University of Groningen Getting down to brass tacks: Is your … · 2017. 1. 17. · idate predictions from inventory theory (Rumyantsev and Netessine, 2007b; Bray and Mendelson,

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Getting down to brass tacks: Is your organization really aligned?Ullrich, Kristoph

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Chapter 5

Demand-Supply Mismatches and Stock Mar-ket Performance: A Retailing Perspective

Alitur vitium vivitque tegendo(The taint is nourished and lives bybeing concealed)

Vergil

Abstract

We provide empirical evidence that the volatility of inventory productivity relative tothe volatility of demand is a predictor of future stock returns in a sample of publiclylisted U.S. retailers over the period 1985-2013. This key performance indicator, entitleddemand-supply mismatch (DSM), captures the fact that low variation in inventory pro-ductivity relative to variation in demand is indicative of the superior synchronizationof demand- and supply-side operations. Applying the Fama and French (1993) three-factor model augmented with a momentum factor (Carhart, 1997), we find that zero-costportfolios formed by buying the two lowest and selling the two highest quintiles of DSMstocks yield abnormal stock returns of up to 1.13%. These strong market anomalies re-lated to DSM are observed over the entire sample period and persist after controlling foralternative inventory productivity measures and firm characteristics that are known topredict future stock returns. Further, we reveal that DSM is indicative of lower futureearnings and lower sales growth and provide evidence that the observed market ineffi-ciency results from investors’ failure to incorporate all of the information that inventorycontains into the pricing of stocks.

134 5. Demand-Supply Mismatches and Stock Market Performance

5.1 Introduction

Simply operating more efficient and cost-effective supply chains is not sufficient

to gain a sustainable competitive advantage (Lee, 2004). Rather, a company’s

ability to respond quickly to changes in demand or supply without stockpiling un-

necessary inventory needs to be complemented by cost efficiency. In the retailing

industry, inventory accounts for, on average, 34% of total assets and 60% of current

assets, with a mean value of US$ 519.7 M. Therefore, retailers must manage invento-

ries with the greatest caution. Excessive inventory stock may lead to liquidity prob-

lems and future markdowns, whereas insufficient stock will lead to lost sales, cus-

tomer dissatisfaction (with all of its negative consequences), and premium freight

charges resulting from expedited shipments (Kesavan and Mani, 2013). Given the

great importance of managing inventories in an efficient and effective manner, most

retailers have adopted modern supply chain and operations management concepts

over the last decades, which have led to widespread improvement in the manage-

ment of inventories across the retailing industry (e.g., Chen et al., 2007; Alan et al.,

2014).

The great relevance of inventory management in retailing contexts also inspired

scholars to test and improve conventional inventory performance measurement me-

thods (Gaur et al., 2005; Rumyantsev and Netessine, 2007a) and to empirically val-

idate predictions from inventory theory (Rumyantsev and Netessine, 2007b; Bray

and Mendelson, 2012; Jain et al., 2014). In addition, scholars have shown that re-

tailers indeed realized significant increases in inventory productivity over the past

years and attribute these improvements to the adoption of concepts such as quick

response, inventory pooling, and revenue management (e.g., Chen et al., 2007). De-

spite increasing awareness of the importance of inventory management in practice

and in the scientific community, Sloan (1996), Kesavan et al. (2010), and Kesavan

and Mani (2013) note that there is growing evidence that Wall Street investors do

not leverage all of the information that inventory contains. In addition, as manage-

rial bonus payments are commonly tied to the stock market performance (Currim

5.1. Introduction 135

et al., 2012; Alan et al., 2014), it is not only relevant to investors but also to managers

whether the stock market appreciates superior inventory management.

This explains the extensive effort by academics to determine whether operations

management (OM) practices are related to stock market performance and whether

OM-related accounting information can be leveraged to predict stock returns. In

their pioneering work, Chen et al. (2005, 2007) investigate the development of firm-

level inventories in the manufacturing and retailing industries and analyze whether

abnormal inventories are indicative of abnormal stock returns. The authors reveal

that inventory holdings generally declined during the 1981-2000 and 1981-2004 pe-

riods and that firms with abnormally high inventories yield abnormally poor long-

term stock returns. Although Chen et al. (2005) find that manufacturing firms with

slightly lower than average inventory holdings (deciles 4 and 3) yield positive ab-

normal returns, Chen et al. (2007) do not find evidence that abnormally low inven-

tory holdings yield abnormal returns for retailers and wholesalers. Building upon

these findings, Steinker and Hoberg (2013) utilize a dataset of manufacturing firms

over the 1991-2010 period and show that abnormal stock returns monotonically de-

crease in abnormal year-over-year inventory growth and that abnormal stock re-

turns increase in within-year inventory volatility.

Kesavan et al. (2010) and Kesavan and Mani (2013) explore the impact of inven-

tory-related information on analysts’ earnings and sales forecasts. Kesavan et al.

(2010) reveal that including information on the cost of goods sold, inventory levels,

and gross margins as endogenous variables in a sales forecast improves forecast ac-

curacy, although analysts typically do not consider all of this information. Kesavan

and Mani (2013) complement the finding that analysts fail to fully incorporate the

information contained in past inventory by providing evidence for an inverted U-

shaped relationship between abnormal inventory growth and one-year-ahead earn-

ings, implying that inventories are necessary to capitalize on (additional) demand

but become detrimental once they exceed a certain point. Recently, Alan et al. (2014)

reveal that inventory productivity predicts the stock returns of publicly listed U.S.

retailers and that, despite its predictive power, investors fail to incorporate inven-


tory information into investment decisions.

Thus, the above literature suggests that investors do not explicitly analyze pub-

licly available financial data to obtain information regarding firms’ OM. Interest-

ingly, if such information is announced to the major business press, investors do in-

corporate this information in stock valuations (Hendricks and Singhal, 2005a, 2009,

2013). That pattern indicates that investors consider major events related to firm

operations in pricing stocks, but they fail to predict them based on careful analysis

of the available inventory data (Kesavan and Mani, 2013). In particular, Hendricks

and Singhal (2005a, 2009, 2013) use event studies applied to stock market and ac-

counting data to study the effect of public announcements regarding supply chain

management and OM on firm performance. Considering supply chain glitches, the

authors show that undersupply leads to significant declines in both current and fu-

ture stock returns and that oversupply is costly and causes obsolescence risk, as

reflected in stock market reactions. Hendricks and Singhal (2013) complement this

finding by reflecting on their previous work and demonstrate that announcements

related to excess inventory, compared with announcements related to product in-

troduction delays and production disruptions, clearly have the greatest effect on the

equity volatility of companies.

In addition to OM scholars, accounting researchers also explore the predictive

power of inventory-related information. Bernard and Noel (1991) demonstrate that

unexpected changes in the raw materials and work-in-process inventories of man-

ufacturing companies are positive indicators of future sales, whereas the effect of

inventory changes on future earnings is essentially neutral. Regarding finished-

goods inventory, the authors show that for both manufacturers and retailers, un-

expected changes in inventory are negative indicators of future earnings, despite

the presence of a countervailing positive relationship with future sales. This result

partially contrasts with that of Abarbanell and Bushee (1997), who find only weak

economic justification for a relationship between inventory and future earnings for

manufacturers; they do not establish such a relationship for retailers. Kesavan and

Mani (2013) reveal, in line with Sloan (1996), that analysts fail to reflect all of the

5.1. Introduction 137

information contained in accruals, which leads to mispriced stocks, as implied by

the abnormal returns of the bottom and top deciles of portfolios formed based on

accruals. This finding is particularly important because subsequent research reveals

through accrual decomposition that most of the predictive power and hedge returns

of accruals result from inventory components (Thomas and Zhang, 2002).

