This paper provides an overview of the existing literature on the use of artificial intelligence (AI) in various fields, including economics, finance, mining, manufacturing, and innovation. The paper identifies the drivers and effects of AI deployment in the context of innovation and highlights the challenges and opportunities that arise from the use of AI. The studies reviewed in this paper cover various topics related to forecasting, including the impact of AI on professional skills, hybrid forecasting techniques for predicting commodity prices, and novel deep reinforcement learning algorithms for crude oil price forecasting. The paper’s contribution lies in its systematic and comprehensive approach to reviewing the literature, which allows for a better understanding of the impact of AI on various fields and the identification of strategies to address the challenges that arise from its deployment.

The precise prediction of carbon emissions trading prices is the foundation for the stable and sustainable development of the carbon financial market. In recent years, influenced by a combination of factors such as the pandemic, trading regulations, and policies, carbon prices have exhibited strong random volatility and clear non-stationary characteristics. Traditional single-perspective prediction methods based on conventional statistical models are increasingly inadequate due to the homogenization of features and are struggling to adapt to China's regional carbon emissions trading market. Therefore, this paper proposes a multi-perspective fusion-based prediction method tailored to the Chinese market. It leverages carbon emissions trading information from key cities as relevant features to predict the price changes in individual cities. Inspired by the development of artificial intelligence, this paper implements various time series models based on deep neural networks. The effectiveness of the multi-perspective approach is validated through multiple metrics. It provides scientific decision-making tools for domestic carbon emissions trading investors, making a significant contribution to strengthening carbon market risk management and promoting the establishment and rational development of a unified carbon market in China.

How does democracy relate to the initial economic policy responses to Covid-19? Using a cross country analysis, we find that countries with a higher degree of democracy have stronger economic policy responses than their peers. However, when we separate monetary and financial policies from fiscal policy, democracy is not associated with the latter when we control for the income level of a country. Finally, for countries with lower levels of labor participation, high levels of income inequality are associated with weaker policy responses.

Bank deposit is closely related to systemic risks. In addition, considering that resident deposits in China have significant seasonal characteristics, this paper focuses on which component of deposits drives the systemic risk volatility, that is, it can supplement the existing forecast information. We use X-13ARIMA-SEATS to decompose deposit into three subsequences. The research findings show that the forecast effect of subsequence models is better than that of benchmark series. Most importantly, the model with trend component has the best forecast performance.

Calculating the amount of regulatory capital to cover unexpected losses due to operational events in the upcoming year has caused problems because of difficulties in fitting probability distributions to data. It is consequently difficult to judge an appropriate level of capital that reflects the risk profile of a financial institution. We provide theoretical and empirical analyses to link the calculated capital to the sum of losses using appropriate statistical approximations. We conclude that, in order to reasonably reflect the associated risk, the capital should be approximately half the sum of losses, with a wide bound for the ratio of capital to sum.

China's carbon emission trading market has been formally established, but few studies have been conducted to analyze the impact of this policy on the regional urbanization level. Therefore, this paper evaluates whether the carbon trading pilot policy can enhance the regional urbanization level in China through the difference-in-differences method and analyzes the mediating role of industrial structure upgrading in this process. The results prove that the carbon trading market policy can accelerate the transformation and upgrading of industrial structure in the region so that it promotes the development of regional urbanization. Moreover, the effects of the policy are concentratedly manifested in the eastern region of China.

In this paper, a portfolio analysis is carried out using the Sharpe ratio to identify the optimal market portfolio. The measure of investment performance with a Sharpe ratio is compared to results obtained with bootstrapped resamples of the Sharpe ratio. The results indicate that the choice of the market portfolio is highly affected by the uncertainty regarding the estimation of the expected returns and the variance-covariance matrix between the returns, that is, the estimation risk associated with these parameters.

