-DSD-LSTM model exhibited even stronger performance. Notably, the proposed method yielded promising results across diverse datasets, achieving competitive performance in comparison to existing methods, even without employing signal decomposition techniques. [ABSTRACT FROM AUTHOR]

This paper investigated the use of linear models to forecast crude oil futures prices (WTI) on a monthly basis, emphasizing their importance for financial markets and the global economy. The main objective was to develop predictive models using time series analysis techniques, such as autoregressive (AR), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), as well as ARMA variants adjusted by genetic algorithms (ARMA-GA) and particle swarm optimization (ARMA-PSO). Exponential smoothing techniques, including SES, Holt, and Holt-Winters, in additive and multiplicative forms, were also covered. The models were integrated using ensemble techniques, by the mean, median, Moore-Penrose pseudo-inverse, and weighted averages with GA and PSO. The methodology adopted included pre-processing that applied techniques to ensure the stationarity of the data, which is essential for reliable modeling. The results indicated that for one-step-ahead forecasts, the weighted average ensemble with PSO outperformed traditional models in terms of error metrics. For multi-step forecasts (3, 6, 9 and 12), the ensemble with the Moore-Penrose pseudo-inverse showed better results. This study has shown the effectiveness of combining predictive models to forecast future values in WTI oil prices, offering a useful tool for analysis and applications. However, it is possible to expand the idea of applying linear models to non-linear models. [ABSTRACT FROM AUTHOR]

The impact of climate risk on economic and financial fields has attracted considerable interest from academics and practitioners. Recent literature documents that climate risk can affect oil volatility through the dual channel of economic activities and financial markets. Inspired by this, this paper uses climate policy uncertainty (CPU) to examine the predictability of crude oil price volatility. The empirical results indicate that changes in the CPU, rather than the original CPU, can forecast oil volatility. The revealed volatility predictability is both in-sample and out-of-sample. Asset allocation exercise shows that this finding is also economically significant. Furthermore, the predictive ability is mainly concentrated during economic expansions and the CPU contains climate risk that is not available for other predictors. Finally, CPU change indicators can explain the changes in crude oil consumption, which is the economic source of their predictive power. [ABSTRACT FROM AUTHOR]

This study aims to identify the sources of spillovers affecting tourism tokens and classify the type of assets to which they correspond. Using daily data for different asset classes from June 2018 through November 2022, we employ a TVP-VAR methodology to test the connectedness between two tourism tokens, two leading travel equity indices, and the two dominant cryptocurrencies, namely, Bitcoin and Ethereum. The findings show that tourism tokens are relatively independent of fluctuations in the traditional sources affecting the travel and leisure sector, such as the U.S. dollar, the price of oil, or travel equity indices. These results hint that tourism tokens are more closely related to cryptocurrencies rather than pure travel goods. The results may help decision-makers in the travel and hospitality industries considering the use of tourism tokens identify the potential forces impacting them. [ABSTRACT FROM AUTHOR]

Recent theoretical developments in economics distinguish between risk and ambiguity (Knightian uncertainty). Using state-of-the-art methods with intraday stock market data from February 1993 to February 2021, we derive financial ambiguity and empirically examine the effect of shocks to it on the price and volatility of crude oil. We provide evidence that ambiguity carries important information about future oil returns and volatility perceived by investors. We validate these results using Granger causality and in-sample and out-of-sample forecasting tests. Our findings reveal that financial ambiguity is a possible factor that explains future drops in oil prices and their increased variability. Our findings will benefit scholars and investors interested in how financial ambiguity shapes short-term oil prices. [ABSTRACT FROM AUTHOR]

This study aims to identify the factors most likely to affect renewable energy consumption (REC) across mostly homogenous country groups worldwide. Classifying countries into a relatively homogenous group is taken from their economic and social development level measured with the Human Development Index. We delimited highly, medium-, and low-developed countries and checked whether the sets of determinants for using renewable energy sources are the same. We constructed a panel dataset as a basis for the panel Bayesian model averaging (panel BMA) as a factor selection method. The most likely factors were found and compared between the groups of countries. Then, the panel fixed-effects models for each country group were estimated. The results allowed us to confirm that CO2 per capita emissions, terms of trade, GDP, foreign direct investment, crude oil price, and energy consumption from alternative sources are the most critical drivers of REC in group I. The most important factors in group II are CO2 per capita, labor force, forest area, and gas and coal consumption. In the third group, REC consumption differs from that of the more advanced groups and strongly depends on foreign direct investment inflow. The results allow the formulation of policy recommendations on a global scale. [ABSTRACT FROM AUTHOR]