In this chapter, we apply portfolio-based asset pricing methods to analyze whe-

ther retailers’ ability to manage inventory effectively (i.e., minimizing demand- sup-

ply mismatches) predicts future stock returns. Driven by the fact that demand and

supply variability are the primary factors responsible for supply chain inefficiency,

we develop a novel key performance indicator (KPI), entitled DSM (demand-supply

mismatch), which measures the relative volatility of the inventory productivity of a

firm. Our KPI is supported by the extensive body of literature on the “Bullwhip

effect,” which shows that unnecessarily amplified inventory volatility has negative

consequences along various dimensions (e.g., Lee et al., 1997; Warburton, 2004; Chen

and Lee, 2012; Cui et al., 2015). In contrast to prior studies that relate inventory pro-

ductivity measures directly to financial metrics (Gaur et al., 2005; Cannon, 2008;

Alan et al., 2014) or that normalize inventory productivity by an industry peer’s

performance (Chen et al., 2005, 2007; Kesavan and Mani, 2013), our DSM measure

accounts for the volatility of inventory productivity over time. Knowing that higher

demand volatility causes higher inventory volatility, we normalize the volatility of

inventory productivity by the volatility of demand. This relative volatility measure

provides additional and distinct insights into how well firms can match demand

with supply. In practice, many managers benchmark their inventory productivity

(e.g., inventory turnover) against industry standards on an annual basis. Suppose a

firm has an (annual) average inventory turnover that is equal to the industry bench-

mark; the typical assumption in such a case is that the firm’s operations are well

managed. If, however, the volatility of the inventory turnover of that firm is consid-

erably higher relative to its demand volatility, then the firm may face some periods

with excessively high inventories and others with stockouts. As such, our measure

of DSM overcomes the problem that average measures of inventory productivity


smooth out important information.

Similar to prior research demonstrating that inventory productivity is indicative

of future stock returns (Alan et al., 2014), we show that the information content in

the relative volatility of inventory productivity is an additional predictor of future

stock returns. We find that zero-cost portfolios formed on this inventory-productivity-

to-demand-volatility ratio generate abnormal returns of up to 1.13%. We consider dif-

ferent measures of inventory productivity, including inventory turnover and inven-

tory days, and show that both measures lead to qualitatively similar results. Consis-

tent with the literature regarding the bullwhip effect, we approximate DSM further

as the volatility of purchases/orders relative to the volatility of demand. The results

we obtain from that alternative operationalization confirm a negative relationship

between demand-supply mismatches and stock market performance.

The remainder of the chapter is structured as follows: In §5.2, we present our

research setup, derive our measure of DSM, describe the data, and elaborate on our

portfolio formation methodology. In §5.3, we present the results from the portfolio

formation and perform additional robustness checks. In §5.4, we provide potential

explanations for the observed market inefficiency, and in §5.5, we explore the impli-

cations of our work, discuss the limitations of our study, and propose directions for

future research.

5.2 Research Setup

5.2.1 Relative Inventory Productivity Volatility Measure (DSM)

Throughout the chapter, we use the following notations to account for time-specific

(fiscal year t “ 1, . . . , 29 and quarter q “ 1, . . . , 4q and company-specific pi “

1, . . . , 424q effects. Accordingly, for fiscal year t, quarter q, and firm i, we denote

the ending inventory as INVitq , sales revenue as SALitq , and the cost of goods sold

as COGSitq . Contingent on the valuation method for inventory – first-in, first-out

(FIFO) or last-in, first-out (LIFO) – artificial differences in the reported ending inven-

5.2. Research Setup 139

tories and cost of goods sold may occur. Therefore, prior research suggests adding

back the LIFO reserve to the ending inventory and subtracting the annual change

in the LIFO reserve (LIFOit ´ LIFOi,t´1) from the cost of goods sold (Kesavan

et al., 2010; Kesavan and Mani, 2013; Alan et al., 2014). Because the LIFO reserve

is only reported on an annual basis, we incorporate the valuation peculiarities into

our quarterly measures as follows. We define inventory turnover as

ITitq ”

ˆ

pCOGSitq ´1

4LIFOit `

1

4LIFOi,t´1q{pINVitq `

1

4LIFOitq

˙

(5.1)

and analogously inventory days over a quarter of 90 days as13

IDitq ” 90

ˆ

pINVitq `1

4LIFOitq{pCOGSitq ´

1

4LIFOit `

1

4LIFOi,t´1q

˙

. (5.2)

Prior research that investigates the relationship between inventory management

and firm performance relates inventory turnover and gross margin – capital inten-

sity – and sales surprise-adjusted inventory turnover directly to financial metrics

(Gaur et al., 2005; Cannon, 2008; Alan et al., 2014) or normalizes inventory produc-

tivity by an industry peer’s performance to obtain measures of abnormal inventory

and abnormal inventory growth (Chen et al., 2005, 2007; Kesavan and Mani, 2013).

Our metric captures another aspect of inventory performance by incorporating the

variability of inventory productivity over time and relating it to the variability in

demand. We do not use the volatility of the inventory levels because the majority

of firms face seasonal swings in demand, which induce a natural seasonal pattern

in inventory levels. To incorporate the fact that the levels of inventory productivity

may vary among firms, for instance depending on the inventory objectives, we use

the coefficient of variation (CV) of the inventory productivity as the base volatility

measure. The CV of inventory productivity relates the standard deviation of the

inventory productivity to its mean. In addition, we incorporate the fact that those

firms that face higher demand volatility (naturally) hold higher inventory levels

13All of the subsequently presented results remain qualitatively unchanged even without the LIFO-FIFO adjustment.


(safety stocks). Therefore, we relate the CV of inventory productivity to the CV of

demand to account for the market conditions under which inventory needs to be

managed efficiently.

Specifically, we operationalize DSM as the CV of inventory productivity relative

to the CV of demand on the basis of four quarterly observations, i.e.,

DSM ”CV pinventory productivityq

CV pdemandq. (5.3)

We employ inventory turnover and inventory days as proxies for inventory pro-

ductivity, and demand is approximated by sales.14 For notational convenience, we

define DSM ITSAL ”CV pIT qCV pSALq and DSM IDSAL ”

CV pIDqCV pSALq . For example, using the

CV of inventory turns relative to the CV of sales, DSM ITSAL of firm i in year t is

computed as follows: DSM ITSALit ”

¨

˚

˚

˚

˚

˝

d

14

4ř

q“1

ˆ

ITitq ´14

4ř

k“1

ITitk

˙2

1{44ř

k“1

ITitk

˛

‹

‹

‹

‹

‚

¨

˚

˚

˚

˚

˝

1{44ř

k“1

SALitkd

14

4ř

q“1

ˆ

SALitq ´14

4ř

k“1

SALitk

˙2

˛

‹

‹

‹

‹

‚

.

The computation of DSM IDSAL follows accordingly.

Prior studies analyze annual data and employ the fiscal year-end inventory rather

than the average inventory to determine inventory productivity measures. Using

quarterly data to calculate an annual inventory productivity metric (DSM) therefore

follows the principles of OM theory and standard OM textbooks (e.g., Cachon and

Terwiesch, 2013, pp. 10-23).

Steinker and Hoberg (2013) utilize quarterly data to investigate the relationship

between inventory volatility and financial performance in manufacturing indus-

tries. The authors deseasonalize the inventory time series to obtain a measure of

inventory volatility; however, we do not filter out the seasonal component, as our

14Using COGS as proxy for demand, as is common in the OM literature (e.g., Cachon et al., 2007; Kesa-van et al., 2010; Bray and Mendelson, 2012; Chen and Lee, 2012; Jain et al., 2014), does not meaningfullychange the results.


measure intends to capture a firm’s capability to respond “smoothly” to all sources

of sales fluctuations in terms of inventory, including seasonality. Hence, DSM mea-

sures the degree to which a firm under- or overreacts in the adjustment of inventory

to changes in demand patterns.

