As a key component of supply-side structural reform in the financial sector, Fintech plays a crucial role in enhancing firms' counter-cyclical resilience. Using data from Chinese A-share listed companies and treating monetary-policy-related interest rate fluctuations as external financial shocks, this paper investigates the impact and underlying mechanisms of Fintech development on corporate resilience. The findings indicate that Fintech significantly mitigates the adverse effects of monetary policy adjustments on firm operations, thereby effectively improving corporate resilience. Mechanism analysis reveals that Fintech enhances resilience primarily through three channels: reducing information asymmetry between banks and firms, increasing firms' risk-taking capacity, and decreasing dependence on traditional collateral assets. Further heterogeneity analysis shows that the resilience-enhancing effect of Fintech is more pronounced in regions with lower marketization, in firms facing greater financing constraints, and in areas with more volatile housing prices. This study provides theoretical support for further development of Fintech policies aimed at fostering resilience among micro-economic entities.

This study aims to explore how a firm’s exposure to economic policy uncertainty (EPU) affects the use of fair value (FV) measurement for non-financial assets. The analysis focuses on the selection of FV models for different types of non-financial assets. It examines how internal factors and external policy shocks (EPU) influence the adoption of FV measurement through regression analyses. The results reveal that firms with greater sensitivity to EPU are significantly less likely to adopt FV accounting for non-financial assets. This tendency is especially pronounced among firms that benefit from uncertain environments and is consistent with the conservatism principle. The effect varies by firm characteristics and industry context – being more evident among larger firms, those with high tangible assets or volatile cash flows and those operating in regulated or non-real estate sectors. This study contributes to the literature by linking macroeconomic uncertainty to accounting policy choice, highlighting EPU as a key external determinant of FV adoption. It extends prior work focused on internal drivers by empirically documenting how firms adapt accounting practices in response to policy uncertainty.

We propose a new investment strategy, the improved cross-asset time-series momentum (I-XTSM) strategy, to predict future returns and make investments. Using data on 25 investment portfolios and common commodities for the period from January 1990 to December 2023, we find that the I-XTSM strategy increases profitability substantially in the stock market and avoids momentum collapse effectively. We also document that its profitability is driven by the predictive power of the industrial metal assets' past signals. Even after considering transaction costs and market exposure, the I-XTSM demonstrates superior performance and explains the excess profits of other momentum strategies.

Utilizing cross-correlation-based Planar Maximally Filtered Graph, and conditional Value-at-Risk-based extreme risk spillover network approaches, we analyze the structure and dynamics of price contagion and risk transmission between different commodity groups in the global commodity futures market during the Global Financial Crisis (GFC) and different phases of the COVID-19 pandemic. As expected, owing to the fundamental differences between the two crises, we find very divergent commodity network structures and non-identical direction of risk transmission between commodities in these two crises. Gold and silver, however, continued to play their traditional role of risk transmitters in both crises – right at the beginning of the GFC but towards the latter part of the COVID19 crisis.

PurposeThis paper aims to construct a systematic theoretical analysis framework from the perspective of knowledge management capabilities, and to explore the empowering effect and the underlying theoretical mechanisms of artificial intelligence (AI) technology application on enterprise technological innovation, thereby providing theoretical references and empirical evidence for policymaking and management practices.Design/methodology/approachThis paper selects the operation data and technical invention patent data of Chinese listed companies from 2012 to 2023 to deeply explore the empowering effect of AI on enterprise technological innovation. This paper uses a fixed-effects model to perform regression analysis on the samples, and conducts systematic robustness tests, heterogeneity analysis and extensibility analysis.FindingsThis study finds that the application of AI technology can systematically enhance enterprises' knowledge management capabilities (including knowledge acquisition, integration, application and transformation), thereby empowering technological innovation. This conclusion remains robust after a series of robustness checks. Moreover, compared with logical AI technologies, learning AI technologies exhibit a more pronounced empowering effect on enterprise's technological innovation. The heterogeneity analysis reveals that the empowering effect of AI technology on technological innovation is stronger in enterprises with low financing constraints, high human capital and technology-intensive characteristics. The extended analysis further shows that AI technology application significantly promotes both incremental and radical technological innovations, though its impact on the latter demonstrates a notable lagged effect.Originality/valueBased on the theory of knowledge management, this paper constructs a systematic theoretical analysis framework and explores the internal theoretical mechanism of AI empowering enterprise technological innovation from the perspective of knowledge management capabilities. Meanwhile, this paper innovatively uses machine learning methods to conduct text analysis on the annual reports of listed companies, and simultaneously uses the index of AI technology investment to directly quantify and measure the level of AI application of enterprises, and conduct classified research on it (logical AI and learning AI). In addition, this paper deeply explores the differentiated impact of AI on incremental technological innovation and breakthrough technological innovation.