This study conducted a macro credit risk stress testing framework for assessing the impact of global transmissions on Tanzania's banking sector from Q12006 to Q42020, using the Global Vector Autoregressive Regression (GVAR) model with seven countries. The adoption of the GVAR model enabled us to take into account non-linear relationships, capture business cycle effects and the global interaction. This study focused on assessing the projected responsiveness of Tanzania's credit risk to shocks originating from domestic, global and foreign variables in relation to USA, China and South Africa. This study found that the global transmissions have an impact on the Tanzanian banking sector, with a significant exposure witnessed from the increase in the crude oil price index. On a domestic level, the results revealed that projected inflation rate shocks have a more responsive impact on banking sector credit risk compared to real GDP growth. In addition, GDP growth shocks from foreign countries have a slight impact on the banking sector compared to exchange rate risk transmission, given the growing global interconnectedness, thus leading to a spillover effect on the banking sector. Conclusively, Tanzania's banking sector is not invulnerable to domestic, foreign and global shocks, and therefore warrants monitoring to enhance resilience. [ABSTRACT FROM AUTHOR]

Electric scooters are increasingly gaining popularity in India owing to rising global crude oil prices and rising levels of vehicular pollution. Most of them are currently powered by expensive in-wheel (IW) permanent magnet (PM) brushless DC motors. Owing to their simplicity, and ruggedness while being cost-effective (since they do not employ PMs), switched reluctance motors (SRMs) are a viable alternative. Despite these benefits, SRMs possess drawbacks such as low torque density and inferior efficiency. Recently, a multi-teeth (MT) SRM with an improved performance was reported. However, the design of MTSRM topologies and their electromagnetic performance have not been explored sufficiently. In this paper, a design formula governing the selection of the number of MT and rotor poles for MTSRMs has been proposed. Using this, a novel four-phase 8/18 IW-MTSRM is derived and proposed for an E-scooter. The characteristics of the proposed SRM are numerically compared with a conventional 8/10 SRM based on magnetic characteristics, efficiencies and steady-state operation for the complete torque-speed range. Results indicate that the proposed 8/18 MTSRM has a higher peak torque capacity, torque density, superior drive cycle efficiency and reduced torque ripple. Further, the FEA model is validated experimentally on a downsized 8/18 MTSRM prototype. [ABSTRACT FROM AUTHOR]

Migration and private remittances have been continuously increasing over the years. For many developing countries, the quantum of remittances is greater than other forms of capital, such as FDI, FII, etc. The remittances may be used to fill the gap in potential funding sources for economic development in emerging countries like India. Migrations from India to the United States (US) and the flow of remittances to India have been continuously increasing over the years. The paper studies the macroeconomic factors influencing remittances to India. The study employs ARDL technique to identify the macroeconomic factors such as GDP, term premium, CPI, etc. of US, in addition to the domestic factors of India and price of Brent and other commodities such as gold, silver, and platinum that influence the flow of remittances to India. Further, the study also employs NARDL techniques to identify the asymmetric impact of macroeconomic variables in US on remittance flows to India. CDM technique is also used to decipher the shock evolution of independent variables with regard to remittance flows to India. [ABSTRACT FROM AUTHOR]

We build and estimate a dynamic, structural model of the world oil market to quantify the impact of the shale revolution. We model the shale revolution as a decrease in shale production costs and find that the resultant increase in shale production lowers oil prices by 24% in the short run and 48% once the shale oil transition is complete. Current oil price volatility is lowered by 8% to 23% depending on the horizon. We also find that OPEC core acts to keep its market share constant in the face of the dramatic increase in shale production. [ABSTRACT FROM AUTHOR]

Recently, there has been an increased focus on enhancing the accuracy of machine learning techniques. However, there is the possibility to improve it by selecting the optimal tuning parameters, especially when data heterogeneity and multicollinearity exist. Therefore, this study proposed a statistical model to study the importance of changing the crude oil prices in the European Union, in which it should meet state-of-the-art developments on economic, political, environmental, and social challenges. The proposed model is Elastic-net quantile regression, which provides more accurate estimations to tackle multicollinearity, heavy-tailed distributions, heterogeneity, and selecting the most significant variables. The performance has been verified by several statistical criteria. The main findings of numerical simulation and real data application confirm the superiority of the proposed Elastic-net quantile regression at the optimal tuning parameters, as it provided significant information in detecting changes in oil prices. Accordingly, based on the significant selected variables; the exchange rate has the highest influence on oil price changes at high frequencies, followed by retail trade, interest rates, and the consumer price index. The importance of this research is that policymakers take advantage of the vital importance of developing energy policies and decisions in their planning. [ABSTRACT FROM AUTHOR]

