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With the development of economy, China’s OFDI constantly increase in recent year. Meanwhile, OFDI has spillover effect on economic development and technological development of home country. Thus, accurate OFDI prediction is a prerequisite for the effective development of international investment strategies. The purpose of this paper is to predict China’s OFDI accurately using a novel multivariable grey prediction model with Fourier series.

This paper applied a multivariable grey prediction model, GM(1,N), to forecast China’s OFDI. In order to improve the prediction accuracy and without changing local characteristics of grey model prediction, this paper proposed a novel grey prediction model to improve the performance of the traditional GM(1,N) model by combining with residual modification model using GM(1,1) model and Fourier series.

The coefficients indicate that the export and GDP have positive influence on China’s OFDI, and, according to the prediction result, China’s OFDI shows a growing trend in next five years.

This paper proposed an effective multivariable grey prediction model that combined the traditional GM(1,N) model with a residual modification model in order to predict China’s OFDI. Accurate forecasting of OFDI provides reference for the Chinese Government to implement international investment strategies.

In the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms. Bankruptcy prediction can be regarded as a grey system problem because while factors such as the liquidity, solvency and profitability of a firm influence whether it goes bankrupt, the precise manner in which these factors influence the discrimination between failed and non-failed firms is uncertain. In view of the applicability of multivariate grey prediction models (MGPMs), this paper aimed to develop a grey bankruptcy prediction model (GBPM) based on the GM (1, N) (BP-GM (1, N)).

As the traditional GM (1, N) is designed for time series forecasting, it is better to find an appropriate permutation of firms in the financial data as if the resulting sequences are time series. To solve this challenging problem, this paper proposes GBPMs by integrating genetic algorithms (GAs) into the GM (1, N).

Experimental results obtained for the financial data of Taiwanese firms in the information technology industries demonstrated that the proposed BP-GM (1, N) performs well.

Among artificial intelligence (AI)-based techniques, GBPMs are capable of explaining which of the financial ratios has a stronger impact on bankruptcy prediction by driving coefficients.

Applying MGPMs to a problem without relation to time series is challenging. This paper focused on bankruptcy prediction, a crucial issue in financial decision-making for businesses, and proposed several GBPMs.

This paper investigates whether weather affects stock market returns in Fiji's stock market.

The author employed an exponential general autoregressive conditional heteroskedastic (EGARCH) modeling framework to examine the effect of weather changes on stock market returns over the sample period 9/02/2000–31/12/2020.

The results show that weather (temperature, rain, humidity and sunshine duration) have robust but heterogenous effects on stock market returns in Fiji.

It is useful for scholars to modify asset pricing models to include weather-related variables (temperature, rain, humidity and sunshine duration) to better understand Fiji's stock market dynamics (even though they are often viewed as economically neutral variables).

Investors and traders should consider their mood while making stock market decisions to lessen mood-induced errors.

This is the first attempt to examine the effect of weather (temperature, rain, humidity and sunshine duration) on stock market returns in Fiji's stock market.

This review scopes evidence on the use of social robots for older adults with depressive symptoms, in the scenario of smart cities, analyzing the age-related depression specificities, investigated contexts and intervention protocols' features.

Studies retrieved from two major databases were selected against inclusion and exclusion criteria. Studies were included if used social robots, included older adults over 60, and reported depressive symptoms measurements, with any type of research design. Papers not published in English, published as an abstract or study protocol, or not peer-reviewed were excluded.

28 relevant studies were included, in which PARO was the most used robot. Most studies included very older adults with neurocognitive disorders living in long-term care facilities. The intervention protocols were heterogeneous regarding the duration, session duration and frequency. Only 35.6% of the studies had a control group. Finally, only 32.1% of the studies showed a significant improvement in depression symptoms.

Despite the potential for using social robots in mental health interventions, in the scenario of smart cities, this review showed that their usefulness and effects in improving depressive symptoms in older adults have low internal and external validity. Future studies should consider factors as planning the intervention based on well-established supported therapies, characteristics and needs of the subjects, and the context in which the subjects are inserted.

This study aims to investigate the role of board gender diversity in explaining the effects of board members’ political connections on banking performance in the Eurozone.

This paper analyses panel data on 83 banks supervised by the European Central Bank (ECB) for the period 2013–2017, using a generalized moment method-type estimation methodology.

Results suggest that when gender diversity is high, there is a U-shaped nonlinear relationship between political connections and banking performance. Empirical evidence also indicates that differentiating characteristics of women, such as greater ethical concern and risk aversion, help mitigate the negative effects of political connections on banking performance, safeguarding the institutions’ interests from the adverse effects of personal agendas. In addition, these results also suggest that a minimum of 14% of gender diversity can contribute to greater social justice and beneficial structural change.

The period studied may not yet fully reflect the impact of the assessment of the board members’ suitability.

The paper contributes to the growing literature on political connections and gender diversity, providing greater insight into their role as determinants of banking performance. The study also suggests the benefits and possible limitations of the regulator’s two impositions – gender diversity quotas and members’ repute (members’ political connections).

The effect of gender diversity on the impact of board members’ political connections on banking performance has not been studied, as these relationships have not been analysed separately for banks directly supervised by the ECB.

The purpose of this paper is to establish a new evaluation system to assess the degree of integration between industry and the internet. And use the gray correlation matrix method to evaluate the “internet + industry” integration degree of China’s provinces.

