This paper examines the information content of selected US industries focusing on the dynamic linkages among these industries, the stock market and a number of fundamental variables. The period of investigation spans from January 1960 to December 2021. The empirical strategy includes several methodologies such as regressions, vector autoregressions and volatility models. The idea is to investigate the dynamic linkages among these series at both the mean and the volatility levels. The results point to significant industry returns’ explanatory power for many predictors of economic activity including the stock market. Further, time-varying analysis of the linkages among the industries and the stock market’s returns reveal that certain industries such as Oil and Financials provide consistent information leadership over other industries and across decades. Further, upon assessing the industry–market return volatility spillovers, it was found that a market risk–return profile may not always be economically significant and timely for investors. Finally, crises, financial or otherwise, affect industries but to differing degrees.

Credit scoring remains one of the most important subjects in financial risk management. Although the methods in this field have grown in sophistication, further improvements are necessary. These advances could translate in major gains for financial institutions and other companies that extend credit by diminishing the potential for losses in this process. This research seeks to compare statistical and artificial intelligence (AI) predictors in a credit risk analysis setting, namely the discriminant analysis, the logistic regression (LR), the artificial neural networks (ANNs), and the random forests. In order to perform this comparison, these methods are used to predict the default risk for a sample of companies that engage in trade credit. Pre-processing procedures are established, namely in the form of a proper sampling technique to assure the balance of the sample. Additionally, multicollinearity in the dataset is assessed via an analysis of the variance inflation factors (VIFs), and the presence of multivariate outliers is investigated with an algorithm based on robust Mahalanobis distances (MDs). After seeking the most beneficial architectures and/or settings for each predictor category, the final models are then compared in terms of several relevant key performance indicators (KPIs). The benchmarking analysis revealed that the artificial intelligence methods outperformed the statistical approaches.

We explore the connectedness and portfolio implications between Islamic and conventional bonds in global and GCC regions. We also compare which bonds performed better during our sample period. Unlike previous studies, we focus on Islamic bond markets compared to their conventional counterparts and highlight the GCC bonds (Islamic and conventional) in respect of global bonds. We apply the DCC-GJR-GARCH (1,1) method, the Sharpe ratio, and the portfolio implications strategy over the period from September 1, 2013, to February 23, 2022. Our time-varying results suggest that the relationship among all variables varies over time, but most of them are positive, suggesting that there are fewer diversification opportunities between Islamic and conventional bonds. Hedging and diversification benefits are found only in the limited period among these variables, especially between GCC bonds and global bonds, and global Sukuk and GCC Sukuk. The findings of risk-adjusted returns reveal that Islamic bonds outperform their conventional counterparts. Moreover, mixed results are found in the case of hedging costs, and the majority of the fund, based on optimal weights, should be invested in Islamic bonds. Our study endows investors and regulators in the global and GCC markets with new insights on how to shield their investments and the financial system from financial crises through a hedging strategy with Islamic finance.

Regulation of financial institutions has two key purposes: Solvency (prudential regulation) and consumer protection. Prudential regulation is implemented mainly by capital requirements, but governments also provide insurance for customer deposits, as a backup tool. In this article, we discuss the critical conditions for deposit insurance and capitalization to act as substitutes for each other, under cyclical economic environment. We make two assumptions. The first one is that deposit insurance is fairly priced and there is no moral hazard. The second one is that insurance creates incentives for moral hazard among insured banks, resulting in increased risk taking. We also discuss the critical conditions for deposit insurance and capitalization to be complementary under different proportions of deposit insurance.

This study explores the cause and effect of endogenous risk aversion in land pricing, where state intervention through taxation remains a general practice. Using a consumption-based asset pricing model incorporating taxation, it is shown that high taxation, due to the indexation effect, supporting land prices and reducing individuals' risk expectations, could lead to an endogenous decrease in risk aversion, which could result in market dysfunction because risk aversion plays a key role in the market mechanism. China, with its wholly state-owned land and the general use of land sales to cover financial deficits, is a typical case for empirical tests. The tests confirm that there, the rise in land prices was driven by the increase in reserve prices set by local governments, a strong means of taxation, and not by the market, indicating the endogenous decrease in risk aversion.