This study delved into various aspects of noodle production, emphasizing the impact of waste fermented black carrot powder (WFBCP) concentration and drying techniques. Higher concentrations of WFBCP increased cooking times, particularly in freeze-dried (FD) samples, resulting in varying water absorption and notable cooking losses. Color variations, influenced by both WFBCP concentration and drying methods, led to generally darker hues in the FD samples. Significant differences in the water content were observed across treatments. Ash content increased significantly with higher WFBCP concentrations, particularly at 40%, indicating the impact of WFBCP on ash formation. Noodle samples exhibited a significant rise in oil content with increasing WFBCP concentration, reaching a peak at 20%. Total Phenolic Content notably increased at higher WFBCP concentrations, peaking at 40%, suggesting potential nutritional benefits. Similarly, antioxidant activity increased significantly with higher WFBCP concentrations, indicating enhanced protection against food oxidation. Total monomeric anthocyanin content surged with increased WFBCP concentration, establishing WFBCP as a noteworthy source of anthocyanins. HPLC analysis revealed elevated levels of phenolic compounds when WFBCP was added to noodles, with FD-WFBCP showing the highest content. Freeze-drying effectively preserved the phenolic compounds, while the protein content remained stable. Sensory analysis, conducted using a 0–10 scale, emphasized the impact of processing methods and WFBCP concentrations on attributes such as color, stickiness, chewing, flavor, and overall rating. This study underscores the importance of precise control over WFBCP concentrations and processing for enhancing noodle attributes. Principal component analysis, explaining 90.8% of the total variation, highlights distinct differences between control and treated samples. [ABSTRACT FROM AUTHOR]

Many studies have examined the asymmetric effect of US dollar-denominated crude oil prices on petroleum product prices. The 'rockets and feathers' argument suggests that a crude price increase raises petroleum product prices more than a corresponding decrease in crude prices lowers product prices. However, for the countries that do not use the US dollar as a medium of exchange, petroleum product prices are also affected by the exchange rates. This paper analysed the asymmetric effects of both US dollar-denominated crude oil prices and exchange rates on local currency-denominated diesel prices for 27 European countries in the short run as well as long run. The overall empirical evidence suggests that, in the short run, diesel prices react more to crude oil price increases than to a decrease, parallel to the 'rockets and feathers' argument. However, contrary to that argument, the long-run adjustment is the opposite. As for exchange rate shocks, again the 'rockets and feathers' argument holds and diesel prices respond more to exchange rate depreciation than appreciation in the short and long run. [ABSTRACT FROM AUTHOR]

This study investigates the extent and efficiency of global energy transition by examining the impact of renewable electricity generation on non-renewable electricity generation in 73 economies from 1990 to 2020. This is done to empirically scrutinise the assumption of perfect displacement as adopted by Intergovernmental Panel on Climate Change and other environmental and economic analysts. The estimated models also control for the effects of globalisation, economic growth, and international crude oil prices. The PMG-ARDL estimation demonstrates that renewable energy is able to partly displace the non-renewable energy and that the global energy transition is far from perfect displacement. More specifically, the coefficient of renewable energy generation is − 0.157, indicating that more than 6 units of alternative energy are necessary for displacing one unit of fossil fuel energy at the global level. However, the findings reveal that globalisation, economic growth, and crude oil prices negatively influence the energy transition by pushing up the non-renewable energy generation. To delve into finer details, the ARDL findings uncover that 1% increase in globalisation, economic growth, and crude oil prices increases the non-renewable energy generation by 0.88%, 0.75%, and 0.03%, respectively. The Driscoll–Kraay and feasible generalised least squares estimation techniques also confirm these baseline findings. Potential policy implications for enhancing the efficiency of global energy transition are also discussed. [ABSTRACT FROM AUTHOR]

This paper examines the predictability of the changes in Brent oil futures prices using a multilayer perceptron artificial neural network that exploits the information contained in the largest possible set of economic indicators. Feature engineering is employed to identify the most important predictors of the change in Brent oil futures prices. We find that oil‐market‐specific variables are important predictors. Our findings also suggest that forecasts of the change in the Brent oil futures prices from the multilayer perceptron that exploits the informational content of all and oil‐market‐specific predictors exhibit higher statistical forecast accuracy than the random walk. Tests of forecast optimality indicate that the forecasts generated using oil‐market‐specific predictors are optimal. We discuss the policymaking and practical relevance of our results. [ABSTRACT FROM AUTHOR]

Extending the popular HAR model with additional information channels to forecast realized volatility of WTI futures prices, we show that machine learning‐generated forecasts provide better forecasting quality and that portfolios that are constructed with these forecasts outperform their competing models resulting in economic gains. Analyzing the selection process, we show that information channels vary across forecasting horizon. Variable selection produces clusters and provides evidence that there are structural changes with regard to the significance of information channels. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of global geopolitical risks and global supply chain pressures on global inflation for the monthly period of 1999M1–2022M12. The investigation is based on a structural vector autoregression model, where the effects of global oil prices and global monetary policy are controlled for. Four alternative measures of inflation are used, including headline, core, food, and energy inflation. The empirical results show that disruptions in global supply chains are the main drivers of global inflation in the long run as the corresponding shocks explain the lion's share of volatilities in headline inflation (by 32%), core inflation (by 30%), and food inflation (by 22%), followed by oil price shocks and policy rate shocks. In comparison, energy inflation is explained the most by oil price shocks (by 55%) followed by supply chain shocks and policy rate shocks. Positive supply chain pressure and oil price shocks have positive and statistically significant effects on headline inflation even after five years, whereas positive policy rate shocks have negative and statistically significant effects on headline inflation in the long run. In contrast, positive shocks to geopolitical risk result in higher headline inflation only up to one year, with insignificant effects in the long run. Several policy implications follow. [ABSTRACT FROM AUTHOR]

