Financial openness and Chinese regional growth imbalance: New insight from spatial spillovers

This paper uses spatial panel methods and Chinese provincial data from 2003 to 2017 to study the spatial spillovers of financial openness on economic growth. The results show, first, a positive direct effect and an overall negative spatial spillover of financial openness on provincial growth. Second, there are two spatial spillover channels: a positive growth externality and a harmful resource competition among provinces. Third, we estimate the state dependence and dynamics of spatial spillover, and find that the negative spatial spillover is smaller in provinces with high levels of financial openness and in the long term; thus, the negative spatial spillover declined over time. These results are robust to the choice of SDM and GNS spatial econometrics methods and under different spatial weight matrices.     

Financial development,portfolio investments and the real economy in Africa

According to the conservative view, capital flows enhance economic growth. Focussing on Africa’s real economy, this study investigates the linkage between portfolio investments and real sector growth, and whether financial sector development strengthens this association. The study covers 30 countries over the period 1990–2017. We adopt the Lewbel instrumental variable general method of moments (IV-GMM) two-step robust estimator, which relies on heteroscedasticity for identification, while dealing with instrument insufficiency, unavailability, endogeneity and omitted variable bias. We found that portfolio equity has no growth impact on Africa’s real sector. Debt flows deter the growth of the overall real sector as well as the manufacturing and industrial sectors, but have no impact on agriculture and service growth. We found that financial development does strengthen the positive association between capital flows and economic growth, but this is dependent on the type of sector and portfolio investment, as well as on the degree of financial development. We control for known determinants of economic growth.     

建筑工程高支模板支架搭设施工工艺要点浅析

对于建筑工程项目来讲,高支模施工技术是应用较为频繁的技术形式,因此必须要结合项目实际情况,加强高支模板支架搭设工作。文章就结合具体工程项目分析建筑工程高支模板支架搭设施工工艺要点。     

委托理财导致上市公司脱实向虚吗?——基于企业创新的视角

依据2008-2017年上市公司委托理财与专利数据,本文实证研究了委托理财对企业创新的影响。研究发现随着委托理财规模与理财收益对公司业绩贡献的增加,上市公司总体上创新数量变化不明显,但创新质量却显著下降。对于不同的委托理财而言,上市公司购买银行与非银行委托理财对创新质量的影响均为负面,但对创新数量的影响却正好相反。两种类型委托理财对创新数量的影响可能相互抵消,从而导致总体上委托理财对创新数量的影响不显著。进一步研究发现,购买更多的委托理财导致上市公司更加倾向于进行风险低的开发性创新,减少风险高的探索性创新。购买银行理财的上市公司更倾向于机会主义创新,而购买非银行理财的上市公司更可能消极创新。总之,基于企业创新视角的分析发现大规模和投机性的委托理财会干扰上市公司创新行为,导致其经营"脱实向虚"。     

金融资产配置与违约风险:蓄水池效应,还是逐利效应？

本文以2007—2016年中国A股上市公司为研究对象,检验了企业金融资产配置对违约风险的影响。实证研究发现:金融资产持有量越多,企业的违约风险越低,金融资产配置的“蓄水池效应”显著;在货币政策宽松时期,金融资产配置导致的代理冲突显现,宽松的货币政策会抑制金融资产投资对违约风险的降低作用。政府规制也会有一定的公司治理作用,将产业政策纳入讨论发现:对于产业政策支持的行业来说,企业金融资产配置能够降低违约风险,但是宽松的货币政策会刺激管理层的短视投资行为,抑制政府规制的公司治理作用。进一步地,本文提出会计稳健性的提升是企业金融资产配置降低违约风险的重要路径。本文的研究结论丰富了企业金融资产配置动机和违约风险影响因素的讨论,能够为政府部门防范经济运行中的内在风险提供有益借鉴。     

Does bond market development enhance the banking sector’s efficiency in resource allocation? Industry-level evidence from Korea

Korea’s financial system used to be bank-based, with banks playing the leading role in financing corporations. As highlighted by Park et al. (2019), however, bond markets have developed rapidly in Korea and other Asian countries. The corporate bond market competes with banks as a source of finance for large borrowers. As such, bond markets may affect banking sector operation, a process known as disintermediation. In this paper, we examine whether bond market development improves the efficiency of resource allocation in Korean bank lending. We propose two channels through which bond market development affects the efficiency of bank lending. Since the two channels have opposing effects on the efficiency of banking, the issue must be settled by empirical analysis. We find that bank loans are much less efficient than bond financing in allocating resources across industries. Furthermore, banks are particularly inefficient in resource allocation in industries that rely more on bond financing. This suggests that competition from bond financing does not improve allocative efficiency of bank loans.     

Financial development and economic growth in a microfounded small open economy model

The global financial crisis since 2008 revived the debate on whether or not and to what extent financial development contributes to economic growth. This paper reviews different theoretical schools of thought and empirical findings on this nexus, building on which we aim to develop a unified, microfounded model in a small open economy setting to accommodate various theoretical possibilities and empirical observations. The model is then calibrated to match some well-documented stylized facts. Numerical simulations show that, in the long run, the welfare-maximizing level of financial develop is lower than the growth-maximizing level. In the short run, the price channel (through world interest rate) dominates the quantity-channel (through financial productivity), suggesting a vital role of international cooperation in tackling systemic risk of the global financial system.     

Classification of intraday S&P500 returns with a Random Forest

Stock markets can be interpreted to a certain extent as prediction markets, since they can incorporate and represent the different opinions of investors who disagree on the implications of the available information on past and expected events and trade on their beliefs in order to achieve profits. Many forecast models have been developed for predicting the future state of stock markets, with the aim of using this knowledge in a trading strategy. This paper interprets the classification of the S&P500 open-to-close returns as a four-class problem. We compare four trading strategies based on a random forest classifier to a buy-and-hold strategy. The results show that predicting the classes with higher absolute returns, ‘strong positive’ and ‘strong negative’, contributed the most to the trading strategies on average. This finding can help shed light on the way in which using additional event outcomes for the classification beyond a simple upward or downward movement can potentially improve a trading strategy.     

Failure to learn from failure: The 2008 mortgage crisis as a déjà vu of the mortgage meltdown of 1994

This article traces the developments in the market for residential mortgage-backed securities (MBS) during the period 1970–2008. Drawing on an analysis of trade publications, business press, and interviews with practitioners, it shows that an MBS market meltdown in 1994 provided clear signals of problems with MBS. The market participants did not re-evaluate their use of risk management tools or adjust security design in response to the 1994 crisis, suggesting a lack of understanding of the implications of the crisis. The 1994 meltdown showed that MBS were vulnerable to systematic risks and that these risks could precipitate an MBS market crash. Furthermore, the 1994 meltdown demonstrated that large-scale investment in MBS could affect the primary mortgage market, thereby rendering the MBS risks unpredictable. After 1994, MBS investment shifted to MBS backed by mortgages with default risk – a development that led to the crash of 2008. By drawing parallels between the 1994 and 2008 crises, this article shows how the MBS market failed to self-correct. The results suggest that financial market participants do not always incorporate relevant information in their decision-making and that market participants have difficulties in both foreseeing the effect of financial innovations on markets and interpreting these effects.     

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.