Spence’s signaling model (Spence, 1973) suggests that education can signal workers’ unobserved ability to employers thereby mitigating discrimination. There have been several studies concerning education’s impact on labor market discrimination against minority or disadvantaged groups. Our approach in this inquiry is unique in that we utilize the data of PhD recipients, a group of people with the highest education attainment, to test Spence’s theory. Another novelty of this paper is that in addition to examining possible discrimination against women and foreign-born, as has been done in previous studies, we further explore possible discrimination against the physically challenged individuals. Our baseline results show conflicting results that Ph.D. education can reduce discrimination against disability and foreign-born but not against gender. Further analysis by the Blinder-Oaxaca Decomposition shows that the wage gaps of gender and disability come more from the unobserved part than the explained part, while the foreign-born wage gap come more from the observable human capital differences. Since prejudice is an unobserved factor and we know that the disadvantaged groups are likely to suffer from prejudice (Oaxaca, 1973; Blinder, 1973; Montes-Rojas et al., 2017; Deshpande and Khanna, 2018), we conjecture that prejudice might be attributable to the unexplained part of the wage gaps. Furthermore, prejudice might be deeply rooted in one’s mind, thus difficult to remove even with the influence of education. Hence, our results reveal that it would be hard for Ph.D. education to eradicate the discrimination against gender and disability, but not against foreign-born.

This study simultaneously tests Wagner’s law on one hand and Keynes proposition on the other hand related both government spending and output in Ivory Coast that experiencing long run economic growth and widened deficit. That challenges the country’s fiscal sustainability. With annual data from 1980 to 2020, results show that Wagner’s law holds, the elasticity of government spending to output is greater than one. There is bidirectional causality between government size and output validating Keynesian idea that public expenditure is an exogenous factor and a policy instrument for increasing national income. Wagner law and public deficit can justify Ivorian over-indebtedness.

Based on provincial panel data from 1998-2018, this paper estimates research and development (R&D) factors, and a stochastic frontier analysis (SFA) model is constructed to examine the effects of R&D factors on regional total factor productivity (TFP). The results show that both R&D capital stock and R&D personnel can significantly promote regional TFP, but the productivity-enhancing effect of R&D factors is different between regions. Specifically, R&D capital and R&D personnel can promote TFP in eastern and central provinces, and the promotion effect is not significant in western provinces. In addition, compared with investment-driven regions, innovation-driven regions are more likely to enhance TFP by R&D factors.

I present a theoretical model and an empirical approach for jointly estimating the effectiveness of fiscal policy and the stochastic process of sovereign interest rate shocks. The theoretical model has features relevant to small open and emerging economies. Interest rate shocks affect the ability of firms to finance payroll expenses. This theoretical feature creates a propagation mechanism for interest rate shocks and affects government spending multipliers. This paper proposes a strategy for jointly estimating government spending multipliers and the interest rate shock process parameters.

We assess the impact of the Strategic Economic Plan on Singapore’s long-term GDP per capita. To our knowledge, we are the first to evaluate the effect of the 1991 plan on Singapore’s successful growth trajectory using methodologies aimed at causal identification. In other words, this article applies the Synthetic Control method and World Bank data from 1970 to 2018. We estimate an average increase of 6,641.71 US$ in Singapore’s GDP per capita. Results remain robust to placebo and leave-one-out tests and the Synthetic Difference-in-Differences method.

This paper investigates the impacts of COVID-19 on women’s employment and gender disparity with a longitudinal dataset spanning the pandemic. We exploit the regional intensities of social vulnerability and temporal variation to implement the difference-in-differences (DID) estimation. The results indicate that the pandemic and its associated lockdowns generate a significant and negative impact on women’s employment but not on men’s employment. Moreover, a counterfactual analysis using pre-pandemic data further supports the causal nature of the documented relationships. The evidence suggests that economic downturns caused by public health emergencies, unlike previous economic recessions, have a greater impact on women, and differentiated policies should be designed.

With the daily data from Nov 20, 2019 to Oct 31, 2022, this paper examines the dynamic nonlinear effects of RCEP on Dual Circulation and Greater Bay Area stock market from a quantile perspective. The rolling window quantile regressions detect the positive effects of RCEP on Dual Circulation and Greater Bay Area stock markets with significant time-varying characteristics. Meanwhile, QQ results show that the impacts from RCEP index are more significant under extreme conditions. In addition, we further use a nonparametric QC test to provide evidence on the predictive power of RCEP for Dual Circulation and Greater Bay Area with stock market.

