Chapter 2. Literature Review
2.2 Inventory, Sales and Earnings Related Literature
2.2 Inventory, Sales and Earnings Related Literature
Broadly speaking, past LIFO research has focused on two key questions. The first question is about the sophistication of managers’ inventory accounting method decision. For example, Bar-Yosef (1992) and Cushing (1992) discuss whether
managers would choose LIFO to minimize the company’s tax payment, or they would choose FIFO to avoid lower reported earnings. Hughes, P.J (1994) analyzes the manager's choice of both an inventory accounting method and capital structure in order to communicate private information about the firm's future cash flows.
The second question is about investors' reactions to LIFO adoptions. For example, Biddle (1988) focuses on analysts’ forecast errors and stock price behavior near the earnings announcement dates of LIFO adopters. Jennings (1992) examines investor and stock price reaction to LIFO adoption decisions. Kang (1993) discusses the stock price effects of LIFO tax benefits. Guenther (1994) analyzes the effect that the ―LIFO reserve‖ has on firm value, and the results indicate a significant negative relation between the LIFO reserve and the value of equity because larger LIFO reserves may be associated with greater accounting costs and may be a proxy for the average expected effect of future inflation on the firm’s input prices.
However, few literatures consider the effect of inventory accounting methods on financial statements analysis. This study examines how inventory accounting methods affect inventory and how the inventory affects future sales and earnings.
According to the IASB, LIFO is generally not a reliable representation of actual inventory flows. International Accounting Standard (IAS) 2 sets out the accounting treatment for inventories and provides guidance on determining their cost. IAS 2 points out that the LIFO method treats the newest items of inventory as being sold
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first, and consequently the items remaining in inventory are recognized as if they were the oldest; therefore, the use of LIFO results in inventories being recognized in the balance sheet at amounts that bear little relationship to recent cost levels of
inventories. Some respondents argued that the use of LIFO has merit in certain
circumstances because it partially adjusts profit or loss for the effects of price changes.
However, the Board concluded that it is not appropriate to allow an approach that results in a measurement of profit or loss for the period that is inconsistent with the measurement of inventories for balance sheet purposes. As a result, the Board decided to eliminate the allowed alternative of using the LIFO method.
Several studies have addressed that Inventory is one of the fundamental signals for Future Earnings. Chi-Wen Jevons Lee (1988) finds significant association between the Earnings and Profit ratio (E/P ratio) and the inventory accounting methods.
According to common economic intuition, each dollar of pretax cash flow in a FIFO firm should lead to higher accounting earnings, higher tax payments and a higher stock price than in a FIFO firm, so the E/P ratios of the FIFO firms should be higher than those of the LIFO firms. However, Lee finds the E/P ratios of the LIFO firms are higher than those of the FIFO firms. Although he hasn’t established a complete causal link, he shows that inventory accounting can affect a company’s stock valuation.
Bernard (1991) examines the relation between inventory disclosures, future sales and future earnings. He uses a ―lead time‖ or ―production smoothing‖ model and a
―stockout model‖ of inventory to evaluate the predictive ability of inventory. He finds that an unexpected change in total inventory is a negative leading indicator of future earnings and profit margins, because an inventory buildup generally reflects decline in future sales, but the increase in inventory is positively related to future sales, because inventory reflects management's private information about demand. This paper
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reveals a strong relation between inventory and future sales and earnings, and
provides valuable insight that inventory disclosures can improve predictions of future sales and earnings.
Thiagarajan. (1993) Abarbanell (1997) analyzes the underlying relations between accounting-based fundamental signals and security prices. He finds that inventory is one of the fundamental signals for future earnings for several reasons. One of the reasons is that increase in finished goods inventory that outstrips sales demand is predicted to indicate bad news for earnings. The other reason is that inventory changes in excess of sales changes are negatively associated with future earnings performance. The study shows that inventory is one of the crucial elements for earnings information analysis.
