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With the increasing importance of the stock market, it is of great practical significance to accurately describe the systemic risk of the stock market and conduct more accurate early warning research on it. However, the existing research on the systemic risk of the stock market lacks multi-dimensional factors, and there is still room for improvement in the forecasting model. Therefore, to further measure the systemic risk profile of the Chinese stock market, establish a risk early warning system suitable for the Chinese stock market, and improve the risk management awareness of investors and regulators. This paper proposes a combination model of EEMD-LSTM, which can describe the complex nonlinear interaction. Firstly, 35 stock market systemic risk indicators are selected from the perspectives of macroeconomic operation, market cross-contagion and the stock market itself to build a comprehensive indicator system that conforms to the reality of China. Furthermore, based on TEI@I complex system methodology, an EEMD-LSTM model is proposed. The EEMD method is adopted to decompose the composite index sequence into intrinsic mode function components (IMF) of different scales and one trend term. Then the LSTM algorithm is used to predicted and model the decomposed sub-sequences. Finally, the forecast result of the composite index is obtained through integration. The empirical results show that the stock market systemic risk index constructed in this paper can effectively identify important risk events within the sample period. In addition, compared with the benchmark model, the EEMD-LSTM model constructed in this paper shows a stronger early warning ability for systemic financial risks in the stock market. [ABSTRACT FROM AUTHOR]

This paper studies the issuing pricing character of green bonds with the evidence from China and further analyzes the reason why green factor affects bond issuing pricing by analyzing credit rating's intermediary effect and exploring the mediating of policy incentives and third-party green certification. Furthermore, heterogeneity analysis is conducted to analyze how green factor impacts on bond issuing pricing at different quantiles and for companies in different industries or with different ownership. The findings include: (1) Green factor generally reduces the issuance interest rate of green bonds, though this effect is not significant at the low quantiles or at the early development stage. (2) Green factor's impact on bond issuing pricing occurs partly by improving bond credit rating, fully by this way for companies in the construction industry, but directly for companies in other industries. (3) For non-state-owned companies, green factor has direct effect on bond issuing pricing, but this effect is not significant for state-owned companies. (4) Third-party green certification and incentive policies enhance the reducing effect of green factor on the issuance interest rate. This study provides a theoretical reference for environment-friendly investors, financiers with green projects and the regulators caring about green finance. [ABSTRACT FROM AUTHOR]

This paper aims to explore whether the development of competitive computer gaming (eSports) industries is sustained in China. In doing so, we apply the Generalized Supremum Augmented Dickey-Fuller (GSADF) method to investigate when the bubbles originated and crashed in the Chinese eSports market from 2014 to 2021. Utilizing the data of the Chinese Electronic Sports Thematic Index (CESI), the empirical outcomes show that the Chinese eSports industry may deviate from its market value and that the industry witnesses bubble behaviors. The latent reasons might be attributed to government policies, economic growth, stock market crises, and venture capital investments. Therefore, to promote the stabilized and sustained development of the eSports industry in China, regulators and authorities should pay attention to the evolution of bubbles. Moreover, maintaining policy consistency and continuity may be beneficial in stabilizing investors' expectations and reducing speculative investment behaviors. [ABSTRACT FROM AUTHOR]

Our paper investigates how qualified foreign institutional investors (QFII) impact stock liquidity in the Chinese A-share market using data for 2005 to 2019. Contrary to previous findings, we find that QFII enhance stock liquidity. Specifically, QFII's participation is negatively associated with the individual stock illiquidity and positively related to stock trading volume. Moreover, using a step-by-step procedure, we provide evidence that QFII raises stock liquidity by ameliorating information asymmetry. QFII will attract more market attention and improve firm disclosure quality. We address possible endogeneity with fixed effects and instrumental variables, and our findings are robust to self-selection bias, stock market shocks, and alternative explanatory variables. [ABSTRACT FROM AUTHOR]

The world's dependency on cobalt mines in Congo and cobalt refineries in China is seen as serious security issues with potentially dangerous implications for the energy transition. However, Chinese refineries have a similar supply security issue as most of its cobalt concentrates are imported. Most supply security studies take a country perspective on market concentration and supply risks. However, control of the mines and refineries lies with the producing companies, not the governments of the countries where they are located. This paper analyses the corporate structure of the cobalt industry at the mine and the refinery stages over a longer time period to establish changes in the level of corporate concentration and to put the situation in 2018 in perspective. The level of corporate concentration at the mine stage is low and does not raise concerns for market failures or a lack of competitiveness. Corporate concentration of refined cobalt depends on the Chinese government's influence over Chinese production: if the state control over individual refineries is assumed to be strong, the corporate concentration is high. Mine stage supply security could be strengthened by improving the general political stability in the DRC to make the country more attractive for investors other than the present ones. Increased local beneficiation would strongly benefit Congo and reduce China's influence. This is a long and complicated process and its success is not at all certain. At the refinery stage, the solution is much easier: reliability of supply could be improved by constructing refineries in countries outside China. [ABSTRACT FROM AUTHOR]

This paper employs a multidimensional fixed effects model and examines the impact of the digital economy on the performance of firms in traditional industries. We have identified a pattern similar to the "IT productivity paradox" observed in the United States during the 1980s. Using data from A-share listed Chinese firms from 2011–2019, we find that the digital economy significantly reduces the short-term performance of firms by intensifying market competition and increasing adjustment costs. On the contrary, the digital economy improves firms' long-term performance through accumulation of human capital and greater innovation capability. Competitive strategies strengthen the effect of the digital economy on the long-term performance of firms. Additionally, our research demonstrates that technology-intensive firms and small firms experience more pronounced negative effects in the short term but firms with high-tech titles suffer smaller shocks than other technology-intensive firms. These findings presents novel perspectives on the influence of the digital economy on firm performance, offering valuable insights for managers, investors, and policymakers involved in economic decision-making. [ABSTRACT FROM AUTHOR]