This paper studies non-compliant firms with gender legislation and why some businesses are taking the risk of not binding the gender quotas. It is conducted on firms listed on the SBF120 index, after the introduction of the gender law of Copé and Zimmermann, in 2011. Our findings show that gender diversity on advisory committees, unlike monitoring committees, is likely to decrease the non-compliance likelihood. Non-compliant firms have busy members, specifically among men and long-tenured CEOs who are serving in non-dual structures. Regarding women's profiles, non-compliant boards are prone to hire short-tenured and local female candidates. Finally, their financial and social performances are not damaged while their corporate risks are decreased. When penalties are not required against non-compliant firms, refractory businesses do not bear “real” costs.

College choice and choice of major are the most important decisions for future earnings. It is still unclear, however, what makes a greater difference—college or major—or whether a choice of college matters more for some majors, but not the others. Using cross-classified models and College Scorecard data, I show that a discipline is more consequential for future earnings than a college. The effect of STEM is substantial but is less pronounced at institutions with higher overall median earnings. The effect of college selectivity on earnings is more pronounced for non-STEM disciplines. Institutional characteristics—such as tuition, shares of graduates receiving different forms of financial aid, institutional size and location, and type of college—correlate with earnings of graduates. Racial and gender composition of an educational program correlate with expected earnings of its graduates even after control for other institutional and disciplinary characteristics. Models presented here provide a better understanding of the effect of college and major choices on future earnings.

This study examines the role of news sentiments in the GCC equity markets’ connectedness. We collected news titles for the period from 22nd June 2006 until 31st December 2020 from Gulf News, which is the most widely read English newspaper in the Arab World. We filter these news titles using a carefully designed list of keywords that capture public sentiment on matters related to financial markets. Next, we classify the news titles to compute the geographically distinguished sentiment indexes that allow for a detailed analysis of the source of news sentiment spillovers to compare the impact of domestic versus regional sentiments on the equity markets of GCC countries. Our quantile regression results reveal that equity markets in the GCC are most sensitive to news sentiments when underperforming. Moreover, our results from the connectedness approach suggest that the UAE equity markets are most influenced by domestic sentiments, whilst the KSA equity market is most influenced by regional sentiments from the other GCC countries. Mixed results are found for other countries. The time-varying component of this study also shows that the influence of news spillovers intensified during the major crises events, including the COVID-19 outbreak.

Banks are at the core of economic functioning in various countries and are the cause of their development in various fields. In a changing environment, they must deal with certain risks and maintain financial stability as the ultimate objective. The primary objective of this study is to examine the correlation between credit risk and bank stability within the Tunisian banking sector, with a specific focus on understanding the moderating impact of the COVID-19 pandemic. To achieve this, we employ several econometric techniques, including panel unit root and cointegration tests, panel Vector Error Correction Model (VECM), Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS). Our empirical analysis relies on a panel dataset that encompasses a comprehensive sample of 8 Tunisian banks over 2000-2020. The results of our analysis unveil a significant negative relationship between credit risk and bank stability, indicating that higher levels of credit risk exert a detrimental effect on the overall stability of Tunisian banks. Furthermore, our study highlights that this adverse impact is further exacerbated during the COVID-19 pandemic, suggesting that the pandemic acts as a moderator. The findings of this study hold substantial implications for policymakers, regulators, and bank managers in Tunisia. They emphasize the critical importance of implementing robust risk management practices to mitigate credit risk and bolster bank stability. Additionally, the research underscores the need to consider the unique challenges introduced by external shocks, such as the COVID-19 pandemic, when assessing the overall stability of the banking system.

The diplomatic relations between Azerbaijan and Pakistan have undergone significant development since the late 1990s. Despite some studies focusing on diplomatic and political relations, the economic dimension of this partnership has not been systematically covered. In this paper, a robust least squares (RLS) approach is used to model bilateral trade between the two countries and identify statistically significant determinants, including economic, institutional, and cultural factors, after analyzing the main economic and subsectoral trade dynamics of both countries. The results reveal that Azerbaijan's imports from Pakistan are significantly associated with the country's higher income and market size, while Pakistan's elevated exchange rate and trade costs hinder bilateral trade. In addition, Pakistan's rising manufacturing output increases exports to Azerbaijan but decreases imports from Azerbaijan. Among institutional determinants, government effectiveness, the trade freedom index (TFI), and political stability play significant roles in bilateral trade. Lastly, it was found that cultural proximity increases Pakistani exports to Azerbaijan but not imports from Azerbaijan. The analysis also indicates that domestic savings stimulate Pakistani exports. These findings underscore the complex nature of bilateral trade between Azerbaijan and Pakistan and highlight the need for institutional arrangements to harmonize trade flows and boost welfare gains from trade. The results may be of great importance to intergovernmental working groups, domestic policymakers, and businesses.