The past decade witnessed a surge in anti-corruption activities in post-Soviet oil-dependent countries. Despite the scale of the anti-corruption measures, and their political implications, the extant literature paid limited attention to the comparison of these campaigns, including differences in their motivations, functions, objectives, and targets. Aiming to fill this research gap, this study employs a mixed-methods research design that combines fixed-effects panel data analysis with a paired-case qualitative analysis of Russia and Kazakhstan. I argue that anti-corruption policies are the result of either economic necessity or the desire to secure power through buttressing regime legitimacy. Consequently, a significant drop in oil price due to the COVID-19 crisis is likely to result in a major anti-corruption campaign targeting high-level officials in oil-rich countries. [ABSTRACT FROM AUTHOR]

The demand for fermentation-based chemicals is expected to grow in the coming years because of the increasing emphasis on using bio-based chemicals over petrochemicals. Growing crude oil prices and rising concerns about carbon discharge are the main reasons pushing the shift toward bio-based chemicals. 2,3-Pentanedione is one such high-value fine chemical that is currently produced in smaller quantities through a multi-stage chemical synthesis or extracted from milk waste. Here we report a green route to produce 2,3–pentanedione directly from vapour-phase condensation of crude lactic acid over polymorphic ZrO2. The catalyst exhibits high selectivity, activity and stability for the direct condensation of raw lactic acid to 2,3-pentanedione and achieved 99.7% LA conversion and 95.5% selectivity at 325 °C. [ABSTRACT FROM AUTHOR]

The demand for fermentation-based chemicals is expected to grow in the coming years because of the increasing emphasis on using bio-based chemicals over petrochemicals. Growing crude oil prices and rising concerns about carbon discharge are the main reasons pushing the shift toward bio-based chemicals. 2,3-Pentanedione is one such high-value fine chemical that is currently produced in smaller quantities through a multi-stage chemical synthesis or extracted from milk waste. Here we report a green route to produce 2,3–pentanedione directly from vapour-phase condensation of crude lactic acid over polymorphic ZrO2. The catalyst exhibits high selectivity, activity and stability for the direct condensation of raw lactic acid to 2,3-pentanedione and achieved 99.7% LA conversion and 95.5% selectivity at 325 °C. [ABSTRACT FROM AUTHOR]

The subject of predicting global crude oil prices is well recognized in academic circles. The notion of hybrid modeling suggests that the integration of several methodologies has the potential to optimize advantages while reducing limitations. Consequently, hybrid techniques are extensively used in contemporary research. In this paper, a novel decompose-ensemble prediction approach is proposed by integrating various optimization algorithms, namely biography-based optimization (BBO), backtracking search algorithm (BSA), teaching-learning-based algorithm (TLBO), cuckoo optimization algorithm (COA), multi-verse optimization (MVO), and multilayer perceptron (MLP). Furthermore, the aforementioned approaches, namely BBO-MLP, BSA-MLP, and TLBO-MLP, include the de-compose-ensemble technique into the individual artificial intelligence model in order to enhance the accuracy of predictions. In order to validate the findings, the forecast is conducted using the authoritative data on oil prices. This study will use three primary indicators, including EMA 20, EMA 60, EMA 100, ROC, and AUC assessments, to assess and evaluate the efficacy of the five methodologies under investigation. The below findings are derived from the conducted research: Based on the achieved AUC values of 0.9567 and 0.9429, it can be concluded that using a multi-verse optimization technique is considered the most suitable strategy for effectively handling the dataset pertaining to crude oil revenue. The next four approaches likewise have a significant AUC value, surpassing 0.8. The AUC values for the BBO- MLP, BSA-MLP, TLBO-MLP, and COA-MLP approaches were obtained as follows: (0.874 and 0.792) for training and testing stages, (0.809 and 0.792) for training and testing stages, (0.9353 and 0.9237) for training and testing stages, and (0.9092 and 0.8927) for training and testing stages, respectively. This model has the potential to contribute to the resolution of default probability and is very valuable to the credit card industry. Broadly speaking, this novel forecasting approach serves as a notable predictor of crude oil prices. [ABSTRACT FROM AUTHOR]