The literature has confirmed that when managers increase profits through earnings management, the readability of annual reports may be reduced Lo (2017), Ye (2018). Whether this conclusion is suitable for Chinese corporate social responsibility (CSR) reports, however, is still unclear. Based on the panel data of 5083 Chinese non-financial listed companies from 2010 to 2019, this paper adopts multiple linear regression to investigate the impact of earnings management on the readability of Chinese CSR reports. The results show that: (1) There is a significant negative correlation between earnings management and the readability of Chinese CSR reports, with the readability of Chinese annual reports as a mediating variable. (2) The negative effect is more significant when companies are not punished for violations, when the internal control index is low, when companies lack ISO14001 certification and when companies do not have independent third-party authentication for Chinese CSR reports. (3) When earnings management just exceeds zero, the readability of Chinese CSR reports decreases. (4) The economic consequences of reducing the readability of Chinese CSR reports are that financing costs are increased and environmental performance is decreased. To improve the quality of information disclosure of listed companies, the recommendations are as follows: First, the government should issue CSR reporting standards to reduce the manipulation of Chinese CSR reports. Second, Chinese CSR reports disclosed by listed companies must be audited by independent third parties to enhance the credibility of the information. Third, the company needs to strengthen its external and internal supervision to reduce the manipulation space for the readability of Chinese CSR reports. This study extends the negative relationship between earnings management and the readability from annual reports to Chinese CSR reports. To prevent investors from detecting earnings management, the readability of Chinese CSR reports may be reduced. At the same time, the study has definitely added value to the existing literature in the domain of CSR. [ABSTRACT FROM AUTHOR]

At present, with the increasing digital competition among enterprises, it is of great importance for enterprises to promote digital transformation and achieve sustainable development. Taking China's A-share listed companies from 2007 to 2021 as samples, this paper performs empirical tests in order to explore the impact of enterprise digital transformation on the comparability of accounting information and its mechanism. The results show that digital transformation has significantly enhanced accounting information comparability, which is still significant after a series of robustness tests. The mechanism test shows that the improvement of the comparability of accounting information by the digital transformation is mainly achieved by alleviating earnings management and agency problem. Further research shows that when the degree of market competition or transparency is high, the role of enterprise digital transformation in enhancing the comparability is more significant. This paper enriches the literature on the economic consequences of digital transformation and the factors affecting the comparability of accounting information. At the same time, the conclusions of this paper confirm the governance effect of enterprise digital transformation, provide evidential support for investors to improve decision-making efficiency, and urge enterprises to accelerate digital transformation. [ABSTRACT FROM AUTHOR]

This paper analyzes the crowding-out effect of firms' environmental, social, and governance (ESG) activities, arguing that ESG rating convergence among firms makes investors hesitant, driving down economic and social welfare growth. This is yet another adverse consequence of overcompetition in ESG practices in addition to greenwashing. We first build a model to show that, although ESG incentive policies can improve social welfare in the short run, they may reverse long-term social welfare growth, resulting in an ESG-paradox situation. Empirically, we use various proxies of provincial social welfare and the distribution of ESG score changes for China's A-share companies and detect a significantly reduced impact on the social welfare growth of a Chinese province by either a lower standard deviation or skewness of annual ESG score growth calculated for all firms registered within that province. Overall, our findings reveal the complexity of formulating ESG incentive policies and suggest that firms should strike a strategic balance between their business operations and social responsibility when determining their optimal ESG input level. [ABSTRACT FROM AUTHOR]

This paper attempts to extend the approach of quantitative investment and provide investors with suggestions about volatility timing. Based on the volatility-managed portfolios strategy, we propose two volatility–tail risk-managed portfolios strategies by combining the volatility and tail risk forecasting methods. We subject seven indices in China's stock market to verify the performance of these two strategies. The empirical results show that volatility–tail risk-managed portfolios yield better performance than buy-and-hold portfolios and volatility-managed portfolios in terms of annualized average returns, Sharpe ratio, maximum drawdown and Calmer ratio. Furthermore, we find that the effectiveness also depends on the volatility clustering of portfolios and the selection of investment periods. [ABSTRACT FROM AUTHOR]

This paper employs the Stock Connect programme to examine the impact of stock market liberalisation on the earnings management activities of listed firms in China. Using the time-varying difference-in-difference model, we find that both accrual-based and real earnings management activities decrease after firms join the Stock Connect programme. This reduction effect disappears once the firms leave the programme. These firms may even resume their earnings management activities. We further find that the participation of international investors, the improvement of the information environment, and the strengthening of monitoring power are potential transmission channels for this reduction effect. [ABSTRACT FROM AUTHOR]

