Analysts issuing forecasts on weekends

Abstract

Purpose This study aims to investigate the motivation of financial analysts issuing forecasts on weekends and the impact of such behavior on forecast accuracy and analysts’ careers. Design/methodology/approach Logistic regression and ordinary least squares models with Huber–White standard errors were used in this study. Findings This paper first documented the emerging trends of the weekend forecasts after 2000. Longitudinal data from 2002 to 2011 validated that analysts’ conscientious timing of information release in line with their workload and confidence level gives more accurate forecasts. Further, given the same accuracy, analysts exhibiting diffident behaviors (analysts who are predicted to work on weekdays but in fact work on weekends) are not fired or demoted by brokerage houses, but those exhibiting inactive behaviors (analysts who are predicted to work on weekends but did not do so) are more likely to be dismissed or demoted by brokerage houses, indicating that brokerage houses are aware of the negative effect of both behaviors, but treat them differently. Research limitations/implications Weekend versus weekday proxies for an analyst’s timing of information release consider only one of many timing options. Other timing proxies, the nature and the composition of the information release of analysts are not examined in this study. Practical implications For practitioners, the results indicate that depending on the alignment, capital market can predict analysts’ future forecast accuracy, and hence, respond accordingly. For example, in addition to analyst forecast level or change, investors could pay attention to when the information is released to the market and possible reasons behind the choice of timing. Investors can thus better assess the forecast accuracy of one specific forecast and respond with the right action. Furthermore, analysts can better project their own forecast accuracy and career potential by assessing to what extent their forecasts are released conscientiously. Social implications This study examines analysts’ forecast behavior, but generate some insights on linking the analysts and investors in the capital market. Originality/value This study is the author’s original work.

The association of analysts’ cash flow forecasts with stock recommendation profitability

“…Number of firms followed (NFIRM) : We use the number of firms that an analyst follows in a calendar year, NFIRM, to control for the complexity of the analyst’s forecasting job, as well as the time and resources the analyst devotes to a given firm (Clement, 1999; Liu, 2017). We expect that the greater the number of firms that an analyst follows, the poorer the performance of the stocks that the analyst recommends.…”