Table 5.1 provides a simplified numerical example using the CV of inventory

turns relative to the CV of sales as a measure of DSM (the concept does not change

under an alternative operationalization). Firm A and Firm B have identical sales

time series and identical (mean) inventory turnovers, but Firm A’s inventory pro-

ductivity is more volatile than Firm B’s inventory productivity. This table illustrates

that if the variation in inventory turnover is low relative to a given level of fluctu-

ation in sales (i.e., there are fewer DSMs), then the DSM metric is low, which we

consider to be good (i.e., Firm B outperforms Firm A in terms of DSM).15 As such,

the measure does not assess the efficiency of maintaining inventory stock relative

to sales (i.e., inventory turnover); rather, it relates the variation in relative inven-

tory holdings (inventory dynamics) to sales fluctuations. A firm performs well in

this regard if its demand- and supply-side operations are well synchronized. Low

fluctuation in inventory productivity metrics relative to sales volatility is indicative

of superior information-sharing practices, cross-functionally aligned planning and

forecasting capabilities and a reliable supplier and logistics network, which are all

characteristics that fall into the domain of good SCM practices (Mishra et al., 2013).

Therefore, a high degree of demand-and-supply mismatches may be symptomatic

of operational inefficiencies that may continue into the future, causing costs to rise

and revenues to decrease.

15Note that the volatility of inventory productivity is not affected by ordering frequencies (e.g., thedegree of responsiveness). A simplified example is provided in Appendix 5.A.


Tabl

e5.

1:Ex

ampl

eof

DSM

Peri

odIT

SD(I

T)M

ean(

IT)

CV

(IT)

SAL

SD(S

AL)

Mea

n(SA

L)C

V(S

AL)

DSM

ITSAL

Firm

A

t=1

41.

665.

50.

3012

016

.33

100

0.16

1.88

t=2

61.

665.

50.

3010

016

.33

100

0.16

1.88

t=3

81.

665.

50.

3010

016

.33

100

0.16

1.88

t=4

41.

665.

50.

3080

16.3

310

00.

161.

88

Firm

B

t=1

50.

55.

50.

0912

016

.33

100

0.16

0.56

t=2

60.

55.

50.

0910

016

.33

100

0.16

0.56

t=3

50.

55.

50.

0910

016

.33

100

0.16

0.56

t=4

60.

55.

50.

0980

16.3

310

00.

160.

56N

ote.

SD=s

tand

ard

devi

atio

n.


In particular, on the revenue side of the earnings equation, DSMs can lead to

lost sales due to the unavailability of products, lower realized gross margins due

markdowns of excess inventory, poorer customer service, satisfaction, and loyalty,

if the types of products that customers demand are not available at the right time

and place. On the cost side of the earnings equation, DSMs can lead to an increase

in expenditures due to penalties paid to customers, expedited shipments, obsolete

inventories and write-downs. Moreover, the loss of credibility towards customers

may require the firm to increase marketing and other public relations-related expen-

ditures, may increase the costs of raising capital because investors ask less credible

retailers for premiums, and lastly may decrease employees’ productivity as a result

of volatile workloads (e.g., Hendricks and Singhal, 2005a; Kesavan and Mani, 2013).

As such, the consequences of DSMs are likely to have negative effects on future sales

growth and future earnings. Considering many of these aspects, the IHL Group

(2015) estimates the cost of overstocks and out-of-stocks for U.S. retailers to be 3.2%

and 4.1% of revenues, which accumulate to US$ 123.4B and US$ 129.5B annually, re-

spectively. Accordingly, we expect that DSM contains valuable information for the

analysis of firms when portfolio investment decisions are made. Therefore, moti-

vated by the voluminous amount of literature on the negative impact of amplifying

order/inventory variability on supply chain operations (Lee et al., 1997; Chen and

Lee, 2012), we investigate whether the relative volatility of inventory productivity

is predictive of future stock returns.

Traditionally, the literature regarding the bullwhip effect relates the order (rather

than inventory) variability to the demand variability. In line with this stream of re-

search, we propose a third proxy for DSM which relates the CV of purchases to the

CV of demand. Consistent with prior research (e.g., Bray and Mendelson, 2012;

Larson et al., 2015), we operationalize purchases (orders) of firm i in fiscal year

t and quarter q as PURitq ” INVitq ´ INVit,q´1 ` COGSitq , such that the third

proxy for DSM can be defined as DSMPURSAL ”CV pPURqCV pSALq . Because the compu-

tation of purchases requires two periods of data, it is clear that DSMPURSAL con-

tains information from five periods if we determine the CV of purchases based on


four subsequent observations of purchases. Therefore, the information contained in

DSMPURSAL will be on average 45 days older than the information contained in

DSM ITSAL and DSM IDSAL, which may have an effect on its predictive power.

In Figure 5.1 (upper chart), we depict the inventory turnover, DSM ITSAL, and

(annual) average stock returns of Sears Holdings Corporation, one of the largest

retailers by annual revenue in the United States (2014: US$ 31,198B). The lower

chart presents the mean-adjusted inventory turnover, mean-adjusted DSM ITSAL,

and (annual) average stock returns of the same company. Inventory turnover and

DSM do not behave identically, implying that the two measures capture different

aspects of operational performance and that DSM might be related to stock mar-

ket performance. For example, in the periods 1995-1997 and 2004-2006, inventory

productivity in terms of inventory turnover is rather low – compared to the firm’s

average – which, according to prior research (e.g., Alan et al., 2014), should be neg-

atively related to stock returns; however, we do not observe such a relationship

during these periods. In contrast, the firm’s capability to reduce DSMs (as implied

by low values of DSM ITSAL) may explain the relatively high stock returns during

these periods. Therefore, we analyze whether our measure of DSM can be leveraged

to predict future stock returns.

5.2.2 Data Description

For the purpose of our study, we collect financial data for U.S. public retailers dur-

ing the 1983-2013 period. The sampling period is rooted in prior research (e.g., Ra-

jagopalan and Malhotra, 2001), which finds that modern inventory management

practices, such as just-in-time, were primarily adopted in the 1980s; furthermore, it

ensures a sufficiently large sample size for the application of econometric methods.

We use three types of data: first, we extract quarterly financial data from Stan-

dard & Poor’s COMPUSTAT R©-North American database for all publicly listed U.S.

retailers, identified by the four-digit Standard Industrial Classification (SIC) code

assigned to each firm based on its primary industry segment. Public companies are


Figure 5.1: Inventory turnover, DSM, and average stock returns of Sears HoldingsCorp.


obliged to provide GAAP-compliant financial and operational information to en-

able investors to assess their performance. Because we are particularly interested in

the within-year dynamics of a supply chain, to obtain our measure of DSM, we use

quarterly rather than yearly data. Second, we combine quarterly data with monthly

stock returns from the Center for Research in Security Prices (CRSP) and data on

common risk factors and monthly risk-free rates from the Fama-French Portfolios

and Factors database accessed through Wharton Research Data Services (WRDS),

which serve as the basis for the portfolio formation method. Quarterly financial

statements do not include information on inventory valuation methods and the as-

sociated LIFO reserves. Therefore, we further obtain annual accounting information

from Standard & Poor’s COMPUSTAT R©-North American database.

The U.S. Department of Commerce classifies retailer categories and assigns four-

digit SIC codes if there is significant commonality among the product portfolios of

these firms. In line with prior empirical OM research that utilizes secondary data

to analyze inventory performance in retailing industries (e.g., Kesavan et al., 2010;

Kesavan and Mani, 2013; Alan et al., 2014), we exclude retailers that are classified

as eating and drinking establishments (SIC 5812-5813) and automotive dealers and

service stations (SIC 5511-5599) because service is a significant component of their

business models. We also exclude some four-digit categories for which inventories

have little commonality with other retailers in our sample, such as lumber and other

building materials dealers (SIC 5211) and SIC categories in which firms’ inventory

decisions may be largely dependent on economic conditions and raw material prices

(e.g., jewelry stores, SIC 5944).