Abstract We propose a new predictor—the innovation in the daily return minimum in the U.S. stock market ( $$\Delta {MIN}^{US}$$ Δ MIN US )—for predicting international stock market returns. Using monthly data for a wide range of 17 MSCI international stock markets during the period spanning over half a century from January 1972 to July 2022, we find that $$\Delta {MIN}^{US}$$ Δ MIN US have strong predictive power for returns in most international stock markets: $$\Delta {MIN}^{US}$$ Δ MIN US negatively predicts the next-month stock market returns. The results remain robust after controlling for a number of macroeconomic predictors and conducting subsample and panel data analyses, indicating that $$\Delta {MIN}^{US}$$ Δ MIN US has significant predictive power and it outperforms other variables in international markets. Notably, $$\Delta {MIN}^{US}$$ Δ MIN US demonstrates excellent predictive power even during the periods driven by financial upheavals (e.g., Global Financial Crisis and European Sovereign Debt Crisis). Both panel regressions and out-of-sample tests also support the robust predictive performance of $$\Delta {MIN}^{US}$$ Δ MIN US . The predictive power, however, disappears during the non-financial crisis caused by COVID-19 pandemic, which is originated from the health sector rather than the financial sector. The results provide a new perspective on U.S. extreme indicator in stock market return predictability.

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Do Chinese judges follow prior decisions fraught with judicial errors? If they do, why do they do so? The development of policies on the reform of China’s judicial system requires an answer to these questions. Unfortunately, empirical findings that may lead to an answer, especially those based on data collected from case reports, are lacking. To fill this gap, we undertook an analysis of 310 case reports on a complex issue to discover (i) the influence of two wrongly decided precedents announced by the Supreme People’s Court and (ii) the extent to which the rank of the deciding court affects the force of this influence. Our findings suggest that judges may rely on an officially designated precedent even if the latter is wrongly decided and the strength of the influence is negatively correlated with the seniority of the deciding court. Our findings are useful for isolating factors affecting judicial decision making in China, which are necessary for making decisions on the reform of judicial system in that country.

Since the global privatization of the water sector, water investment opportunities have become much more sought after. This study investigates the link between four water indices (S-Network Global Water Index, World Water Index, S&P Global Water Index and MSCI ACWI Water Utilities Index) and four water markets (Asia, Europe, Latin America and the US), using daily data for the period January 2004 to October 2022. Utilizing the Johansen test for cointegration during the pre-Global Financial Crisis (GFC), GFC, post-GFC, COVID-19 and full-sample periods, we find the existence of cointegrating vectors in the different periods. The Granger causality approach within a cointegration framework also allows us to assess the dynamic linkages between the stock price indices of the four water indices and the four water markets. To further confirm the empirical results in the cointegration framework, we employ the autoregressive distributed lag-based bounds approach for a robustness check. The testing results confirm the presence of cointegration in different (pre-GFC, GFC, post-GFC, COVID-19 and full-sample) periods. The analysis of the different periods allows us to better understand the relationship between global water indices and world water markets, which has important implications for investments (such as portfolio diversification) in the global water sector.