The oil market is one of the most important markets for the global economy. Often, oil prices influence the financial results of whole countries and sectors. Therefore, the modeling and prediction of crude oil prices are of high importance. Most up-to-date publications have used daily closing rates in crude oil price modeling, not considering the variability in prices during the day. The application of this kind of price representation leads to a loss of information about the range of price changes during the day, which influences the accuracy of the models and makes them useless in short-term course predictions. In this paper, we introduce the concept of a new state model in a binary-temporal representation, which uses trend analysis, which is one of the main methods used in the prediction of the direction of future changes in the course trajectory. The model described in this paper stands as the first tool that allows for predicting course changes in a given range. The presented work also summarizes the research results of modeling crude oil prices from the last six years, which prove the effectiveness of the mentioned modeling method. [ABSTRACT FROM AUTHOR]

Commodity markets, such as crude oil and precious metals, play a strategic role in the economic development of nations, with crude oil prices influencing geopolitical relations and the global economy. Moreover, gold and silver are argued to hedge the stock and cryptocurrency markets during market downsides. Therefore, accurate forecasting of crude oil and precious metals prices is critical. Nevertheless, due to the nonlinear nature, substantial fluctuations, and irregular cycles of crude oil and precious metals, predicting their prices is a challenging task. Our study contributes to the commodity market price forecasting literature by implementing and comparing advanced deep-learning models. We address this gap by including silver alongside gold in our analysis, offering a more comprehensive understanding of the precious metal markets. This research expands existing knowledge and provides valuable insights into predicting commodity prices. In this study, we implemented 16 deep- and machine-learning models to forecast the daily price of the West Texas Intermediate (WTI), Brent, gold, and silver markets. The employed deep-learning models are long short-term memory (LSTM), BiLSTM, gated recurrent unit (GRU), bidirectional gated recurrent units (BiGRU), T2V-BiLSTM, T2V-BiGRU, convolutional neural networks (CNN), CNN-BiLSTM, CNN-BiGRU, temporal convolutional network (TCN), TCN-BiLSTM, and TCN-BiGRU. We compared the forecasting performance of deep-learning models with the baseline random forest, LightGBM, support vector regression, and k-nearest neighborhood models using mean absolute error (MAE), mean absolute percentage error, and root mean squared error as evaluation criteria. By considering different sliding window lengths, we examine the forecasting performance of our models. Our results reveal that the TCN model outperforms the others for WTI, Brent, and silver, achieving the lowest MAE values of 1.444, 1.295, and 0.346, respectively. The BiGRU model performs best for gold, with an MAE of 15.188 using a 30-day input sequence. Furthermore, LightGBM exhibits comparable performance to TCN and is the best-performing machine-learning model overall. These findings are critical for investors, policymakers, mining companies, and governmental agencies to effectively anticipate market trends, mitigate risk, manage uncertainty, and make timely decisions and strategies regarding crude oil, gold, and silver markets. [ABSTRACT FROM AUTHOR]

This study investigates the extent and persistence of major crisis events in the crude oil market and economy and searches for general rules of event impact. Although the short-term effects of such crises may quickly become evident, their long-term implications can be challenging to uncover. To this end, we analyzed 50 major crisis events across four categories using a Proxy structural vector autoregressive (Proxy-SVAR) model. The results show that these events substantially impacted crude oil production, world crude oil prices, and inflation in China and the United States. Generally speaking, major crisis events have a more severe impact on the crude oil market, but their impact on the economy lasts longer. Notably, geopolitical conflicts have led to a sharp decline in S&P500, resulting in a simultaneous reduction in the industrial production indices of the world, China, and the US. Among the different types of crises, the financial crisis had the most prolonged impact, persisting for approximately four years. The public health emergency represented by COVID-19 resulted in a decline in actual oil prices, with its impact on the oil market continuing for over three years. Considering our findings, we suggest four policy measures to enhance economic resilience. [ABSTRACT FROM AUTHOR]

This research explores economic growth dynamics in five ASEAN countries (1980–2020), analyzing the short and long-term impact of financial development (FD), oil prices, investment (INV), and inflation on GDP growth. Panel ARDL analysis reveals a transient positive link between FD and long-term GDP growth, necessitating a holistic approach for short-term stability. Similarly, oil prices exhibit long-term volatility, urging diversification strategies. In contrast, consistent and significant relationships exist between INV and GDP growth in both time frames, underscoring investment's pivotal role. The study emphasizes managing inflation for sustained growth, offering vital insights for policymakers, economists, and analysts in fostering ASEAN's economic stability. [ABSTRACT FROM AUTHOR]