Thomas and Zhang (2003) indicate that the negative relation between accruals and future abnormal returns is due mainly to inventory changes, and inventory changes represent the one component that exhibits a consistent and substantial relation with future returns. They document several key empirical regularities for extreme inventory change companies and explore the relation between sales and inventory changes. They think firms with inventory increases experience higher profitability, growth, and stock returns over the prior five years, but those trends reverse after the extreme inventory change. They also think quarterly cost of goods sold (COGS) and sales ratio and selling, general and administrative (SG&A) expenses and sales ratio exhibit similar patterns. In addition, LIFO companies with inventory increases represent one subgroup of extreme inventory change companies that exhibits
abnormal return and profitability patterns unlike those observed for other companies.
Jennings and Thompson (1996) investigate the relative usefulness of LIFO and non-LIFO financial statements as a basis for valuation. It is often argued that LIFO
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income statements are more useful as a basis for valuation than those prepared under alternative cost-flow assumptions because LIFO cost of goods sold is based on relatively current inventory costs. In contrast, non-LIFO balance sheets are alleged to be more useful for valuation because their inventory values better represent the net assets available to generate future resource inflows. Jennings and Thompson use LIFO reserve disclosures to construct ―as if‖ non-LIFO income statements and
balance sheets for 991 LIFO users and compare the extent to which elements of actual LIFO financial statements and their ―as if‖ non-LIFO counterparts explain the
observed distribution of equity values for these firms. The comparisons indicate that LIFO cost of goods sold is a more useful indicator of future resource outflows, LIFO reserve disclosures are useful supplements to the LIFO balance sheet, and LIFO-based income statements explain slightly more of the cross-sectional variation in equity values than their ―as if‖ non-LIFO counterparts.
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In this chapter, Section One will develop the hypotheses for this study, which are based on two economic models. Section Two will present the data selection process.
Section Three will discuss the research methodology and design, and Section Four will examine the empirical models and variables.
3.1 Research Hypotheses
3.1.1 The Production Smoothing Model
The production smoothing model is one of the most widely studied models of inventory in economic literature (Blinder 1986). A necessary motive for a company to smooth production is that demand varies through time. If there is a random element to demand, a company may decide to smooth production and treat inventories as a buffer stock. Therefore, a firm is said to smooth production if the variance of production is less than the variance of sales.
The information structure of the production smoothing model presumes that both cost shock and demand shock would affect production decisions. According to Guido Lorenzoni (2006), demand shock is a sudden event that causes a shift in consumer expectations, which increases or decreases demand for goods or services temporarily, while cost shock is an event that causes a sudden increase of decrease of production costs. The production smoothing model assumes that managers can observe cost shock and part of demand shock before choosing its level of production, price, and expected sales. After these decisions are made, the rest of the demand shock is observed and actual sales are determined. The inventory levels for next period then follow and modify the prior production decision.
Consequently, we can see that when the production is smoothed, the resulting
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inventory levels represent management’s expectations about future demand and cost structures, which may also include management’s private information. As a result, inventory levels can be positive leading indicators of future sales when interpreting financial statements. In addition, unless competitive forces totally eliminate any impact of sales changes upon earnings, inventory levels should also be positive leading indicators of future earnings.
Under LIFO, the changes in inventory mostly represent the changes in inventory volume, while under IFRS, the changes in inventory represent the changes in both inventory volumes and current costs. It is because under LIFO, the items remaining in inventory are recognized as if they were the oldest, so the inventory costs remain the same throughout the year. Thus, any change in inventory levels reflects the inventory volume change. Under IFRS, because the items in inventory are measured by
inventory’s current cost, the changes in inventory levels may result from the changes in costs or volume.
This study further assumes that when inventory volume is the only factor that affects inventory levels, inventory levels will be stronger indicators of future sales and earnings. Therefore, this study assumes that inventory levels reported under LIFO method should be stronger positive indicators of future sales and earnings than inventory levels reported under IFRS method.
Hypothesis 1:
Under LIFO method, inventory levels are stronger positive indicators of future sales than under IFRS method.
Under LIFO method, inventory levels are stronger positive indicators of future earnings than under IFRS method.
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3.1.2 The Stockout Model
The stockout model is one of the inventory models that are more consistent with existing data (e.g., Kahn [1987]). In the stockout model, if actual sales are less than the available stock, the company may carry the remainder into the next period as inventory. If, on the other hand, actual sales are more than the available stock and the company ―stocks out,‖ it generates losses, and if a buyer is willing to let the company sell the product in next period at this period’s price, the company will occur a backlog in next period. As a result, when making production decision, a company must weigh against the possibility of stockout and the possibility of holding excessive inventory.
