Special Session 62: Mathematical problems arising in recognizing the data value chain efficiency

Unleashing the empowered effect of data resource on inclusive green growth: Based on double machine learning

Shuanglian Chen Guangzhou University Peoples Rep of China Co-Author(s):

Abstract: Inclusive green growth is considered an essential strategy for achieving economic high-quality development, requiring the empowered effect of data resource. We ana-lyze whether data resources drive inclusive green growth using Double Machine Learning (DML) methods on a sample of 301 prefecture-level cities from 2000 to 2021. Our study examines the mediating roles of talent, technology, and capital and explores siphoning and spillover effects. We find that a 1% increase in data resources correlates with a 2.4% rise in inclusive green growth. Notably, effects vary by city size, location, and policy timing. Data resources mainly influence growth through talent rather than technology, with capital having a negative mediating effect. This research offers insights into integrating data resources with inclusive green growth.

Valuing Financial Data: The Case of Analyst Forecasts

Ziqing Du Guangzhou University Peoples Rep of China Co-Author(s):    Li Zhenghui; Xu Yanting

Abstract: Investors will seek and utilize various effective financial market information and data in the investment decision-making process, seeking investment portfolios that maximize benefits and minimize risks. This article first conducts a theoretical analysis of the investor welfare effect of financial market data. Based on this, a sample of 185 A-share companies in China from the first quarter of 2008 to the fourth quarter of 2023 is selected to explore whether financial market data brings benefits to investors using analyst data as a case study. Simultaneously considering the impact of investor wealth level, target asset size level, target asset information disclosure level, and target asset analyst attention on the investor welfare effect of financial market data. The results indicate that: (1) Analyst data has opposite effects on investor welfare under different market conditions and is positively correlated with investor wealth levels. (2) There are significant differences in the impact of analyst data on investor interests for assets of different scales and levels, with small-scale damage being the least and medium scale damage being the greatest. (3) Regardless of the transparency of the underlying asset information, analyst data harms the interests of investors. Market information is valuable for investors holding highly transparent underlying assets. (4) The analyst data for assets with low levels of attention from analysts does not show significant utility to investors, while assets with high levels of attention show a clear negative utility.

The Evolution of and Endogenous Mechanism in the Global ICT Trade Dependence Network

jiajia He Guangzhou University Peoples Rep of China Co-Author(s):    Zhenghui Li, Yanting Xu

Abstract: This paper uses information and communication technology (ICT) product trade data on 126 major economies from 1996 to 2020 to construct a global ICT trade dependence network, analyze its structural characteristics, and investigate endogenous factors by constructing a temporal exponential random graph model. The study reveals that the global ICT trade dependence network exhibits a center-periphery structure. The two largest trade communities in the network are the Asia-Pacific community, led by China and the US, and Europe, centered on Germany. The regionalization and clustering characteristics of the network structure have become more pronounced since 2018 because of trade friction between the US and China. The formation and evolution of the ICT trade dependence network have been influenced not only by traditional gravity model factors but also by endogenous network structures, overlooking endogenous mechanisms that can lead to biased empirical results.

The dark side of financial digitalization: Corporate digital finance and speculative financial investments

Zhehao Huang Guangzhou University Peoples Rep of China Co-Author(s):

Abstract: In this paper, we devote to exploring the effects of digital finance on the corporate financialization. We first built a two-stage portfolio model to analyze how the digital finance development impacts the financial investment of corporations with different financialization motives, which is followed by empirical examinations corresponding to the theoretical analysis, where the corporate digital finance index was constructed by text analysis in a keyword network describing the logical relationship among 138 keywords standing for corporate digital transformation and corporate finance. Two core results are achieved. First, digital finance development pushes the corporate financialization. Serial robustness tests and endogeneity test were also carried out to support this result. Second, digital finance development has heterogeneous effects on corporate financialization. It drives speculative financialization positively but has no significant impact on risk-hedging financialization, while speculative financial investments increase corporate uncertainty and affect core business development. Finally, some additional heterogeneity analysis with respect to managers` financial capability, ownership nature and industry characteristics are carried out to complete our paper. Some targeted policy recommendations are proposed in the conclusion.

Volatility spillover between carbon market and related markets in time-frequency domain based on BEKK-GARCH and complex network analysis

Tinghui Li Guangzhou University Peoples Rep of China Co-Author(s):

Abstract: With rising public attention to climate issues and sustainable development, the connection be-tween carbon market and its related markets has become closer. Based on BEKK-GARCH method and complex network theory, this study tries to explore the volatility spillover effects and net-work topology among China`s carbon market, non-renewable energy market, renewable energy market, high-tech market and climate policy uncertainty index from the perspective of time do-main and frequency domain. The findings include that, firstly, there are significant asymmetric volatility spillover effects among the above-mentioned markets. Secondly, the volatility spillover effects between the markets are time-varying, especially during crisis periods in which the volatility spillover effects are significantly higher than that during the stable period. Thirdly, with the increase of time, the closer the connection between the markets. In the long run, the closeness of the connection is directly proportional to the spillover effect. Finally, climate policy uncertainty is the main source of risk. For the full sample period, from the perspective of frequency domain, non-renewable energy market and carbon market are the main risk recipients. Based on the above research findings, the authors suggest stepping up efforts on market monitoring and intervention during crisis periods, strengthening cross-market risk management, paying attention to climate policy uncertainty, and attaching importance to time and frequency varying characteristics of volatility spillovers, so as to provide useful information for policy makers and investors.