Financial markets are increasingly interlinked. Therefore, this study explores the complex relationships between the Tadawul All Share Index (TASI), West Texas Intermediate (WTI) crude oil prices, and Bitcoin (BTC) returns, which are pivotal to informed investment and risk-management decisions. Using copula-based models, this study identified Student's t copula as the most appropriate one for encapsulating the dependencies between TASI and BTC and between TASI and WTI prices, highlighting significant tail dependencies. For the BTC–WTI relationship, the Frank copula was found to have the best fit, indicating nonlinear correlation without tail dependence. The predictive power of the identified copulas were compared to that of Long Short-Term Memory (LSTM) networks. The LSTM models demonstrated markedly lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Scaled Error (MASE) across all assets, indicating higher predictive accuracy. The empirical findings of this research provide valuable insights for financial market participants and contribute to the literature on asset relationship modeling. By revealing the most effective copulas for different asset pairs and establishing the robust forecasting capabilities of LSTM networks, this paper sets the stage for future investigations of the predictive modeling of financial time-series data. The study highlights the potential of integrating machine-learning techniques with traditional econometric models to improve investment strategies and risk-management practices. [ABSTRACT FROM AUTHOR]

Background: Mastalgia, or breast pain (or mastodynia), is globally a common symptom experienced by women of reproductive age; treatment for these conditions is not yet standardized. Most of the drugs used for mastalgia are expensive and have side effects, so, the aim of this study was to compare different agents for reducing mastalgia in fibrocystic disease of women in outpatient clinics at Zagazig University. Methods: this prospective random comparative study has been carried out in Zagazig University out patients clinics specifically in breast clinic subjects in period between May-2019 to March -2020 on 68 patients enrolled with 3 months period of follow up. Results: In EPO group 30% of patients showed complete resolution and 61% showed partial response. The danazol group received, 58% of patients showed complete resolution and 41% showed partial improvement. In Tamoxifen group 47% of patients showed complete resolution while 52 % showed partial improvement. In the cabergoline group 14% of patient showed complete resolution and 42% showed partial improvement. Conclusions: Evening prime rose oil is effective in reducing the severity of mastalgia with minor tolerable side effects and should be used as first line of management. Danazol is the most effective agent but its side effects make it less favorable agent. Tamoxifen is the second most effective agent among the other agents with reversible tolerable side effects. Cabergoline is significantly decrease breast pain especially cyclic mastalgia, with notable side effects. [ABSTRACT FROM AUTHOR]

During the development process of heavy oil with steam huff and puff, the wellbore undergoes alternating conditions of temperature rise and fall, leading to a significant decline in the strength of the cement sheath. This work studied the inhibition effect and mechanism of the composite material composed of mullite and basalt fiber on the strength decline of cement stone (the hardened body of cement slurry) under simulated wellbore temperature alternating conditions from 80 °C to 260 °C. The pore structure distribution of cement stone was investigated by mercury intrusion porosimetry (MIP), the microstructure and phase composition of cement stone was characterized by scanning electron microscope (SEM) and energy dispersive spectrometer (EDS), together with quantitative X-ray diffraction (XRD) analysis. After 4 rounds of curing under alternating temperature conditions, the compressive strength of cement stone containing mullite and basalt fiber composite material was 28.6 MPa at 260 °C, which was only 9.2 % lower than that at 80 °C. However, the compressive strength of the blank cement stone without composite materials decreased to 22.5 MPa at 260 °C, which was 20 % lower than that at 80 °C. After 4 rounds of alternating temperature cured, the harmful pores of the cement stone without composite materials increase, and the xonotlite flakes and stacks together. The cement stone added with mullite has a finer pore structure after 4 rounds of alternating temperature cured because mullite reacts with portlandite to form C-A-S-H gel. The xonotlite remains fibrous, and a small amount of new phase hibschite is formed. Furthermore, basalt fiber can react with C-A-S-H gel at high temperatures, and the two phases are closely combined to further improve the high-temperature resistance of cement stone. [ABSTRACT FROM AUTHOR]

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This study is the first to investigate the asymmetric effects of oil price volatility on stock returns for the Ho Chi Minh Stock Exchange (HOSE). We utilized weekly series of VN30-Index, WTI crude oil prices, geopolitical risks (GPR) index, and gold prices spanning from 6 February 2012 to 31 December 2023 as data sources. Using a nonlinear autoregressive distributed lag (NARDL) bounds testing approach, we found that, in the shortterm, oil price volatility has negative asymmetric effects on market returns. Specifically, in the shortterm, a 1 percent increase in oil price volatility immediately leads to a 2.6868 percent decrease in the market returns, while a similar magnitude decrease in oil price volatility is associated with a 6.3180 percent increase in the market returns. In addition, the results obtained from the NARDL model indicated that, in the longterm, the negative and positive changes of oil price volatility have significantly negative effects on the market returns. Finally, the findings derived from the error correction model (ECM) show that a 98.21 percent deviation from the equilibrium level in the previous week is converged and corrected back to the long-term equilibrium in the current week. [ABSTRACT FROM AUTHOR]