With the advancement of science and technology and the popularization of the internet, information resources have become more and more abundant, but the attention of investors has become relatively scarce. In the face of surplus information resources, how investors allocate their attention will affect investors' decision making. This study examines the dynamic relationship between investors' attention and China's stock market liquidity, utilizing weekly data based on the component stocks of the Shenzhen Stock Exchange 100 Index in China from January 1, 2018, to December 31, 2020. The analysis reveals that investors' attention, measured by the Google Trends search volume index, negatively affects future stock liquidity. Moreover, a long-term cointegration relationship exists between investor attention and stock liquidity. The negative relationship between investor attention and stock liquidity is robust for an alternative liquidity measure using a stock fixed-effects model. The inverse correlation between investor attention and stock liquidity was more potent before the coronavirus disease 2019 pandemic and weakened amid the pandemic. The findings in this paper can enrich the standard of investors' investment decisions and help investors make objective, rational, and scientific investment decisions. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore how earnings management techniques are affected by corporate financial debt risk (FDR), internal control (IC) effectiveness and CEO education. Design/methodology/approach: The study uses a sample from listed firms in China from 2010 to 2017, comprising different industries, including agriculture, forestry, livestock farming and fishing; mining; manufacturing; electric power, gas and water production and supply; construction; transport and storage; information technology; the real estate industry; social services; and communication and cultural. The regression analysis is used to test the hypotheses. The two-stage least squares technique is used to check for endogeneity issues. Findings: The study finds that firms are less likely to manage real earnings when they have more robust IC and FDR. Likewise, companies with weak ICs are more likely to manipulate real earnings. Besides, the study finds an influence of CEO education on the relationship between IC, FDR and real earnings management (REM). These results can be applied to the sectors in the sample covered by the research, and the authors do not overlook the energy industry sector for the importance of its role in the economy. Research limitations/implications: There are some limitations for the researcher when performing any research, and this study is no exception. Researchers are urged to take these circumstances into consideration when generalizing or comparing the results because the methods used to calculate the measurement variables in each study may differ somewhat from those used in other research. In addition, expanding the current research design to incorporate additional nations may be an area of interest for future research and could aid in evaluating the effects of nation-specific elements (such as inflation, culture, legal systems and political considerations) on the usefulness of IC and decreasing FDR. Second, the current study focuses on the impact of IC and FDR on REM; this paper does not dissect the "black box" of IC and consider how each element affects earnings management. Future research may need to focus specifically on how effective IC would affect earnings management and precisely what IC mechanisms would discourage the management of earnings. Practical implications: Helping companies listed in China to make decisions and improve investors' vision of the results of real companies' businesses, as well as helping management to avoid falling into debt risk and the consequent effects and manipulation of earnings. Originality/value: By highlighting the significance of IC and debt risk in enhancing information quality in China, the results contribute to the body of work examining the relationship between IC, FDR and REM. In addition, this study uses a CEO's education to moderate this link. [ABSTRACT FROM AUTHOR]

Neuropsychological studies propose that listeners unconsciously assess speakers' trustworthiness via their facial expressions. Building on this theory, we investigate how investors respond to CEOs' dynamic hemifacial asymmetry of expressions (HFAsy) shown in CNBC's video interviews about corporate earnings. Consistent with the neuropsychological prediction that facial asymmetry induces distrust, we document that (1) the stock market reacts negatively to the CEO's HFAsy shown in the interview video, and (2) the market reaction to favorable earnings news is negatively associated with the CEO's HFAsy. We also find a significantly positive association between the short-window abnormal bid-ask spreads and HFAsy, suggesting a larger investor disagreement for firms with high-HFAsy CEOs. Furthermore, we show that analyst forecast revisions are negatively associated with CEOs' HFAsy. Our results are robust to conducting a falsification test using the previous quarter as a pseudo-event, employing an alternative measure of HFAsy, and using more granular trading data at the second level. Overall, our study provides evidence that investor trust and trading behavior are affected by the dynamic hemifacial asymmetry of expressions appearing on CEOs' faces. This paper was accepted by Brian Bushee, accounting. Funding: Financial support from the National Natural Science Foundation of China [Grants 71772123, 72132006, 72172038, 72241425, 72273088, 72372103, and 72310107002], the National Social Science Foundation of China [Grant 23ZDA039], Fudan University (Sci-Tech Innovation Foundation of School of Management), and the Research Fund of Shanghai Data Exchange Corporation is gratefully acknowledged. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4922. [ABSTRACT FROM AUTHOR]

Enterprises have different behavioral characteristics depending on their ownership structure and their nature and play different roles in promoting economic development. In order to explore the innovation behavior characteristics of private enterprises, state‐owned enterprises, Hong Kong‐, Macao‐, and Taiwan‐funded enterprises, and foreign‐funded enterprises in China's high‐tech industry, this paper constructs spatial lag models and varying coefficient spatial lag models within the framework of knowledge production functions. It not only evaluates the technological innovation performance of various types of enterprises in high‐tech industries but also comprehensively explores the differences in the impact of external environments such as spatial dependence and business environment on the innovation efficiency of various types of enterprises. The results of the study provide a reference for enterprise managers and investors to efficiently allocate the scarce resources for innovation investment. [ABSTRACT FROM AUTHOR]

This paper mainly studies the interdependence structure and risk transmission between China's north–south capital flow and the stock market and foreign exchange market. The results show that the correlation between southward capital and the mainland stock market is stronger, and the correlation between northward capital and the Hong Kong stock market is stronger. The correlation between northward capital and RMB exchange rate is greater than that between southward capital and RMB exchange rate. After the outbreak of COVID-19, the correlation between capital flows and the stock market has strengthened, while the correlation with the foreign exchange market has weakened. Furthermore, there is a bidirectional risk transmission between the north–south capital flow and China's stock market and foreign exchange market. In general, the north–south capital flow has a greater impact on the stock market, and the exchange rate of RMB/US dollar has a greater impact on the north–south capital flow. This study has implications for investors and policymakers. [ABSTRACT FROM AUTHOR]

