Examining the asymmetric impact of macroeconomic policy in the UAE: Evidence from quartile impulse responses and machine learning

The current paper examines the asymmetric effects of changes to monetary and fiscal variables on different types of firms in the UAE. We compute impulse responses based on local projections and select shock and switching variables using machine learning. We examine 180 firms listed in the UAE exchanges and find significant asymmetries among financial and non-financial firms and among low- and high-debt firms when there is a shock to macroeconomic monetary or fiscal variables. Quartile analysis shows that firms belonging to the first and last quartile of debt respond negatively to expansionary policies, while middle-quartile firms respond more positively. Our results demonstrate the importance of comprehending the heterogeneity in the micro characteristics of the underlying corporate environment when evaluating macroeconomic policies. Our work can facilitate the design and implementation of policy in the UAE and helps explain the transmission mechanisms towards corporations

The Rise of GCC – East Asian Trade: A cointegration Approach to Analysing trade Relationships

The economic relationship between the Gulf Cooperation Council states and the countries of East Asia has grown in significance in recent years at the expense of the European Union and the USA. Empirical analysis of GCC‐East Asian trade indices shows that East Asian states now dominate GCC trade flows and a number of GCC‐East Asian Free Trade Agreements are cementing the long‐term economic ties between the two regional blocs which are based on the complementarity of their economic structures and comparative advantage. Within East Asia Japan is increasingly having to compete with China and India for both security of energy supplies from the GCC and to supply GCC markets with their manufactured exports. Using a new approach to analysing trade relationships we utilise cointegration techniques to analyse GCC trade patterns over time. We find econometric evidence of a long‐term trade relationship between the GCC states and those of East Asia, in particular China, whose continued economic growth has allowed it to diversify its economic and political dependence away from North America and ‘look East’ for new strategic alliances

Twitter and market efficiency in energy markets: Evidence using LDA clustered topic extraction

We use an extended sample of tweets relating to energy markets in order to examine and quantify the existence of market efficiency. The tweets are used as a proxy for publicly available information and we examine the degree to which this information determines market movements on the next trading day for nine energy market indices. We mine the topics of increasing and decreasing days using latent Drichtlet allocation and find that the topics of tweets in increasing and decreasing days differ. We validate our approach by feeding the extracted topics into three classifier machines and find that the classifiers provide forecasts on market movements with accuracy 57.83% (39.02%) in bull (bear) markets. Our findings support the presence of semi-strong efficiency, since we find evidence of price movements not reflecting public information, while the asymmetry of forecast accuracy over increasing and decreasing markets suggests a different rate of information propagation across market regimes. Our findings can provide useful input to valuation models linked to market efficiency

Demographic Change and Economic Growth: The Role of Natural Resources in the MENA Region

This paper analyses the conditional effect of demographic change on economic development in the MENA region. We employ fixed-effects panel analysis on data from 19 countries in the region and demonstrate a negative impact of natural rents on the relationship between the working-age population and economic growth. Once the critical level of approximately 16% of resource rents (as share in total GDP) is reached, a one-unit increase in working-age population appears to harm economic growth. Further tests show that this finding is mainly driven by the negative effects of resource rents on female labor force participation. However, other drivers are a large public sector, low private sector development and inefficient labor market policies and issues such as the “Dutch disease”. The main finding remains after robustness checks in the form of controlling for competing hypotheses. Policy makers are advised to encourage economic diversification, female employment and private sector development

Islamic banking, efficiency and societal welfare: a machine-learning, agent-based study

Purpose: This paper models the benefits of Islamic banking on the efficiency of the banking sector and on societal happiness. This paper aims to examine how the adoption of Islamic banking to various degrees affects economics outcomes. Design/methodology/approach: This study uses machine-learning tools to build a happiness function and integrate it in an agent-based model to test for the direct and indirect welfare effects of implementing Islamic banking principles. Findings: This study shows that even though Islamic banking systems tend to reduce economic activity, financial stability and societal happiness is improved. Additionally, a banking sector using Islamic principles across all its members is better equipped to handle banking crises because contagion to both economic activity and societal welfare is greatly reduced. At the same time, adoption of the profit-and-loss sharing (PLS) paradigm by banks may also slow down economic growth. Research limitations/implications: The findings extend existing literature on the advantages of Islamic banking, by quantifying the welfare benefits of the PLS paradigm on happiness and financial stability. Originality/value: To the best of the authors’ knowledge, this paper is the first to combine agent-based modelling with machine learning tools to examine the benefits of the Islamic banking model on financial stability, social welfare and unemployment