The objective of this research is to identify a bio-based friction modifier (FM) with tribological performance comparable to conventional FMs. Promising alternatives to conventional FMs, such as the FMs derived from natural sources, including rapeseed and salmon oil, were selected. Increasing concerns about crude oil prices, environmental impact, and the depletion of fossil resources have further fueled the search for renewable, biodegradable, and environmentally friendly raw materials for lubricants Tribological tests were conducted using a rheometer under non-conformal contact. The normal force, temperature, and sliding speed were varied to simulate conditions such as those found in a food extruder. To simulate cold extrusion applications, water and bio-based FM mixtures were used. The best-performing bio-based FMs were then mixed with a polyalphaolefin to simulate warm extrusion conditions. The results were compared to those obtained from mixtures of a polyalphaolefin and selected conventional FMs. The main finding of this study demonstrated that rapeseed and salmon oils, with a peak coefficient of friction (COF) of 0.16, are the best-performing bio-based FMs for reducing friction. When mixed with distilled water for cold extrusion (case 1) and with polyalphaolefin for warm extrusion (case 2), they performed similarly to the conventional FM, tallow amine, also with a maximum COF of 0.16, and significantly better than polyalphaolefin alone (maximum COF of 0.25). Consequently, rapeseed and salmon oils are suitable bio-based FM candidates to replace conventional FMs in food-grade lubrication. [ABSTRACT FROM AUTHOR]

The oil and gas trade is one of the main ways to promote regional economic development by improving the effectiveness of resource allocation. While regional energy cooperation could lead to growth in the energy trade, blind investment will reduce effective yields. Kazakhstan and China maintain a stable oil and gas trade, but resource exports to China are not growing as expected. The aim of this research is to analyze the competitiveness and complementarity of Kazakhstan and China in the oil and gas trade, as well as the main factors affecting the oil and gas trade between Kazakhstan and China. By creating a linear regression equation to analyze the gravity model of the oil and gas trade between Kazakhstan and China, it was revealed that a 1% growth of the gross domestic product in both countries would lead to a 1.471% increase in the oil and gas trade. However, an increase in oil and gas production in Kazakhstan will not contribute to the expansion of the oil and gas trade with China. Kazakhstan and China could improve their oil and gas trade by strengthening financial cooperation, improving energy efficiency, increasing investment in infrastructure such as oil refineries and pipelines, and developing new oil and gas fields in Kazakhstan. [ABSTRACT FROM AUTHOR]

Purpose: This study examines the relationship between oil price uncertainty (OPU) and corporate inventory investments using a sample of 6,072 USA manufacturing firms from 1992 to 2019. Design/methodology/approach: The author's study employs a panel dataset to examine the relationship between OPU and corporate inventory investments. The author uses several alternative specifications such as fixed effects models, an instrumental variable analysis, an impact threshold for confounding variable (ITCV) analysis, alternative measures, additional control variables and the percent bias analysis to account for endogeneity issues. Findings: Corporate inventory investments decrease in response to high OPU. This decrease in inventory investments happens regardless of firms' expected stockout costs, information environment and reliance on external financing. As a potential mechanism, an uncertainty-induced increase in cash holdings contributes to this reduction in inventory investments. Also, the effect of OPU is non-linear and asymmetric. In response to the volatility of positive (negative) oil price changes, inventory investments decrease (increase) up to a certain point and increase (decrease) after that. Further, uncertainty-induced adjustments in inventory investments positively influence the operating performance of firms. Originality/value: The author's study adds to the growing literature that examines the impact of OPU on corporate outcomes. Inventory investments directly affect business operations and could better reflect firms' responses to an uncertain environment. [ABSTRACT FROM AUTHOR]

If a financial asset's price movement impacts a firm's product demand, the firm can respond to the impact by adjusting its operational decisions. For example, in the automotive industry, automakers decrease the selling prices of fuel-inefficient cars when the oil price rises. Meanwhile, the firm can implement a risk-hedging strategy using the financial asset jointly with its operational decisions. Motivated by this, we develop and solve a general risk-management model integrating risk hedging into a price-setting newsvendor. The optimal hedging strategy is calculated analytically, which leads to an explicit objective function for optimizing price and "virtual production quantity" (VPQ). (The latter determines the service level—that is, the demand-fulfillment probability.) We find that hedging generally reduces the optimal price when the firm sets the target mean return as its production-only maximum expected profit. With the same condition on the target mean return, hedging also reduces the optimal VPQ when the asset price trend positively impacts product demand; meanwhile, it may increase the VPQ by a small margin when the impact is negative. We construct the return-risk efficient frontier that characterizes the optimal return-risk trade-off. Our numerical study using data from a prominent automotive manufacturer shows that the markdowns in price and reduction in VPQ are small under our model and that the hedging strategy substantially reduces risk without materially reducing operational profit. This paper was accepted by Kay Giesecke, finance. Funding: Financial support from the Hong Kong Research Grants Council [General Research Fund 17200521] is gratefully acknowledged. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2023.4942. [ABSTRACT FROM AUTHOR]