The initial extract of the annual data comprises 4,802 firm-year observations

across 498 firms. We omit observations that have missing values on the variables

COGS, sales, or inventory and combine the data with quarterly observations. Af-

ter these adjustments, our final data set contains 15,951 firm-quarter observations

across 424 firms. Table 5.2 shows the description of each segment and the corre-

sponding SIC codes, provides exemplary firms, and presents the number of firms

and the number of firm-quarter observations of each segment.


This data set is supplemented by monthly stock returns, which are required for

the subsequent analyses. Following the guidelines of Alan et al. (2014), we replace

the stock return with the delisting return if a stock return for a particular month is

not available due to the delisting of a firm; if neither the stock return nor the delist-

ing return is available, we set the stock return as equal to the value-weighted market

return. Firms that do not have stock return information for any month in the CRSP

are omitted. This approach provides the final data set upon which our subsequent

analyses are based. Table 5.3 presents the summary statistics of the main variables

that will be used throughout the chapter.


Tabl

e5.

2:Sa

mpl

ede

scri

ptio

n

Des

crip

tion

Segm

ent

Four

-dig

itEx

ampl

esof

firm

sN

o.of

firm

sN

o.of

firm

-SI

Cco

dequ

arte

rob

s.

Gen

eral

mer

chan

dise

stor

es53

5311

,533

1,53

99W

al-M

art,

Cos

tco,

Tar-

get,

J.C.P

enne

y90

3,37

3

Gro

cery

stor

es54

5411

Kro

ger,

Safe

way

,A

l-be

rtso

ns75

3,14

2

App

arel

and

acce

ssor

yst

ores

5656

00,5

621,

5651

,566

1G

AP,

Foot

Lock

er,

Nor

dstr

om11

25,

355

Rad

io,

TV,

cons

umer

elec

tron

ics,

and

mus

icst

ores

5757

31,5

734

Best

Buy,

Cir

cuit

Cit

y,C

ompU

SA43

1,18

5

Cat

alog

,m

ail-

orde

rho

uses

,an

don

line

reta

ilers

5959

61A

maz

on.c

om,

Buy.

com

,Sys

tem

ax10

42,

896

Tota

l42

415

,951


Tabl

e5.

3:D

escr

ipti

vest

atis

tics

Var

iabl

ena

me

Not

atio

nM

ean

Med

ian

SD10

thpe

rc.

90th

perc

.

Sale

s(U

S$M

)SAL

4,03

6.31

725.

1412

,333

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5.2.3 Portfolio Formation

On the basis of portfolio-asset pricing models, we illustrate the financial implica-

tions of DSMs over and beyond the financial benchmark. In the following, we ex-

plain the portfolio formation method and the underlying sequence of events. On

July 31 in year t, we invest US$ 1 divided equally in each firm of each portfolio.

We weight each firm equally as opposed to applying a value-weighting approach

because giving more weight to larger firms such as Wal-Mart or Best Buy could sig-

nificantly influence the mean excess returns of the relatively small number of firms

in each portfolio and thus affect the generalizability of our findings.

To construct the portfolios on July 31 of each year t, we rank firms according to

their DSM value in ascending order and construct five quintiles using accounting

information for the fiscal period from February 1 of year t ´ 1 until January 31 of

year t. As is standard in the asset-pricing literature (Fama and French, 1993), this

method ensures that there is a time gap of at least six months (January 31 of year t

until July 31 of year t) for the accounting information to be announced and absorbed

by the market. The portfolios that are formed on July 31 of each year t are liquidated

on July 31 of each year t`1, and new portfolios are formed on the basis of the newly

available accounting information. In addition to the quintile portfolios, we also form

zero-cost (also called long-short or arbitrage) portfolios; these portfolios are formed

by taking a US$ 1 long position in firms with low DSMs (i.e., quintiles one and two)

and a US$ 1 short position in firms with high DSMs (i.e., quintiles four and five),

thereby utilizing 80% of the sample firms.

Figure 5.2 depicts the sequence of events in the portfolio formation process. Be-

cause our sample period is from 1983 until 2013, we form the first portfolio for the

purpose of our analysis on July 31, 1985, and the last portfolio on July 31, 2012.

Whereas Fama and French (1993), who developed this method, utilize information

from the fiscal-period January 1 of year t´ 1 until December 31 of year t´ 1 to form

portfolios on June 30 of year t, we use the fiscal-year end cutoff date of January 31

to form portfolios on July 31 of each year t. This approach is used because a siz-

5.3. DSM and Stock Market Performance 151

Figure 5.2: Sequence of events

able portion of retailers and the majority of the firms in our sample (46.49%) have

their fiscal-year end in January, implying a 17-month information delay if we use

the fiscal-year end cutoff date of December 31.

5.3 DSM and Stock Market Performance

In the following three sections, we first present the average excess returns (in ex-

cess of the risk-free rates) of the portfolios based on our DSM metrics. Then, to test

whether the observed excess returns can be explained by commonly known risk fac-

tors, we apply the Fama and French (1993) three-factor plus momentum (Carhart,

1997) model. Finally, we follow the Fama and MacBeth (1973) regression-based ap-

proach to test whether DSM contains unique information after controlling for alter-

native inventory productivity measures and firm-characteristics that are known to

predict stock returns.

5.3.1 Portfolio Excess Returns

Table 5.4 shows the average monthly excess returns (in %) of equally weighted quin-

tile portfolios based on the proposed metrics. We find that there is a negative trend


Table 5.4: Average monthly excess returns of quintile and zero-cost portfolios basedon DSM

Portfolio rank DSM ITSAL DSM IDSAL DSMPURSAL

1 (low) 1.74%*** 1.76%*** 1.10%***9.15 9.19 6.86

2 1.37%*** 1.09%*** 0.94%***7.18 5.80 5.64

3 0.76%*** 0.93%*** 0.83%***3.63 4.60 4.76

4 0.66%*** 0.78%*** 0.65%***3.53 3.96 3.57

5 (high) 0.24% 0.27% 0.08%0.95 1.04 0.42

Zero-cost 1.03%*** 0.88%*** 0.65%***5.73 4.86 5.98

Note. *p ă .1, **p ă .05, ***p ă .01. T-statistics are reported below the average excess returns.

for DSM across all of the portfolios, regardless of whether we consider the CV of

inventory turnover, the CV of inventory days, or the CV of purchases as a proxy

for inventory productivity: that is, the average monthly excess return decreases by

portfolio rank; thus, DSM and stock returns are negatively correlated. In addition

to the excess returns of the quintile portfolios, Table 5.4 also presents the zero-cost

portfolio returns. These results indicate that firms with lower values for DSM tend

to outperform those with higher values in terms of stock returns. For example, the

average monthly excess return of the zero-cost portfolio formed on DSM ITSAL by

having a short position in quintiles four and five and a long position in quintiles one

and two is 1.03% (p ă .01). It is noteworthy that this method of forming zero-cost

portfolios is not based only on firms with extremely high or low values of DSM (or

even driven by outliers); rather, it is based on the utilization of 80% of the dataset;

hence, it is a robust finding. Test statistics for each of the quintiles as well as for the

zero-cost portfolio are reported below the average excess returns.


