Dr Juanxi Wang

This paper study the role of digital finance in driving industrial and green transformation by considering reginal heterogeneity. The result aim to provide insights for policymakers and practitioners to develop targeted strategies for promoting sustainable industrial development through the leveraging of digital finance and technological innovation.
This paper examines how digital finance affects the industrial and green transformation in China, considering its heterogeneity across various aspects and dimensions. It uses panel data from 31 provinces and municipalities in China during 2011-2020 to analyse the relationship between digital finance and industrial transformation, with technological innovation as a mediating factor. The findings are robust and tested by using various robust and endogeneity approaches.
The results show that digital finance has a positive and significant impact on the advancement, efficiency, and green aspects of industrial transformation, which is mediated by technological innovation. As the levels of digital finance development and technological innovation improve, the incentive effect of digital finance strengthens. Additionally, the positive impact of digital finance on the efficiency of industrial transformation is only observed in the eastern regions, while in the central and western regions, digital finance primarily influences the advancement and green aspects of industrial transformation.
This paper introduces the digital finance and ecological indicators into the analysis of industrial structure transformation, presenting a unique contribution to existing literature. To our knowledge, this is one of the first papers to include digital finance, technological innovation and green transformation of industrial structure within the same research framework.

Existing research has overlooked the interaction of debt in the financial assessment of asset-light strategies. This research assesses how financial leverage (high or low long-term debt) interacts with management contracts as well as franchising to exert an impact on the value of hotel companies. Panel data of 195 observations in 2007-2019 were analysed with ordinary least square, robust regression, stepwise regression and quantile regression. The research findings clarify the impacts of asset-light strategies by differentiating the previously discovered U-shaped impact into a U-shape for management contracts and an inverted U-shape for franchising. Furthermore, when financial leverage is introduced, the above relationships hold at a low leverage level; but are weakened at a high level. Lastly, these U-shapes are asymmetrical, being more responsive to asset-light strategies than when there is no leverage interaction. This research hence clarifies agency costs and their relevance to asset-light strategies with varying financial leverage.

This paper examines the dynamic effects of Social Influence on asset prices in the presence of heterogeneous expectations among investors. In our model, the choices of investors’ trading strategies are influenced not only by past payoffs but also by their neighbors’ choices in the social network. To obtain tractable results with generic implications for social structure, we use a mean-field approximation approach rather than specifying the exact structure of social network. Analytical conditions for the existence and local stability of equilibria of price dynamics are established and validated through numerical simulations. Our analysis shows that social influence increases the dimension of the dynamical system and that equilibria can only be expressed implicitly as solutions of certain equations. We also investigate the long-run behavior of price and fraction of trading strategies using numerical simulation under a scale-free network and a power function type social influence factor. Our results suggest that the system tends to be stable when social influence is small but exhibit complex periodic orbits and even chaos when social influence is large. These findings yield valuable insights into the role of social influence in financial markets.

Financial crises have repeatedly been coined as a potential application area in the recent literature on constructing early warning signals through identifying characteristics of critical slowing down on the basis of time series observations. To test this idea, we consider four historical financial crises—Black Monday 1987, the 1997 Asian Crisis, the 2000 Dot-com bubble burst, and the 2008 Financial Crisis—and investigate whether there is evidence for critical slowing down prior to these market collapses. We find statistical evidence for critical slowing down before Black Monday 1987, while the results are mixed or insignificant for the more recent financial crises.

Like stock market prices, housing prices often exhibit temporary booms and busts. A possible explanation for the observed abrupt changes is offered by the stochastic catastrophe model. This paper addresses the question whether the catastrophe model can describe and predict the dynamics of housing markets. We fit a stochastic cusp catastrophe model to empirical housing market data for six OECD countries, US, JP, UK, NL, SE and BE. Two different estimation approaches are considered – Cobb׳s method and Euler discretization. The analysis shows that while Cobb׳s approach describes the long-run stationary density better, Euler discretization is more tailored for time series, as it provides better one-step-ahead predictions. Proceeding using the Euler discretization method we discuss the dynamics of housing markets in terms of the multiple equilibria cusp catastrophe model. By considering the long-term interest rate as an exogenous variable we obtain new insights into the policy implications of interest rate levels, in particular concerning the stability of housing markets.