This paper examines the nonlinear (NL) asymmetric long- and short-run interconnections between energy indices (S&P 500-ENERGY, MSCI-CHINA, and MSCI-WORLD) and the crypto (Bitcoin) market. The daily data have been used from January 1, 2013, to February 24, 2023, archived from the wind database. We use a nonlinear autoregressive distributed lag (NARDL) framework for the research to be empirical. The NARDL test reveals the presence of a nonlinear asymmetric relationship, both in the long-run and short-run, among the MSCI-China, MSCI-World, S&P500-ENERGY indices, and the price of Bitcoin. The S&P 500 energy index and Bitcoin price have an inverse asymmetric connection. The prices of Bitcoin are positively influenced by both positive and negative shifts in the MSCI-CHINA and MSCI-WORLD indices. Overall, this study underscores the growing significance of the cryptocurrency market as a hedge against risks, offering evidence that Bitcoin can be perceived as a safe haven amidst the turbulence of the energy market. This insight holds implications for Bitcoin investors, suggesting its potential as a refuge in times of energy market instability. [ABSTRACT FROM AUTHOR]

Considering the heterogeneity of China's different sectoral stock markets, this paper adopts the time‐domain spillover index model, extreme spillover index model, and frequency‐domain spillover model to analyse the spillover effects of China's commodity and sectoral stock markets under normal conditions, extreme conditions, and frequency‐domain conditions. The empirical results highlight three interesting and noteworthy aspects for investors and regulators: first, the spillover behaviours of China's sectoral stock markets reveal significant heterogeneity. The Energy, Materials, Industries, Optional, Consumption, Information, and PublicUtilities markets are net transmitters, while the Pharmaceutical, Finance, and Telecom ones are net receivers. Second, the spillover effects between China's commodity and sectoral stock markets are enhanced under extreme conditions and are approximately 18.49% higher than those under normal conditions, and there is asymmetry between left‐tail and right‐tail spillovers, which was observed during China's stock market crash. Finally, the spillover effect between China's commodity and sectoral stock markets is dominated by short‐term spillovers, and there is a positive correlation between short‐term and long‐term spillovers. [ABSTRACT FROM AUTHOR]

The financialisation of rental housing (FRH) has been recognised as a major force behind neighbourhood change and a threat to the right to adequate housing worldwide in/after the 2010s. However, the differentiation of FRH and its impact on housing rents across countries, cities, and neighbourhoods remain understudied. This paper fills this gap by taking China as a case. First, through the lens of state entrepreneurism, we conceptualise how the interplay between the state and financial investors caused the shift of the FRH economy from the venture capital-led asset-light business model to the state-orchestrated asset-heavy business model. Second, we examine how FRH affects local housing rent differently across business models and cities using the housing economics approaches and big data analytics. The analysis results suggest that the differentiation of the socio-spatial consequences of FRH is caused not only by financial investors' motivation to capitalise on the local population and neighbourhoods but also by the state's motivation to employ financialisation to serve its policy objectives. This paper advances the understanding of the variegated FRH under state entrepreneurialism. It also informs the policies about which neighbourhoods and cities are most in need of interventions to mitigate the negative effects (e.g. , rent inflation and neighbourhood environment downgrade) brought about by FRH. • The differentiation of the financialisation of rental housing (FRH) and its impact on rents across countries, cities, and neighbourhoods remain understudied. • We conceptualise the logic and examine the socio-spatial impact of variegated FRH economy in five Chinese cities. • Financial investors' motivation to capitalise on the local population and neighbourhoods and the state's motivation to employ financialisation for its policy objectives caused the differentiated FRH economy. • The asset-light FRH causes rent inflation while the asset-heavy addresses the housing demand but leads to neighbourhood environment downgrade. [ABSTRACT FROM AUTHOR]

Different from developed markets, China capital market is dominated by retail investors. In the digital age, retail investors are increasingly making their voice heard through online channels, which is a new form of shareholder activism in China. This paper examines the association between retail investor activism (RIA) and corporate greenwashing behavior (CGB) using the data of question raised by investors on official investor interactive platforms (IIPs) in China. We discover that the RIA on IIPs is negatively associated with CGB. The mechanism analysis shows that the RIA helps to curb CGB through promoting information transparency and improving internal corporate governance level. We also find that the association between RIA and CGB is strengthened in regions with weaker green finance and environmental regulation, and in industries with weaker product market competition. This indicates that the RIA, a type of private governance, can serve as a substitute to the governance effect of the government and the product market. Further studies show that RIA on unofficial channels such as stock forums or search engines can promote CGB, contrary to the conclusion about IIPs. Our research suggests that strengthening the rights of retail investors through official channels helps to reduce CGB and demonstrates the importance of official channels in the governance role of RIA in emerging markets. [ABSTRACT FROM AUTHOR]

We examine the impact on bond credit ratings of reduced information processing costs from two investor interactive platforms in China, i.e. 'Easy Interaction' and 'e-Interaction'. These online platforms provide investors with a channel to directly communicate with listed companies and learn much of corporate operations. Replies and explanations from management assist investors in integrating disclosed information and reducing information processing costs. The reduced costs likely impose reputational concerns on credit rating agencies (CRAs), leading to more stringent rating standards and less rating inflation. We find that more interactions result in lower credit ratings of corporate bonds. This effect is more pronounced for bonds rated by global-partnered CRAs and bonds whose issuers have higher information complexity, and less pronounced for bonds whose issuers have official consultation websites. Further analyses show that reduced information processing costs are associated with more information contents in rating downgrades and timelier ratings. Our findings provide evidence for the disciplinary effect of reduced processing costs on CRA behavior. [ABSTRACT FROM AUTHOR]