Figure 5.3 presents the time series for the average excess returns of the zero-cost

portfolios based on DSM and, as such, provides support for the persistence of a

proposed relationship between DSM and subsequent stock returns. The time series

does not indicate any systematic variations over time. Analyzing the year-to-year

performance of the zero-cost portfolios reveals that companies with low DSMs out-

perform those firms with high DSMs, for DSM ITSAL and DSM IDSAL in 27 of 29

firm-year observations (ą 93%), and for DSMPURSAL in 23 of 29 firm-year obser-

vations (ą 88%), some years of economic turbulence are the exceptions. Therefore,

we conclude that our results are not driven by a particular subperiod of our sample.

Figure 5.3: Average excess returns of zero-cost portfolios based on DSM

5.3.2 Portfolio Adjustment for Common Risk Factors

To investigate whether the DSM measure is only a proxy for common risk fac-

tors, we employ the Fama and French (1993) three-factor plus momentum (Carhart,

1997) model, according to which the following regression explains the excess re-

turn of portfolio p in month m, ERpm, with p P t1, 2, 3, 4, 5, zero-costu and with


m P t1, . . . , 12u:

ERpm “ αp`β1pRMRFm`β2pSMBm`β3pHMLm`β4pUMDm` εpm, (5.4)

where RMRF is the excess return (in excess of the risk-free rate) of the value-weight-

ed market return; SMB is the return of a zero-cost portfolio consisting of the return

of a portfolio of big companies, in terms of market capitalization, subtracted from

the return of a small-company portfolio; HML is the return of a zero-cost portfolio

of high book-to-market ratio stocks minus the return of a portfolio of low book-to-

market ratio stocks; and UMD is the return of a zero-cost portfolio of the last year’s

high-return portfolio subtracted from the return of the last year’s low-return portfo-

lio. The intercept αp is called the monthly abnormal return of portfolio p because it

reflects the expected value of the return in excess of the passive investments of port-

folio p if all of the other independent variables assume a value of zero. Accordingly,

it will not be different from zero if the aforementioned factors entirely explain the

excess return of portfolio p.

Table 5.5 presents the regression results of the asset-pricing framework explained

above across the different measures of DSM. We find a negative trend for αp in the

quintiles’ rank order; specifically, we observe negative abnormal returns for quin-

tiles five and four and monotonically increasing positive abnormal returns in quin-

tiles two and one. Table 5.5 also presents the abnormal returns of the zero-cost port-

folios. Contingent on the proxy for DSM, we observe positive abnormal returns for

the zero-cost portfolios while controlling for risk factors; these returns range from

1.13% to 0.73%, all of which are statistically significant (p ă .01). These findings

suggest that DSM explains future stock returns and is not just a proxy for common

risk factors.

Comparing the other factors across the portfolio ranks, we do not observe any

systemic patterns for RMRF, HML, and UMD. However, the coefficient of SMB tends

to increase with the portfolio rank; that is, the factor loading of SMB is higher for

firms that experience more DSMs, suggesting that firms that experience many DSMs


exhibit behavior similar to that of small firms. This result partially contrasts with

prior finance research, which finds that smaller firms outperform larger firms in

terms of stock market performance (the so-called small-firm effect (e.g., Reinganum,

1981)). However, the finding that smaller firms tend to have more DSMs than larger

ones is completely in line with OM theory. That is because larger firms benefit from

demand and inventory pooling, which can be subsumed under the notion of “risk

pooling” (e.g., Eppen, 1979; Corbett and Rajaram, 2006), tend to be more diversi-

fied and are therefore less sensitive to demand or supply shocks of single product

branches (e.g., Hendricks and Singhal, 2009), tend to have more operational and

financial flexibility as well as market power to cope with DSMs (e.g., Hendricks

and Singhal, 2009), and often have more financial resources available to make in-

vestments related to the IT infrastructure, which many suggest facilitates the inven-

tory management practices (Gaur et al., 2005; Hendricks and Singhal, 2005a; Mishra

et al., 2013), all of which reduce the amount of DSMs.

5.3.3 Distinctiveness of DSM

The above-used sorting approach has one major shortcoming: while it performs

well in determining whether portfolios formed on the basis of a particular variable

(in our case, DSM) yield abnormal stock returns, it cannot determine whether the

variable upon which portfolios are formed contains unique information. For exam-

ple, Kesavan and Mani (2013) identify a relationship between abnormal inventory

growth and abnormal stock returns, and Alan et al. (2014) show that adjusted in-

ventory turnover predicts abnormal stock returns. Therefore, it remains unclear

whether the information content in DSM is different from other inventory-related

factors shown to predict abnormal stock returns. One obvious question that follows

from this shortcoming is whether the portfolio ranks assigned to firms based on the

proposed measures of DSM differ from those of existing metrics. To answer this

question, we follow Kesavan and Mani (2013) and define the abnormal inventory

growth of firm i in year t (AIGit) as retailers’ annual inventory growth rates ad-


Table 5.5: Fama-French-Carhart four factor regression results for zero-cost and quin-tile portfolios

Portfoliorank

α RmRf SMB HML UMD R2

DSMITSAL

1 (low) 1.21%*** 0.96*** 0.56*** 0.23*** -0.28*** 57.57%2 0.71%*** 1.11*** 0.59*** 0.34*** -0.34*** 63.12%3 -0.01% 1.07*** 0.61*** 0.57*** -0.20*** 63.04%4 -0.04% 1.15*** 0.78*** 0.47*** -0.38*** 65.98%5 (high) -0.40%*** 1.01*** 1.02*** 0.41*** -0.38*** 58.50%

Zero-cost 1.13%*** -0.05** -0.32*** -0.16*** 0.06** 69.23%

DSMIDSAL

1 (low) 1.16%*** 1.00*** 0.64*** 0.28*** -0.31*** 58.64%2 0.46%*** 1.13*** 0.49*** 0.24*** -0.30*** 61.28%3 0.36%*** 1.06*** 0.63*** 0.38*** -0.39*** 61.18%4 -0.04% 1.11*** 0.73*** 0.49*** -0.36*** 65.07%5 (high) -0.38%*** 1.02*** 1.08*** 0.34*** -0.36*** 58.65%

Zero-cost 1.04%*** -0.02* -0.30*** -0.17*** 0.04* 68.03%

DSMPURSAL

1 (low) 0.44%*** 1.03*** 0.47*** 0.27*** -0.23*** 58.20%2 0.33%*** 1.04*** 0.61*** 0.14*** -0.32*** 66.37%3 0.19%*** 1.06*** 0.69*** 0.35*** -0.32*** 65.24%4 -0.03% 1.07*** 0.70*** 0.42*** -0.26*** 66.42%5 (high) -0.59%*** 0.95*** 0.89*** 0.38*** -0.22*** 65.37%

Zero-cost 0.73%*** 0.02*** -0.25*** -0.19*** -0.03*** 68.19%Note. *p ă .1, **p ă .05, ***p ă .01.

justed for several covariates (such as COGS and gross margin of the recent period,

and the previous period’s COGS, inventory, accounts payable to inventory ratio,

and capital investment) that prior research has shown to affect stocking decisions

(Gaur et al., 2005; Rumyantsev and Netessine, 2007b; Kesavan et al., 2010). Unlike

Kesavan and Mani (2013), we do not adjust a retailer’s annual inventory growth

rate by store growth and do not normalize scale-dependent variables by the num-

ber of stores to preserve sample size. In line with Alan et al. (2014), we define the


Table 5.6: Percentage overlap of portfolio ranks formed on alternative metrics (lowertriangle) and z-statistics of the Wilcoxon signed-rank test (upper triangle)

1 2 3 4 5

1 DSM ITSALRank 1 -0.28 -7.46 14.65 -13.00

2 DSM IDSALRank 71.16% 1 -7.32 15.02 -12.96

3 DSMPURSALRank 25.09% 28.21% 1 27.11 -7.54

4 AIGRank 21.26% 21.63% 20.16% 1 -29.80

5 AITRank 20.92% 21.58% 19.24% 18.79% 1

adjusted inventory turnover of firm i in year t, AITit, as the deviation (residual)

from each retailer’s segment-specific average inventory turnover while controlling

for the firm-specific gross margin, capital intensity, and sales surprises. In Appendix

5.B, we provide further details regarding the construction of each of these variables.

