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Review of Managerial Science
Erschienen in: Review of Managerial Science 2/2021

20.05.2019 | Original Paper

Market reaction to asymmetric cost behavior: the impact of long-term growth expectations

verfasst von: Lisa Silge, Arnt Wöhrmann

Erschienen in: Review of Managerial Science | Ausgabe 2/2021

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Abstract

We investigate whether asymmetric cost behavior (also termed cost stickiness) and investors’ assessment of asymmetric cost behavior are affected by firms’ long-term growth expectations. Using a sample of US firms for the period 1990–2014, we first predict and find that cost stickiness, though a short-term phenomenon, is greater when firms have high rather than low long-term growth expectations. Second, we predict that unexpected cost stickiness is negatively evaluated by investors. Investigating cumulative abnormal returns surrounding earnings announcement dates, we find support for this prediction. Third, we investigate this finding in more detail, dependent on long-term growth. We argue that the reasons for cost asymmetry differ between firms with high versus low long-term growth expectations. We expect these differences to result in differing investor reactions. In line with this prediction, our results reveal that investors react more negatively to unexpected cost stickiness when a firm has low long-term growth opportunities. This finding supports the assumption that investors perceive agency motives as more likely to explain the unexpected cost stickiness for these firms.
Vorheriger Artikel A chip off the old block? The role of dominance and parental entrepreneurship for entrepreneurial intention
Nächster Artikel The mediating effect of strategic posture on corporate governance and environmental reporting

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Fußnoten
1
Because the capital market will react only to new information, we focus on unexpected cost stickiness for this hypothesis, i.e., the portion of costs of idle capacity that is not expected by the capital market.
 
2
We use US firms to ensure high data availability and comparability with prior studies.
 
3
We find that our measure for long-term growth prospects provides information about future sales growth in addition to measures suggested by prior research.
 
4
Adjustment costs are costs that a firm incurs to reduce committed resources and to replace those resources if demand is restored (Anderson et al. 2003).
 
5
While long-term growth expectations are likely to affect expected cost of idle capacity, there is no reason to believe that costs of the same adjustment (e.g., for hiring a new employee) will differ between low- and high-growth firms.
 
6
In contrast, Kama and Weiss (2013) and Dierynck et al. (2012) show that depending on motivations underlying managers’ resource adjustments, agency-driven incentives can lead to inefficiently low cost stickiness. These authors show that when sales decrease, managers reduce costs more aggressively in the presence of incentives to meet earnings targets, thereby resulting in a lower degree of cost stickiness.
 
7
We again refer to the different drivers of cost stickiness when developing H3. More precisely, we use the different drivers to explain how investors’ evaluate cost stickiness, an implication not examined in Chen et al. (2012).
 
8
For our operationalization of unexpected cost stickiness, we argue that the capital market forms its expectation of the level of cost stickiness based on the level four quarters ago. In summary, therefore, unexpected cost stickiness is operationalized as the difference between current cost stickiness and cost stickiness one year earlier.
 
9
Compared with the original method to detect cost stickiness suggested by Anderson et al. (2003), the Weiss measure can be used as an independent variable to examine the impact of cost stickiness on capital market evaluation. Many prior studies follow Anderson et al. (2003) and use a regression model to estimate cost stickiness at the industry or firm level, which results in an estimated regression function with one static regression coefficient that captures the degree of cost stickiness of the sample firms in the sample period. Thus, this measure is constant over time and/or is not firm specific. To analyze the market reaction to cost stickiness, we need a firm- and time-specific measure of cost stickiness. Thus, we use the cost stickiness measure suggested by Weiss (2010).
 
10
To minimize data loss, we use STICKYi,t-8 to calculate ∆STICKYi,t if data on cost stickiness for t-4 is missing. However, if we use only cost stickiness of t − 4 to calculate ∆STICKYi,t, our results for H2 and H3 are inferentially identical. Further, our results are unchanged if we define ∆STICKYi,t as the difference between cost stickiness in the current period and cost stickiness in the most recent of four fiscal quarters with available data.
 