Using samples of corporate bonds issued by Chinese A-share firms from 2014 to 2021, we examine whether information about bond defaults transmits to other bonds underwritten by a common underwriter. We document that during the underwriting process for a new corporate bond, if any prior bonds underwritten by the same underwriter have defaulted, the yield spread of the new corporate bond increases significantly. Specifically, such a new bond, on average, has a yield spread that is 16.8 basis points higher than one without a common underwriter. The results suggest that investors perceive the default risk of a new bond increases due to a deteriorated reputation of the underwriter. Additional analyses suggest that the adverse effect of the new bond yield spread is less salient when an underwriter has a strong reputation, is state-owned, or has foreign shareholding; the issuer is state-owned or dual listed overseas. Overall, default information in the bond market transmits through common underwriters. [ABSTRACT FROM AUTHOR]

In this study, we examine the green innovation effect of investor preference on Chinese listed companies from 2008 to 2020. We observe that investors with environmental protection preferences can significantly increase enterprise green innovation. The baseline conclusions remain robust after we conduct multiple sensitivity tests, such as establishing a shift-share instrumental variable (Bartik IV), using the Heckman's two-step method, changing the regression methods, substituting the core variables, adopting the DID method, and considering investors' ownership. Our analysis shows that the green innovation effect of environmental investors is highly pronounced in companies with a high level of indebtedness and low managerial ability. We verify that environmental investors can improve the green innovation level of enterprises through two potential mechanisms, namely, easing corporate financing constraints and incentivising managers. In addition, political and public attention can strengthen the positive relationship between environmental investors and green innovation. [ABSTRACT FROM AUTHOR]

Since the late 1970s' reform and opening-up, China's economy has achieved remarkable success, maintaining an average annual GDP growth of 9.7% from 1978 to 2006. In 2007, China achieved a GDP of 3.38 trillion USD, surpassing Germany to become the world's third-largest economy, and by the second quarter of 2010, it had become the second-largest economy globally. These developments have attracted international investors' attention to Chinese equities. However, China's poor reputation for corporate governance may deter investors, despite their positive outlook on China's economy. Corporate managers in China are known to engage in income smoothing, taking actions to dampen fluctuations in their firms' publicly reported net income. In 2004, the OECD proposed that a strong disclosure regime promoting real transparency can help attract capital and maintain confidence in the capital market, with income smoothing being a key concern for both institutional and individual investors. This study investigates how auditor industry specialization (AIS) affects income smoothing behavior in Chinese listed companies. Using a sample of 27,208 firm-year observations from 2006 to 2018 collected from the Taiwan Economic Journal Database, the results indicate that AIS is negatively related to managers' income smoothing behavior, underscoring the importance of industry expertise in mitigating such practices. Additionally, an independent t-test highlights the difference between Big Four and non-Big Four auditors in influencing client companies' income smoothing behaviors. The study further demonstrates the significant moderating effect of Big Four auditors on the relationship between accounting firms with top industry expertise and client companies' income smoothing, reflecting the unique characteristics of the Chinese market. This study verifies the suppressive effect of AIS on firms' income smoothing and indirectly suggests that governments (or the Chinese Institute of Certified Public Accountants, CICPA) should regularly disclose accounting firms/auditors with industry expertise publicly. This regime can help global investors make informed investment decisions. [ABSTRACT FROM AUTHOR]

This study uses machine learning to investigate the effects of firm and CEO characteristics on stock price crash risk by collecting massive data on publicly listed firms in China. The results show that eXtreme Gradient Boosting (XGBoost) is the most effective model for predicting stock price crash risk, with relatively satisfactory performance. Meanwhile, the SHapley Additive exPlanations (SHAP) method is used to interpret the importance of features. The results show that the average weekly return of a firm over a year (RET) contributes the most and is negatively associated with crash risk, followed by Sigma, IPO age, and firm size. We also found that, among CEO characteristics, CEO pay contributes substantially to crash risk at the firm level. Our findings have important implications for research into the impact of firm and CEO characteristics on stock price crash risk and provide a novel way for investors to plan their investment decisions and risk-taking behavior rationally. [ABSTRACT FROM AUTHOR]

Stock time-series data has the characteristics of high dimensionality and nonlinearity, which brings great challenges to stock forecasting. Aiming at the impact of stock correlation and the prediction information contained in stock image features, we propose a long short-term memory model based on clustering and image feature extraction, named Kmeans-CAE-LSTM. Firstly, the Kmeans algorithm is used for stock clustering, where the most correlated stocks are found. Secondly, a convolutional autoencoder (CAE) is applied to extract stock price image features. Finally, the stock technical data and image features are respectively input into the double-layer long-term short-term memory network to predict the stock price of the next trading day. The empirical research results on 11 industries in China's stock market show that the hybrid model has achieved the best prediction effect, which further proves the predictive ability of stock image data and can provide investors with new ideas for stock prediction and asset portfolio. [ABSTRACT FROM AUTHOR]

