Chapter 4. Empirical Results and Analysis
4.2 Empirical Results
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4.2 Empirical Results
4.2.1 Basic Prediction Model
Table 4-2 Basic Sales Prediction Model
Coefficients: Std. Error t value p value
0.022051 0.004594 4.80 1.71e-06 ***
X 0.658251 0.018111 36.35 < 2e-16 ***
*** denotes significant at 0.001 level; ** denotes significant at 0.01 level;
*denotes significant at 0.01 level;
Adjusted R-squared: 0.4183
F-statistic: 1321 on 1 and 1835 DF, p-value: < 2.2e-16 X:
: Sales in quarter t
Table 4-2 reports the estimates of the Basic Sales Prediction Model. X is equal to the first lag seasonal difference in sales scaled by the base quarter. The model shows that the first lag seasonal difference in sales explains much of the current seasonal difference in sales, with the R-squared equals 0.4183. The coefficient on the first lag seasonal difference in sales is positive and significant at 0.001 level, indicating that the variable is positively and highly related to current seasonal difference in sales, and the variable is a necessary control variable in forming sales prediction.
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Table 4-3 Basic Earnings Prediction Model
Coefficients: Std. Error t value p value
0.008923 0.008563 1.042 0.298
X 0.200658 0.022920 8.755 <2e-16 ***
*** denotes significant at 0.001 level; ** denotes significant at 0.01 level;
*denotes significant at 0.01 level;
Adjusted R-squared: 0.03957
F-statistic: 76.65 on 1 and 1835 DF, p-value: < 2.2e-16 X:
: Sales in quarter t
: Income before extraordinary items in quarter t
Table 4-3 reports the estimates of the Basic Earnings Prediction Model. X is equal to the first lag seasonal difference in earnings scaled by the base quarter. The R-squared is equal to 0.03957. This shows that the first lag seasonal difference in earnings doesn’t explain the current seasonal difference in earnings well. However, the coefficient on the first lag seasonal difference in earnings is positive and
significant at 0.001 level, implying that there is still a strong association between the first lag seasonal difference in earnings and current seasonal difference in earnings, and the variable is a necessary control variable in forming earnings prediction.
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Table 4-4 Basic Profit Margins Prediction Model
*** denotes significant at 0.001 level; ** denotes significant at 0.01 level;
*denotes significant at 0.01 level;
Adjusted R-squared: 0.009495;
F-statistic: 6.867 on 3 and 1833 DF, p-value: 0.0001342 X1:
: Income before extraordinary items in quarter t
Table 4-4 reports the estimates of the Basic Profit Margins Prediction Model. X1 is the first lag profit margins, X2 is the fourth lag profit margins, and X3 is the first lag seasonal difference in profit margins. The R-squared is equal to 0.009495. This shows that the Independent variables in profit margins cannot explain much of the current profit margins. However, the coefficients on all the independent variables are positive and significant at 0.001 levels, indicating that there is still a strong
association between these variables and current profit margins. The estimates of are significant, suggesting that the model used for sales and earnings would have been inadequate for profit margins. The estimates of are significant, suggesting the
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presence of seasonality in margins. The estimates of are not significant, conforming the stationarity in the series.
4.2.2 Inventory Expectations Models
Table 4-5 Inventory Expectations Model—LIFO method
*** denotes significant at 0.001 level; ** denotes significant at 0.01 level;
*denotes significant at 0.01 level Adjusted R-squared: 0.8049
: Total inventory under LIFO method in quarter t
Table 4-5 reports the estimates of the LIFO Inventory Expectations Model. The
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model explains much of the variance in inventories, with the R-squared equals 0.8049.
The coefficients on the fourth lag of inventory-to-sales ratio and the first lag in seasonal difference are both positive and significant at 0.001 levels, indicating that these variables are necessary control variables in forming inventory expectations.