Generating output growth by adding more inputs into the production process may not be sustainable in the long run for any economy, given the limited resources. On the other hand, if productivity growth dominates the production process, it will generate more output without excessive increase in input use. Hence, this paper examines whether the output growth in Indonesia’s manufacturing sector is excessive inputs driven or productivity driven. Productivity driven growth is measured by Total Factor Productivity (TFP) growth, which is decomposed into its major components of technological progress and technical efficiency within the framework of varying coefficients stochastic frontier analysis (VSFA) using Indonesia’s annual Large and Medium Manufacturing Industries Survey data over the period 2002–2014. The measurement of the components of TFP growth not only provides more insights and better understanding of the dynamic nature of the production processes, but also has important policy implications. The mean TFP growth during the period 2002-2014 was estimated to be 4.3 per cent and was mostly contributed by technological progress experienced by firms. The policy implication is that technical efficiency could still be improved for the selected technology to reap the full benefit of increasing output from the chosen technology.

Green-biased technological progress takes into account the influence of energy input and pollution emissions, which is of great significance to China's green development. This paper decomposes technological progress into two categories: green input-biased technological progress (IBTC) and green output-biased technological progress (OBTC), using the Slacks-based measure integrating (SBM) model. The factor bias in technological progress is determined based on data from 34 industries in China from 2000 to 2015. The results show that green-biased technological progress exists significantly in the industry, and most of it promotes the growth of green total factor productivity. IBTC first tends to consume energy to pursue capital between capital input and energy input, while it tends to save energy after the Eleventh Five-Year Plan. Between labor input and energy input, it is biased towards saving labor and consuming resources. OBTC is biased towards promoting industrial growth and curbing pollution emissions. Medium and light-polluting industries are biased toward promoting industrial growth and curbing pollution emissions, while heavy-polluting industries are biased towards emitting more pollution.

This paper attempts to introduce an overlapping generations structure into Paul Krugman's "The world's smallest macroeconomic model" (Krugman (1999)) to examine the implications of fiscal policy, particularly fiscal deficits, in a framework suitable for policy analysis. In that paper, Krugman argued that under the price rigidity assumption, a shortage in the money supply leads to underemployment and recession, so increasing the money supply would eliminate underemployment and restore full employment. But how can the money supply be increased? I show that in order to restore full employment out of a recession, a fiscal deficit is needed to increase the money supply. I also show that in a growing economy, fiscal deficits are necessary to maintain full employment at constant prices or inflation. Fiscal deficits are not only effective in pulling the economy out of recession, they are even necessary for growth to continue without recession or inflation. The fiscal deficit in this paper represents the difference between government spending and government revenues. If this difference is positive, we say that the government is in deficit. Krugman's original model is a one-period static model. I intend to extend this model to a dynamic overlapping generations model.

In this paper I will show that budget deficit (or fiscal deficit) is necessary to achieve full employment under constant prices or inflation, using a model of endogenous growth in which consumers hold money for the reason of liquidity and live forever. Budget deficit need not be offset by future budget surpluses. I consider the continuous time case by taking the limit of the discrete time case when the time interval approaches zero. A continuous time dynamic model seems to be more general than a discrete time model. When the actual budget deficit is greater (smaller) than the value which is necessary and sufficient for full employment under constant prices, an inflation (a recession) occurs. The main argument of this paper is that a growing economy requires the continuation of budget deficit, and that we should not think of paying off the resulting government debt with taxes.

The high price of energy due to green energy policy will cause adjustments across the US economy predicted in the present general equilibrium model that includes energy Btu input with capital and labor to produce manufactures and services. This same model in trade theory examines the effects of a tariff on an imported factor of production such as a natural resource or capital.  Corresponding error correction estimates of the reduced form equations of the model in annual 1970-2018 data prove robust and suggest model modifications. A parametric approach to noncompetitive pricing based on unit cost diminishing with output brings the model much closer to the estimates. Manufactures are revealed to have a higher degree of noncompetitive pricing than services. Assuming constant elasticity of substitution production, a weak degree is revealed to provide the best fit to the error correction estimates. The high price of green energy will cause an inelastic decrease in energy input resulting in increased energy revenue. Outputs of both sectors fall, more in manufactures due to its energy intensity. The capital return and wage also fall given the weak substitution of capital and labor relative to the price of energy. The wage rises slightly in the model but falls considerably in the error correction estimate. The only clear winner is energy input with increased revenue. The government owns a large share of hydrocarbon reserves and will benefit from a higher price of energy.

