Abstract:In order to stimulate the technological innovation of new energy enterprises, the Chinese government has issued a large number of relevant subsidy policies. However, the complex influence mechanism of government subsidies on technological innovation of new energy enterprises is still unclear, and the incentive effect has not yet reached a consensus. Therefore, this paper focuses on the data of China’s A-share listed new energy manufacturing enterprises from 2011 to 2020, and constructs a two-way fixed effect model of panel data to investigate the influence of government subsidies on technological innovation of new energy enterprises. The findings are as follows: 1) Government subsidies generally promote the technological innovation of new energy enterprises. In the sample interval, for every 1〖WTXT〗%〖WTBZ〗 increase in government subsidies, the patent applications of new energy enterprises increase by 0.82〖WTXT〗%〖WTBZ〗; 2) R&D investment and innovative human capital play a partial intermediary role in the process of government subsidies to promote technological innovation of new energy enterprises, while enterprise financialization plays a moderating effect which enhances the promoting role of government R&D subsidies to the technological innovation of new energy enterprises; 3) There are significant differences between R&D and non-R&D subsidies on technological innovation of new energy enterprises: for every 1〖WTXT〗%〖WTBZ〗 increase in R&D subsidies in the short term, the patent applications of new energy enterprises increase by 1.064〖WTXT〗%〖WTBZ〗, and the relationship between R&D subsidies and technological innovation of new energy enterprises shows an inverted U-shape in the long term; for every 1〖WTXT〗%〖WTBZ〗 increase in non-R&D subsidies, the patent applications of new energy enterprises increase by 0.61〖WTXT〗%〖WTBZ〗; 4) The promoting effect of government subsidies on technological innovation of new energy enterprises is significant in the areas with high intellectual property development level, but not in the areas with low intellectual property development level. These research results will provide powerful decision-making reference for the government to formulate new energy industry policies and new energy enterprises to make good use of government subsidies.

Abstract:Digital transformation has fundamentally reshaped the landscape of traditional industries, necessitating institutional entrepreneurship to overcome existing constraints. Despite its significance, the process of institutional entrepreneurship during digital transformation remains underexplored in current literature. This study leverages institutional entrepreneurship theory and enterprise survey data to investigate how digital leadership impacts digital transformation performance. Our findings reveal that digital leadership significantly enhances digital transformation outcomes by fostering institutional change activities. Furthermore, the capability for information search is found to positively moderate the relationship between digital leadership and institutional change activities. By integrating institutional entrepreneurship theory into the realm of digital transformation, this research broadens the theoretical framework and offers fresh insights. It also advances the application and development of institutional entrepreneurship theory within the context of digital transformation, providing valuable guidance for enterprises seeking to improve their digital transformation performance.

Abstract:Will the digital transformation of firms be affected by policy uncertainty? This paper investigates the mechanisms by which policy uncertainty affects corporate digital transformation behavior through a theoretical model. Based on this, this paper uses the data of China’s A-share listed companies to conduct an empirical study on the relationship between policy uncertainty measured by local government official changes and corporate digital transformation. The results show that the increase in policy uncertainty can significantly impede the process of digital transformation. In addition, the relationship between policy uncertainty and digital transformation is moderated by the professional background of officials and corporate financial constraints. This paper provides evidence for better driving the digital transformation of Chinese firms.

Abstract:Epidemic diffusion has made great damages to human life and development. Controlling epidemic diffusion is an important issue for all social societies. During the fight against public health emergencies, government have made arduous efforts to control virus diffusion to ensure people’s safety, such as strengthening media propaganda, controlling population travels, and so on. In this paper, sets of heterogeneous data about disease diffusion are collected and combined. After a careful analysis of the data, the disease diffusion process is divided into 3 stages. Media propaganda and population travels are quantified with parameters, and the classical states of disease transition model are extended from 3 to 5. A two-associative-strategy disease diffusion control model, based media propaganda and population travels, is presented. Numerical simulations and empirical data fittings demonstrate that the two-strategy control model is efficient and feasible in deceasing epidemic diffusion.

Abstract:A supply chain in which a manufacturer sells a product through an online platform is considered to investigate the impacts of the manufacturer’s pricing strategy and the platform’s product information provision strategy on customer’s purchasing and product return behaviors due to fit uncertainty. How the platform should choose product information provision strategy and information accuracy is examined, and how the manufacturer should choose its pricing strategy and selling price is explored. It is found that information provision can reduce product return rate if both price and information accuracy are sufficiently high, at the expense of a possible decrease in demand. The manufacturer and the platform have three strategy profiles: A low price set by the manufacturer to mitigate returns that induces the platform to choose a low information accuracy to increase demand, a high price set by the manufacturer that induces the platform to disclose full product information to mitigate the customer’s fit uncertainty, and a moderate price set by the manufacturer to control returns rate that induces the platform to decrease information accuracy and increase demand. The conditions under which each of the three strategy profiles can dominate are identified. The impacts of the customer’s unit misfit cost and the salvage value of a returned product on the manufacturer’s equilibrium price and the platform’s information accuracy are also discussed.

Abstract:The trend toward decentralization in supply chains has amplified the importance of behavioral factors. This paper investigates a supply chain system involving multiple suppliers and retailers, specifically focusing on integrating retailers’potential regret behavior into the supply chain decision-making model. The differences between regret-driven behavioral decision-making and artificial intelligence-assisted automated decision-making are compared, and the impact of retailers’regret is analyzed on their profits, upstream supplier profits, and the overall profitability of the supply chain. The results show that regret prompts deviations in retailers’order quantities relative to those determined through automated inventory decisions, which leads to an increase in the wholesale price that provides suppliers with competitive pricing advantages. These effects significantly influence the profits of supply chain members and the overall system and may be amplified as the supply chain expands. Therefore, managers should recognize and anticipate the implications of retailers’regret behavior to develop more effective decision-making strategies.

