Is socially responsible investment useful in Mexico? A multi-factor and ex-ante review

The present paper continues the firsts reviews made to socially responsible investment in Mexico. We extended these reviews by using a non-parametric multivariate equality test, along with a multi-factor market cap model, and a Monte Carlo simulation. Our results show that the IPCS index, the IPCcomp and the IPC have a statistically equal mean-variance performance, suggesting that this sort of investment style (SRI) is a good substitute of the broad market investment style in the long term. Among the causes of this finding is the fact that the IPCS and the IPCcomp indexes have almost the same large and small cap stock concentration and the IPC index (a large-cap one) is not as diversified and mean-variance efficient as the former.     

组合预测模型在货运量预测中的应用

本文对灰色预测模型和ARIMA预测模型进行组合,建立了组合模型,并应用于货运量的预测,实证预测表明,组合模型的预测精度优于单一的预测模型,预测结果与实际货运量拟合较好。     

Forecasting the past: the case of US interest rate forecasts

This study evaluates 10-year US government bond yield forecasts and three-month US Treasury bill rate forecasts for the period between October 1989 and December 2004. In total, 136 forecast time series with approximately 13,800 forecast data were scrutinized, making this the most extensive analysis of interest rate forecasts to date. Not one of the forecast time series proved to be unbiased. In the majority of cases, information from the past was not efficiently integrated into the forecasts. The sign accuracy is significantly better than random walk forecasts in only a very few of the forecast time series. The modified Diebold–Mariano test for forecast encompassing reveals that the information content of most of the forecast time series is lower than that of the naïve forecasts, the simple ARIMA models, the implicit forward rates, or average interest rate expectations. The forecasting process is dominated by the present and past market situation.     

A model of West African millet prices in rural markets

In this article we specify a model of millet prices in the three West African countries of Burkina Faso, Mali, and Niger. Using data obtained from USAID’s Famine Early Warning Systems Network (FEWS NET) we present a unique regional millet price forecasting model that takes advantage of the panel nature of our data and accounts for the distance of rural markets from capital cities. Another novel aspect of our analysis is our use of the Normalized Difference Vegetation Index (NDVI) to detect and control for variation in conditions for productivity. We find that including NDVI information significantly improves price forecasts.     

行业因素对综合预警模型的判别效果的影响研究--以基于主成分分析的Logit模型为例

以2010～2012年期间我国沪深A股因财务困境陷入ST的公司和按照1∶2比例配比的正常公司作为研究对象,并根据行业特点对样本进行划分。同时,选取能够反映企业盈利能力、股东获利能力、现金流量能力、营运能力、发展能力、偿债能力的30个财务指标,在主成分分析的基础上构建各年度的Logit模型,对各行业的违约概率和判别准确度分别进行分析。结果表明,不同行业的违约概率和判别准确度均存在显著差异且存在共性特征。     

Does foreign investor demand for information affect forecast accuracy? Evidence from the Chinese stock markets

Prior international accounting studies have examined mainly the supply side of analyst research. Specific trading rights and financial reporting systems in China provide a unique opportunity to test both demand and supply factors of analyst information. For shares traded only by less-informed foreign investors, the increased information demand may create incentives for analysts to exert greater efforts than for shares traded by local investors. This study provides evidence that analyst effort (proxied by analyst coverage) and expertise (proxied by consensus cross-analyst forecast variability) affect forecast accuracy more significantly for shares traded by foreign investors than for shares traded by local investors. Additionally, I find that the relation between analyst characteristics and relative forecast accuracy is stronger for shares traded by the foreign investors. The findings are consistent with analysts responding to demands for information by less-informed investors.     

Do German security analysts herd?

We employ an innovative methodology suggested by Bernhardt et al. (J. Financ. Econ. 80:657–675, 2006) to examine the herding (or anti-herding) behavior of German analysts regarding earnings forecasts. This methodology avoids well-known shortcomings often encountered in related studies, such as correlated information signals, unexpected common shocks to earnings, systematic optimism or pessimism, or forecast target mismeasurement. Our findings suggest that German analysts anti-herd, that is, they systematically issue earnings forecasts that are further away from the consensus forecast than their private information indicates. Furthermore, we analyze the association between herding behavior and different characteristics, including the size of the brokerage, general or firm-specific experience, and the coverage of firms on the Neuer Markt. We mainly confirm findings for the United States, for example, that anti-herding is more severe in cases of higher competition among analysts. Contrary to anecdotal evidence, we also find anti-herding behavior in earnings forecasts for Neuer Markt firms during the “new economy” bubble.

内部评级体系定量验证模型及在中国银行业的应用

定量验证是运用一定的统计方法来检验商业银行已运行内部客户评级体系的准确性、审慎性及稳定性。准确性验证主要检验商业银行的内部评级模型对授信客户信用状况好坏的风险识别能力;审慎性验证主要检验银行所采用的政策与标准在辨别其内部评级和风险参数量化上的保守程度;稳定性验证则主要检验在风险不变的情况下,银行所采用政策和标准能够保持评级与估值总体上不发生变化。本文介绍了常用的定量验证模型及流程,并以中国某商业银行进行定量验证的实践为例,介绍了如何进行银行内部评级体系的定量验证及在具体实践中可能遇到的问题及解决方案。     

Return prediction and stock selection from unidentified historical data

The experimental approach was applied to test the value of historical return series in technical prediction. Return sequences were randomly drawn cross-sectionally and over time from S&P500 records and participants were asked to predict the 13th realization from 12 preceding returns. The hypothesis that predictions (nominal or real) are randomly assigned to historical sequences is rejected in permutation tests, and the best-stock portfolios by experimental predictions significantly outperform the worst-stock portfolios in joint examination of mean return and volatility. The participants dynamically adjust their predictions to the observed series and switch from momentum riding to contrarian extrapolation when recent trends get extreme. The implicit tuning of predictions to specific series captures variabilities that could not be inferred by schematic statistical forecasting.     

Analysts' Forecasts Following Forced CEO Changes

This paper examines analysts' earnings forecasts during the period of uncertainty following a change of chief executive officer (CEO). It distinguishes between forced and non‐forced CEO changes, and examines whether analysts utilize their information advantage to reduce the heightened uncertainty of a forced change of CEO. Examining a sample of Australian companies followed by analysts between 1999 and 2009, we find that forecasting accuracy is lower and earnings forecasts are more optimistic for firms experiencing forced CEO turnover compared to firms not undergoing such a change. However, dispersion is not statistically different. The results suggest that forced CEO turnover events provide a challenge to the forecasting environment for analysts. During CEO changes, investors should be aware that forecasts are less accurate and have an optimistic bias.