Mitigating panic buying behavior in the epidemic: An evolutionary game perspective

The panic buying behavior under public health emergencies will lead to many adverse consequences, such as material waste, price fluctuation and uneven distribution of epidemic prevention materials, which will pose a threat to the social stability and economic development. In this paper, we construct a tripartite game model to explore the strategic choices of the public, merchants and the government in order to effectively respond to the panic buying behavior in the epidemic. The results demonstrate that: (1) Eight evolutionary stable strategies emerge in the panic buying events. The worst scenario can be improved by adjusting some relevant parameters. (2) The probability of the public choosing the strategy of “not involving in panic buying” depends on the potential benefits and losses of snapping up, rather than the extent of price rising. (3) The probability of merchants choosing the strategy of “not bid up price” depends on the intangible benefits. (4) The probability of the government choosing the strategy of “active supervision” depends on the supervision costs and government credibility, rather than the amount of fines. In addition, strategic suggestions to mitigate panic buying behavior are put forward from the perspective of each stakeholder.     

金融支持国防知识产权成果转化问题及策略研究

国防知识产权成果转化应用是国防科技创新体系建设落地见效的关键环节，引入现代金融体系能够有效促进国防知识产权成果转化。目前我国国防知识产权转化工作在组织管理体制、工作运行体系、法规政策制度和信息化建设等方面初显成效，但仍面临整体转化形势不容乐观、金融支持体系尚未成熟等问题，根本原因在于国防知识产权与发明人割裂导致一般金融产品难以通过审核，使融资渠道狭窄；其保密性进一步加大了与金融机构间的信息不对称，使得金融资源配置效率低下；国防知识产权专用性较强，相应抬高了融资成本。为有效解决上述问题，提出3条改进措施，以实现对国防知识产权转化的金融支持，即①从顶层设计上优化解密脱密流程，明确发明人与知识产权的权属关系，细化财税优惠等支持措施；②加强政府财政税收政策支持，引导创新利用多元社会资本进入支持转化领域；③建立各主体参与的金融服务协调保障机制、监测评估体系，启动国防知识产权转化的金融支持试点工作，构建符合国情的国防知识产权转化金融支持体系。     

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.     

New development: A new COFOG for South Korea

The South Korean government's Classification of the Functions of Government (COFOG) is grounded in the System of National Accounts (SNA), rather than Government Finance Statistics (GFS). This article explains why a GFS-based classification might be beneficial to South Korea for the purposes of fiscal management and the comparison of government expenditures with other countries.     

基于三大品牌价值排行榜的中国品牌横向分析

论文以Interbrand、Brandz、Brand Finance三大品牌价值排行榜的数据为基础,通过与美英日德四个发达国家的对比,从品牌整体实力、品牌国际影响力、品牌分布格局三个方面分析我国品牌的发展现状。发现我国品牌存在发展不协调、国际影响力低、品牌表现不稳定的问题,并对我国品牌发展格局进行展望。     

Destruction and settlement norms as determinants of conflict: An evolutionary perspective

This paper studies conflict in a simple bargaining framework using an evolutionary game-theoretic approach. Our findings suggest that a player does not always regard the winning probability as an acceptable settlement rule. He accepts a division according to winning probability when the destruction caused to him in conflict is more than a threshold, which in turn depends on cost of conflict to the opponent and the size of the population. Further, our analysis shows that the norms with positive weight to disagreement payoff are effective against conflict, and incentivize peace. Contrary to the findings of extant studies carried out in finite population evolutionary game setting, the settlement possibility set is identical under both ESS and Nash equilibrium in our model with settlement norms.     

Economic forecasting with evolved confidence indicators

We present a machine-learning method for sentiment indicators construction that allows an automated variable selection procedure. By means of genetic programming, we generate country-specific business and consumer confidence indicators for thirteen European economies. The algorithm finds non-linear combinations of qualitative survey expectations that yield estimates of the expected rate of economic growth. Firms’ production expectations and consumers’ expectations to spend on home improvements are the most frequently selected variables – both lagged and contemporaneous. To assess the performance of the proposed approach, we have designed an out-of-sample iterative predictive experiment. We found that forecasts generated with the evolved indicators outperform those obtained with time series models. These results show the potential of the methodology as a predictive tool. Furthermore, the proposed indicators are easy to implement and help to monitor the evolution of the economy, both from demand and supply sides.     

Drivers of research impact: evidence from the top three finance journals

We study the characteristics of all published papers in the top three finance journals (JF, JFE and RFS), and how these paper characteristics affect the number of citations in Google Scholar and the Web of Science database. First, we find the characteristics in the universalist perspective remain constant while the characteristics in the constructivist and presentation perspectives increase over time. Second, some characteristics are significantly different between the high-impact and the low-impact papers. Third, paper quality, research method, journal placement and paper age are the most important drivers. Last, different drivers play different roles in different journals.     

Rethinking the Career Anchors Inventory framework with insights from a finance transformation field study

It is claimed that the re-engineering and automation of routine work should free accountants to adopt broader, so-called ‘value-adding’ roles as partners to business decision-makers. The ‘new world of work’, involving new process-based methods, single instance ERP systems and the wholesale relocation of professional work to lower cost areas, brings opportunities for accountants in systems design and adding value to the business. For many others, the new reality may be a more competitive and less secure working environment, with a mid-level career bottleneck reflecting a polarisation between the upper and lower layers of the finance function.Based upon Schein's theory of career anchors and using exploratory sequential mixed methods, we consider the impact on the career perceptions of individual accountants as accounting expertise, traditionally embodied in people, is codified and embedded within end-to-end business processes in the context of a finance shared service centre. Key findings include new perceptions of careers with greater emphasis on the acquisition of general managerial competence and leadership skills, with the importance of cultivating a sense of ‘global connectedness' to maintain individual employability. Suggestions are made to update Schein's Career Orientation Inventory to better reflect the reality of accounting in the ‘new world of work'.     

新时期农业科技与金融融合的示范园模式与“公司+农户”模式探索

农民如何依托土地脱贫增收成为我国农业发展的关键问题。农业科技与金融的融合是家庭联产承包责任制下提升农业产出效率的有效手段。通过创新农业经营主体，建立政府引导、市场主导的“示范园模式”与“公司+农户”模式加强农业科技与金融融合；构建完善的试验-示范技术扩散体系，帮助农民进行技术模仿，提升农业技术能力；结合金融扶持，帮助农民提升对接市场技术的能力，提高土地产出效率是促进农民脱贫增收的有效途径。