Is it U.S. GAAP or IFRS? Understanding how R&D costs affect ratio analysis

While financial reporting standards under U.S. GAAP and IFRS are fundamentally similar, differences do exist that may affect our analysis of company financial statements. This is particularly true when comparing a U.S. company following U.S. GAAP to a firm that uses IFRS. To illustrate, we compare research and development (R&D) accounting methods under both sets of standards and illustrate how they affect the analysis of financial results of firms in a specific industry—automotive manufacturers. Our results provide insight into settings in which differences in R&D accounting may have the greatest impact on financial analysis.     

Commemorating the 50‐Year Anniversary of Ball and Brown (1968): The Evolution of Capital Market Research over the Past 50 Years

We commemorate the 50th anniversary of Ball and Brown [1968] by chronicling its impact on capital market research in accounting. We trace the evolution of various research paths that post–Ball and Brown [1968] researchers took as they sought to build on the foundation laid by Ball and Brown [1968] to create a body of research on the usefulness, timeliness, and other properties of accounting numbers. We discuss how those paths often link back to the groundwork laid and questions originally posed in Ball and Brown [1968].     

Twenty-Five Years after the Fall of the Berlin Wall: An Empirical Revisit of West German Consumers' Attitudes Toward Products and Brands from Former East Germany

More than 25 years after the German reunification, data show that products/brands from the eastern regions of Germany (“Neue Länder”) still do not have significant shares in the country's western part (“Alte Länder”). To analyze potential reasons for this phenomenon, our current study replicates a previous study that investigated selected attitudes of Alte Länder consumers toward products/brands from the Neue Länder. It is shown that factors such as consumer ethnocentrism, product judgment, willingness to buy, and economic animosity continue to influence consumer behavior and as such our study offers potential explanation for the failure of Neue Länder products/brands in the western regions of Germany.     

基于经济新常态的长春市水资源开发利用潜力评价

以长春市为研究对象,基于未来经济处于新常态的发展态势,构建包含水资源、社会、经济、生态环境4个子系统的评价指标体系,并确定各指标的评价等级标准;基于模糊数学理论以及层次分析法构建包括目标层、准则层、指标层的3层模糊综合评价模型,以2015年为现状水平年,应用模型对长春市水资源开发利用潜力进行综合评价。结果表明:长春市水资源开发利用程度处于中等偏上水平,各辖区水资源开发利用规模不平衡,可通过产业结构调整提高全市用水效率,为经济持续增长提供保障。     

China and international market integration: Evidence from the law of one price in the Middle East and Africa

We model market integration in the Middle East and Africa by analyzing price dispersion and testing the law of one price (LOP) on highly-comparable actual local retail prices of 135 goods and services across 23 countries in the region over the period of 1990–2016. Second-generation panel estimators are applied to four price benchmarks: Regional average, South Africa, China, and US prices. Cross-regional price dispersion diminishes considerably over time up to 2008, particularly for non-tradeables around China price. The test of LOP indicates the percentage of convergent prices is highest in China price benchmark, followed by US, South Africa, and regional average benchmarks. Direct estimation of the convergence speed confirms this order. Overall, the results show evidence of increasing market integration in Middle East and Africa but it appears to be driven by global forces and, especially, the rise of China as a new economic power. The results show that some emerging market economies, such as China, can step up and promote integration while traditional economic powerhouses, such as the USA and UK, disengage from international economic relations.     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

昆明市工程招投标监管的探索与发展研究

针对工程招标投标领域存在的突出问题,昆明市近几年在工程招标投标监管方面进行了许多新的探索.从昆明市招标投标监管机构设置、标前监管、标中监管、标后监管4个角度对昆明市工程招标投标监管现状进行分析,并对工程招标投标监管领域的一些新趋势进行探讨,提出强化招标投标监管的建议.新技术为招标投标监管提供了新工具,需要进一步探索新技术在招标投标监管领域的应用.     

中国零售业自营与联营问题的流通经济学分析

零售业自营与联营之争由来已久，在当前供给侧结构性改革、完善促进消费体制机制以及零售业寻求创新发展的背景下，有必要对这一问题展开进一步的理论探讨。文章结合马克思流通经济理论指出，自营和联营的本质区别在于是商业资本还是产业资本承担商品流通职能，自营和联营并不是非此即彼的互斥关系，但如果零售业完全倚重其中一种模式并在全社会推向极端，则需要反思商业资本和产业资本是否各得其所和高效分工。在中国流通体制变革中，虽然联营模式在特定历史时期帮助众多零售企业渡过生存危机并推进内资零售快速扩张，但发展至今，已呈现零售业普遍联营的情况，使零售业整体面临着制约创新发展的新问题。数字经济为零售业回归自营提供了有利契机，零售企业应充分利用新技术摆脱联营制的路径依赖，依托数字化开展深度自营并全面提升流通效率。     

碳排放交易制度的节能减排路径：效率提升还是结构转型？——来自中国省级数据的准自然实验

以中国2013年以来实行的碳排放交易试点政策作为准自然实验事件，基于2000-2017年中国30个省市的面板数据，运用双重差分法实证检验碳排放交易制度的节能减排效应及影响机制。研究发现，碳排放交易制度有利于实现中国经济“节能”与“减排”的双重目标，并通过了一系列稳健性检验；作用机制检验表明，能源效率提升和能源结构转型都是碳排放交易制度实现节能减排目标的重要路径；能源效率提升在碳排放交易制度实现“节能”和“减排”中分别发挥了10.19%和5.93%的作用，而能源结构转型分别发挥了48.87%和52.95%的作用。这意味着中国碳排放交易制度实现节能减排的主要动力来自能源结构转型，而非能源效率提升。能源结构转型涉及问题更加宏观和深层，这为中国加快推进节能减排进程，特别是完成2030年碳达峰和2060年碳中和的国际承诺提供政策启示。     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.