In this paper, we investigate house price shocks on the macroeconomic variables (financial market, inflation, and real sector) of the G7 economies. We use the GVAR to capture the spillover effects from the U.S. housing market and oil prices on these economies from 1991M3–2022M10. We identify the U.S. house price shock using the Structural Generalized Impulse Response Function, house supply and demand variables, and regional divergence. We find that the domestic stock markets and industrial production are the most sensitive to house price shocks. We further compare the importance of house and oil prices on domestic fluctuations. The estimates reinforce the previous findings: U.S. house prices are responsible for a quarter of the domestic volatility of the stock markets and industrial production. In the other macroeconomic segments, the effects of house prices are present, but in lower values. Our results show that house prices provoke more domestic fluctuations than oil prices. Finally, we also found that short and long‐term credit markets, as well as stock markets, transmit the house price shock to industrial production. Consequently, we provide potential channels to comprehend the spillover effect of U.S. house prices on international markets. [ABSTRACT FROM AUTHOR]

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This paper examines the impact of oil price and quantity shocks on income inequality in the USA. Using micro income data, we construct measures of pre‐tax money income inequality at the state level for the period 1980–2017. We find that a $10 increase in oil price increases inequality, measured by the ratio of pre‐tax income of the 90th percentile to 10th percentile (p90/p10), by 3.8 percentage points in states with high levels of oil endowments per capita. The increase in capital income to households at the top of the distribution drives the observed change in inequality. In addition to price shocks, we study the impact of quantity shocks that arise from oil discoveries. Quantity shocks have no effect on the overall income distribution. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept of statistical arbitrage through the definition of a mean-reverting trading strategy that captures persistent anomalies in long-run relationships among assets. We model the statistical arbitrage proceeding in three steps: (1) to identify mispricings in the chosen market, (2) to test mean-reverting statistical arbitrage, and (3) to develop statistical arbitrage trading strategies. We empirically investigate the existence of statistical arbitrage opportunities in crude oil markets. In particular, we focus on long-term pricing relationships between the West Texas Intermediate crude oil futures and a so-called statistical portfolio, composed by other two crude oils, Brent and Dubai. Firstly, the cointegration regression is used to track the persistent pricing equilibrium between the West Texas Intermediate crude oil price and the statistical portfolio value, and to identify mispricings between the two. Secondly, we verify that mispricing dynamics revert back to equilibrium with a predictable behaviour, and we exploit this stylized fact by applying the trading rules commonly used in equity markets to the crude oil market. The trading performance is then measured by three specific profit indicators on out-of-sample data. [ABSTRACT FROM AUTHOR]

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This study examines the causality relationship between oil price movements and geopolitical risks for a group of 18 geopolitically sensitive countries, using monthly data by implementing all quantiles distributions. Contrary to earlier studies, which have applied the Granger‐causality through the conditional mean regression, this research estimates the association between the variables through Granger causality within quantiles. Evidence of a two‐way causality is found linking the changes in geopolitical risk and fluctuations in oil prices in the case of Thailand, Argentina, Israel, China, Mexico India, Korea, Indonesia, South Africa, Turkey, Philippines, Venezuela, Ukraine, and others. In addition, it is confirmed that oil prices Granger cause geopolitical risks for the countries like Brazil, Malaysia, and Colombia. Furthermore, a one‐way causality direction is found from changes in geopolitical risk to shifts in oil prices in Russia and Saudi Arabia, which are observed as super oil rich states. This study findings highlight the importance of government policies and business strategies that aim at containing the effects of geopolitical risk and the resulting oil price movements. [ABSTRACT FROM AUTHOR]

Crude oil is a vital energy source for industrialised and developing countries. Both investors who trade crude oil derivatives and firms that use oil as raw material for production monitor oil supply and demand. Disruption in the flow of oil in the commodity markets, therefore, leads to oil price volatility that affects major economies worldwide. Often, geopolitical and natural conditions could adversely impact the oil price and the financial markets react to such fluctuations in oil prices. This paper analyses the effects of oil price changes on sectorial stock returns in India. Using daily returns of S&P BSE Sensex, S&P BSE Oil & Gas and S&P BSE-Auto sectoral indices from January 2000 to March 2020, we examined the impact of Brent crude oil price fluctuations on the two major sectorial stock indices and overall market index. The results of the Granger causality test show significant results on S&P BSE-Sensex and S&P BSE-Oil and Gas Index Returns except S&P BSE-Auto Index Returns. Further for contemporaneous association between the oil price changes and sectorial returns, we operationalised the simple regression analysis. The results from the regression analysis show statistically significant impact of crude oil price fluctuations on the sectorial returns. [ABSTRACT FROM AUTHOR]