The coefficients in the LIFO inventory expectations model show that the LIFO inventory-to-sales ratios are negatively and significantly related to current sales, indicating that production cannot adjust instantaneously to demand changes, and that inventory-to-sales ratios decline as sales increase. According to Bernard (1991), if inventory is a buffer for sales, there should also be a positive relationship between current inventory-to-sales and past sales, as production is adjusted for inventory excesses or shortfalls in the previous quarter. This is the case, with current
inventory-to-sales ratios positively related to sales changes lagged on quarter, and the coefficient roughly equal in magnitude to the coefficient on current sales changes. The buffering behavior is consistent with the stockout model of inventory, while it is inconsistent with production smoothing model, for which inventory-to-sales ratios would be a leading indicator of sales.
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Table 4-6 Inventory Expectations Models—IFRS method
*** denotes significant at 0.001 level; ** denotes significant at 0.01 level;
*denotes significant at 0.01 level Adjusted R-squared: 0.8076
: Total inventory under IFRS method in quarter t
Table 4-6 reports the estimates of the IFRS Inventory Expectations Model. The model explains the variance in inventories well, with the R-squared equals 0.8076.
The coefficient on the fourth lag of inventory-to-sales ratio and the first lag in seasonal difference are both positive and significant at 0.001 levels, indicating that
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these variables are essential control variables in forming inventory expectations. The IFRS inventory are also negatively related to current sales changes and positively related to first lag sales changes, indicating the buffer effect of first lag sales change.
4.3 Final Prediction Models
Table 4-7 Final Sales Prediction Model
*** denotes significant at 0.001 level; ** denotes significant at 0.01 level;
*denotes significant at 0.01 level;
Adjusted R-squared: 0.4124
F-statistic: 417 on 3 and 1775 DF, p-value: < 2.2e-16 X:
: Sales in quarter t
: The unexpected inventory reported under LIFO method in quarter t : The unexpected inventory reported under IFRS method in quarter t
In the final prediction models, the residuals from the inventory expectations model are added as a regressor in prediction models for sales, earnings, and margins to evaluate the predictive ability of inventory. Table 4-7 reports the estimates of the Final Sales Prediction Model. The model shows that the first lag seasonal difference
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in sales explains the current seasonal difference in sales quite well, with the R-squared equals 0.4183. The coefficient on the first lag seasonal difference in sales is positive and significant at 0.001 level, indicating that the variable is strongly related to current seasonal difference in sales.
In the model, is the estimated unexpected inventory from LIFO inventory expectations model, and is the estimated unexpected inventory from IFRS inventory expectations model. According to Bernard and Stober (1989), if the production smoothing model holds, the unexpected inventory would contain information about future demand, and positive unexpected inventory would predict sales and earnings increases. If the stockout model holds, then unexpected inventory would contain information about the difference between current sales and current demand, and positive unexpected inventory would predict sales and earnings decrease.
If neither of these models hold and the simple decision rules are adequate to describe the production decision, then unexpected inventory is noise, and would not be able to predict future sales or earnings. As a result, the coefficients of and represent either the ability of LIFO inventory and IFRS inventory to predict future sales, or the noises in the models.
Table 4-7 shows the positive relation between unexpected inventory and future sales for LIFO inventory, and negative relation between the two variables for IFRS inventory. The results show that the predictability of LIFO inventory for future sales tends to be consistent with production smoothing model, while the predictability of IFRS inventory for future sales tend to be consistent with stockout model. However, the coefficients on both LIFO and IFRS inventory are statistically insignificant. These results indicate that the effect of production smoothing model and stockout model on inventory is not significant.
