Do mutual fund flows affect the French corporate bond market?

We study whether investors’ withdrawals from mutual funds affect corporate bond prices. As mutual funds have become major players in the financial markets, they are likely to exert downward pressures on asset prices when facing investors’ redemptions, particularly in the less liquid markets such as corporate bonds. We use a novel dataset on the French bond funds and show that both flows in and out of mutual funds lead to a significant effect on the corporate bond yields. This effect is asymmetric as redemptions provoke a change in yields of greater magnitude than inflows. Moreover, all corporate bonds are not equally affected by investors’ withdrawals from funds: The more a bond is detained by funds, the higher the impact of redemptions on its yield. These three results are robust to various changes in econometric specifications.     

Price connectedness between green bond and financial markets

We study price connectedness between the green bond and financial markets using a structural vector autoregressive (VAR) model that captures direct and indirect transmission of financial shocks across markets. Using heteroskedasticity to identify the structural VAR model parameters, our empirical findings reveal that the green bond market is closely linked to the fixed-income and currency markets, receiving sizeable price spillovers from those markets and transmitting negligible reverse effects. We also show that, in contrast, the green bond market is weakly tied to the stock, energy and high-yield corporate bond markets. These findings have implications in terms of portfolio and risk management decisions for environmentally aware investors holding positions in green bonds.     

Credit constraints and productivity of SMEs: Evidence from Canada

To what extent firms are constrained by external credit is usually unobserved in commonly used firm-level data. We use a survey of financing among Canadian small and medium-sized enterprises to measure the likelihood of a firm being constrained by credit. We find that firm size, current debt-to-asset ratio and cash flow are robust indicators of being financially constrained, while long-term debt to asset ratio is not a significant indicator of credit constraints. We then estimate the firm-level total factor productivity, taking into account the measured credit constraints. Omitting credit constraints leads to an upward bias of productivity estimates, by 4 percent. In addition, we find no strong evidence that suggests credit constraints lead to slower productivity growth. Finally, we confirm that both investment and employment growth are negatively affected by the measured credit constraints.     

Financial development,economic growth and convergence clubs

To study the role of financial development in economic growth, we apply an array of convergence tests designed to capture nonlinear transitional dynamics to real outputs per capita. Strong evidence of multiple convergence clubs is observed, implying that the clubs are formed based on the initial level of real output per capita and average growth rate. Our empirical results show that the stage of economic growth of each country plays an important role for the composition of the convergence clubs. Furthermore, financial development emerges to be a significant determinant, albeit plays differently in the economic growth of each convergence club.     

Incorporating textual information in customer churn prediction models based on a convolutional neural network

This study investigates the value added by incorporating textual data into customer churn prediction (CCP) models. It extends the previous literature by benchmarking convolutional neural networks (CNNs) against current best practices for analyzing textual data in CCP, and, using real life data from a European financial services provider, validates a framework that explains how textual data can be incorporated in a predictive model. First, the results confirm previous research showing that the inclusion of textual data in a CCP model improves its predictive performance. Second, CNNs outperform current best practices for text mining in CCP. Third, textual data are an important source of data for CCP, but unstructured textual data alone cannot create churn prediction models that are competitive with models that use traditional structured data. A calculation of the additional profit obtained from a customer retention campaign through the inclusion of textual information can be used by practitioners directly to help them make more informed decisions on whether to invest in text mining.     

Does more finance mean more inequality in times of crisis?

Many recent empirical studies show that both banking crises and financial development (FD) play an important role in understanding the dynamics of income inequality (IncI) over the last decades. However, so far no study has investigated the role of FD in the amplification of IncI following banking crises. This paper seeks to address this issue based on a sample of 69 banking crises in 54 countries over the 1977–2013 period. Our analysis suggests that FD is associated with a significant increase in IncI in the aftermath of banking crises. This result is robust to a broad range of alternative specifications and is unaffected by various potential sources of endogeneity. We also show that the relationship between FD and the redistributive consequences of banking crises is not subject to a threshold effect and is stronger for developing countries.     

能源企业技术创新能力对财务绩效的影响——基于企业规模的门槛效应研究

能源消费和供给的重要性使得政府给予能源企业技术创新更多关注和支持，能源企业经营存在较大风险使得能源企业技术创新能力对财务绩效的影响呈现一定特殊性。以我国能源企业2013-2018年技术创新投入与产出能力指标为样本数据，采用因子分析法和动态面板门槛效应模型，在评价能源企业技术创新能力的基础上，分别研究能源企业技术创新投入和产出能力对财务绩效的影响及企业规模门槛效应。研究结果表明，能源企业的技术创新投入和综合能力对财务绩效的影响存在显著的门槛效应，而产出能力的表现并不显著。研究结果可为国家和能源企业的技术政策制定和创新投入决策提供参考。     

I (Could Have) Told You! Early warning indicators and crisis performance of the 12 “old” Euro-countries under the economic crisis 2008–09

The impact of the economic crisis 2008-09 was remarkably different in the 12 “old” member states in the Euro-zone. Five of them were hit especially badly; four of them even had to be bailed out by the rest in one way or another. This paper asks if one could have foretold, based solely on information available prior to 1999, which of the countries then about to enter the Euro-zone would run into economic trouble once a serious economic crisis occurred. The focus is on the (post)predictive power of three kinds of leading indicators: economic indicators, political indicators (indicating quality of governance), and indicators derived from the theory of optimal currency areas (OCA). Since there are more indicators than cases, PLS-regression is used to gauge the (post)predictive strength of the indicators examined. The results show that political indicators have quite some (post)predictive power in this case, whereas indicators derived from OCA-theory do not do too well. Economic indicators perform better than indicators derived from OCA-theory, but generally less well than the political indicators. Thus, the experience from the latest economic crisis in the Euro-zone suggests that more emphasis should be placed on the quality of governance record of a country when deciding if it should be deemed fit to become a member.     

Foreign direct investment and economic growth: Is more financial development better?

We investigate the extent to which an increase in financial development affects the positive effect of foreign direct investment on economic growth. Although the financial sector is beneficial for economic growth, the effect of further financial development on growth is found to become insignificant. Using a dynamic panel threshold model on 62 middle- and high-income countries spanning the period 1987–2016, we re-examine the possible nonlinearity between finance, foreign direct investment, and growth. Consistent with the “vanishing effect” of financial development, we find significant evidence that foreign direct investment fosters growth in general, but the growth effect of foreign direct investment becomes negligible when the ratio of private sector credit to gross domestic product exceeds 95.6%. This finding is robust to different econometric methods, various subsamples and interaction analyses, and distinct financial development indicators.     

Personalized goal-based investing via multi-stage stochastic goal programming

In this paper, we propose a goal-based investment model that is suitable for personalized wealth management. The model only requires a few intuitive inputs such as size of wealth, investment amount, and consumption goals from individual investors. In particular, a priority level can be assigned to each consumption goal and the model provides a holistic solution based on a sequential approach starting with the highest priority. This allows strict prioritization by maximizing the probability of achieving higher priority goals that are not affected by goals with lower priorities. Furthermore, the proposed model is formulated as a linear program that efficiently finds the optimal financial plan. With its simplicity, flexibility, and computational efficiency, the proposed goal-based investment model provides a new framework for automated investment management services.