The sensitivity of the credit default swap market to financial analysts’ forecast revisions

We examine the impact of analysts’ earnings per share (EPS) and cash flow per share (CPS) forecast revisions on the market for credit default swaps. We find that while the issuance of both EPS and CPS forecast revisions are inversely associated with changes in credit default swap (CDS) spreads, cash flow forecast revisions have a larger effect. We demonstrate that the relationship between CPS forecast revisions and CDS spreads tends to be stronger in cases of financial distress. We provide evidence that cash flow forecasts dominate earnings forecasts in some situations and that participants in the CDS market discriminate between analysts' forecast revisions and recommendation changes.     

Income growth in the 21st century: Forecasts with an overlapping generations model

We forecast income growth over the period 2000–2050 in the US, Canada, and France. To ground the forecasts on relationships that are as robust as possible to changes in the environment, we use a quantitative theoretical approach which involves calibrating and simulating a general equilibrium model. Compared to existing studies, we allow for life uncertainty and migrations, use generational accounting studies to link taxes and public expenditures to demographic changes, and take into account the interaction between education and work experience. Forecasts show that growth will be weaker over the period 2010–2040. The gap between the US and the two other countries is increasing over time. France will catch-up and overtake Canada in 2020. Investigating alternative policy scenarios, we show that increasing the effective retirement age to 63 would be most profitable for France, reducing the gap between it and the US by one third. A decrease in social security benefits would slightly stimulate growth but would have no real impact on the gap between the countries.     

Real-time macroeconomic forecasting with leading indicators: An empirical comparison

This paper demonstrates that the Conference Board’s Composite Leading Index (CLI) has significant real-time predictive ability for Industrial Production (IP) growth rates at horizons from one to six months ahead over the period 1989-2009. A popular but unrealistic analysis, which combines real-time data for CLI and final vintage data for IP as predictor variables, obscures the actual predictive content of the CLI, in the sense that in that case, the improvements in forecast accuracy relative to a univariate AR model are not significant. The CLI appears to be less useful for forecasting growth rates of the Conference Board’s Composite Coincident Index (CCI) in real time, as a univariate AR model performs better. This result is mostly due to its disappointing performance during the first five years of the forecast period. The CLI may not be the best way of exploiting the information contained in the underlying individual leading indicator variables. The use of principal components instead of CLI leads to more accurate real-time forecasts for both IP and CCI growth rates.     

基于P-Star指示器的通货膨胀预测模型及应用

保持较低的通货膨胀率是各国政府制定宏观经济政策的主要目标之一。为保证经济政策在调节价格水平时充分发挥作用,我们必须找到能够准确预测价格水平的指示器。本文介绍了用于预测通货膨胀率的P-Star指示器并建立了中国的P-Star模型。该模型的思想是:存在一个长期均衡价格,即P-Star指示器,实际价格围绕这个均衡价格波动,因此可以通过长期均衡价格预测未来价格走势与通货膨胀率。我们建立的P-Star模型具有很好的预测效果,同时预测结果表明2007年的通货膨胀率将略高于2006年。     

Takeover prediction using forecast combinations

The ability to identify likely takeover targets at an early stage should provide investors with valuable information, enabling them to profit by investing in potential target firms. In this paper we contribute to the takeover forecasting literature by suggesting the combination of probability forecasts as an alternative method of improving the forecast accuracy in takeover prediction and realizing improved economic returns from portfolios made up of predicted targets. Forecasts from several non-linear forecasting models, such as logistic and neural network models and a combination of them, are used to determine the methodology that best reduces the out-of-sample misclassification error. We draw two general conclusions from our results. First, the forecast combination method outperforms the single models, and should therefore be used to improve the accuracy of takeover target predictions. Second, we demonstrate that an investment in a portfolio of the combined predicted targets results in significant abnormal returns being made by an investor, in the order of up to double the market benchmark return when using a portfolio of manageable size.