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Analyst team forecasts are the most frequent form of earnings expectations available to investors, with teams issuing more than 70% of research reports in 2016. Prior research provides differing evidence on whether analyst teams issue higher or lower quality forecasts than individual analysts.

I use a sample of more than 17,000 hand-collected analyst reports representing 7,586 forecasts from 89 companies in three industries from 1994–2005.

I document that analyst teams benefit from an assembly bonus, and issue more accurate forecasts than individual analysts only in time periods when teams would be expected to benefit from an assembly bonus.

I outline multiple factors within the control of brokerage houses that impact teams’ relative forecast quality, such as the number of members in the team, how long the team has worked as a unit and the costliness of integrating information when forming a forecast.

Given the preponderance of analyst teams and the strength of market reaction to their forecasts, it is valuable to document factors both in the past and present likely to affect analyst teams’ relative forecast accuracy.

This study aims to determine the key indicators affecting the resilience of the construction supply chain to flooding and calculate the resilience of the urban construction supply chain in three cases city.

This study combines expert opinions and literature review to determine key indicators and establish a fuzzy EWM-GRA-TOPSIS evaluation model. The index weight was calculated using the entropy weight method, and GRA-TOPSIS was used for comprehensive evaluation.

The results of the study show that the three cities are ranked from the high to low in order of Hangzhou, Hefei and Zhengzhou.

The innovative method adopted in this study comprising EWM-GRA-TOPSIS reduced the influence of subjectivity, fully extracted and utilized data, in a way that respects objective reality. Further, this approach enabled the absolute and relative level of urban construction supply chain resilience to be identified, allowing improvements in the comprehensiveness of decision-making. The method is relatively simple, reasonable, understandable, and computationally efficient. Within the approach, the entropy weight method was used to assign different index weights, and the GRA-TOPSIS was used to rank the resilience of the construction supply chain in three urban cities. The development of resilience provides a robust decision-making basis and theoretical reference, further enriching research methods, and having strong practical value. The study serves to improve risk awareness and resilience, which in turn helps to reduce losses. It also provides enhanced awareness regarding the future enhancement of supply chain resilience for urban construction.