Forecasting Oil Price Trends with Sentiment of Online News Articles

AbstractWith the rapid development of the Internet and big data technologies, a rich of online data (including news releases) can helpfully facilitate forecasting oil price trends. Accordingly, this study introduces sentiment analysis, a useful big data analysis tool, to understand the relevant information of online news articles and formulate an oil price trend prediction method with sentiment. Three main steps are included in the proposed method, i.e., sentiment analysis, relationship investigation and trend prediction. In sentiment analysis, the sentiment (or tone) is extracted based on a dictionary-based approach to capture the relevant online information concerning oil markets and the driving factors. In relationship investigation, the Granger causality analysis is conducted to explore whether and how the sentiment impacts oil price. In trend prediction, the sentiment is used as an important independent variable, and some popular forecasting models, e.g., logit regression, support vector machine, decision tree and back propagation neural network, are performed. With crude oil futures prices of the West Texas Intermediate (WTI) and news articles of the Thomson Reuters as studying samples, the empirical results statistically support the powerful predictive power of sentiment for oil price trends and hence the effectiveness of the proposed method

Reservoir and lithofacies shale classification based on NMR logging

© 2020 Chinese Petroleum Society Shale gas reservoirs have fine-grained textures and high organic contents, leading to complex pore structures. Therefore, accurate well-log derived pore size distributions are difficult to acquire for this unconventional reservoir type, despite their importance. However, nuclear magnetic resonance (NMR) logging can in principle provide such information via hydrogen relaxation time measurements. Thus, in this paper, NMR response curves (of shale samples) were rigorously mathematically analyzed (with an Expectation Maximization algorithm) and categorized based on the NMR data and their geology, respectively. Thus the number of the NMR peaks, their relaxation times and amplitudes were analyzed to characterize pore size distributions and lithofacies. Seven pore size distribution classes were distinguished; these were verified independently with Pulsed-Neutron Spectrometry (PNS) well-log data. This study thus improves the interpretation of well log data in terms of pore structure and mineralogy of shale reservoirs, and consequently aids in the optimization of shale gas extraction from the subsurface

The Role of Financial Risks and Financial Sustainability in the Iraqi Stock Markets during COVID-19 Pandemic

This paper compares the company's ability to sustain a diversified resource base for long-term service to its customers without need for external funding sources through two stressful systems of financial markets, the financial crisis aftermath 2008 and the COVID-19 epidemic. Our findings indicate that financial sustainability has been affected by disasters. Banks can impose significant risks on the economy, one of the main concerns about the causes of the current financial crisis is that banks engaged in excessive risk-taking, it has faced during the 2008 crisis liquidity and credit risks, while Through the 2020 crisis which is the COVID-19 pandemic crisis, the most significant financial risks that banks experienced were credit risks due to banks ’dependence on traditional, weak, safe assets, including the dollar, the Swiss franc, and other treasury bonds during the COVID-19 pandemic. This led to a lack of cash reserves held by banks, which led to weak investor tolerance towards financial risks, especially as the nature of crises is changing, as the results have proven that departures from riskiest directorships are more beneficial during the COVID-19 crisis because managers deal with the crisis in the same way as a crisis 2008 On the other hand, departures from riskiest directorships lead to higher administrative costs due to a lack of expertise

Machine learning methods for systemic risk analysis in financial sectors.

Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.This research has been partially supported by grants from the National Natural Science Foundation of China (#U1811462, #71874023, #71771037, #71725001, and #71433001)

Knowledge mapping of credit risk research: scientometrics analysis using CiteSpace

To understand the development track of credit risk research clearly and discover the hidden internal connections between literatures, this article utilises the scientific information measurement software–CiteSpace–to conduct a scientometric analysis (citation analysis, co-citation analysis and co-occurrence analysis) of 2,384 articles on credit risk from Web of Science (W.o.S.) during 1998 and 2017. According to the research results, some useful conclusions can be drawn as follows: (1) Credit risk research has become interdisciplinary and subject involved are ‘Business Finance’, ‘Economics’, ‘Operations Research Management Science’, ‘Mathematics Interdisciplinary Applications’; (2) The U.S., Europe, and Asia make the majority of contributions and there are numerous collaborations among countries; (3) The key researchers with influence and authority in this field mainly are Merton Robert Cox and Jarrow Robert Alan; (4)‘Rollover risk’, ‘Arbitrage-free pricing’, ‘Default cycle’, ‘Credit risk evaluation’ and ‘Correlated default’ are the major research area; (5)‘Crisis’, ‘Contagion’, ‘Monetary policy’, ‘Counterparty risk’ and ‘Systemic risk’ have become major research hotspot currently. Finally, we hope this scientometric analysis can provides some inspiration for credit risk researchers