Due to its effectiveness as a risk-hedging trading strategy in financial markets, futures arbitrage is highly sought after by investors in turbulent market conditions. The essence of futures arbitrage lies in formulating strategies based on predictions of future futures price differentials. However, contemporary research predominantly focuses on projections of single indicators for the subsequent temporal juncture, and devising efficacious arbitrage strategies often necessitates the examination of multiple indicators across timeframes. To tackle the aforementioned challenge, our methodology leverages a PSO Deep-ConvLSTM network, which, through particle swarm optimization (PSO), refines hyperparameters, including layer architectures and learning rates, culminating in superior predictive performance. By analyzing temporal-spatial data within financial markets through ConvLSTM, the model captures intricate market patterns, performing better in forecasting than traditional models. Multistep forward simulation experiments and extensive ablation studies using future data from the Shanghai Futures Exchange in China validate the effectiveness of the integrated model. Compared with the gate recurrent unit (GRU), long short-term memory (LSTM), Transformer, and FEDformer, this model exhibits an average reduction of 39.8% in root mean squared error (RMSE), 42.5% in mean absolute error (MAE), 45.6% in mean absolute percentage error (MAPE), and an average increase of 1.96% in coefficient of determination (R2) values. [ABSTRACT FROM AUTHOR]

This study investigates the impact of green equity indices on global market dynamics using a time-varying parameter vector autoregression (TVP-VAR) model. We uncover a significant shift in the role of the global market, transitioning from a shock transmitter to a shock receiver, as the influence of green finance grows. By directly comparing green equity indices with their corresponding global parent indices, we adopt a global perspective that transcends the limitations of studies focusing on specific regions, such as the USA, China, or Europe. This novel approach minimizes the potential biases in the transmission channels within regional markets, enabling a more comprehensive understanding of the relationship between green finance and global market dynamics. Moreover, by focusing on equity indices we ensure a consistent comparison of financial instruments, avoiding the complexities that arise when comparing different asset classes such as green bonds and conventional equities. For global investors, our results highlight the importance of dynamic and flexible hedging strategies that adapt to the distinct characteristics of green assets and their growing influence on the global market. Risk managers should incorporate these time-varying spillover effects into their models to better assess and mitigate potential risks. Policymakers should consider the growing influence of green finance on the broader market when formulating regulations and incentives to support sustainable investing, as our findings underscore the increasing importance of this sector in shaping market dynamics. [ABSTRACT FROM AUTHOR]

What is geopolitical risk to multinational enterprises in a world flooded with geopolitical changes? Does such risk impact foreign direct investment flows? To answer these questions, we attempt to develop the concept of comprehensive geopolitical risk by expanding the border of geopolitical risk in the literature to include both implicit and explicit risks that overseas investors face in host countries. We further construct an index by mining big data from a news report database, which we use to examine how comprehensive geopolitical risk impacts FDI outflows from China to 154 host countries between 2003 and 2020. Our findings indicate that comprehensive geopolitical risk does negatively impact FDI outflows, especially in the energy sector. Implicit geopolitical risk also significantly deters FDI outflows. [ABSTRACT FROM AUTHOR]

Using machine learning coupled with stock price data to predict stock price trends has attracted increasing attention from data mining and machine learning communities. An accurate prediction results can help investors reduce investment risks and improve investment returns. The research on correlation stocks is one of the most important directions among many studies. Due to the high volatility and randomness of stock data, the correlation between stocks changes over time, which makes the stock correlation in static correlation stock sets often inconsistent with reality. Furthermore, various raw data related to stocks contain sufficient stock history information to analyze the future trend of stocks, but traditional prediction models cannot make good use of this information, which restricts the learning ability of the model and reduces the prediction accuracy. In this paper, we propose a stock prediction model combining multi-view stock data features with dynamic market correlation information (MDF-DMC). The model extracts stock trend features by combining multi-view raw data of a single stock with a Multi-layer Perceptron Mixer (MLP-Mixer); The improved Transformer encoder learns the correlation between the stock to be predicted and all the selected stocks in the stock market dynamically and extracts the features of the market correlation. We have conducted a large number of experiments on a total of 578 stocks in the stock markets of China and the United States, and the results show that our model has achieved excellent accuracy and returns across all data sets. • Stock movements are influenced by both itself and the market. • Stock multidimensional data can be used to improve the forecasting performance. • The attention mechanism can effectively extract the correlation of market dynamics. [ABSTRACT FROM AUTHOR]

Predicting stock market trends is the basic daily routine task that investors should perform in the stock trading market. Traditional market trends prediction models are generally based on hand-crafted factors or features, which heavily rely on expensive expertise knowledge. Moreover, it is difficult to discover hidden features contained in the stock time series data, which are otherwise helpful for predicting stock market trends. In this paper, we propose a novel stock market trends prediction framework SMART with a self-supervised stock technical data sequence embedding model S3E. Specifically, the model encodes stock technical data sequences into embeddings, which are further trained with multiple self-supervised auxiliary tasks. With the learned sequence embeddings, we make stock market trends predictions based on an LSTM and a feed-forward neural network. We conduct extensive experiments on China A-Shares market and NASDAQ market to show that our model is highly effective for stock market trends prediction. We further deploy SMART in a leading financial service provider in China and the result demonstrates the effectiveness of the proposed method in real-world applications. [ABSTRACT FROM AUTHOR]