The perfect bail‐in: Financing without banks using peer‐to‐peer lending

We explore the potential outcomes for financial stability when using peer‐to‐peer lenders to finance economic activity. Combining Random Regression Forests, a machine‐learning process, with an agent‐based model, we perform simulations on artificial economies with various degrees of adoption of peer‐to‐peer lending. We find that as peer‐to‐peer lenders proliferate, there is increased financial instability, lower GDP and higher unemployment. On the other hand, peer‐to‐peer lending increases the total volume of loans given out but demonstrates a preference towards consumer loans (over corporate loans), which has a negative effect in the long run. Finally, introducing peer‐to‐peer lenders increases the access of the unbanked to services which conventional banking is not able to offer within the extant regulatory framework. Our results can help policymakers as they address the issue of regulation in the peer‐to‐peer finance industry

Determining Terrorism Proxies for the Relationship With Tourism Demand: A Global View

This study examines the determinants of the relationship between terrorism and tourism, by testing different proxies to assess both the frequency and the severity of terrorist activity. The methodological approach includes implementing principal component analysis into four different sets of possible proxies for terrorism in order to examine their relationship with international tourism arrivals over the period 1998–2018. The dataset includes world tourist flows and terrorist incidents anywhere in the world in order to avoid regional effects. The empirical results show that all candidate proxies exhibit a long-run, negative relationship with tourism, while there is also an impact of tourism on terrorism, with conflicting directions between the short run and the long run. The findings suggest that increased terrorist activity may cause destination substitution in the short run but will have adverse effects in the long run. In addition, authorities should be prepared for a rise in terrorist incidents during periods with increased tourist flows. Finally, research on terrorism should take into account the qualitative characteristics of terrorist activities

From Heroes to Scoundrels: Exploring the effects of online campaigns celebrating frontline workers on COVID-19 outcomes

This paper examines the effects of online campaigns celebrating frontline workers on COVID-19 outcomes regarding new cases, deaths, and vaccinations, using the United Kingdom as a case study. We implement text and sentiment analysis on Twitter data and feed the result into random regression forests and cointegration analysis. Our combined machine learning and econometric approach shows very weak effects of both the volume and the sentiment of Twitter discussions on new cases, deaths, and vaccinations. On the other hand, established relationships (such as between stringency measures and cases/deaths and between vaccinations and deaths) are confirmed. On the contrary, we find adverse lagged effects from negative sentiment to vaccinations and from new cases to negative sentiment posts. As we assess the knowledge acquired from the COVID-19 crisis, our findings can be used by policy makers, particularly in public health, and prepare for the next pandemic

Evaluation of DNA ploidy in relation with established prognostic factors in patients with locally advanced (unresectable) or metastatic pancreatic adenocarcinoma: a retrospective analysis

<p>Abstract</p> <p>Background</p> <p>Most patients with ductal pancreatic adenocarcinoma are diagnosed with locally advanced (unresectable) or metastatic disease. The aim of this study was to evaluate the prognostic significance of DNA ploidy in relation with established clinical and laboratory variables in such patients.</p> <p>Methods</p> <p>Two hundred and twenty six patients were studied retrospectively. Twenty two potential prognostic variables (demographics, clinical parameters, biochemical markers, treatment modality) were examined.</p> <p>Results</p> <p>Mean survival time was 38.41 weeks (95% c.i.: 33.17–43.65), median survival 27.00 weeks (95% c.i.: 23.18–30.82). On multivariate analysis, 10 factors had an independent effect on survival: performance status, local extension of tumor, distant metastases, ploidy score, anemia under epoetin therapy, weight loss, pain, steatorrhoea, CEA, and palliative surgery and chemotherapy. Patients managed with palliative surgery and chemotherapy had 6.7 times lower probability of death in comparison with patients without any treatment. Patients with ploidy score > 3.6 had 5.0 times higher probability of death in comparison with patients with ploidy score < 2.2 and these with ploidy score 2.2–3.6 had 6.3 times higher probability of death in comparison with patients with ploidy score < 2.2.</p> <p>Conclusion</p> <p>According to the significance of the examined factor, survival was improved mainly by the combination of surgery and chemotherapy, and the presence of low DNA ploidy score.</p

Inflation and the war in Ukraine: Evidence using impulse response functions on economic indicators and Twitter sentiment

In this paper, we propose the use of social media information as a real-time decision-making tool for significant war events, using the war in Ukraine as a case study. We proxy the public\u27s perception of the progression of events using sentiment analysis on 42 million tweets and calculate impulse response functions on 5-min data for 15 economic and financial indicators. European indices (currencies and markets) experience an immediate negative response to conflict escalation “shocks”, while crude oil registers a delayed negative response. US stock markets seem unaffected, while the US Dollar responds positively to negative events of the war. Our findings suggest that user generated content can be used as a decision-making tool when important war events unfold. This approach can monitor the public\u27s perception of such events as well as capture their potential economic impact, which carries increased importance in times of increasing prices