Conditional Mandates on Management Earnings Forecasts: The Impact on the Cost of Debt

Exploiting a unique conditional disclosure mandate on management earnings forecasts (MEFs) in China, we examine the differential effects of voluntary and mandatory MEFs on the cost of debt. We find that firms providing voluntary MEFs have lower cost of debt than do mandatory forecasters and nonforecasters. The results of the channel analyses reveal that voluntary forecasters have greater commitment to voluntary MEFs in future periods than do mandatory forecasters and nonforecasters, and the precision, accuracy, and timeliness of MEFs are higher for voluntary forecasters than for mandatory forecasters. Additional analyses show that the differential effects of voluntary and mandatory MEFs on cost of debt are stronger for voluntary forecasters operating in opaque information environments, issuing high-quality and confirming forecasts, controlled by private shareholders, and operating in highly competitive product markets. Overall, our results indicate that, compared with mandatory MEFs, voluntary MEFs are more informative for credit investors, particularly for firms facing greater information risk and operating uncertainty.     

Size of nested cities

Models of cities based on conventional spatial market theory are unable to replicate a realistic size distribution. The stochastic process approach to size distribution, which assumes proportionate growth, does not provide an economic foundation for spatial trades. There is an apparent irreconcilability. We propose that since there is a continuum of equilibria in models of spatial markets with endogenous location, proportionate growth can work as equilibrium selection. We present computations for an urban configuration that has not been presented in the literature before. A small city locates inside a larger city's agricultural supply zone. This generates a larger variation in city size that may include a realistic size distribution.     

Integrating uncertain user-generated demand data when locating facilities for disaster response commodity distribution

This paper presents a new facility location problem variant with application in disaster relief. The problem is unique in that both verified data and unverified user-generated data are available for consideration during decision making. The problem is motivated by the recent need of integrating unverified social data (e.g., Twitter posts) with data from more traditional sources, such as on-the-ground assessments and aerial flyovers, to make optimal decisions during disaster relief. Integrating social data can enable identifying larger numbers of needs in shorter amounts of time, but because the information is unverified, some of it may be inaccurate. This paper seeks to provide a “proof of concept” illustrating how the unverified social data may be exploited. To do so, a framework for incorporating uncertain user-generated data when locating Points of Distribution (PODs) for disaster relief is presented. Then, three decision strategies that differ in how the uncertain data is considered are defined. Finally, the framework and decision strategies are demonstrated via a small computational study to illustrate the benefits user-generated data may afford across a variety of disaster scenarios.     

A Kaleckian Perspective on Financialisation and Distribution in Three Main Eurozone Countries before and after the Crisis: France,Germany and Spain

The purpose of this article is twofold. First, we examine if, and to what extent, a general Kaleckian analysis of the potential effects of financialisation on income shares in advanced capitalist economies is of relevance for the three Eurozone countries under investigation—France, Germany and Spain—in the period before the recent financial and economic crisis. Second, we study changes in the financialisation–distribution nexus that have occurred in the course of and after the financial and economic crisis. We find that the countries examined here have shown broad similarities regarding redistribution before the crisis, although there are some differences in the underlying determinants. These differences have continued during the period after the crisis and have led to different results in the development of distribution since then.     

农村集体经营性建设用地流转模式再审视：基于产权让渡与市场化的视角

研究目的:厘清集体经营性建设用地流转模式,推进集体建设用地入市改革。研究方法:以产权让渡及市场化为视角,采用矩阵分类法划分集体经营性建设用地流转模式,剖析不同流转模式特征与演进路径。研究结果:基于本研究视角可将集体经营性建设用地流转划分为6种模式,不同模式具有不同特征、运行条件、优缺点及收益分配格局,且其存在关联并遵循一定的演进规律。研究结论:规范市场下农地发展权及使用权流转模式将是当前改革的方向,需从法律法规上明确农地发展权相关规定;不同地区需因地制宜地选择流转模式及类型,探索具体实施办法,循序渐进推进流转改革与创新。     

Analyst earnings forecast precision and local advantage: evidence from American depositary receipt firms

Prior research has documented a finding that local analysts provide more accurate earnings forecasts than nonlocal analysts in many settings. However, little is known about local and nonlocal analysts’ relative earnings forecast abilities for ADR stocks. In this study, we find that the local advantage disappears for ADR stocks and that nonlocal analysts in fact outperform local ones in this case. We investigate the source of this ‘local disadvantage’ and find evidence against hypotheses based on accounting standards and exchange rates. We document that the local advantage decreases with increased investor interest in foreign firms, consistent with our nonlocal investors’ interest hypothesis.     

Option trading after the opening bell and intraday stock return predictability

Prior literature finds that information is reflected in option markets before stock markets, but no study has explored whether option volume soon after market open has predictive power for intraday stock returns. Using novel intraday signed option-to-stock volume data, we find that a composite option trading score (OTS) in the first 30 min of market open predicts stock returns during the rest of the trading day. Such return predictability is greater for smaller stocks, stocks with higher idiosyncratic volatility, and stocks with higher bid–ask spreads relative to their options’ bid–ask spreads. Moreover, OTS is a significantly stronger predictor of intraday stock returns after overnight earnings announcements. The evidence suggests that option trading in the 30 min after the opening bell has predictive power for intraday stock returns.     

Wealth transfer through private placements: Evidence from China

We examine private issuance of public equity (PIPE) in China, and our results suggest that PIPE investors benefit from the price manipulation before and after issuance. These investors tend to cash out after lockup expiration and make large profits. We also find evidence that the trading of PIPE investors after lockup expiration is informed. Tests about the abnormal returns in the 3 years after lockup expiration suggest that at least part of the benefits PIPE investors receive come from wealth transfer from outside investors. Overall, PIPE issuers in China seem to use an opaque mechanism to compensate PIPE investors.     

Real earnings management around CEO turnovers

Following CEO turnovers, US firms adjust real business activities to manage earnings downwards (REM bath). This effect is most pronounced in firms with low levels of institutional ownership. REM baths early in CEOs’ tenure can be confounded with legitimate adjustments to business activities. However, we show that they are not accompanied by increases in R&D or capital expenses, nor are they explained by restructuring expenses. CEOs with short tenure record more negative REM measures in their first year of tenure, when compared with CEOs with long tenure.     

Factor state–space models for high-dimensional realized covariance matrices of asset returns

We propose a dynamic factor state–space model for high-dimensional covariance matrices of asset returns. It makes use of observed risk factors and assumes that the latent integrated joint covariance matrix of the assets and the factors is observed through their realized covariance matrix with a Wishart measurement density. For the latent integrated covariance matrix of the assets we impose a strict factor structure allowing for dynamic variation in the covariance matrices of the factors and the residual components as well as in the factor loadings. This factor structure translates into a factorization of the Wishart measurement density which facilitates statistical inference based on simple Bayesian MCMC procedures making the approach scalable w.r.t. the number of assets. An empirical application to realized covariance matrices for 60 NYSE traded stocks using the Fama–French factors and sector-specific factors represented by Exchange Traded Funds (ETFs) shows that the model performs very well in- and out of sample.