This paper establishes a new evaluation system to assess the degree of integration between industry and the internet using the matrix gray relational analysis method.

The main indexes and its rankings of the provinces’ integration degree and the rankings of the provinces’ integration degree are obtained.

The ranking of the degree of integration of various provinces in the country has certain guiding significance in promoting the development of “internet +” and “industry 4.0.”

Establishing a new model for the quantitative assessment of the degree of fusion, this method has a positive impact on the quantitative assessment of “internet + industrial” integration.

In this study, the authors explore the effects of politically controlled boards on bank loan performance in both state-owned commercial banks (SCBs) and private sector commercial banks (PCBs) in Bangladesh.

The data consist of 409 bank-year observations from 46 sample SCBs and PCBs of Bangladesh for the period 2008–17. The authors apply ordinary least squares pooled regression with year fixed effect for baseline econometric analyses and generalized method of moments regression for robustness tests after addressing the endogeneity issue.

The regression results reveal that the presence of bank “boards controlled by politically affiliated directors” (PA) have significant positive effects on non-performing loans (NPLs). Similarly, the presence of “boards controlled by politically affiliated directors without substantial ownership interests” (PAWOI) show positive association with NPLs. In contrast, the presence of “boards controlled by politically affiliated directors with substantial ownership interests” (PAOI) exhibit an inverse relationship with NPLs. These findings support ‘agency conflict’ arguments and document that both PA and PAWOI are detrimental to bank loan performance in Bangladesh, while PAOI do not have significant effect on increasing NPLs.

This study contributes to the existing bank governance literature by providing evidence from an emerging economy perspective, where politically affiliated directors (PADs) exploit their positions for personal and/or political gain at the cost of other stakeholders by taking advantage of relaxed regulatory oversights and investor protections.

Although prior research documents that analysts sometimes herd their forecasts, very few studies investigate how investors’ judgments are influenced by their perceptions of the likelihood of analyst herding. I conduct an experimental study to investigate the conditions under which investors’ assessments of uncertainty about future earnings are influenced by their perceptions of the likelihood of analyst herding. As expected, and consistent with motivated reasoning, the results show that the temporal order of analyst forecasts influences investors’ estimates of the likelihood of analyst herding and investors’ uncertainty judgments when analyst forecasts are preference-inconsistent but not when analyst forecasts are preference-consistent. This study provides a potential explanation for the mixed findings of prior research in regard to investors’ reactions to the likelihood of analyst herding. In addition, this study extends research on investors’ credulity by providing evidence that motivated reasoning and skepticism may serve as a mechanism that contributes to that credulity.

This paper aims to analyze the effect of surface roughness and hardness of leadframe on the bondability of gold (Au) wedge bond using in situ inspection of laser interferometer and its relationship with the deformation and wire pull strength.

The in situ inspection of ultrasonic vibration waveform through the changes of vertical axis (y-axis) amplitude of wire bonder capillary was carried out using laser interferometer to analyze the formation of Au wedge bond. The relationship between the changes of ultrasonic waveform of capillary with the deformation and the pull strength was analyzed to evaluate the bondability of Au wedge bonds.

It was observed that the changes in vertical axis amplitude of ultrasonic vibration waveform of wire bonder capillary can be used to describe the process of bonding formation. The loss of ultrasonic energy was exhibited in ultrasonic vibration waveform of wire bonding on leadframe that has higher value of roughness (leadframe A) as compared to that of leadframe that has lower value of roughness (leadframe B). The lower pull strength obtained by Au wedge bond further confirms the reduction of bond formation because of the higher deformation on leadframe A as compared to that of leadframe B.

The relationship between in situ measurement using laser interferometer with the bondability or deformation and wire pull strength of Au wedge bonds on different surface roughness and hardness of leadframes is still lacking. These findings provide a valuable data in analyzing the bonding mechanisms that can be identified based on the in situ measurement of ultrasonic vibration and the bondability of Au wedge bonds.

This paper investigates the effect of political connections on the capital structure of banks before and after the financial crisis in Gulf Cooperation Council (GCC) countries.

This paper employs the natural experiment that the financial crisis offers and uses a difference-in-differences model to investigate the effect of political connections on capital structure. Capital structure is measured by the total debt to total assets ratio. Control variables include bank size, growth, profitability, coverage ratio and volatility. The research sample includes all the banks in the GCC from 2005 to 2016.

The authors find that political connections negatively affect banks capital structure decisions. The results contradict the claim that politically connected firms tend to sustain higher debt due to government privilege and a lower chance of bankruptcy. Additionally, the results show that after the financial crisis, politically connected banks de-lever more compared to non-connected counterparts. This could suggest that the degree of support received by connected banks changes or that they exploit their retained earnings for financing (individual country results, however, suggest that leverage increases in Qatar).

This paper provides several contributions. First, GCC countries present an interesting and important area in which to study the relation between political connections and capital structure as it represents a mix of newer markets that seek to attract investors and foreign capital. Second, to the best of our knowledge, the present study is the first to examine the effect of the political connection and capital structure in GCC region where royal families play a significant role, especially for banks. Third, our paper is the first to link connections with leverage after the financial crisis in the banking sector. Moreover, our paper is the first to investigate this phenomenon in the GCC countries using manually collected primary data.