Abstract:This paper addresses the common joint replenishment and delivery (JRD) problem that occurs in a multi-item inventory system. An ε-optimal algorithm and a dual lower bound are proposed via deeply analyzing the mathematical properties of the model. The randomized numerical experiments show that the proposed ε-optimal algorithm outperforms the existing meta heuristics both in terms of both accuracy and efficiency, and the largest increase in accuracy can reach 31〖WTXT〗%〖WTBZ〗. The dual lower bound is very tight, with the gap averaging below 0.84〖WTXT〗%〖WTBZ〗. Moreover, the proposed ε-optimal algorithm is also very fast, and a 100-item instance can be solved in twenty-three seconds. In addition, the time complexity of the ε-optimal algorithm is analyzed. The ε-optimal algorithm can achieve O(n) polynomial-time complexity under real-world situations, which enriches the algorithmic design theory of the JRD research. Finally, the effect of coordinated delivery is analyzed. Experiments show that coordinated delivery can achieve cost reduction when the inventory management level of the central warehouse is higher than that of the retailers.

Abstract:With the trend toward personalization and diversity in customer demand, enterprises are faced with the challenge of how to respond to customer demand quickly and provide personalized product service solutions. To locate personalized product service solutions accurately, a personalized product-service configuration method based on trust degree and forgetting curve is proposed. First, the method introduces the trust degree and time weight into the calculation of preference similarity and score similarity. Then, it integrates user similarity and scheme similarity to reduce the impacts of data sparsity and cold starts on rating prediction. Finally, by comparing it with existing methods, the proposed method can improve the accuracy of product service scheme configuration.

Abstract:Using a unique data set obtained from a Chinese fintech lending company, this paper categorizes the information used for loan default prediction into four types: Self-reported information, central bank credit information, e-commerce credit score, and online shopping behaviors. The roles played by the two types of digital footprints, e-commerce credit score and online shopping behaviors, in predicting loan default are investigated and the underlying mechanisms through which digital footprints improve 〖JP2〗credit availability are explored. Our results show that the digital footprints can help lenders to improve the accuracy in predicting a borrower’s〖JP〗 default likelihood by about 50〖WTXT〗%〖WTBZ〗. The prediction power of the e-commerce credit score is significantly the highest among the four types of information. Nevertheless, online shopping behaviors can not only provide additionally useful information beyond the e-commerce credit score, but also perform as well as the self-reported information disclosed by borrowers. Furthermore, the preliminary evidence on the mechanisms through which digital footprints improve credit availability shows that e-commerce credit score and online shopping behaviors can be used to establish credit scores for “unbanked customers” and accurately evaluate the credit worthiness of borrowers whose credit quality is always wrongly underestimated.

Abstract:In policy communication practice, exploring the impact of ambiguous policy communication on investor expectations and market stability is with significant theoretical and practical relevance. Using a learning-to-forecast experiments, this paper distinguishes between two dimensions of policy communication information: the policy trigger dimension and the adjustment scale dimension and examines the impacts of ambiguous communication from either dimension. The experimental findings reveal that reducing ambiguity in the policy trigger dimension significantly enhances asset price stability, while reducing ambiguity in the adjustment magnitude dimension either fails to enhance or may even destabilize asset prices. This counterintuitive result suggests that the effects of ambiguous communication are dimension-specific; Thus, minimizing ambiguity in the policy trigger dimension while maintaining it in the adjustment magnitude dimension can effectively bolster the stabilizing impact of policy communication. Furthermore, we examine the mechanisms underlying these dynamics by analyzing market participants’expectation formation strategies. Results indicate that when ambiguity arises from the adjustment magnitude dimension, participants are more inclined to adopt adaptive expectation strategies—leading to greater price stability—while being less likely to employ trend-following strategy or learning, anchoring and adjusting strategy, which are associated with greater price instability. Our analysis contributes to the understanding of heterogeneous market responses to varying dimensions of policy communication ambiguity.

Abstract:Building a high-quality capital market with survival of the fittest is the key for finance to serve the real economy and the supply-side reform. This study extracts fundamental information from a high-dimensional big dataset of over 90 accounting indicators by means of several approaches in machine learning and econometrics, evaluates the quality of listed firms with an aggregate fundamental index, and investigates the correlation between the index and the performance of stock price. Results show that the quality index can significantly and positively predict stock returns. Among them, the partial-least-squares-based measurement has the strongest predicting power, with an annualized return of approximately 38〖WTXT〗%〖WTBZ〗, which cannot be explained away by CAPM, three-factor or five factor models. Furthermore, this paper explores the impact mechanism from the perspective of behavioral finance and macroeconomic cycle, and finds that market sentiment, limits to arbitrage, firm policy, and macro business cycle can help dissect the quality premium among listed firms. Our study suggests that the pricing efficiency in Chinese stock market has steadily improved, and it has stepped into the stage of survival of the fittest and value investment.

Abstract:This paper proposes the concept of bull market risk, i.e., the time variation in the probability of a future bull market state, and explores whether it is priced. Since a bull spread option portfolio reflects investors’ex-ante expectations about future bull market risk-neutral probability, its short-term return is used to measure bull market risk. This measurement, which belongs to the implied information method, aligns more closedly with the ex-ante attributes of risk and can avoid the Peso problem by using historical data. Based on China’s stock and option market data, the paper finds that bull market risk cannot be explained by traditional factor models. What’s more, an individual stock’s exposure to bull market risk, which is defined as bull beta, has a significantly robustly positive relation with its future return, indicating that bull market risk is priced in the cross-section.