1. Introduction
This study examines whether or not certain firm characteristics, specifically growth properties, are associated with stronger incentives in order to avoid negative revenue surprises. We define revenue surprises as the difference between the latest consensus of analyst revenue forecasts and actual firm revenues consistent with prior studies. The latest consensus (median) of analyst annual revenue forecasts reported one month before the current period earnings announcement is used as a proxy for market revenue expectations. We find that growth firms are more likely to emphasize revenue surprises than value firms to the extent that market participants place heavier weight on the revenue signals of growth firms versus value firms. We additionally focus on the use of two possible tools for growth firms to achieve favorable revenue surprises: (1) revenue manipulation, and (2) revenue expectation management. Since costs associated with both mechanisms may be different depending on the firms’ growth properties, we examine which mechanism best allows growth firms to meet or beat market expectations for revenues versus value firms.
Prior literature provides evidence that the market rewards significantly higher equity premiums for firms meeting or beating both analyst earnings and revenue forecasts, and conversely penalizes firms for missing them (Jegadeesh and Livnat [
1]; Rees and Sivaramakrishnan [
2]; Chandra and Ro [
3]). Nelson et al. [
4] show that many attempts in earnings management to meet or beat market expectation involved revenue manipulation. More importantly, Ertimur et al. [
5] finds that market participants react negatively to growth firms missing revenue expectations even if these firms successfully meet or beat earnings expectations. Furthermore, Kama [
6] reports that the impact of revenue surprises on stock returns is higher in research and development (R&D) intensive firms. These findings suggest that the costs associated with missing revenue expectations are much greater for growth firms versus value firms. These high costs might provide stronger incentives for growth firms to closely observe revenue signals. These increased incentives may accordingly lead growth firm managers to undertake additional actions such as manipulating reported revenues upward and managing revenue expectations downward in order to generate favorable revenue surprises. For example, Stubben [
7] uses univariate analysis and presents evidence that growth firms use more upward revenue manipulation to meet or beat analyst revenue forecasts than value firms. We build on this research by examining how growth firm managers avoid missing market revenue expectations.
We hypothesize that growth is positively associated with the likelihood of achieving either zero or positive revenue surprises because the importance of valuation revenue information is higher for growth firms. Using a book-to-market ratio as a growth proxy, we find that growth firms are more likely to meet or beat analyst revenue expectations versus value firms (cf. Collins et al. [
8]).
Since the costs and benefits derived from earnings management and expectations management may vary by growth properties, we test which is more commonly used by growth firm managers in order to achieve either zero or positive revenue surprises. We accordingly examine the impacts of an interaction term between growth proxy and a proxy for upward revenue manipulation as well as the impact of an interaction term between growth proxy and a proxy for downward revenue expectation management on the likelihood of meeting or beating analyst revenue forecasts. We proxy for revenue manipulation by estimating discretionary revenue using the Stubben [
9] model. Following Matsumoto [
10] we also proxy for revenue expectation management using a measure of the revenue forecast guidance. These results suggest that revenue manipulation increases or decreases the likelihood of meeting or exceeding revenue expectations for growth firms and value firms, respectively; while expectation management decreases or increases the likelihood for growth firms and value firms, respectively. Revenue manipulation and revenue expectation management are, respectively, accordingly a more- or less-commonly used tool for growth firms in achieving favorable revenue news versus value firms. These findings imply that growth firms are more inclined to distort their reported revenue numbers in order to achieve short-term objectives relative to value firms. It may have negative impacts on a growth firm’s future performance and eventually deteriorate its sustainability.
This study contributes to the literature in highlighting the importance of revenue information for certain firms. Prior research provides evidence that managers have strong incentives to focus on revenue signals because market participants may consider revenue-related information more important and value-relevant under various circumstances, such as a specific industry (e.g., internet business industry) (Bowen et al. [
11]), firms having negative earnings (Hayn [
12]; Callen et al. [
13]), firms having a high volatility of earnings (Ertimur and Stubben [
14]), and firms having high growth properties (Ertimur et al. [
5]; Kama [
6]). We add to this research by providing additional evidence that growth firms are more likely to meet or exceed analyst revenue expectations than value firms.
Moreover, this study also contributes to the research examining the mechanisms used to successfully reach analyst revenue expectations. Although some prior studies investigate revenue manipulation in order to achieve zero or small positive revenue surprises (Stubben [
9]), there is no prior work on whether or not firms use expectations management, revenue management, or both as a tool to achieve expected revenues. This paper provides implications for future research, in that the practices used by managers in order to avoid missing an important revenue target are influenced by certain firm characteristics, as shown by the differing mechanism effectiveness for growth properties.
The remainder of this paper is organized as follows.
Section 2 discusses the related literature and hypotheses. We describe the sample selection in
Section 3.
Section 4 explains the research design and variables.
Section 5 contains descriptive statistics and empirical results. Finally,
Section 6 provides the concluding remarks.
3. Sample Selection
We use the consensus of analyst annual revenue forecasts obtained from the Institutional Brokers Estimate System (I/B/E/S) as a proxy for market revenue expectations (Bartov et al. [
18]; Ertimur et al. [
5]; Rees and Sivaramakrishnan [
2]). We obtain annual analyst revenue forecasts from the I/B/E/S, which began providing revenue forecasts in a machine-readable format in 1996. Limited observations are available between 1996 and 1998, so we accordingly limit our sample to the years between 1999 and 2010. We also follow Bartov et al. [
18] by requiring that each firm have at least three revenue forecasts in order to ensure that there is an initial forecast, a forecast revision, and a final forecast during the fiscal period. We also confirm that the first available revenue forecast is disclosed after the prior revenue announcement date, and that the last available forecast is released before the current announcement date. We use the fourth quarter earnings announcement date as the annual revenue announcement date. For comparability we estimate revenue surprises by comparing revenue forecasts versus actual revenue from I/B/E/S. We use annual accounting data to calculate discretionary revenues, and other variables were compiled from the COMPUSTAT database. Consistent with Matsumoto [
10] we exclude financial institutions, utilities industries, and regulated industries the Standard Industrial Classification (SIC) codes between 5999 and 7000, between 4799 and 5000, and 3999 and 4500 respectively) because these firms are likely to have different earnings management incentives from other firms. The total number of firm-year observations included in the final sample is 29,520.
6. Conclusions
This study investigates whether a firm’s growth properties are associated with its likelihood of meeting or beating analyst revenue forecasts. We expect that growth firms pay closer attention to achieving zero or positive revenue surprises than value firms. This is in part because revenue information is more relevant for the market in making appropriate valuation decisions relative to earnings information. Our findings provide evidence that high growth firms are more likely to either meet or exceed analyst revenue expectations versus low growth firms.
This study also examines whether the use of two possible mechanisms (revenue manipulation and revenue expectation management) for avoiding negative revenue surprises varies conditional on a firm’s growth property. We postulate that the use of these tools might differ in their growth properties, although they are both effective mechanisms for generating favorable revenue information. Our results confirm that both mechanisms increase the likelihood of achieving either zero or positive revenue surprises. However, we find that upward-revenue manipulation is more actively used by growth firms than value firms to meet or exceed analyst revenue forecasts, while downward-revenue expectation management is less utilized by growth firms. The reported existence of revenue manipulation by growth firms in order to achieve short-term goals may not be sustainable in the long run, and can misguide users of financial statements in their decision making. Although this study provides empirical evidence of upward-revenue manipulation used by growth firms, future research needs to investigate the role of other players in financial market—including, but not limited to, auditors, policymakers, and regulators—to minimize such opportunistic behaviors.