Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries

The paper investigates the time-varying correlation between stock market prices and oil prices for oil-importing and oil-exporting countries. A DCC-GARCH-GJR approach is employed to test the above hypothesis based on data from six countries; Oil-exporting: Canada, Mexico, Brazil and Oil-importing: USA, Germany, Netherlands. The contemporaneous correlation results show that i) although time-varying correlation does not differ for oil-importing and oil-exporting economies, ii) the correlation increases positively (negatively) in respond to important aggregate demand-side (precautionary demand) oil price shocks, which are caused due to global business cycle’s fluctuations or world turmoil (i.e. wars). Supply-side oil price shocks do not influence the relationship of the two markets. The lagged correlation results show that oil prices exercise a negative effect in all stock markets, regardless the origin of the oil price shock. The only exception is the 2008 global financial crisis where the lagged oil prices exhibit a positive correlation with stock markets. Finally, we conclude that in periods of significant economic turmoil the oil market is not a safe haven for offering protection against stock market losses

Discovering the most important data quality dimensions in health big data using latent semantic analysis

Big Data quality is a field which is emerging. Many authors nowadays agree that data quality is still very relevant, even for Big Data uses. However, there is a lack of frameworks or guidelines focusing on how to carry out big data quality initiatives. The starting point of any data quality work is to determine the properties of data quality, termed ‘data quality dimensions’ (DQDs). Even these dimensions lack precise rigour in terms of definition in existing literature. This current research aims to contribute towards identifying the most important DQDs for big data in the health industry. It is a continuation of previous work, which, using relevant literature, identified five DQDs (accuracy, completeness, consistency, reliability and timeliness) as being the most important DQDs in health datasets. The previous work used a human judgement based research method known as an inner hermeneutic cycle (IHC). To remove the potential bias coming from the human judgement aspect, this research study used the same set of literature but applied a statistical research method (used to extract knowledge from a set of documents) known as latent semantic analysis (LSA). Use of LSA concluded that accuracy and completeness were the only similar DQDs classed as the most important in health Big Data for both IHC and LSA

A qualitative assessment of machine learning support for detecting data completeness and accuracy issues to improve data analytics in big data for the healthcare industry

Tackling Data Quality issues as part of Big Data can be challenging. For data cleansing activities, manual methods are not efficient due to the potentially very large amount of data. This paper aims to qualitatively assess the possibilities for using machine learning in the process of detecting data incompleteness and inaccuracy, since these two data quality dimensions were found to be the most significant by a previous research study conducted by the authors. A review of existing literature concludes that there is no unique machine learning algorithm most suitable to deal with both incompleteness and inaccuracy of data. Various algorithms are selected from existing studies and applied against a representative big (healthcare) dataset. Following experiments, it was also discovered that the implementation of machine learning algorithms in this context encounters several challenges for Big Data quality activities. These challenges are related to the amount of data particular machine learning algorithms can scale to and also to certain data type restrictions imposed by some machine learning algorithms. The study concludes that 1) data imputation works better with linear regression models, 2) clustering models are more efficient to detect outliers but fully automated systems may not be realistic in this context. Therefore, a certain level of human judgement is still needed