Motivated by the significant role of uncertainty in affecting investment decisions and China's economic leadership in Asia, this paper investigates the predictive role of exposure to Chinese economic policy uncertainty at the individual stock level in large Asian markets. We estimate the monthly uncertainty exposure (beta) for each stock and then employ the portfolio-level sorting analysis to investigate the relationship between the China’s uncertainty exposure and the future returns of major Asian markets over multiple trading horizons. The raw returns of the high-minus-low portfolios are then adjusted using conventional asset pricing models to investigate whether the relationship is explained by common risk factors. Finally, we check the robustness of the portfolio-level results through firm-level Fama and MacBeth (1973) regressions. Applying portfolio-level sorting analysis, we reveal that exposure to Chinese uncertainty is negatively related to the future returns of large stocks over multiple trading horizons in Japan, Hong Kong and India. We discover this is unexplained by common risk factors, including market, size, value, profitability, investment and momentum, and is robust to the specification of stock-level Fama and MacBeth (1973) regressions. Our analysis demonstrates the spillover effects of Chinese economic policy uncertainty across the region, provides evidence of China's emerging economic leadership, and offers trading strategies for managing uncertainty risks. The findings of the study significantly improve our understanding of stock return predictability in Asian markets. Unlike previous studies, our results challenge the leading role of the US by providing a new intra-regional return predictor, namely, China’s uncertainty exposure. These results also evidence the continuing integration of the Asian economy and financial markets. However, contrary findings for some Asian markets point toward certain market-specific features. Compared with market-level research, our analysis provides deeper insights into the performance of individual stocks and is of particular importance to investors and other market participants.

Purpose The purpose of this study is to investigate the relationship between gold–platinum price ratio (GP) and stock returns in international stock markets. The study addresses three empirical questions: (1) Does GP have robust predictive power in international stock markets? (2) Does GP outperform other macroeconomic variables in international stock markets? (3) What is the relationship between GP and stock market returns during economic recessions? Design/methodology/approach The study mainly uses OLS regressions to perform empirical tests for a comprehensive set of 17 advanced international stock markets and overall world market. The monthly data is used for the period January 1978 to July 2019, 499 observations for each market. Findings The study finds that the first-difference of GP (ΔGP), not the initial-level of GP, has strong predictive power for stock returns, both in short- and long-time horizons. The results remain robust after controlling for a number of macroeconomic predictors. The out-of-sample test results are significant, confirming the robustness of the predictive power of ΔGP. Originality/value This study is the first to examine the ability of the ΔGP to predict stock returns, and provide novel evidence on the relationship between ΔGP and international stock markets. The study draws on behavioral finance theory, specifically the myopic loss aversion, the herd effect and the limited attention theory, to explain the predictability of stock returns in international stock markets.

Purpose This paper aims to unveil the importance of knowledge management on a firm’s strategic emergency response during the great negative shock from global public health threats. Through analyzing how representative firms in China’s new economy industries dealt with the COVID-19 pandemic before, during and after the crisis, the significant problems confronted by these firms are pointed out, and the important role knowledge management capabilities played in solving these problems is proven. Design/methodology/approach The open data of listed companies regarding the important role knowledge management played in firms’ strategic emergency response during the COVID-19 pandemic are qualitatively analyzed. Based on theoretical sampling, this paper selects representative samples of enterprises and analyzes the positive response measures they took after being hit by this public health event to gain qualitative insight into the importance of knowledge management capabilities in strategic emergency response. Findings Three aspects of the important role of knowledge management capabilities in a firm’s strategic emergency response during the COVID-19 pandemic are introduced: before the crisis, firms should strengthen the acquisition, sharing and integration of knowledge so that they can intensify their monitoring for uncertain risks; during the crisis, firms should boost the transmission, transformation and diffusion of knowledge to improve emergency cooperation; and after the crisis, companies should reinforce knowledge evaluation, creation and application to enhance “immunity” in similar emergencies. Research limitations/implications This paper has important implications for bolstering strategic emergency management practice and knowledge management capability among firms. Future research must focus on the following two aspects for further investigation: the dynamic relationship between firm knowledge management capability and strategic emergency response ability; and the collaboration system between firm knowledge management and strategic emergency response behaviors. Originality/value This paper discusses the important role knowledge management capabilities play in firms’ strategic emergency responses based on insights gained from the significant changes that the COVID-19 pandemic caused to representative Chinese new economy firms. By analyzing the three stages of before, during and after the emergency, this paper proposes the exact efforts that new economy companies should make in improving knowledge management capability.