We quantify financial returns to backers in reward-based crowdfunding projects on Kickstarter and show that such investments provide profitable opportunities in addition to non-monetary benefits. The average unconditional annualized return is 11.5% and the average return on successful projects is 30%. Hence, backing money near the end of a campaign, when the probability of success is already high, is a profitable strategy. The most attractive is the Design category, where successful projects yield 73%, on average. Short-term projects are more profitable than long-term ones. Financial return is an important type of extrinsic motivation in reward-based crowdfunding, which has generally been neglected in academic literature. Reward-based crowdfunding outperforms other forms of crowdfunding and other common alternative investments.

The Weibull distribution is extensively useful in the field of finance, insurance and natural disasters. Recently, It has been considered as one of the most frequently used statistical distributions in modelling and analyzing stock pricing movement and uncertain prediction in financial and investment data sets, such as insurance claims distribution. It is well known that the Bayes estimators of the two-parameter Weibull distribution do not have a compact form and the closed-form expression of the Bayes estimators cannot be obtained. In this paper and the Bayesian setting, it is assumed that the scale parameter of the Weibull model has a gamma prior under the assumption that its shape parameter is known. A simulation study is performed using random claims amount to compare the performance of the Bayesian approach with traditional maximum likelihood estimators in terms of Root Mean Square Errors (RMSE) and Mean Absolute Error (MAE) for different sample sizes, with specific values of the scale parameter and shape parameters. The results have been compared with the estimated result via the maximum likelihood method. The result revealed that the Bayesian approach behaves similarly to the maximum likelihood method when the sample size is small. Nevertheless, in all cases for both methods, the RMSE and MAE decrease as the sample size increases. Finally, applications of the proposed model to the insurance claim data set have been presented.

Information leakage in the stock market has been widely proven. Information disclosure is sometimes uneven, and there is significant information asymmetry between ordinary investors and professional institutional investors. In this paper, Regression Discontinuity design (RDD) model is first employed to analyze the information leakage issues. Based on the daily closing stock prices of 15 capital service listed companies, we analyze the difference between the market reaction time and the disclosure time of two stamp tax policies. We found that the sample policies information may leaked to the market about two days earlier. This paper provides a new method analyzing information leakage.

To investigate successful technology transfer, the potential path of innovations from the university research bench to the knowledge recipient is modelled. Universities exist in highly regulated environments and the initial path of decision-making is a hierarchical model and where decisions flow upward from manager to manager until a small number of candidate innovations for commercialization remain. These are then routed for further processing to the link connecting to the knowledge recipient, the Technology Transfer Office (TTO). In the TTO, a hierarchical decision-making model can be acceptable in terms of outcomes, but ambidextrous co-operative team structures are much superior in cases where staff have good insight and decision-making abilities. This report represents the first Structured Equation Model investigation of the management architecture of a TTO.

This paper intends to analyze efficiency, in its weak form, in the stock markets of Austria (ATX), Poland (WIG), the Czech Republic (PX Prague), Hungary (BUX), Croatia (CROBEX), Serbia (BELEX 15), Romania (BET), and Slovenia (SBI TOP), from February 16, 2018, to February 15, 2023. To achieve the research aim, we intend to answer the following research question: i) Have events in 2020 and 2022 heightened the persistence of Central European stock markets? Results suggest that persistence in returns has increased significantly during the first wave of Covid-19 and the Russian invasion in the year 2023, but we also saw that most stock markets already exhibit long memories, implying that the research question has been partially validated. This research can provide valuable insights to investors, policymakers, and others interested in financial risk management.

Cryptocurrencies have gained popularity over the past five to six years. Most recently, events like the FTX bankruptcy fueled the interest in regulation. Moreover, it is possible that the FTX event disrupting the cryptocurrency market was a factor in Silicon Valley Bank's failure. While several countries consider regulation, from soft regulation, like Japan, to more rigid standards, like the total ban in China, we study the effect of other news or events on cryptocurrency prices. This paper looks at historical closing prices for Bitcoin, the largest of the cryptocurrencies, and how prices react to various events. Then we focus on modeling the time series considering an 'event,' China's ban on cryptocurrency exchanges, using intervention analysis. We find that intervention analysis provides a reliable approach to quantifying the impact regulation may have on cryptocurrency pricing.