Vehicle Coordinated Strategy for Vehicle Routing Problem with Fuzzy Demands

The vehicle routing problem with fuzzy demands (VRPFD) is considered. A fuzzy reasoning constrained program model is formulated for VRPFD, and a hybrid ant colony algorithm is proposed to minimize total travel distance. Specifically, the two-vehicle-paired loop coordinated strategy is presented to reduce the additional distance, unloading times, and waste capacity caused by the service failure due to the uncertain demands. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed approaches

Financial sector development and Investment in selected countries of the Economic Community of West African States: empirical evidence using heterogeneous panel data method

Abstract This study investigated the impact of financial sector development on domestic investment in selected countries of the Economic Community of West African States (ECOWAS) for the years 1985–2017. The study employed the augmented mean group procedure, which accounts for country-specific heterogeneity and cross-sectional dependence, and the Granger non-causality test to test for causality in the presence of cross-sectional dependence. The results show that (1) The impact of financial sector development on domestic investment depends on the measure of financial sector development utilised; (2) Domestic credit to the private sector has a positive but insignificant impact on domestic investment in ECOWAS, whereas banking intermediation efficiency (i.e., ability of the banks to transform deposits into credit) and broad money supply negatively and significant influence domestic investment; (3) Cross-country differences exist in the impact of financial sector development on domestic investment in the selected ECOWAS countries; and (4) Domestic credit to the private sector Granger causes domestic investment in ECOWAS. The study recommends careful consideration in the measure of financial development that is utilised as a policy instrument to foster domestic investment. We also highlight the importance of employing country-specific domestic investment policies to avoid blanket policy measures. Domestic credit to the private sector should be given priority when forecasting domestic investment into the future

The Rise and Fall of Cryptocurrencies: Defining the Economic and Social Values of Blockchain Technologies, assessing the Opportunities, and defining the Financial and Cybersecurity Risks of the Metaverse

This paper contextualises the common queries of "why is crypto crashing?" and "why is crypto down?", the research transcends beyond the frequent market fluctuations to unravel how cryptocurrencies fundamentally work and the step-by-step process on how to create a cryptocurrency. The study examines blockchain technologies and their pivotal role in the evolving Metaverse, shedding light on topics such as how to invest in cryptocurrency, the mechanics behind crypto mining, and strategies to effectively buy and trade cryptocurrencies. Through an interdisciplinary approach, the research transitions from the fundamental principles of fintech investment strategies to the overarching implications of blockchain within the Metaverse. Alongside exploring machine learning potentials in financial sectors and risk assessment methodologies, the study critically assesses whether developed or developing nations are poised to reap greater benefits from these technologies. Moreover, it probes into both enduring and dubious crypto projects, drawing a distinct line between genuine blockchain applications and Ponzi-like schemes. The conclusion resolutely affirms the continuing dominance of blockchain technologies, underlined by a profound exploration of their intrinsic value and a reflective commentary by the author on the potential risks confronting individual investors

Insurance and inequality in Sub-Saharan Africa:Policy thresholds

Insurance and inequality in Sub-Saharan Africa: Policy thresholdsIn this study, we examine how insurance affects income inequality in sub-Saharan Africa, using data from 42 countries during the period 2004-2014. Three inequality variables are used, namely: the Gini coefficient, the Atkinson index and the Palma ratio. Two insurance premiums are employed, namely: life insurance and non-life insurance. The empirical evidence is based on the Generalized Method of Moments (GMM). Life insurance increases the Gini coefficient and increasing life insurance has a net positive effect on the Gini coefficient and the Atkinson index. Non-life insurance reduces the Gini coefficient and increasing non-life insurance has a net positive effect on the Palma ratio. The analysis is extended to establish policy thresholds at which increasing insurance premiums completely dampen the net positive effects. From the extended analysis, 7.500 of life insurance premiums (% of GDP) is the critical mass required for life insurance to negatively affect inequality, while 0.855 of non-life insurance premiums (% of GDP) is the threshold required for non-life insurance to negatively affect inequality. Policy thresholds are provided at which insurance penetration decreases income inequality in sub-Saharan Africa.Economic