In Table 5.6, we report in the lower triangle the percentage overlap of portfolio

ranks across alternative metrics. For example, the rank assigned to a firm based

on AIT , denoted as AITRank, is equal to the rank assigned to a firm based on

DSM ITSAL, denoted as DSM ITSALRank , for 20.92% of the observations. The upper

triangle of Table 5.6 presents the z-statistics of the Wilcoxon signed-rank test. As

expected, there is no statistically significant difference between DSM ITSALRank and

DSM IDSALRank . The differences between portfolio ranks based upon all of the other

metrics are statistically significant, which provides some evidence that our prox-

ies for DSM capture different aspects of operational performance than do AIG and

AIT .

Fama and French (2008) recommend the regression-based approach of Fama and

MacBeth (1973) to determine whether the information content of DSM is distinct

from the information contained in commonly known inventory metrics. A high cor-

relation between DSM and AIG and/or AIT might imply that DSM is just a proxy

for one of these variables. Therefore, we present in Table 5.7 the Pearson correlation


Table 5.7: Correlation matrix

Variable 1 2 3 4 5

1 DSM ITSAL 12 DSM IDSAL 0.95 13 DSMPURSAL 0.61 0.55 14 AIG -0.07 -0.05 -0.09 15 AIT 0.10 0.09 -0.04 -0.04 1

Note. All of the reported correlation coefficients are significant at the 5% level.

coefficients of DSM, AIG, and AIT. All of these measures are significantly corre-

lated with one another (p ă .05), implying that it might not be clear whether the

relationship between DSM and abnormal stock returns persists after controlling for

alternative inventory productivity metrics.

Hence, to account for the possibility that a combination of established inventory

productivity metrics captures large portions of the variation in stock returns that

we would attribute to DSM, we conduct cross-sectional regressions in the fashion

of Fama and MacBeth (1973). This approach facilitates testing whether DSM has

predictive power after controlling for known drivers of stock returns. Following

their framework and to be consistent with our portfolio formation procedure, we

first run monthly cross-sectional regressions from February through January of re-

tailer’s excess returns in each year against DSM and other anomaly variables from

the prior fiscal year. For example, we run regressions of each retailers’ excess returns

in February 2012 against these retailers’ DSMs computed from accounting informa-

tion released in the period from February 2011 to January 2012. After obtaining

regression coefficients for each of the cross-sectional regressions, of which we report

the average in Table 5.8, we calculate t-statistics that are based on the time-series

standard deviations of the monthly slopes.

In addition to AIG and AIT, we include a set of control variables in the regres-

sions that are also suggested to predict stock returns. The control variables are as fol-

lows: Accruals, as measured by ACCit ” IBitÓCFitÉIDOitTAi,t´1

, where IB, OCF, EIDO,


and TA denote income before extraordinary items, operating cash flows, extraordi-

nary items and discontinued operations, and total assets, respectively (Hribar and

Collins, 2002); Asset growth, ASSGit, as measured by the change in the natural

logarithm of total assets from t ´ 2 to t ´ 1 (Fama and French, 2008); Momentum,

MOM1im, as measured by the most recent one-month stock return prior to the port-

folio formation (Jegadeesh, 1990); Cumulative momentum, MOM2im, as measured

by the cumulative stock return from monthm´12 to monthm´2, not including the

return of m´ 1 (Fama and French, 2008); Market capitalization, MCit, as measured

by the natural logarithm of the market cap in January of t (Fama and French, 2008);

Book-to-market ratio, BTMit, as measured by the natural logarithm of the ratio of

the book equity for the last fiscal year-end in t ´ 1 divided by the market equity in

December of t ´ 1 (Fama and French, 2008); Operating leverage, as measured by

OLit ”NFAitTAi,t´1

, with TA denoting the net fixed assets (e.g., Saunders et al., 1990;

Alan et al., 2014); Capital intensity, as measured by CIit ”PPEit

TAitÍNVit(e.g., Jain

et al., 2014); and Inventory growth, INV Git, as measured by the change in ending

inventory, deflated by total assets, from fiscal year t´ 2 to t´ 1 (Kesavan and Mani,

2013). Finally, we control for the change in AIT, ∆AIT , measured as the percentage

change of AIT from t´ 1 to t (Alan et al., 2014).

Each column in Table 5.8 relates to a different measure of DSM. Throughout

columns (1)-(3), we use each stock’s quintile rankings of DSM to allow a direct

comparison with our portfolio results and to mitigate the effect of extreme obser-

vations. For example, in column (1), the coefficient of DSM relates to each stock’s

quintile rankings of DSM ITSAL, DSM ITSALRank , and equals -.003 (t=-2.345). All else

being equal, this coefficient implies that a zero-cost portfolio formed onDSM ITSAL

by having a short position in quintiles four and five and a long position in quin-

tiles one and two yields, on average, a ´.003”

p1`2q´p4`5q2

ı

˚ 100 “ 0.9% monthly

excess return, which confirms our findings from §5.3.2. Because the negative coeffi-

cient sign of DSM persists along all of the rank operationalizations of DSM (columns

(1)-(3)) and all of the continuous measures of DSM (columns (4)-(6)), with only one

exception of insignificance, we conclude that the predictive power of the proposed


inventory-productivity-to-demand-volatility ratio prevails, despite controlling for

other measures.

In line with Alan et al. (2014), our estimates confirm the positive relationship

between AIT and stock returns. Retailers that manage inventories more efficiently

than their industry peers generate higher returns. Our results further imply a nega-

tive relationship between AIG and future stock returns, which follows prior research

(Kesavan and Mani, 2013). That is, retailers that experience abnormally high inven-

tory growth rates generate lower stock returns. Overall, the results of the Fama and

MacBeth (1973) regression-based approach provide reassurance that DSM is a pre-

dictor of future returns and contains distinct information from commonly known

inventory metrics.


Table 5.8: Fama and MacBeth (1973) approach: Average slopes and t-statistics frommonthly cross-sectional regressions

DSM quintile rank

Column 1 2 3

Variable DSM ITSALRank DSM IDSAL

Rank DSMPURSALRank

Intercept 0.039*** 0.022* 0.019*2.853 1.932 1.741

DSM -0.003** -0.002** -0.002*-2.345 -2.093 -1.661

ACC -0.356*** -0.180*** -0.072***-5.145 -5.339 -3.084

ASSG -0.034*** -0.037*** -0.008-2.660 -2.822 -0.603

MOM1 -0.033*** -0.045*** -0.044*-2.969 -3.820 -1.658

MOM2 0.002 0.005 0.0020.649 1.339 0.369

MC -0.013*** -0.007*** -0.014***-3.730 -2.958 -0.820

BTM 0.000 0.001 0.0040.101 0.289 1.478

OL -0.017 -0.009 -0.017-1.229 -0.721 -1.061

CI 0.013 0.015 0.0191.121 1.259 1.227

INVG -0.403*** -0.341*** -0.087*-4.124 -3.987 -1.741

AIG -0.003 -0.002 -0.015-0.427 -0.285 -0.648

AIT 0.010*** 0.008** 0.017*2.675 2.12 1.681

∆AIT 0.000 0.000 0.0000.237 0.393 0.086

Note. *p ă .1, **p ă .05, ***p ă .01. This table shows average slopes from monthly cross-sectional regressionsto predict future stock returns. Below the coefficients, we report t-statistics that are based on time-series standarddeviations of the monthly slopes. To mitigate the influence of outliers, we have winsorized all continuous independentvariables at the .02-level.


