上海A股市场分析师分歧对未来股票回报的预测性研究

本文采用2004年9月至2009年5月间相关数据,通过组合构建策略,分别控制了市值效应、账面值市值比效应和动量效应,发现上海A股市场上分析师分歧对未来股票回报不具有预测性,并分析了造成这一现象的主要原因,进一步在此基础上提出了促进我国证券分析师行业规范化发展的一些政策建议。     

Revisiting dividend yield dynamics and returns predictability: Evidence from a time-varying ESTR model

Since the bubble of the late 1990s the dividend yield appears non-stationary indicating the breakdown of the equilibrium relationship between prices and dividends. Two lines of research have developed in order to explain this apparent breakdown. First, that the dividend yield is better characterised as a non-linear process and second, that it is subject to mean level shifts. This paper jointly models both of these characteristics by allowing non-linear reversion to a changing mean level. Results support stationarity of this model for eight international dividend yield series. This model is than applied to the forecast of monthly stock returns. Evidence supports our time-varying non-linear model over linear alternatives, particularly so on the basis of an out-of-sample R-squared measure and a trading rule exercise. More detailed examination of the trading rule measure suggests that investors could obtain positive returns, as the model forecasts do not imply excessive trading such that costs would not outweigh returns. Finally, the superior performance of the non-linear model largely arises from its ability to forecast negative returns, whereas linear models are unable to do.     

香港人民币债券发行的公告效应及其影响因素研究

本文以2007年7月至2011年8月在香港发行人民币债券的74家公司发行的87支离岸人民币债券为研究对象,运用事件研究方法对这些离岸人民币债券发行的公告效应及其影响因素进行了实证研究。结果表明,在我们研究的事件窗口中,在香港发行人民币债券会对公司股价产生显著为负的公告效应,此效应近似等于我国公司在国内股权融资所产生的公告效应,小于可转债和公司债发行时所产生的效应。横截面的数据回归分析表明,股票的累计异常收益率与公司的规模、信用评级和债券的相对规模成正相关关系,与债券的期限、公司的固定资产比率、负债率和成长性不存在显著的相关关系。     

欧债危机国家国债与股票收益率相关性研究

欧债危机爆发后,欧元区国家国债与股票市场的相关性呈现出新的特点。在深入分析国债和股票市场风险传导机制的基础上,提出了一个分析两者关系的理论框架,将股债相关性的变化归因于贴现率、资金流、风险三大因素的复合效应。利用希腊、葡萄牙、西班牙和德国的数据,对国债和股票收益率的相关性进行了多角度的实证研究,结果表明,本文的理论框架能够很好地解释危机前后各种股债关系的变化。     

On returns and network configuration in supply chain dynamics

This research focuses on how two common modeling assumptions in the Bullwhip Effect (BWE) literature (i.e., assuming the return of the excess of goods and assuming a serial network) may distort the results obtained. We perform a robust design of experiments where the return condition (return vs. no return) and the configuration of the Supply Chain Network (SCN) (serial vs. divergent) are systematically analyzed. We find an important interaction between these assumptions: the impact of returns on the BWE strongly depends on the SCN configuration. This study highlights the importance of accurately modeling SCNs to properly assess SCNs managers.     

Returns,volatility and investor sentiment: Evidence from European stock markets

In this study, we show that patterns in returns behave as if investors, influenced by their level of optimism, selected stocks according to their volatility. Our goal is to confirm the contribution of behavioral finance while showing that investor sentiment can be profitably used by practitioners. We incorporate volatility in the relationship between investor sentiment and future returns, this is the main originality of our approach. Our methodology consists in comparing returns, volatility and higher-order moments of portfolios managed with investor sentiment against those obtained either with passive (buy and hold) portfolio management or with a minimum variance portfolio. Portfolios managed with investor sentiment have better returns and involve less risk under certain conditions.     

Can volume predict Bitcoin returns and volatility? A quantiles-based approach

Prior studies on the price formation in the Bitcoin market consider the role of Bitcoin transactions at the conditional mean of the returns distribution. This study employs in contrast a non-parametric causality-in-quantiles test to analyse the causal relation between trading volume and Bitcoin returns and volatility, over the whole of their respective conditional distributions. The nonparametric characteristics of our test control for misspecification due to nonlinearity and structural breaks, two features of our data that cover 19th December 2011 to 25th April 2016. The causality-in-quantiles test reveals that volume can predict returns – except in Bitcoin bear and bull market regimes. This result highlights the importance of modelling nonlinearity and accounting for the tail behaviour when analysing causal relationships between Bitcoin returns and trading volume. We show, however, that volume cannot help predict the volatility of Bitcoin returns at any point of the conditional distribution.     

Litigation and mutual-fund runs

We investigate whether anticipation of adverse events (litigation about market timing and late trading) may trigger mutual-fund runs. We find that runs start as early as three months prior to litigation announcements. Pre-litigation runs accumulate to 31 basis points of the total net assets over a three-month window; post-litigation runs may last more than six months and accumulate to 1.25 percent over the first three-month window. Additionally, investors who run before litigation announcements earn significantly higher risk-adjusted and peer-adjusted returns than those who run after litigation. The difference in returns is particularly pronounced for funds holding illiquid assets. Finally, securities held by litigated fund families significantly underperform vis-á-vis other securities in terms of lower abnormal returns and liquidity. Our analysis suggests that a pro-rata ownership design is insufficient to prevent mutual-fund runs.     

Returns to scale at large banks in the US: A random coefficient stochastic frontier approach

This paper investigates the returns to scale of large banks in the US over the period 1997–2010. This investigation is performed by estimating a random coefficient stochastic distance frontier model in the spirit of Tsionas (2002) and Greene (2005, 2008). The primary advantage of this model is that its coefficients can vary across banks, thereby allowing for unobserved technology heterogeneity among large banks in the US We find that failure to consider unobserved technology heterogeneity results in a misleading ranking of banks and mismeasured returns to scale. Our results show that the majority of large banks in the US exhibit constant returns to scale. In addition, our results suggest that banks of the same size can have different levels of returns to scale and there is no clear pattern among large banks in the US concerning the relationship between asset size and returns to scale, due to the presence of technology heterogeneity.     

Innovative intensity and its impact on the performance of firms in Brazil

This article evaluates whether firms that invest in research and development (R&D) have better future performance and if stock market fully value such intangible investment. The results of annual cross-sectional regressions indicate a strong association between the intensity of R&D and future performance, even after controlling for other variables that affect future performance. However, after controlling for firm characteristics and risk factors, the innovative intensity was not significant in predicting future returns. In general, the results suggest that the R&D intensity is not useful for firm valuation in Brazil.