Income and population growth are key determinants of housing demand, while land use regulations are designed to affect housing supply. Previous studies of housing price determinants focus either on specific regulations in particular cities/regions, or on selective subsets of major cities and regulations. This study examines the impact of land use regulations on housing prices from 1989 to 2006 in an unusually large sample of 250 major US cities. Aside from factors that are commonly associated with housing demand (income, population growth and density), housing prices are found to be associated with local cost-increasing land use regulations (approval delays) and with statewide regulations. Since statewide regulations factor prominently into the results, specific examples of the impact of different types of land use regulations are provided for 5 cities in the state of Washington. The estimated increase in housing prices associated with regulations is, on average (over 250 cities), substantially larger than the effects of income and population growth. While the estimated dollar costs associated with regulations may be sizable at times, the results are remarkably consistent with previous studies that were based on smaller cross sections.

Consideration of optimal commodity storage with different discount rates. Finding that, even with a lower discount rate than private storage, optimal government-financed storage may not narrow price fluctuations compared with optimal privately financed storage because a government has to choose a probability of buffer stock failure greater than zero to economize on storage costs that could conceivably become very large. There is no presumption that government financed storage would be larger, with narrower intervention bands and more stable prices, than with privately-financed storage. The welfare enhancing effect of government financed storage compared to private storage is therefore indeterminate.

We theoretically examine whether a US-Japan trade agreement would be effective as a buffer against the US-China trade war. There are two ways to mitigate the negative impacts of US protectionism: a “domestic pressure effect to suppress external pressure” and a “free trade agreement (FTA) effect. The former is an effect arising from Japanese protectionist political pressure to suppress the negative impacts of US tariff increases on Japan's political support function. The latter is an effect of the Japan-US free trade agreement. In the absence of a US–Japan trade agreement, both Japan and China’s political support functions decrease when the US raises tariffs. By contrast, when an FTA is agreed between Japan and the US, an increase in US tariffs causes the Chinese political support function to decrease, while the Japanese political support function increases. The US–Japan trade agreement thus plays an effective role as a buffer in mitigating the negative impacts of the US–China trade war on Japan.

In reality firms most often face negatively sloped demand curves. Then, for a given level of consumers’ surplus, levies on prices yield higher fiscal revenues than specific duties. Therefore, according to the prevailing view, the switch from unit to ad valorem taxation is supposed to generate more welfare; some even speak of an associated Pareto-improvement. However, this is not true because taxing prices merely transfers profits to the Treasury, while total rent remains unaffected. Since excise duties diminish the welfare gain in comparison with untaxed trade, an appropriately designed income tax allows all parties to benefit. Sales should be taxed only exceptionally.

Green innovation, driven by China's new development concept, plays a crucial role in high-quality economic development. In line with the green development trend, businesses increasingly prioritize whether their corporate environmental responsibilities (CER) can effectively enhance corporate green innovation (CGI) levels. This paper examines the influence and mechanism of CER on CGI using a dynamic perspective, drawing from 1,640 manually-collected panel data of Shanghai and Shenzhen A-share listed companies between 2010 and 2017. The primary findings indicate that the impact of CER on CGI possesses phase-specific characteristics and a dual effect of "crowding in" and "crowding out." The current phase of CER negatively affects green innovation, while the lag phase has a positive effect. CER's impact on various CGI types is heterogeneous: specifically, it follows an "inverted-N" trajectory (inhibition-promotion-inhibition) for "strategic green innovation" and has a promotional effect on "substantive green innovation," which is stronger and has a longer time lag. The mechanism analysis reveals that financing constraints play a critical mediating role. A heterogeneity analysis based on multiple dimensions (ownership, industry, and location) suggests that CER has a more significant driving force for CGI among state-owned firms, high-polluting industries, and enterprises in inland areas. Finally, the paper presents corresponding suggestions for government and corporate entities.