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This paper aims to examine and compare the dividend ratios’ statistical and economic ability to predict the equity premium in the UK and US markets and two US sub-indices (S&P 500 Growth and S&P 500 Value).

In this paper, the authors use the linear regression models to examine the dividend ratios’ statistical ability to predict the equity premium. The in-sample and out-of-sample approaches, including Diebold and Mariano (1995) statistics, and Goyal and Welch’s (2003) graphical approach, are used. Also, the mean-variance analysis is used to test the economic significance.

The paper findings indicate that the dividend ratios have in-sample and out-of-sample predictive abilities in both UK and US markets and both US sub-indices. However, the results show that the dividend ratios have a less impressive predictive ability in the US market compared to the UK market and less in the US value index than the US growth index. This could indicate that there is no relation between the number of companies that distribute dividends in each index and the informativeness of dividends ratios. Furthermore, the tests show the dividend ratios’ predictive ability departure during particular periods and in some indices.

Results and implications of this research are exclusively applied to the US and UK markets. These results can also be applied with caution to other markets, taking into consideration the distinctive characteristics of these markets.

Results revealed in this paper imply that the investors in any of the indices may experience economic gain by adopting a dynamic trading strategy using the information content of the dividend ratios prediction models instead of the benchmark model, which is the prevailing simple moving average model.

This paper adds value through testing the prediction models’ economic significance in two well-developed markets, in addition to exploring the relationship between the number of companies distributing cash dividends and the dividends ratio prediction ability. Unlike most of the previous studies in which dividend ratios’ prediction ability is attributed to the number of companies that distribute dividends in the market, this paper denied this interpretation by studying two S&P 500 sub-indices. To the best of the authors’ knowledge, this is the first study to test the prediction models’ ability for these sub-indices.

This study conducts a comparative analysis of the diversification effects of China's national carbon market (CEA) and the EU ETS Phase IV (EUA) within major commodity markets.

The study employs the TVP-VAR extension of the spillover index framework to scrutinize the information spillovers among the energy, agriculture, metal, and carbon markets. Subsequently, the study explores practical applications of these findings, emphasizing how investors can harness insights from information spillovers to refine their investment strategies.

First, the CEA provide ample opportunities for portfolio diversification between the energy, agriculture, and metal markets, a desirable feature that the EUA does not possess. Second, a portfolio comprising exclusively energy and carbon assets often exhibits the highest Sharpe ratio. Nevertheless, the inclusion of agricultural and metal commodities in a carbon-oriented portfolio may potentially compromise its performance. Finally, our results underscore the pronounced advantage of minimum spillover portfolios; particularly those that designed minimize net pairwise volatility spillover, in the context of China's national carbon market.

This study addresses the previously unexplored intersection of information spillovers and portfolio diversification in major commodity markets, with an emphasis on the role of CEA.

This study aims to contribute to the literature on migration by examining the nexus between migration-related fears and housing affordability in France, Germany, the UK and the USA using new datasets for migration-related fears.

This study adopts the feasible quasi-generalized least squares approach wherein a predictor can be isolated in the estimation process. Thus, rather than specifying a multi-predictor model that may also lead to parameter proliferation, a single-predictor model (for the predictor of interest) is formulated while also accounting for other salient features resulting from suppressing other important factors that may not be of interest to the current study. Such salient features include persistence, endogeneity and conditional heteroscedasticity issues.

Overall, the results show heterogeneous responses of housing affordability to migration fears across the four developed countries, as the latter deteriorates housing affordability in Germany and the USA and improves it in France and the UK. Similarly, the GFC makes housing less affordable in all four countries as low interest rate passes the mediation test in the nexus. The results, especially for low interest rates, are robust to different uncertainty measures.

As is often the case with economic phenomena, no single model can capture all the factors influencing an economic variable. Thus, besides examining the nexus between migration fears and housing affordability, the authors also account for the role of GDP per capita, given the influence of population and income dynamics on housing affordability. However, incorporating GDP per capita alone does not substantially enhance the model’s ability to predict housing affordability. Future research should explore additional macroeconomic and social factors, such as human capital development, to further enhance this subject.

The findings have significant implications for policymakers regarding the use of low interest rates to counteract the adverse effects of migration-related fear on housing affordability. Specifically, to mitigate the potential negative impact of migration and the associated fear on housing affordability, monetary authorities could adopt a more accommodative stance on mortgages. By allowing real estate investors to obtain loans at lower rates, this approach would help increase housing supply and reduce the housing gap exacerbated by migration influx.

The values of this study lie in its examination of housing affordability in relation to migration fears from both the demand and supply sides of the market. Furthermore, the analyses are conducted to cover out-of-sample forecast evaluation as in-sample predictability may not guarantee out-of-sample prediction.

The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors.

This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation.

The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%.

The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.

The increased capital requirements and the implementation of new liquidity standards under Basel III sparked various concerns among researchers, academics and other stakeholders. The question is whether Basel III regulation is ideal, that is, adequate to deal with a crisis, such as the 2007–2009 global financial crisis? The purpose of this paper is threefold: First, perform a stress testing exercise on the US banking sector, while examining liquidity and solvency risk indicators jointly under the Basel III regulatory framework. Second, allow the study to cover the post-crisis period, while referring to key Basel III regulatory requirements. And third, focus on the resilience of domestic systemically important banks (D-SIBs), which are supposed to support the US financial system in times of stress and therefore whose failure causes the entire financial system to fail.

