JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Wiley and Accounting Research Center, Booth School of Business, University of Chicago are collaborating with JSTOR to digitize, preserve and extend access to Journal of Accounting Research. This paper examines the value relevance of earnings by testing their ability to predict two future benefits of equity investment: earnings and cash flow from operations. Previous research (discussed below) has given an incomplete view of earnings' predictive ability, as it has typically focused on short horizons while ignoring the longer-term benefits that are also valued, as in Ohlson [1990].I test earnings' ability to predict future earnings and future cash flow from operations1 one through eight years ahead using annual data from *University of Illinois, Urbana-Champaign. This paper is part of my dissertation from the University of California, Berkeley. I would like to thank my committee members, Professors Andrew Rose, Paul Ruud, and, especially, Baruch Lev (chair), for their thoughtful comments and suggestions. I would also like to thank an anonymous reviewer, Dick Dietrich, Paul Newbold, Walter Teets, and Dave Ziebart as well as workshop participants at the . l Benefits of equity investments include future stock returns (Lev [1989]) or future dividends (i.e., Rubinstein [1976], Garman and Ohlson [1980], and Ohlson [1979; 1983; 1990; 1991]), future earnings (i.e., Miller and Modigliani [1961], Litzenberger and Rao [1971], and Ohlson [1990]), or future firm cash flow (Miller and Modigliani [1961], Watts and Zimmerman [1986], and Ohlson [1990]) that can be discounted to determine the value of a firm. Tests of dividend prediction are not included because it is unreasonable 210 ABILITY TO PREDICT EARNINGS AND CASH FLOW 211 1935-87 for 50 firms. I use time-series methods to test firm-specific predictive ability over the entire time period (hereafter in-sample regression tests) and then compare out-of-sample forecast errors to assess earnings' ability to improve earnings or cash flow forecasts up to eight years ahead. Previous research on earnings' ability to predict future earnings (Ball and Watts [1972], Albrecht, Lookabill, and McKeown [1977], Watts and Leftwich [1977], and Lev [1983]) has examined firstor secondorder autocorrelations and/or forecasts over one-or two-year horizons and has provided evidence to support a random walk model (i.e., uncorrelated earnings changes). However, recent evidence suggests that a random walk may not be descriptive of the earnings process. Ramesh and Thiagarajan [1989] reject a random walk earnings model for 17 of 32 firms tested, and Lipe and Kormendi [1993] show that higher-order, rather than random walk, models are descriptive of market-adjusted earning...

This paper provides evidence that will help stock market participants interpret sell‐side analyst buy/sell recommendations. We examine whether recommendation levels (e.g. buy) correspond with traditional predictors of the underlying stock's performance, and whether recommendation revisions (e.g. an upgrade) are consistent with news analysts receive. Consistent with theory, we find that more optimistic recommendations are associated with higher mean forecast errors, forecast revisions, and forecasted earnings‐to‐price ratios. However, contrary to expectations, they also have higher market‐to‐book ratios, higher market values, and lower ratios of value to price (Lee et al. 1999). These results are probably driven by specific differences between buys and the less optimistic recommendations, as holds and sells are rarely distinguishable from each other. Our recommendation revision findings are consistent with our expectations. Upgrades have significantly larger earnings forecast errors, earnings forecast revisions, and unexpected earnings growth than do reiterations or downgrades.

Publications in nonaccounting journals are commonly omitted in accounting faculty productivity studies, and we examine whether that omission matters. We use samples consisting of two groups of new scholars who received an accounting Ph.D. (1987/88 and 1977/78 graduates) and took tenure-track positions at U.S. universities. We combine data from multiple databases to obtain an extensive list of research publications pertaining to these individuals. During the first seven employment years, the 87/88 (77/78) Ph.D. sample published, on average, 1.95 (1.55) total research articles per scholar, including 1.38 (1.15) articles appearing in accounting journals. These findings suggest that new scholars commonly use nonaccounting journals as publication outlets, and that publication counts increase substantially when publications in both nonaccounting and accounting journals are considered. In addition, the 87/88 scholars published in nonaccounting journals more often than did the 77/78 scholars. Finally, publications in nonaccounting journals occur somewhat later in accounting scholars' careers.

Attendance2 is an iOS application (app) that can be purchased for $5 and used with an Apple device to record and summarize a wealth of classroom data. After importing or manually entering roster data, an instructor can take student photos and refer to them while "tapping" the device to enter attendance and participation information. Attendance2 allows instructors to specify two statuses (data items to be collected at each class meeting). Instructors commonly set "Status1" to be "attendance" and "Status2" to be "class participation," but an instructor can set the statuses to be anything, with options limited only by the imagination and needs of the user. Instructors can ensure the security of their data by regularly using the easy "backup" feature, which sends data to an email account or "Dropbox." They can also set points for each status value (e.g., two points for "present," one point for "late") and instantly create a spreadsheet that includes the daily entries and totals (e.g., total absences) for each student.