Analysts’ Cash Flow Forecast Accuracy and Recommendation Profitability

We examine whether analysts who supplement their earnings forecasts with more accurate cash flow forecasts generate more profitable recommendations. Previous research using consensus cash flow forecasts, or the issuance of cash flow forecasts fails to document a significant relation between analysts’ cash flow forecasts and their stock recommendation performance. We argue that analysts’ cash flow forecasts differ in quality and hypothesize that the relative accuracy of an individual analyst’s cash flow forecasts is positively associated with the profitability of that analyst’s stock recommendations. We find that when analysts issue both earnings and cash flow forecasts for a firm, cash flow forecast accuracy predicts stock recommendation profitability even after controlling for earnings forecast accuracy. Our findings are robust to the use of instrumental variables and the use of recommendation change in the analysis. Whether analysts also issue sales forecasts and/or long-term growth forecasts does not affect the results

Income Classification Shifting and Financial Analysts’ Forecasts

Income classification shifting involves opportunistically misclassifying core expenses into nonrecurring items in order to boost core earnings. Recent studies have documented large sample evidence of its existence (e.g. McVay 2006; Fan et al.,2010; Barua et al.,2010). Managers engage in income classification shifting because they believe the market in general and financial analysts in particular focus on core earnings. If financial analysts are experts in forecasting permanent earnings, they should be expected to identify reported core earnings that have been inflated through classification shifting and revise their future earnings forecast accordingly. Consistent with my prediction, I find that given the same amount of earnings news, analysts revise their future quarterly earnings forecasts by half as much for classification shifters than for non-classification shifters, suggesting analysts recognize that income classification shifters’ core earnings are less likely to persist into the future. However, I also find that analysts fail to fully gauge the impact of classification shifting on future earnings, leading to more optimistically biased forecasts for classification shifters. Finally, classification shifting makes it more difficult for analysts to forecast earnings so that their forecasts become less accurate

The ternary Goldbach problem with the Piatetski-Shapiro primes

With the help of the transference principle, we prove that for any $c_1,c_2,c_3\in(1,73/64)$, every sufficiently large odd $n$ can be represented as the sum of three primes $p_1$, $p_2$ and $p_3$, where for each $1\leq i\leq 3$, $p_i$ is of the form $\lfloor n^{c_i}\rfloor$.Comment: This is a very preliminary manuscript, which maybe contains some mistake

Extremely cold and hot temperatures increase the risk of ischaemic heart disease mortality: epidemiological evidence from China.

OBJECTIVE: To examine the effects of extremely cold and hot temperatures on ischaemic heart disease (IHD) mortality in five cities (Beijing, Tianjin, Shanghai, Wuhan and Guangzhou) in China; and to examine the time relationships between cold and hot temperatures and IHD mortality for each city. DESIGN: A negative binomial regression model combined with a distributed lag non-linear model was used to examine city-specific temperature effects on IHD mortality up to 20 lag days. A meta-analysis was used to pool the cold effects and hot effects across the five cities. PATIENTS: 16 559 IHD deaths were monitored by a sentinel surveillance system in five cities during 2004-2008. RESULTS: The relationships between temperature and IHD mortality were non-linear in all five cities. The minimum-mortality temperatures in northern cities were lower than in southern cities. In Beijing, Tianjin and Guangzhou, the effects of extremely cold temperatures were delayed, while Shanghai and Wuhan had immediate cold effects. The effects of extremely hot temperatures appeared immediately in all the cities except Wuhan. Meta-analysis showed that IHD mortality increased 48% at the 1st percentile of temperature (extremely cold temperature) compared with the 10th percentile, while IHD mortality increased 18% at the 99th percentile of temperature (extremely hot temperature) compared with the 90th percentile. CONCLUSIONS: Results indicate that both extremely cold and hot temperatures increase IHD mortality in China. Each city has its characteristics of heat effects on IHD mortality. The policy for response to climate change should consider local climate-IHD mortality relationships