The allocation of assets across different markets is a crucial element of investment strategy. In this regard, stocks and bonds are two significant assets that form the backbone of multi-asset allocation. Among publicly offered funds (The publicly offered funds in China correspond to the mutual funds in the United States, with different names and details in terms of legal form and sales channels), the stock-bond hybrid fund gives investors a return while minimizing the risk through capital flow between the stock and bond markets. Our research on China's financial market data from 2006 to 2022 reveals a cross-asset momentum between the stock and bond markets. We find that the momentum in the stock market negatively influences the bond market's return, while the momentum in the bond market positively influences the stock market's return. Portfolios that exploit cross-asset momentum have excess returns that other asset pricing factors cannot explain. Our analysis reveals that hybrid funds play an intermediary role in the transmission mechanism of cross-asset momentum. We observe that the more flexible the asset allocation ratio of the fund, the more crucial the intermediary role played by the fund. Hence, encouraging the development of hybrid funds and relaxing restrictions on asset allocation ratios could improve liquidity and pricing efficiency. These findings have significant implications for investors seeking to optimize their asset allocation across different markets and for policymakers seeking to enhance the efficiency of China's financial market. [ABSTRACT FROM AUTHOR]

This study examines the dynamic connectedness and hedging opportunities between CSI300 (China Security Index 300) and copper, gold, PTA (purified terephthalic acid), and soybean in China from January 09, 2008, to June 30, 2023. A TVP-VAR and cDCC-FIAPARCH modeling framework was used for the empirical investigation. The results show that the total connectedness index can effectively capture cross-asset information transmission in China's financial markets. Copper returns are the dominant volatility transmitters, while CSI300, gold, and soybean returns are net recipients. The Russian–Ukraine war reinforced the safe-haven role of gold. Finally, investors with CSI300 long positions may benefit from prioritizing gold for hedging, while those with CSI300 short positions profit more from allocating gold to PTA. Portfolio managers and investors can use the findings to track the dynamics of systemic risk and adjust their long/short positions when investing in China's stock and commodity markets. [ABSTRACT FROM AUTHOR]

In recent years, Chinese foreign direct investment (FDI) in Africa has attracted interest from both academia and the wider political world. Some postulate that Chinese companies – unlike Western corporations – have greater potential to initiate and carry out the structural transformation of the continent. Some researchers even indicate that thanks to the inflow of Chinese FDI, Africa may become “the next factory of the world”. The aim of this article is to analyse external effects (also called spillover effects) related to Chinese FDI in Africa using the examples of Angola and Zambia. According to the current state of knowledge, spillover effects are the most desirable effects of FDI in the host country, because the related increase in the productivity of local enterprises is key to the occurrence of structural transformation in local economies. The subject of the analysis presented in this article are three aspects (determinants) of spillover effects: (i) the potential of Chinese FDI to generate spillover effects, (ii) the absorptive capacity of local companies and (iii) local institutional settings favouring the occurrence of spillover effects. The findings indicate that due to the existence of weak and few linkages between foreign investors and local firms, spillover effects occur rarely and are relatively small. They are also not sufficient to initiate the transfer of valuable knowledge and technology. The presented research results were based on over 150 in-depth interviews and two rounds of surveys conducted among Chinese investors, Angolan and Zambian companies and representatives of local authorities. [ABSTRACT FROM AUTHOR]

Investing in large-scale public infrastructures is vital for urban renewal and development, often relying on specific types of buildings to drive investment in municipal infrastructure and supporting service buildings. However, the complex interplay of interests among investors introduces unpredictability, hindering the effectiveness of such investments. This study employs evolutionary game theory to explore how investment benefits influence investor behavior. Using China's large-scale exhibition infrastructures as a case study, a tripartite evolutionary game model is developed, involving the government, investors in exhibition buildings, and investors in supporting services. The strategies of the three parties are analyzed, and simulations explore the effects of different initial strategy values, costs, subsidies, and profits. Five research outcomes are identified, indicating that favorable initial investments and optimal subsidies encourage proactive investment, while high initial costs deter it. Consequently, five recommendations for promoting coordinated investment in exhibition facilities are proposed, including the establishment of communication platforms to enhance stakeholder cooperation. These findings offer insights for decision making in other large-scale infrastructure investments, such as sports facilities and transportation hubs. [ABSTRACT FROM AUTHOR]

I examine whether union membership affects individual foreign direct investment (FDI) preferences in ways that vary across investors' country of origin. I argue that the country of FDI origin will bear upon how union members assess FDI, because it provides cues about what the economic prospects of (unionized) workers will look like under different foreign investors. I argue that the salient attribute of foreign investors is whether they originate from a country that is an important form of patient or impatient capital. Compared with non-members, members will be more supportive of FDI from countries embodying patient than impatient capital. Specifically, I expect the (positive) gap in support between FDI from patient and FDI from impatient capital countries to increase with union membership. Conversely, I expect the (positive) gap in support between FDI from impatient versus patient capital countries to decrease with membership. Evidence from original Swiss survey data corroborates my argument. Respondents were asked to evaluate FDI from China and Europe (entities embodying patient capital) and from the United States (a country embodying impatient capital). The results show that the gap in enthusiasm for European FDI versus American FDI increases with union membership, while the gap in enthusiasm for American FDI versus Chinese FDI decreases with membership. Complementary qualitative analysis of reports, documents, and testimonies by trade unions in continental Europe show that their views are in sync with those of their members, suggesting that unions shape their members' FDI preferences. The findings have important implications for the politics of backlash against economic globalization. [ABSTRACT FROM AUTHOR]

