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Review of Accounting Studies
Erschienen in: Review of Accounting Studies 2-3/2008

01.09.2008

Inventory policy, accruals quality and information risk

verfasst von: Gopal V. Krishnan, Bin Srinidhi, Lixin (Nancy) Su

Erschienen in: Review of Accounting Studies | Ausgabe 2-3/2008

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Abstract

This paper provides evidence consistent with firms with Last-in-first-out (LIFO) inventory policy being priced by the market as having lower information risk than First-in-first-out (FIFO) firms. Furthermore, the paper shows that this pricing differential is sustained after controlling for accruals quality, suggesting that the inventory policy signals some information risk characteristics that are not captured by accruals quality measure. We investigate the relation between inventory policy and accruals quality and find that accruals quality is systematically worse for FIFO firms than for LIFO firms after controlling for correlated omitted variables and known firm attributes. These findings complement the currently established relationship between the cost of capital, market pricing and accruals quality by focusing on the need for understanding the incremental effects of individual accounting policies.
Vorheriger Artikel Discussion of “Investor recognition and stock returns”
Nächster Artikel Discussion of “inventory policy, accruals quality and information risk”

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Fußnoten
1
The accruals quality measure was developed by Dechow and Dichev (2002). They used only the current, previous and subsequent cash flows as regressors in determining the residual whose standard deviation was defined as the accruals quality measure. This was modified by McNichols (2002) who included the change in sales and property, plant and equipment as additional regressors in the regression. Francis et al. (2005) investigated and showed the association of this measure with the cost of capital.
 
2
The “accruals anomaly” literature also examines the effect of accruals on market pricing (Sloan 1996; Richardson et al. 2005; Kraft et al. 2006; Hirshleifer et al. 2006; Khan 2007; Ng 2005). The focus of this stream of research is whether the investors rationally price the stock by considering all the systematic risks that might be indicated by the accruals (Ng 2005; Khan 2007; Kraft et al. 2006) or whether they mis-price the stocks due to fixation (Hirshleifer et al. 2006). We are not primarily interested in the accrual anomaly and whether it represents rational pricing or a behavioral phenomenon. The inventory choice policy is public information and we argue that it must necessarily represent systematic risk to be priced by the investors.
 
3
This is a counter-point to the explanation given in Penman and Zhang (2002) who argue that conservative accounting gives rise to higher investment accounts on the balance sheet which can be used by managers to manage earnings and reduce the quality of earnings. Clearly, if the LIFO reserve (which is used in constructing the C-score in their paper) is dipped into, it could create much higher variability in earnings. However, the dipping into reserves reduces the reported reserves and is part of the reported information. Therefore, investors can correct the income number (to estimate the permanent component) and it will be reflected in the stock price immediately, making the LIFO policy an unlikely signaling mechanism.
 
4
LIFO firms have significantly lower obsolescence and variability of inventory than FIFO firms. Further, LIFO firms have a significantly lower standard deviation of earnings before extraordinary items, adjusted for LIFO reserve. In Sect. 3 of the paper, we provide evidence in support of these claims. Panel A and B of Table 5 show lower obsolescence and variability whereas the last panel of Table 2 shows smaller variability of earnings for LIFO firms.
 
5
Consider private information, η m (the subscript m for managers) and public information ω in two identical firms that differ only in inventory policy. If the expected precision of private information in LIFO firm is higher than that in FIFO firm, i.e., ψ (η m |ω, LIFO) > ψ (η m |ω, FIFO), the relative weights on the public and private information in the two firms will be different. This difference in weights between private and public information results in undiversifiable information risk (Easley and O’Hara 2004).
 
6
LIFO inventory policy reduces the ability of managers to use earnings management to hide the firm’s bad performance (note that use of LIFO reserves is easily seen by investors from financial statements and cannot be argued as “hiding” performance). By choosing LIFO, managers could also signal that they will not indulge in earnings management. This is also too costly to be copied by managers who indulge in earnings management. We thank Prof. Robert Halperin for pointing this out.
 
7
FIFO firms signal both higher volatility as well as higher future sales growth (the 5-year-ahead future sales growth is 0.538 for LIFO and 1.105 for FIFO firms, and the difference is significant at 5% level). In equilibrium, the net benefit for FIFO firms of signaling higher future sales growth exceeds the cost of signaling higher volatility but for LIFO firms, the expected total benefit of tax shield and signaling lower volatility exceeds the cost of signaling lower future sales growth. In effect, this results in a separating equilibrium.
 
8
Some earlier analytical work (Hughes et al. 1988, 1994) suggests that firms that choose FIFO might forego the benefit of switching to LIFO (if any) to provide a signal of superior performance to investors. However in this study, we do not examine the effect of inventory choice on performance. Our focus is on the effect of inventory choice on accruals quality and information risk. Further, we control for the incentives to choose the inventory method in a two-stage analysis.
 