The authors used a sample of the 24 largest US banks observed over the period Q1-2015 to Q1-2021 and a scenario-based vector autoregressive conditional forecasting approach.

The authors found that the model successfully produces accurate forecasts and simulates the responses of the solvency and liquidity indicators to different real and historical macroeconomic shocks. The authors also found that the US banking sector is resilient and can withstand both historical and hypothetical macroeconomic shocks because of its compliance with the Basel III capital and liquidity regulations, which consist of encouraging banks to hold high-quality liquid assets and stable funding resources and to strengthen their capital, which absorbs the losses incurred in a crisis.

The authors developed a framework for testing the resilience of the US banking sector under macroeconomic shocks, while examining liquidity and solvency risk indicators jointly under Basel III regulatory framework, a point not yet well studied elsewhere, and most studies on this subject are based on precrisis data. The authors also focused on the resilience of D-SIBs, whose failure causes the failure of the entire financial system, which previous studies have failed to examine.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

This research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects.

The architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets.

The experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements.

The architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bids more strategically to avoid a bid loss or profit loss.

The ABI data have been forecasted and modeled using linear time series models. However, linear time series models often fail to capture nonlinear patterns, interactions, and dependencies among variables, which can be handled by machine learning models in a more flexible manner. Despite the strength of machine learning models to capture nonlinear patterns and relationships between variables, the applicability and forecasting performance of multivariate machine learning models have not been investigated for ABI forecasting problems. This research first attempted to forecast ABI data for different time horizons using multivariate machine learning predictive models using different macroeconomic and construction market variables.

The purpose of this study is to explore the role of gold as a hedge against inflation in the case of the United Arab Emirates.

The study utilizes monthly data on the local sharia-compliant spot gold contract traded on the Dubai Gold and Commodity Exchange (DGCX) and the corresponding consumer price index series over the period December 2015 to January 2021. The econometric approach employed by the study involves a unit root testing procedure that allows the timing of significant breaks to be estimated. A cointegration analysis is then conducted using a nonlinear autoregressive distributed lag (NARDL) model, taking into consideration the presence of structural breaks in addition to short- and long-run asymmetries.

The results reveal that consumer and gold prices are cointegrated, which implies that investing in gold can hedge against inflation in the long run. No sufficient evidence, nonetheless, is found in support of the ability of gold to serve as a hedge against inflation in the short run.

The findings have several important policy implications for policymakers and investors that are further discussed in the study.

Considering the frequency of extreme events, enhancing the global financial system's stability has become crucial. This study aims to investigate the contagion effects of extreme risk events in the international commodity market on China's financial industry. It highlights the significance of comprehending the origins, severity and potential impacts of extreme risks within China's financial market.

This study uses the tail-event driven network risk (TENET) model to construct a tail risk spillover network between China's financial market and the international commodity market. Combining with the characteristics of the network, this study employs an autoregressive distributed lag (ARDL) model to examine the factors influencing systemic risks in China's financial market and to explore the early identification of indicators for systemic risks in China's financial market.

The research reveals a strong tail risk contagion effect between China's financial market and the international commodity market, with a more pronounced impact from the latter to the former. Industrial raw materials, food, metals, oils, livestock and textiles notably influence China's currency market. The systemic risk in China's financial market is driven by systemic risks in the international commodity market and network centrality and can be accurately predicted with the ARDL-error correction model (ECM) model. Based on these, Chinese regulatory authorities can establish a monitoring and early warning mechanism to promptly identify contagion signs, issue timely warnings and adjust regulatory measures.

This study provides new insights into predicting systemic risk in China's financial market by revealing the tail risk spillover network structure between China's financial and international commodity markets.

The main objective of this study is to investigate the factors that influence the adoption intention of cloud computing services among individual users using the extended theory of planned behavior.

A purposive sampling technique was used to collect a total of 339 data points, which were analyzed using SmartPLS to derive variance-based structural equation modeling and fuzzy-set qualitative comparative analysis (fsQCA).

The results obtained from PLS-SEM indicate that attitude towards cloud computing, subjective norms, perceived behavioral control, perceived security, cost-effectiveness, and performance expectancy all have a positive and significant impact on the adoption intention of cloud computing services among individual users. On the other hand, the findings from fsQCA provide a clear interpretation and deeper insights into the adoption intention of individual users of cloud computing services by revealing the complex relationships between multiple combinations of antecedents. This helps to understand the reasons for individual users' adoption intention in emerging countries.

This study offers valuable insights to cloud service providers and cyber entrepreneurs on how to promote cloud computing services to individual users in developing countries. It helps these organizations understand their priorities for encouraging cloud computing adoption among individual users from emerging countries. Additionally, policymakers can also understand their role in creating a comfortable and flexible cloud computing access environment for individual users.

This study has contributed to the increasingly growing empirical literature on cloud computing adoption and demonstrates the effectiveness of the proposed theoretical framework in identifying the potential reasons for the slow growth of cloud computing services adoption in the developing world.