We adopt a multilayer networks approach to assess how network structural embeddedness affects corporate technological innovation. Our findings indicate an annual increase in both single-layer and multilayer networks, although adoption of the latter by Chinese listed companies is comparatively low. We found that structural embeddedness of multilayer networks positively impacts corporate technological innovation. By reducing uncertainty within the internal environment, these networks bolster technological innovation. Moreover, such embeddedness notably spurs innovation in non-state-owned companies and those with greater internal transparency and robust external oversight. Our analysis reveals an intermediate effect where structural embeddedness in multilayer networks influences innovation. Our work provides new insights into enhancing innovation capacity via network embeddedness and supplies empirical data on utilizing network resources for innovation. We also offer actionable guidance and policy advice for managers, investors, and policymakers, especially relevant amidst economic transformation and pursuit of technological self-reliance of China. [ABSTRACT FROM AUTHOR]

This study examines the impact of investor reaction to crisis events on corporate philanthropy in China. Building on impression management theory, we propose that after suffering negative reactions from investors at the onset of a corporate crisis, firms tend to increase their post-crisis philanthropic giving to rebuild their reputation. Analyzing 149 corporate crises in 2008–2018 befalling publicly listed firms in China, our findings reveal that the subsequent increase of corporate philanthropic giving is adversely connected to crisis-related abnormal returns, suggesting that the severity of investor reaction at the onset of a crisis has a favorable impact on corporate philanthropy. Moreover, the negative relationship between philanthropic giving and crisis-related abnormal returns is more pronounced for firms with a higher demand for reputation repair. Our impression management perspective offers new insights into why firms engage in corporate philanthropy after suffering adverse reactions from investors at the onset of a corporate crisis. [ABSTRACT FROM AUTHOR]

Using unbalanced panel data on 3326 Chinese listed companies from 2014 to 2021, this study investigates the impact of corporate environmental performance on China's excess stock returns. The results show that stocks of companies with better environmental performance earn significantly higher excess returns, indicating the existence of green returns in the Chinese stock market. We further reveal that heightened climate-change concerns can boost the stock market's green returns, while tightened climate policies decrease green returns by increasing long-term carbon risk. Our findings are robust to endogeneity problems and hold great implications for both investors and policymakers. [ABSTRACT FROM AUTHOR]

In light of the increasing investor interest in cryptocurrencies (CR) as alternative financial assets in financial markets, we sought to examine the connection between economic policy uncertainty (EPU) and cryptocurrencies. To do so, monthly data for Bitcoin (BTC), Ethereum (ETH), and Tether (THT) from January 2021 to April 2023 were employed. We utilized quantile regression and Granger causality analysis to investigate the relationship between EPU and cryptocurrencies. The initial results of this study suggest that EPU has little effect on the cryptocurrency market in the short-term. To enhance the strength and validity of these findings, we performed separate evaluations tailored to the unique contexts of the United States and China. The results revealed that the effects of EPU were adverse and statistically insignificant for China, while the situation differed slightly for the United States. Given that the United States has the most developed economy, its policies have a significant influence globally. As a result, cryptocurrencies have the potential to serve as efficient hedging tools. Furthermore, we incorporated nonlinear autoregressive distributed lag (NARDL) analysis to assess the asymmetric impact of EPU on cryptocurrencies by adopting both short-term and long-term perspectives. The outcomes demonstrated that both Bitcoin and Ethereum can serve as hedging tools in the short-term, although this utility diminishes in the long-term. Conversely, Tether displayed a positive association with EPU in the long-term. The findings of this study hold significance for policy-makers, offering valuable insights related to structuring efficient policies. The recommendations include fostering a rational framework for active participation from various stakeholders, including investors, governmental bodies, central banks, stock exchanges, and financial institutions. This collaborative effort aims to mitigate irrational fluctuations and enhance the acceptability of cryptocurrencies. In essence, this research underscores the potential of cryptocurrencies as a secure hedge against short-term EPU. However, we caution against assuming that any single cryptocurrency can consistently serve as a dependable investment haven. [ABSTRACT FROM AUTHOR]

This study examines the effect of information quantity announcement (IQA) on modified audit opinion (MAO). The more frequently the listed companies make announcements, the more issues are disseminated to investors. The auditors who are concerned with the issues may consider issuing a modification opinion. This study also examines whether institutional investors can moderate the impact of IQA on MAO. This study uses multivariate regression on 15,875 year-observations of companies listed in China from 2014 to 2019. This study finds evidence that more information is disseminated to investors, increasing the possibility that the company will receive MAO. The results may indicate that the auditors become more skeptical toward the issues and favor issuing modification opinions consistent with the investors' understanding. The results also show that institutional investors moderate the relationship between IQA and MAO. The findings suggest that the more announcements the company has, the higher the probability of the company being issued with a modified audit opinion. More information indicates that the company may confront many issues that possibly influence the auditor's consideration of their audit opinion. Therefore, policymakers may consider and analyze the advantages or drawbacks of the announcement while setting up the rules and regulations for listed companies in China. Meanwhile, the presence of institutional investors can neutralize the auditors' skepticism toward the issues that were announced. This study is novel and contributes to the literature by examining the effect of IQA on MAO and the moderating role of institutional investors in the context of listed companies in China. [ABSTRACT FROM AUTHOR]

This article provides an overview of the concept of a liquidity trap and its implications for macroeconomic policies. It explains that a liquidity trap occurs during a recession when there is excessive saving and the nominal interest rate drops to its lowest point. The article explores how policymakers can avoid liquidity traps through macroprudential policies, such as leverage regulations, but also highlights the potential unintended consequences of these measures. It discusses the impact of leverage regulations on the interest rate and aggregate demand, and emphasizes the importance of ensuring that savers' marginal utility of consumption remains above the zero lower bound. The article concludes by discussing factors that could make a liquidity trap more likely, such as pessimism, deflation expectations, a collapse in real estate prices, and bank runs. [Extracted from the article]