Table 5.8 Fama and MacBeth (1973) approach: Average slopes and t-statistics frommonthly cross-sectional regressions (Cons.)

Continuous DSM measures

Column 4 5 6

Variable DSM ITSAL DSM IDSAL DSMPURSAL

Intercept 0.058*** 0.062*** 0.035**4.626 4.662 2.160

DSM -0.002* -0.002** -0.001-1.870 -1.995 -0.385

ACC -0.265*** -0.280*** -0.059***-5.392 -5.314 -2.609

ASSG -0.025** -0.020** -0.002*-2.400 -2.055 -1.656

MOM1 -0.018* -0.018* -0.046*-1.822 -1.862 -1.939

MOM2 0.001 0.001 0.0060.331 0.314 0.960

MC -0.019*** -0.022*** -0.004**-4.109 -4.120 -1.980

BTM 0.001 0.001 0.0030.721 0.797 0.759

OL -0.018 -0.018 -0.010-1.545 -1.526 -0.553

CI 0.009 0.009 0.0120.918 0.906 1.104

INVG -0.404*** -0.412*** -0.122***-4.357 -4.349 -2.630

AIG -0.006 -0.007 -0.006-0.833 -0.960 -0.336

AIT 0.015*** 0.015*** 0.008*3.168 3.265 1.744

∆AIT 0.000 0.000 0.0000.104 0.103 0.029

Note. *p ă .1, **p ă .05, ***p ă .01. This table shows average slopes from monthly cross-sectional regressionsto predict future stock returns. Below the coefficients, we report t-statistics that are based on time-series standarddeviations of the monthly slopes. To mitigate the influence of outliers, we have winsorized all continuous independentvariables at the .02-level.

5.4. Explanations of the Market Anomaly 163

5.4 Explanations of the Market Anomaly

There are at least two potential explanations for the observed market inefficiency.

The first is information-based (labeled the “information story”), suggesting that the

information content of DSM might be indicative of near-term sales and earnings.

This is because DSM contains information about how effectively (in terms of sup-

ply) the firm can serve its demand, and thus, may contain information regarding

future write-downs and other inventory-related costs that affect a firm’s earnings

and a firm’s ability to realize sales growth (cp. §5.2.1). If investors do not fully

incorporate this operational information into the pricing of stocks in a timely man-

ner, then the anomaly in stock returns can be explained by the market’s failure to

consider all of the information content of DSM and the anomaly will dissipate over

time into the market. The second explanation of the market anomaly is that DSM

is a proxy for an unknown risk and that the market demands a risk premium from

retailers with low DSM (labeled the “market efficiency story”).

Therefore, to shed some light on the underlying causes of the negative rela-

tionship between DSM and abnormal stock returns, we supplement the previous

analyses along three dimensions: first, we use first-order autoregressive models

for changes in one-year-ahead earnings and one-year-ahead sales (growth) to test

the relationship between our base measure, DSM ITSAL and these variables. Ta-

ble 5.9 summarizes the regression results that we obtain from using a generalized

least squares estimation method, which accounts for panel-specific AR(1) autocor-

relation and a heteroskedastic error structure. We include year- and two-digit SIC

code-based segment dummies in all of the models to control for macroeconomic fac-

tors and segment specifics that may affect retailers’ earnings per share (EPS), sales,

and inventory management practices.

In Model 1a, we regress the change in earnings per share of firm i in fiscal year t,

∆EPSit, measured as the change in EPS deflated by the previous fiscal year’s end-

ing stock price, on ∆EPSi,t´1 and the previous year’s accruals, ACCi,t´1, because

Sloan (1996) and Thomas and Zhang (2002) show that the inventory component of


accruals predicts future earnings. Consistent with the accounting literature, we find

that accruals predict future earnings. In Model 1b, we add DSM ITSALi,t´1 as an addi-

tional predictor. The negative coefficient of DSM ITSALi,t´1 in Model 1b confirms the

negative relationship between DSM and future earnings, which is in line with the

results of the above portfolio analysis and the Fama-MacBeth regressions. In Model

1c, we add AITi,t´1, and AIGi,t´1 as additional controls. Consistent with Kesavan

and Mani (2013) and Alan et al. (2014), our regression results reveal a positive re-

lationship between adjusted inventory turnover and one-year-ahead earnings, and

a negative relationship between abnormal inventory growth and one-year-ahead

earnings while the negative and significant effect of DSM ITSALi,t´1 persists across all

models.

In Model 2a, we regress the change in sales of firm i in year t, ∆SALit, measured

as the change in sales from the previous to the recent fiscal year, deflated by the

previous fiscal year’s sales (i.e., sales growth), on ∆SALi,t´1, the average consumer

price index of the previous year, CPIt´1, data that we obtained from the website

of the U.S. Bureau of Labor Statistics, and the previous fiscal year’s gross margin

as calculated by Kesavan and Mani (2013), i.e., GMi,t´1 ” SALi,t´1{ pCOGSi,t´1´

LIFOi,t´1 ` LIFOi,t´2q. In Model 2b, we add DSM ITSALi,t´1 as an additional pre-

dictor. The negative coefficient of DSM ITSALi,t´1 reveals that DSMs are indicative of

a reduction in future sales (growth). The negative relationship between DSM and

future sales follows our discussion in §5.2.1 because DSMs are indicative of either in-

sufficient inventory or because products from prior periods become obsolete and do

not meet recent customer requirements, leading to lower customer service and re-

tention, all of which affect sales growth. In Model 2c, we add AITi,t´1 and AIGi,t´1

as additional controls to the model but the negative and statistically significant ef-

fect ofDSM ITSALi,t´1 persists. For all models, we compute Wald tests to assure that the

addition of variables improves the model fit (p ă .01). The results of Models 1 and

2 provide some initial evidence of an “information story” because the information

content of DSM seems to be predictive of near-term earnings and sales growth.

5.4. Explanations of the Market Anomaly 165Ta

ble

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Second, to explore whether the relationship between DSM and future stock re-

turns can be explained by an “information story” or a “market efficiency story”, we

shift the portfolio formation date from July 31 of year t to June 30 of year t. This

implies that we use accounting information from the fiscal period from January 1 of

year t´ 1 until December 31 of year t´ 1, rather than using accounting information

from February 1 of year t ´ 1 until January 31 of year t to construct the portfolios.

The one-month shift in the portfolio formation date causes a significant information

disadvantage because the majority of retailers in our sample (46.49%) have their

fiscal-year-end in January. Accordingly, the information used to rank firms accord-

ing to their DSM value and to construct portfolios on June 30 of year t is 17 months

old for these 46.49% of retailers, rather than 6 months old when constructing port-

folios on July 31 of year t.

The time shift severely affects the performance of all portfolios formed on one of

the proposed DSM measures and the performance of zero-cost portfolios declines,

on average, by 18.29% compared to the original formation process. These results

provide additional evidence for an ”information story” because the information

content of DSM seems to dissipate over time into the market. We provide the de-

tailed results of the Fama and French (1993) plus momentum (Carhart, 1997) regres-

sions for the quintile and zero-cost portfolios formed on DSM ITSAL, DSM IDSAL,

and DSMPURSAL in Appendix 5.C.

Third, to gain further insights regarding potential explanations of the relation-

ship between DSM and future stock returns, we track portfolio returns for five years.

In particular, we construct portfolios on July 31 of year t using accounting informa-

tion for the fiscal period from February 1 of year t ´ 1 until January 31 of year t.