Modelling the data generating mechanism of Chinese commodity market by identifying hidden information fow regimes

Zhenghui Li Guangzhou University Peoples Rep of China Co-Author(s):

Abstract: The commodity market contains abundant information from macro economy. Measuring the macroeconomic information fows in the fuctuations of commodity priceindexes are conducive to monitoring the market and forecasting its growing trend.In this paper, a high-order hidden Markov chain (HOHMC)is used to measurehidden macroeconomic information fows in the (hinese commodity futures index.where the time frame is from June 25, 2004 to January 31, 2023. Some interest-ing empirical results are achieved for investors and regulators as follows. First, themacroeconomic hidden information fows can be categorized into high volatility regime and low volatility regime. During a high volatility regime, the Chinese Commodity Index exhibits increased volatility and frequent jumps in behavior. Inthe panic phase of this regime, the market is relatively efcient; during its bub.ble phase, it becomes relatively inefcient. Second, different commodity marketshave heterogeneous data generation mechanisms, with industrial, metal, and energymarkets being more sensitive to exogenous shocks compared to the less sensitiveagricultural market. Third, the macroeconomic hidden information fows that driveChinese commodity market data generation mechanism serve as an explicit leadingindicator for macroeconomic variables.

Does Corporate Greenwashing Affect Investors` Decisions?

Gaoke Liao Guangzhou University Peoples Rep of China Co-Author(s):

Abstract: Greenwashing creates a misleadingly positive image for corporations while leading to the misguidance of investors` behaviors. Based on the sample of Chinese A-share listed companies for the period 2008-2021, this paper investigates the impact of corporate greenwashing on investors` decisions. The empirical results indicate that (i) greenwashing significantly improves short-term returns on corporate stocks, but reduces long-term returns; (ii) in the short term, investors are influenced by expressive manipulation rather than selective disclosure, but this impact is not sustainable, and both have an impact in the long term. These findings provide a new risk reminder for investors` behaviors and have practical significance for government to promote the green development of corporations.

Cryptocurrencies as Safe Havens for Geopolitical risk? A Quantile Analysis Approach

Bin Mo Guangzhou University Peoples Rep of China Co-Author(s):

Abstract: In recent years, global geopolitical risk (GPR) events have had profound effects on economies and financial markets. This paper systematically ana- lyzes the hedging characteristics of traditional safe-haven assets (gold, USD, oil) compared to cryptocurrencies (Bitcoin, Ethereum, Litecoin) under dif- ferent levels of GPR. Utilizing quantile regression and QQ plots, the study explores the dynamic nonlinear impacts of GPR on various assets and em- pirically analyzes the influence of key geopolitical events on asset markets. The findings reveal that cryptocurrencies have relatively weaker hedging functions in the context of geopolitical risks, while traditional safe-haven assets like gold, USD, and oil demonstrate more stable hedging character- istics during periods of uncertainty. Notably, the correlation between GPR and asset prices is more pronounced under extreme market conditions. This research offers new asset allocation recommendations for investors and en- hances the understanding of the hedging properties of cryptocurrencies.

Financial Support and Pathway Trade-offs for Enterprise Digital Transformation

Cunyi Yang Lingnan College, Sun Yat-sen University Peoples Rep of China Co-Author(s):    Zeng Yan, Yang Cunyi

Abstract: The construction of a Digital China prioritizes the digital transformation of enterprises as a vital component. However, ample evidence suggests that this transformation faces critical barriers due to liquidity constraints. In this new era and on this fresh journey, the financial sector must embrace its historic responsibility, actively advancing the narrative of financial technology to facilitate enterprise digital transformation. This paper investigates the financial support and strategic considerations for enterprise digital transformation, with empirical results indicating that digital transformation is a crucial path for low-income enterprises seeking regeneration. However, this often necessitates financial backing. To assess the importance of financial support in enterprise digital transformation, we have constructed and estimated a structural model of enterprise digital transformation financing and decision-making. Structural estimation and counterfactual simulation results demonstrate that financial support is pivotal for enterprise digital transformation. Without it, the number of enterprises undergoing digital transformation would decrease by 36%, the price of digital transformation services would surge by 60%, and only high-income enterprises would be able to undertake digital transformation, leading to a decline in overall welfare and an increase in monopoly. The conclusions of this paper offer new perspectives on the role of finance in enterprise digital transformation, providing policymakers and enterprise managers with empirical evidence and theoretical guidance to promote more efficient digital transformation of enterprises, thereby achieving broader economic benefits.