According to Kahn, under a stockout situation, a company’s sales consist of backlogged sales from previous periods and current demand from this period, so current demand is only partially reflected in current sales; the remainder of current demand is reflected in the frequency of stockouts. A low inventory level indicates a potentially high frequency of stockouts, which further indicates higher level of
demand and sales. On the other hand, a high inventory level indicated a lower level of sales. Consequently, inventory levels are inversely related to future sales. In addition, inventory levels are also leading negative indicators of future earnings, because the lower sales may lead to lower margins, and higher inventory levels lead to higher inventory holding costs.
The stockout model can rationalize the violations of the production smoothing model because it suggests that production can be more variable than sales. Two situations may lead to production counter-smoothing. First, because backlogs may shift sales away from large unexpected demand, while production still responds to previous period’s excess demand, the variance of production is larger than the variance of sales. Second, when demand shock occurs, it changes the ending
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inventory and the expectations about future demand, which increases or decrease optimal production, so the variance of production is larger than the variance of sales.
Under LIFO, the changes in inventory represent the changes in inventory volume, while under IFRS, the changes in inventory represent the changes in both inventory volumes and current costs. This study further assumes that when inventory volume is the only factor that affects inventory levels, inventory levels will be stronger
indicators of future sales and earnings. Therefore, this study assumes that inventory levels reported under LIFO method should be stronger positive indicators of future sales and earnings than inventory levels reported under IFRS method.
Hypothesis 2
Under LIFO method, inventory levels are stronger negative indicators of future sales than under IFRS method.
Under LIFO method, inventory levels are stronger negative indicators of future earnings than under IFRS method.
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3.2 Sample Selection
3.2.1 Data Source
The data for this research are obtained from Standard and Poor’s Quarterly Compustat and United Stated Securities and Exchange Commission, EDGAR company search system. The sources for all the variables are presented as follow:
1. Sales, income before extraordinary items, inventory valuation method, and total inventory under LIFO method are retrieved from Standard and Poor’s Quarterly Compustat.
2. LIFO reserve is collected from United Stated Securities and Exchange Commission, EDGAR company search system.
3. IFRS inventory is calculated by adding LIFO reserve to total inventory under LIFO method.
3.2.2 LIFO Reserve Collecting Process
LIFO reserve is collected by the following process:
1. Enter a search string containing a sample company name
(company-name="American Greetings " AND form-type=(10-q* OR 10-k*)) on United Stated Securities and Exchange Commission, EDGAR company search system, Historical EDGAR Archives search, Boolean and advanced searching.
2. Select the sample company’s quarterly financial report (10-Q) and annual financial report (10-K) from 2005 to 2011.
3. For 10-K, collect the sample company’s LIFO reserve from Part II, Item 8, Financial Statements and Supplementary Data, Notes to Consolidated Financial
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Statements. For 10-Q, collect LIFO reserve from Part I, Financial Information, Item 1, Financial Statements, Notes to Consolidated Financial Statements.
3.2.3 Sample Selecting Criteria
The samples include 80 active US companies, extend from 2005 to 2011, and consist of 1779 observations. All of the companies adopt LIFO method as their inventory valuation method. The data must meet the following data requirements:
1. The data must include 23 continuous quarters of nonmissing data for sales, income before extraordinary items, and total inventory under LIFO method for fiscal years 2005-2011.
2. The sample companies must present inventory under LIFO method for fiscal years 2005 to 2011.
3. To calculate the inventory presented under IFRS inventory valuation method, the sample was restricted to companies which disclosed quarterly detail on LIFO reserve.
Samples were discarded according to the rules listed below.
1. Original data consists of companies in Industry Sector Codes 1001-9540 on the Quarterly Compustat file, which includes 9633 companies.
2. Delete the companies using inventory valuation method other than LIFO for fiscal years 2005 to 2011.
3. Delete the companies which didn’t disclose LIFO reserve in 10-Q and 10-K for fiscal years 2005-2011.
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The following table details the sample selection criteria.