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Table 4-8 Final Earnings Prediction Model
*** denotes significant at 0.001 level; ** denotes significant at 0.01 level;
*denotes significant at 0.01 level;
Adjusted R-squared: 0.03873
F-statistic: 24.88 on 3 and 1775 DF, p-value: 9.1e-16 X:
: Sales in quarter t
: Income before extraordinary items in quarter t
: The unexpected inventory reported under LIFO method in quarter t : The unexpected inventory reported under IFRS method in quarter t
Table 4-8 reports the estimates of the Final Earnings Prediction Model. The model shows that the independent variables do not explain the dependent variable well, with the R-squared equals 0.03873. The coefficient on the first lag seasonal difference in earnings is positive and significant at 0.001 level, indicating that there is a strong association between the two variables.
The results show a positive relation between unexpected inventory and future earnings for LIFO inventory, and a negative relation for the two variables for IFRS
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inventory, implying the smoothing effect for LIFO inventory and stockout effect for IFRS inventory. However, the coefficients on both LIFO and IFRS inventory are statistically insignificant, indicating that neither production smoothing nor stockout model holds. The unexpected inventories from LIFO inventory and IFRS inventory may be the noises in the models.
Table 4-9 Final Profit Margins Prediction Model
1.076e-01 1.181e-02 0.000 1.000000
X1 1.076e-01 3.192e-02 3.370 0.000768***
X2 7.893e-02 2.482e-02 3.180 0.001498 **
X3 -3.356e-02 3.142e-02 -1.068 0.285711
3.752e-02 5.246e-02 0.715 0.474607
-3.960e-02 5.244e-02 -0.755 0.450262
*** denotes significant at 0.001 level; ** denotes significant at 0.01 level;
*denotes significant at 0.01 level;
Adjusted R-squared: 0.03873
: Income before extraordinary items in quarter t
: The proxy of total inventory reported under LIFO method in quarter t : The proxy of total inventory reported under IFRS method in quarter t
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Table 4-9 reports the estimates of the Final Profit Margins Prediction Model. The R-squared is equal to 0.03873, suggesting that the independent variables do not explain the dependent variable well. The coefficient on the first lag seasonal difference in profit margins is positive and significant at 0.001 level, indicating a strong relation between the dependent and independent variables.
The results once again show that the unexpected inventory is a positive leading indicator of profit margins for LIFO inventory, but a negative leading indicator of profit margins for IFRS inventory. The coefficients for LIFO and IFRS inventory are statistically insignificant suggest that the production smoothing effect and stockout effect are not prevailed. The unexpected inventories from LIFO inventory and IFRS inventory are the noises in the models.
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In economic literature, production smoothing model and stockout model address the predictability of inventory disclosure on sales and earnings. Based on these models, Bernard and Noel (1991) show that inventory disclosure predicts sales and earnings. This study further investigates and compares the predictability of the sales and earnings by inventory reported under last in, last out (LIFO) and that under International Accounting Standard 2 (IAS 2). Thus this study compares the predicting ability of inventory on sales and earnings under IFRS and non-IFRS.
This study selects a group of companies adopting LIFO and disclosing LIFO reserves to be the sample companies, and the LIFO reserves are added to the inventories reported under LIFO method to generate the inventories reported under IFRS inventory valuation method. IFRS inventory valuation method is defined as the inventory valuation methods recommended under IAS 2, which may be FIFO method or weighted average method and can reflect a company’s internal inventory policy.
The sales, earnings, and profit margins models developed by Bernard are used to determine the ability of LIFO inventory and IFRS inventory to predict sales, earnings, and profit margins, and whether LIFO inventory has better predictability than IFRS inventory.
The empirical results show a positive relation between the LIFO unexpected inventory and current sales and earnings, and a negative relation between IFRS
unexpected inventory and current sales and earnings. However, the coefficients for the unexpected inventories under LIFO and IFRS are both statistically insignificant, suggesting that the unexpected inventories are merely noises in the models, and that the effects of production smoothing model and stockout model are not prevailed and
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may be inadequate to explain the management’s inventory policies and decisions.
Thus, it is difficult to determine which inventory valuation method can generate the inventory that leads to better sales and earnings prediction.
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