PurposeThe Group Method of Data Handling (GMDH) neural network has demonstrated good performance in data mining, prediction, and optimization. Scholars have used it to forecast stock and real estate investment trust (REIT) returns in some countries and region, but not in the United States (US) REIT market. The primary goal of this study is to predict the US REIT market using GMDH and then compare its accuracy with that derived from the traditional prediction method.Design/methodology/approachTo forecast the return on the US REIT index, this study used the GMDH neural network and the generalized autoregressive conditional heteroscedasticity (GARCH) model. In this test, the training samples, testing samples, and kernel functions of the GMDH model are controlled to investigate their impact on the accuracy of the machine learning approach. Corresponding experiments were performed using the GARCH model, and the accuracies of these two approaches were compared.FindingsCompared with GARCH, GMDH's accuracy is much higher, indicating that the machine learning approach can provide a highly accurate prediction of REIT prices. The size of the training samples and the kernel functions in the GMDH model affect the accuracy of the prediction results. In particular, the kernel function has a significant impact on prediction accuracy. The linear and linear covariance kernel functions are simple to train and yield accurate predictions, whereas the quadratic function is difficult to train. Even with small training samples, GMDH can outperform GARCH in prediction accuracy.Research limitations/implicationsAlthough GMDH shows good performance in predicting the US REIT return, it is still a black-box model, and the algorithm is difficult for financial analysts to develop and customize. The data used in this study come from the US REIT market, which is the world's largest and most liquid market.Social implicationsThis research shows that the GMDH model outperforms the GARCH model in forecasting REIT returns. Hence, investors can use the machine learning approach to make more accurate predictions of the target REITs' returns and thus better investment decisions. Future investors and researchers may use GMDH to forecast the performance of REITs in other markets.Originality/valueThis is the first study to apply the GMDH neural network to the US REIT market and determine the impact of the two factors on its performance. For example, this research first discusses the impact of kernel functions on the US REIT market using the GMDH neural network. It also includes short-term daily prediction returns that were not previously considered, making it a valuable reference for financial industry analysts.

Purpose This paper aims to investigate the primary motivations for China’s outward foreign direct investment (ODI) decisions. Design/methodology/approach Using a panel data sample covering the period 2003–2012 and a comprehensive set of 176 host countries. Findings This study finds that market size, trade variables and natural resource variables are strongly related to the Chinese ODI stocks. This indicates that Chinese ODI decisions are driven by both market- and resource-seeking motives. The subperiod sample test results lend even stronger support to the market-seeking motive for ODI. Originality/value These results seem to emerge from the policy changes that were undertaken during the sample period. Consistent with subgroup tests, this study finds that the main purposes of China’s ODI in the top 100 countries are natural resource explorations and production line replacements.

Recent studies advocate two new benchmark models (the Fama-French five-factor model and the Hou, Xue and Zhang four-factor model) with monthly data. Our daily data approach provides considerable supplement to the monthly data approach presented in recent studies. We adopt the advanced bootstrap methodology by replicating the original data sample, and this approach should effectively alleviate the problem of too much noise in the data of daily return. A two-pass cross-sectional regression and GMM with several useful testing statistics are used to more thoroughly diagnose the specifications of the model. The following consistency is observed when using different frequencies of sample data: the evidence indicates that the two newer benchmark models (the Fama-French five-factor model and the Hou, Xue and Zhang four-factor model) outperform the Fama-French three-factor model in estimating a few well-known portfolios (formed on different anomalies). However, several specification tests do not robustly accept the correct specifications of the Fama-French five-factor model and the Hou, Xue and Zhang four-factor model.

