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。     

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.     

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.     

Private equity characteristics and performance: An analysis of North American venture capital and buyout funds

The paper investigates the relationship between fund performance and fund characteristics of North American private equity (PE) funds, by analyzing the interactions of fund size, fund sequence, and past fund performance on traditional fund return measures. The empirical evidence is based on both linear and polynomial regressions, on a sample of 345 venture capital (VC) and 411 buyout (BO) funds with vintage year over the period 1995–2010. We document a concave relationship between fund size and performance, persistence in PE performance, as well as a convex relationship between fund sequence and performance. We suggest both the optimal fund size and the optimal fund sequence number. Economic implications for investors and general partners are discussed as well.     

Do hedge funds time market tail risk? Evidence from option-implied tail risk

This paper focuses on an unexplored dimension of fund managers’ timing ability: Market-wide tail risk implied by information in options markets. Constructing the option-implied tail risk, we investigate whether hedge fund managers can strategically time the tail risk through adjusting their exposure to changes of it. Using an extensive sample of equity-oriented hedge funds, we find strong evidence of tail risk timing ability of hedge fund managers. Furthermore, tail risk timing ability brings significant economic value to investors. Top-ranked funds outperform bottom-ranked funds by 5–7% annually after adjusting for risk factors. Our results are robust to various robustness checks.     

纳税信用评级结果披露与研发投入

以纳税信用评级披露作为一个天然外生冲击,并基于2013—2016年1214家A股上市公司的微观数据构造准自然实验,使用双重差分法系统评估纳税信用评级结果披露对上市公司研发投入的影响。结果发现:纳税信用评级结果披露显著增加了上市公司的研发投入。基于PSM-DID方法的估计结果与上述结论无明显差异。稳健性检验也表明上述结论的正确性。机制检验表明,纳税信用评级结果披露通过降低企业的融资约束,进而促进企业增加研发投入。此外,分样本回归发现纳税信用评级结果披露只能对中小型企业和民营企业的研发投入产生促进作用。     

How much does tax erode fund excess returns?

We model the tax drag from active fund management based on reported monthly holdings of active equity funds. Tax drag erodes 65 percent of the 0.74 percent excess return in Broad Market funds, but only 21 percent of the 1.80 percent excess return in Small-Cap funds for Australian superannuation (pension) fund investors. Tax drag varies with investment style; market state, which is most detrimental during bull markets; and fund turnover. For high-income individual investors, tax drag is exacerbated to the extent that active management only generates meaningful after-tax excess return for Small-Cap funds of certain styles.     

Structural holes and hedge fund return comovement: evidence from network-connected stock hedge funds in China

Using data from a new hedge fund database, we examine the impact of social networks on the return comovement of stock hedge funds in China. We use structural holes in the college alumni networks of managers to measure the managers’ social network positions. We perform an empirical analysis on a sample of 3,012 hedge fund products in China from 2010 to 2017. We find that greater structural holes are associated with higher return comovement. The positive impact of the structural holes on return comovement is not affected by market cycles, a manager's major in college, or his or her abilities.     

Do capital flows drive credit growth and consumption in Central and Eastern Europe?

Domestic credit expansion in CEE economies, fuelled in part by foreign capital inflows, helped increase household welfare before the 2008 financial crisis caused a contraction across the region. How strong are the linkages between the current account, domestic credit and consumer spending? This study compiles a quarterly dataset of domestic credit as a share of GDP for 11 CEE European Union members and isolates structural breaks in the series’ growth rates that often align with the 2008 crisis. Vector autoregressive methods, particularly impulse response functions, show that increased current-account deficits lead to increased consumption in six of the 11 countries and increased credit growth in three, and that shocks to credit growth increase consumption in six countries. Capital inflows significantly increase consumption through domestic credit in Slovenia, while the Baltics show a large share of significant effects.     

Interenterprise Credit and Adjustment during Financial Crises: The Role of Firm Size

Small and medium‐sized enterprises (SMEs) suffered a sharp contraction in their borrowing from banks during the Great Recession. Analyzing a large firm‐level database for European countries, the paper shows that trade credit amplified the liquidity squeeze on SMEs, with adverse effects on their real activity. SMEs sharply increased their net trade credit and thus transferred financial resources to larger firms. Given the large weight of SMEs in the economy of European countries, the liquidity squeeze of SMEs likely contributed to the depth of the output fall and the slow recovery in Europe during the Great Recession.