11
We decided to use a firm-based instead of an industry-based measure because we expect a firm-based measure to more precisely capture growth expectations. For example, an innovative firm in the transportation industry might expect (and have) higher growth rates than well-established and mature firms in that industry. Further, we believe a firm-based measure is better able to account for multi-segment firms.
 
12
If we use industry fixed effects instead of firm fixed effects, our results for H1, H2, and H3 are unchanged.
 
13
Our results are inferentially identical if we use asset intensity and employee intensity without a logarithm.
 
14
We include a robustness test using the growth of order backlog and analysts’ sales forecasts. However, information on analysts’ forecasts is missing for about 65% of the observations we use in our main analysis, and data on the growth of order backlog is not available for about 69% of our observations.
 
15
A heteroscedasticity-robust version of the Hausman specification test (Arellano 1993; Hausman 1978; Schaffer and Stillman 2016) rejects the random-effects model in favor of a fixed effects model.
 
16
In an additional analysis, we also use analysts’ forecast errors as a proxy for unexpected earnings.
 
17
Sales growth is calculated as the difference between the sales of firm i in quarter t and its sales in the same quarter one year prior, scaled by the sales of firm i in the same quarter one year prior.
 
18
We exclude financial firms (SIC codes 6000-6999) and utility firms (SIC codes 4900-4999) because the structure of their cash flows and financial statements differs substantially from those of all other industries.
 
19
To calculate all variables, we require data for the five preceding fiscal quarters for every observation. Thus, we also gather data for the years 1988 and 1989 to calculate these variables.
 
20
We also delete duplicate fiscal quarters (97) and 4488 observations for which a change in fiscal year occurred during the previous four quarters. These and the other criteria described in the text result in 172,113 firm-quarter observations.
 
21
These missing values occur because sales and SG&A costs do not change in the same direction in the current period or because over the last four quarters, there was either no quarter with increasing sales and increasing SG&A costs or no quarter with decreasing sales and decreasing SG&A costs. In the additional analysis section, we provide a robustness check that uses an alternative measure for STICKYi,t to limit the loss of observations and rule out the possibility that our results are affected by a selection bias.
 
22
For the analysis of H2 and H3, we must delete 20,906 firm-quarter observations due to missing values for ∆STICKYi,t and LAGSTICKYi,t.
 
23
We conduct a median split and define GDP growth above 2.9% as high and GDP growth below or equal to 2.9% as low.
 
24
We report two-tailed tests throughout.
 
25
We find a significant negative coefficient for LTGROWTHi,t although we included alternative growth measures from prior literature in the regression model as control variables into the regression model. Further, adding our main dependent variable LTGROWTHi,t to the regression model leads to an increase of adjusted R-squared from 0.1180 (in Regression Model 1 excluding LTGROWTHi,t) to 0.1185 (in Regression Model 1 including LTGROWTHi,t).
 
26
We use yearly values for order backlog because of limited data availability for quarterly values.
 
27
However, the results should be regarded with caution because, due to data limitations, we use yearly data for order backlog, which reflects the current fiscal quarter less accurately than quarterly data. Further, sample size is reduced by about 65% because of missing data for analyst forecasts. In addition, high variance inflation factors for ln∆OB and MISSING∆OB indicate that the results might be biased due to multicollinearity.
 
28
For the fundamental signals earnings quality and audit qualification, quarterly data are not available. Thus, we do not include those variables.
 
29
Because we do not have quarterly data on the number of employees, we use the number of employees at the next fiscal year end after quarter t.
 
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Metadaten
Titel
Market reaction to asymmetric cost behavior: the impact of long-term growth expectations
verfasst von
Lisa Silge
Arnt Wöhrmann
Publikationsdatum
20.05.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Review of Managerial Science / Ausgabe 2/2021
Print ISSN: 1863-6683
Elektronische ISSN: 1863-6691
DOI
https://doi.org/10.1007/s11846-019-00341-8

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