9
The random walk model for resource prices assumes that based on the current information, a systematic increasing or decreasing trend in the prices cannot be anticipated. The model captures the “real” prices after adjusting for inflation.
 
10
We thank Dan Givoly for pointing out that the difference in mean cost of goods sold between the LIFO and FIFO firms is not significant.
 
11
Auditors do not solely rely on a pre-set sampling rule. They perform an analytical review of accounts to find any outliers and choose them for investigation. In addition, they also choose other accounts randomly for investigation. We focus on the first part, i.e., the choice of accounts based on outliers.
 
12
Reporting of cash flow from operations (compustat annual data item #308) in the statement of cash flows was mandated only in 1988 across board, following SFAS No. 95 (requires firms to present a statement of cash flows for fiscal year ending after July 15, 1988). The use of this reported cash flow would limit the sample to the years 1996 onwards because the computation of AQ also requires 7 years of cash flow data. Therefore, we have chosen to report the results from the larger sample from 1976 using the computed cash flows for all years. However, we have conducted the full analysis with the reported cash flow data for the sample from 1996 to 2003 and all the results are qualitatively similar. This analysis is not reported here in the interest of brevity but is available from the authors on request.
 
13
The reason we choose firms with LIFO or FIFO as predominant inventory policy rather than for all inventories (pure LIFO or FIFO) is that multinational firms with the majority of inventory overseas are often prohibited to use LIFO overseas. The criterion of pure LIFO or FIFO policies would therefore exclude all such firms from the sample.
 
14
The number of LIFO and FIFO firms together exceeds the number of total firms because even though we require no change in inventory policy over the past 8 years, it is still possible that a firm adopted LIFO in the early part of the sample period and switched to FIFO in the later part of the sample period or vice versa.
 
15
There regressions are estimated for each Fama French 48 industry and year combination. The mean coefficients are the average values of these regression coefficients. These regressions are not tabulated and presented in the interest of brevity.
 
16
Dechow and Dichev (2002, pp. 43–44) point out that even though the firm-level time-series specification of accruals quality is theoretically better supported than the cross-sectional industry level specification, the empirical estimate is noisier in the firm-specific case because of less degrees of freedom. We use the cross-sectional measure and mitigate the effect of the measurement error by including the industry dummies in the regression.
 
17
The first stage (prediction model) is estimated on a limited sample of firms that have no missing values for the choice variables. In the second stage, the full sample is used and the predicted LIFO is computed using the coefficients estimated in the first stage. In cases where there are missing values for choice variables, the predicted LIFO variable is estimated by replacing those specific missing variables with the average value for LIFO or FIFO firms to which the observation belongs. We also estimated the second stage model using only the limited sample of the first stage. The results are not qualitatively different from those that are reported in the Table. The same variables are significant in the same direction in both cases. For the sake of brevity, the limited sample second stage results have not been tabulated. However, these results are available with the authors.
 
18
In general, we expect dipping into LIFO inventory would result in increased variability in LIFO firms which goes against our results. However, it is theoretically possible that LIFO dipping might be used as a tool to smooth earnings and this could potentially result in improved accruals quality measure. It is this last possibility that we want to avoid by using the inventory-increasing sub-sample.
 
19
As was done in the case of the sample in Table 5, the first stage prediction model is computed using the firms that have no missing values for the choice variables. However, the second stage is based on a broader sample that might have firms with missing choice variables. We repeated the second stage analysis using the limited sample with no missing choice variable values and got results that are consistent with those reported for the broader sample. The results are available with the authors.
 
20
We also compute the median R 2 values for each model in Panel A of Table 7. The medians and the means are not very different, which shows that the distributions of R 2 values are not skewed. The difference in medians tests give essentially the same results as the difference in means tests for R 2 values.
 
21
We acknowledge that for inventory policy to be a priced risk factor, the significant loading on INVfactor is a necessary but not sufficient condition, as it just shows that assets co-vary with a portfolio designed to mimic exposure to this inventory policy risk factor. To further examine if FIFO firms command a high risk premium, we investigate LIFO/FIFO choice with the implied cost of capital in an additional test.
 
22
For instance, Lamont et al. (2001) show that a financial constraints factor exists, but contrary to expectation, its pricing, using the realized returns remains a puzzle because the financially constrained firms exhibit a negative risk premium.
 
23
We are thankful to the reviewer who suggested that we should address the issue of realized returns not being a good proxy for the expected returns.
 
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Metadaten
Titel
Inventory policy, accruals quality and information risk
verfasst von
Gopal V. Krishnan
Bin Srinidhi
Lixin (Nancy) Su
Publikationsdatum
01.09.2008
Verlag
Springer US
Erschienen in
Review of Accounting Studies / Ausgabe 2-3/2008
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI
https://doi.org/10.1007/s11142-008-9067-2

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