Computer science indicates that thinking means the processes in which a finite number of innate 'instructions' act serially, alternately, and selectively on data. This implies a roundabout method of production of thought, which consumes time and resources, and which requires and produces knowledge stocks. Optimising the computing economy and decision-making timeliness, computations must frequently adopt various methods other than deductive reasoning, thus leading to a subjective turn and the occurrence of innovations. The combinational explosion between instructions and data underscores that the socio-economic world is a one-way and explosive evolution (not too dissimilar) to the Big Bang, which begets the synthesis or unification of economics.

Motivated by the purportedly close relation between economic uncertainty and future stock returns in the US, we investigate the predictive role of this potential factor in the Australian stock market. Applying portfolio-sorting strategies based on economic uncertainty exposure measured by individual stock betas, we find that uncertainty betas negatively relate to future stock returns over short- and medium-term trading horizons. Moreover, common asset pricing models, including the capital asset pricing model (CAPM) and the Fama and French three-, five-, and six-factor models, cannot explain these relations. The results remain robust when applying firm-level Fama and MacBeth regressions.

The availability of potable water is a challenging issue for many Asian countries where economies are still expanding and the population is growing. It is not difficult to observe that water scarcity will become far worse, sooner rather than later. In this study, we investigate the relationships among water indices in five Asian markets (namely China, Hong Kong, Japan, Philippines and Singapore) for the sample period 2005–2018 using the DCC-GARCH model. The empirical results confirm that volatility spillover exists among all the five water indices and there are persistent positive volatility effects. Further, we find that portfolio diversification is possible and benefits may be gained due to the contrasting correlations between pairs of these water indices. The findings of this study may be useful to academics, researchers, policymakers and major water investors worldwide in further comprehending the challenges faced by the Asian water market.

Purpose This study aims to answer the question of how business models (BMs) maintain stability while coping with environmental uncertainties. This study proposes a dynamic co-evolution of knowledge management and business model transformation based on a comparative analysis of the focal firms’ BMs and their main partners in two e-commerce ecosystems in China. Design/methodology/approach The open data of listed companies regarding the introduction of emerging topics on the transformation tendency of BMs in the post-COVID-19 business world is qualitatively analysed. The theoretical foundation is based on a critical review of the literature. Findings Three aspects of the co-evolution between knowledge management and business model transformation are introduced. These three aspects are as follows: knowledge integration helps with multi-system business integration and decision-making collaborations; knowledge sharing helps to enhance cognitive ability and network value based on businesses; and the creation of new knowledge helps enrich the knowledge base and promote the transformation of BMs. Research limitations/implications Solely attributing a firm’s ability to cope with environmental uncertainties to its business model weakens the importance of its knowledge management. This study argues that the co-evolution between knowledge management and business model transformation also plays a key role in a firm’s response to issues post-COVID-19. Originality/value This study calls for the development of a normative theory of co-evolution between knowledge management and business model transformation, implying uncharted territories of knowledge management based on interaction with business model designs in e-business ecosystems.

This paper examines the risk-return trade-off in the Australian Securities Exchange (ASX) using high-frequency data of related assets traded in other markets, where intra-day data are available while the ASX is closed. We consider the S&P/ASX 200 index and the ASX risk-neutral option-implied volatility (VIX) to highlight the importance of overnight information in predicting future index returns. Further, aside from the well-specified traditional approach of monitoring risk-return regressions using the second moment (volatility), we conjointly account for other higher-order moments such as the third and the fourth moments (skewness and kurtosis) to investigate the impact of overnight information corrected moments on predicting the future returns using the cointegrated fractional VAR (CFVAR) model. We find that the monthly compounded realized volatility and realized skewness adjusted with the fractional integration parameter are significantly negatively and positively related with the subsequent monthly returns, respectively. Moreover, the multivariate setting of our study implies that there exists a cointegrating relationship between the realized volatility and VIX, which can be regarded as the variance risk premium.