银行业联合授信制度制约企业的非效率投资了吗*——基于沪深A股公司的分析

中国银保监会出台联合授信管理制度旨在抑制企业过度融资行为，提高资金使用效率。为检验该制度是否达到预期效果，采用2014—2018年沪深A股上市公司样本，运用非平衡面板数据回归实证考察联合授信机制对企业非效率投资的影响，发现联合授信对企业的非效率投资（包含投资不足和投资过度）呈现显著抑制效应，其中联合授信对投资过度的抑制效应最大。进一步研究发现，相较于国有、大规模、低负债、高盈利等异质属性的企业，联合授信对非国有、小规模、高负债、低盈利等属性企业的非效率投资（尤其是投资过度）抑制效应更为显著。     

Stock market reactions to monetary policy surprises under uncertainty

This article investigates how uncertainty impacts the effect of monetary policy surprises on stock returns. Using high-frequency US data, we demonstrate that stock markets respond more aggressively to monetary policy surprises during periods of high uncertainty. We also show that uncertainty asymmetrically influences the transmission of positive and negative monetary policy surprises to stock market prices. The amplifying effect of uncertainty is found to be stronger for expansionary shocks than for contractionary shocks. Our robustness analysis confirms that financial uncertainty has a significant role in shaping the influence of monetary policy on the stock market.     

Development built on crony capitalism? The case of Dangote Cement

This paper critiques the emergence of Dangote Cement as the dominant player in cement manufacturing in Nigeria. It argues that the changed economic environment General Obasanjo met when he became president of Nigeria for a second time in 1999 made it difficult for him to continue the nationalisation policies and the expansion of government involvement in several spheres of economic activity that he helped to promote in the 1970s. The realisation that this strategy, which created numerous crony capitalists, was unsustainable resulted in Obasanjo allying with Dangote and promulgating the Backward Integration Programme (BIP) for the local cement industry. This made it possible for Dangote to risk aggressive investment in the capital-intensive cement production business. This strategy achieved public good by rapidly making Nigeria, an oil rent- and import-dependent economy with enormous limestone reserves, self-sufficient in cement production.     

“引洮工程”引发的受水区污染治理研究

[目的]为了解决甘肃中部安定区、陇西县、渭源县、临洮县、榆中县和会宁县的人畜饮水、工业用水、生态用水及农业灌溉用水问题,引洮工程通过九甸峡水利枢纽抬高水位将黄河上游洮河支流水资源引入受水区。在受水区,在调水增加本区供水总量的同时,相应增加了增量水资源使用后带来的废污水,客观上对污染治理工作水平提出了更高要求,轻视排污问题将加剧受水区乃至下游的水系污染程度,影响受水区经济社会的可持续发展。[方法]文章采用文献研究法从理论上探讨了环境容量、排污权及其关系。采用定额计量法计算了调水带来的COD和氨氮增量。[结果]调水使得受水区COD和氨氮每年分别增加6 914. 30t和1 440. 48t。就COD而言,受水区平均排放量占该区环境容量的51. 12%,似有较多的容量结余,但区内排放极不平衡,榆中和会宁已经超排。就氨氮而言,受水区平均排放量将占该区环境容量的151. 55%,总体超排很严重。区内除临洮以外,其余5县区均超排,其中,榆中和会宁超排1倍以上。[结论]排污量是假定增量污水符合二级排放标准的前提下计算的,如果处理率低、排放达标率低,则超排现象会更为严重。对此,要从宏观上进行综合治理,诸如完善政府、社会与市场共同治污机制、排污权配置的过渡性制度安排、污水处理企业改革及引导受水区进行产业结构调整等。     

Testing vertical price transmission for Vietnam's Robusta coffee

Vietnam has undergone market reforms over the last three decades; and as a consequence, the coffee sector has become increasingly market‐driven. The success of the government's liberalisation policies in terms of market efficiency is investigated by examining the transmission of both positive and negative price changes for Robusta coffee between export and farmgate prices. We used a threshold vector error correction model and high‐frequency daily data. The primary result here is that of a symmetric price transmission between export and farm‐level prices. This result holds when tested with weekly price data, derived from the daily data. Farmgate prices respond faster to decreases than increases in export prices when the long‐run deviation exceeds a certain threshold. These price changes are transmitted within several days. This research also confirms the importance of transaction costs, and other price frictions mostly ignored in prior analyses for coffee.     

Best classification algorithms in peer-to-peer lending

A proper credit scoring technique is vital to the long-term success of all kinds of financial institutions, including peer-to-peer (P2P) lending platforms. The main contribution of our paper is the robust ranking of 10 different classification techniques based on a real-world P2P lending data set. Our data set comes from the Lending Club covering the 2009–2013 period, which contains 212,252 records and 23 different variables. Unlike other researchers, we use a data sample which contains the final loan resolution for all loans. We built our research using a 5-fold cross-validation method and 6 different classification performance measurements. Our results show that logistic regression, artificial neural networks, and linear discriminant analysis are the three best algorithms based on the Lending Club data. Conversely, we identify k-nearest neighbors and classification and regression tree as the two worst classification methods.     

Enhanced Special Drawing Rights: How China Could Contribute to a Reformed International Monetary Architecture

Since the end of the Bretton Woods era, the world has operated on a de facto system of free‐floating exchange rates, with the US dollar as the dominant international currency. The system, characterized by large pro‐cyclical capital flows and chronic imbalances, is inherently unstable, and has contributed to repeated crises, recessions and geopolitical tensions. One potentially “least‐difficult” line of reform would be to allow the evolution of a multi‐currency system, underpinned by an expanded role for Special Drawing Rights (SDRs). Attempts to promote wider use of the SDR have foundered on the liquidity premium. However, for Chinese corporations and institutions, at present restricted in their capital account activities, the SDR liquidity premium would appear less daunting. The Chinese authorities could provide policy encouragement for the use of SDRs by their institutions. This initiative, supported by China's Special Administrative Region Hong Kong, would kick‐start an international SDR ecosystem, and encourage even broader use of SDRs, to the benefit of international monetary stability.     

Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

Institutional Economics as Theory of Policy Change: Impact of Past Policy Failures on Present Policy

Social scientists have developed several theories for understanding or evaluating policy change over time. Since all costs or benefits are not internalized owing to positive transaction costs, policymaking is always implemented under cost underestimation conditions and, therefore, is imperfect. I call this trait policy failure in this article. Furthermore, I show that a new framework combining the social costs approach and the legal/economic approach in institutional economics is suitable and can be applied to evaluating how past policy failures affect present policy, providing as an example the Fukushima nuclear disaster in Japan.     

货币政策、企业家信心与上市公司投资效率*

企业、企业家等微观主体对货币政策的反应，在较大程度上影响货币政策的效果。货币政策可通过对企业家信心的影响，进而对公司投资效率产生作用。基于中国2007—2016年40个季度A股上市公司样本的实证研究表明：企业家信心在货币政策传导过程中有不可忽视的作用。企业家信心增强或者货币政策宽松时会加剧过度投资，减轻投资不足；企业家信心减弱或者货币政策紧缩时可以有效抑制过度投资，但会加剧投资不足。其中，国有企业对企业家信心和货币政策松紧的敏感性更强。进一步，货币政策对企业投资效率的影响，也会通过货币政策和企业家信心的交互作用而发挥作用。这种作用在国有企业和非国有企业之间无显著差异。