Table 3-1 Sample Selection Criteria
Sample Selection Criteria
Original Data 9633
Companies adopting the inventory valuation method other than LIFO
(9447)
Companies which didn’t disclose LIFO reserve in 10-Q and 10-K
(106)
Sample companies 80
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3.3 Research Methods
3.3.1 Descriptive Statistics Analysis
This study utilizes the descriptive statistic analysis to analyze the data from sample companies. The means, medians, first quartiles, third quartiles, and standard errors are calculated and observed to determine whether there is any extreme observation that distorts the data and need to be discarded.
3.3.2 Regression Analysis
This study uses regression models to analyze the data from the sample companies. This study chooses a group of companies adopting LIFO method and disclosing LIFO reserve as the sample companies, and adds the LIFO reserve back to the total inventory reported under LIFO to generate the inventory reported under the company’s internal inventory policy. The inventory valuation method used for internal purpose may be FIFO method or weight average method. These inventory valuation methods are defined as IFRS inventory in this study. Then this study uses the sales, earnings, and profit margins models developed by Bernard (1991) to determine the predictability of LIFO inventory and IFRS inventory for sales, earnings, and profit margins. The results will be examined to determine whether the production smoothing hypothesis holds or the stockout model holds for the inventory flow, and whether LIFO inventory has better predictability than IFRS inventory.
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3.4 Empirical Models and Variable Description
This study examines the hypotheses with regression models which combined inventory expectations models and sales, earnings and profit margin prediction models. Section 3.4.1 and 3.4.2 first identify the sales, earnings and profit margin prediction models and the inventory expectations models based on previous literature.
Then Section 3.4.3 discusses the models which combines the two models to determine the inventory predict ability of sales, earnings, and profit margin, and how this study tests the hypotheses.
3.4.1 Predicting Sales and Earnings
The sales, earnings and margin prediction equations are the first order autoregressive models in seasonal differences. According to Foster (1977), each quarterly sales and earnings appears to have both (a) a seasonal component and (b) an adjacent quarter-to-quarter component. This is apparent from both inspection of the cross sectional autocorrelation function and from one-step ahead forecasting results.
Foster concludes that there is strong evidence of seasonality in the quarterly sales and earnings, and a strong association between seasonal component and adjacent
component of sales and earnings. Accordingly, the models in this section utilize seasonal differences of adjacent quarters to predict sales and earnings. The economic intuition of the models is that when the seasonal difference of sales and earnings between quarter t-1 and quarter t-5 increase, the seasonal difference of sales and earnings between quarter t and quarter t-4 would also increase.
The prediction equations are:
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: Income before extraordinary items in quarter t
However, according to Bernard (1991), a potential problem for sales and
earnings prediction equations is that the seasonal difference may be affected by major changes in the scales of operations, such as major expansion, merger and acquisition, or discontinued operation. Under these circumstances, the seasonal difference for one quarter may not be an appropriate prediction for the adjacent quarter. For example, if a company acquired a subsidiary and sales doubled in quarter t-1, the regressor in the model (
) will reflect the scale change, and the model will predict another sales increase for the adjacent quarter. This result is incorrect.
In order to adjust for this problem, Bernard scales every variable by a
contemporaneous variable and develops another prediction equation, profit margins prediction model. Profit margins are defined as earnings divided by contemporaneous sales. Because profit margins follow a stationary process, the effect of the changes in the scales of operations in this model can be mitigated.
The profit margin prediction model is as follow:
Basic Sales and Earnings Prediction Models
Basic Profit Margins Prediction Model
(1)
(2)
(3)
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3.4.2 Predicting Total Inventory
In this section, the inventory expectations model is developed to estimate the unexpected inventory measure, which will be added to the prediction models to examine the predictability of inventory for sales and earnings. From production smoothing and stockout models, we know that inventory can convey information such as inventory decisions and the characteristics of the decision rules. The purpose of the inventory expectations model is to isolate this information, which is contained in unexpected inventory, for use in predicting sales and earnings.
The unexpected inventory is the difference between actual inventory and expected inventory. Expected inventory is identified by the regressor in the inventory
The unexpected inventory is the difference between actual inventory and expected inventory. Expected inventory is identified by the regressor in the inventory