However, rather than liquidating the portfolios on July 31 of year t ` 1, we hold

them for four additional years (until July 31 of year t ` 5). We compute monthly

average excess returns of the portfolios over each year of the holding period and

report in Table 5.10 the abnormal stock returns that we obtain from the Fama and

French (1993) three-factor plus momentum (Carhart, 1997) model. In line with the

above findings, we observe that the portfolio performance decreases substantially

5.4. Explanations of the Market Anomaly 167

Table 5.10: Longitudinal portfolio performance benchmarked against the Fama-French-Carhart factors

Year 1 Year 2 Year 3 Year 4 Year 5Portfoliorank

α α α α α

DSM ITSAL

1 (low) 1.21%*** 0.47%*** 0.22% 0.04% -0.28%*2 0.71%*** 0.41%*** 0.45% 0.06% 0.07%3 -0.01% -0.08% -0.14% 0.02% 0.16%4 -0.04% 0.03% 0.11% 0.00% 0.57%**5 (high) -0.40%*** -0.15% 0.09% 0.24% 0.01%

Zero-cost 1.13%*** 0.48%*** 0.16%** -0.15% -0.08%

DSM IDSAL

1 (low) 1.16%*** 0.49%*** 0.36% 0.14% -0.16%2 0.46%*** 0.49%*** 0.69%*** 0.23% 0.06%3 0.36%*** 0.19%* 0.09% -0.34%** 0.00%4 -0.04% 0.14% 0.32% 0.27%* 0.51%**5 (high) -0.38%*** -0.30% 0.22% 0.28%* 0.04%

Zero-cost 1.04%*** 0.44%*** 0.17%*** -0.30% -0.08%

DSMPURSAL

1 (low) 0.44%*** 0.21%* -0.09% -0.11% -0.31%*2 0.33%*** 0.39%*** 0.23% 0.34%*** -0.08%3 0.19%** 0.12% 0.14% 0.16% 0.10%4 -0.03% 0.40%*** 0.82%*** 0.08%* 0.20%5 (high) -0.59%*** -0.07% -0.34% 0.11% 0.12%

Zero-cost 0.73%*** 0.17%*** 0.00 -0.24% -0.63%Note.*p ă .1, **p ă .05, ***p ă .01.

over time and that zero-cost portfolios based onDSM ITSAL andDSM IDSAL do not

generate any abnormal returns beyond year t` 3. The zero-cost portfolios based on

DSMPURSAL do not even generate any abnormal returns from year t` 2 onwards.

We suspect that the earlier dissipation of information contained in DSMPURSAL

into the market is because the information content of DSMPURSAL is per definition


Table 5.11: Percentage of firms switching from portfolio ranks 1 or 2 in year t toportfolio ranks 4 or 5 in subsequent years

t` 1 t` 2 t` 3 t` 4 t` 5

DSM ITSAL 23.05% 27.47% 26.83% 27.78% 29.00%DSM IDSAL 21.47% 26.37% 24.42% 26.85% 27.07%DSMPURSAL 18.00% 19.65% 22.33% 21.72% 23.19%

older than the information content of DSM ITSAL and DSM IDSAL. The decrease in

the performance of zero-cost portfolios could also occur because a large number of

firms that are assigned to a particular portfolio rank in period t transits to other port-

folio ranks in subsequent years. Therefore, if the fraction of firms that transit from

low to high portfolio ranks in subsequent years is very high (and/or vice versa),

and if this fraction increases severely over time, then the assessment of the longi-

tudinal portfolio performance would not provide any evidence of an “information

story.” To evaluate this alternative explanation for the decrease in the performance

of zero-cost portfolios, Table 5.11 presents the percentage of firms that transit from

low portfolio ranks (1 or 2) in period t to high portfolio ranks (4 or 5) in subsequent

years.16 For example, 24.42% of the firms that were assigned to portfolio ranks 1 or

2 in period t – based on DSM IDSAL – would have been assigned to portfolio ranks

4 or 5 if the portfolios were rebalanced in t+3. Because only a minority of firms tran-

sits from low to high portfolio ranks, and because this fraction remains rather stable

over time, we feel confident in the above-proposed interpretation: the observed de-

crease in portfolio returns over time results from to the ongoing dissipation of the

information that DSM contains into the market.

In sum, the results of the above three analyses suggest that the market anomaly

can be explained by an “information story”. That is, investors do not immediately

incorporate the information content of DSM into the pricing of stocks. However,

over time, the anomaly dissipates into the market, most likely because investors

16The transition rates from portfolio ranks 4 or 5 in period t to portfolio ranks 1 or 2 in subsequentyears are even lower than those presented in Table 5.11.

5.5. Conclusion 169

consider variables such as EPS and sales growth for the pricing of stocks, which we

show to be affected by DSM of prior periods.

5.5 Conclusion

Grounded in OM theory, this study develops a novel KPI for demand-supply mis-

matches, the DSM, which relates the volatility of inventory productivity to the volatil-

ity of demand. A firm that experiences greater volatility in terms of inventory pro-

ductivity relative to demand volatility suffers from a greater mismatch of demand

and supply. The DSM contains valuable and distinct information regarding firm

operations because high volatility in inventory productivity may imply that a firm

faces periods with excessively high inventories and other periods with stock-outs,

although the average inventory productivity may imply that a firm’s operations are

well managed. We normalize the CV of inventory productivity by the CV of de-

mand to incorporate the fact that firms with higher demand volatility (naturally)

hold higher inventory levels (safety stocks).

Investigating a sample of 424 publicly listed U.S. retailers, we apply portfolio-

asset pricing models and demonstrate that zero-cost portfolios formed on DSMs

generate up to 1.13% abnormal stock returns that cannot be explained by the Fama

and French (1993) three-factor plus momentum (Carhart, 1997) risk factors. These

strong market anomalies related to DSM are observed over the entire period of 1985-

2013 and persist after controlling for alternative inventory productivity measures

and firm-characteristics that are known to predict stock returns. We reveal further

that DSM is indicative of lower future earnings and lower sales growth and pro-

vide evidence that the identified market inefficiency results from investors’ failure

to incorporate all of the information that DSM contains into the pricing of stocks.

As such, our results are relevant to the audience of (i) investors/stock analysts, (ii)

managers, and (iii) researchers.

We encourage investors and stock analysts to leverage quarterly financial in-

formation in addition to annual financial reports to gain a more comprehensive


understanding of operational processes. Based on our measure of DSM, which is

computed on the basis of quarterly data, we identify a market inefficiency. As such,

analysts can leverage this knowledge; moreover, we encourage future research to

delve deeper into the underlying mechanisms that lead to the presented effects.

Operations managers may also benefit from our results because, in line with

prior research (e.g., Chen et al., 2005, 2007; Hendricks and Singhal, 2009; Kesavan

and Mani, 2013), we reveal a strong correlation between inventory management

and stock market performance, thus providing empirical support for budget nego-

tiations. CEOs may place greater emphasis on improvement projects with an OM

focus, as our results clearly imply a relationship between DSMs and stock market

performance. In addition, the KPI of DSM may be employed for benchmarking

a firm’s inventory management performance against industry peers while avoid-

ing average measures of inventory productivity, which can smooth out important

information. Furthermore, prior research quantified the negative financial impli-

cations of supply chain disruptions (i.e., the result of severe DSMs) after these are

announced in major business press (e.g., Hendricks and Singhal, 2005a). Because

our measures of DSM capture DSMs on a longitudinal scale that must not have

been subject to media attention, the DSM measure may be employed as an indicator

of future disruptions. Throughout the paper, we stress the importance of appropri-

ate performance metrics. Therefore, it may be advantageous to also reflect the in-

terdependence between functions through KPIs: for example, CEOs may consider

weighting their marketing budgets with OM-related metrics, such as DSM, rather

than allocating fixed percentages of sales, as is done in most firms (e.g., Fischer et al.,

2011).

Our results also have implications for research, and they complement the OM

and SCM literature that links business practices to financial data. Future research

may employ the DSM measure for use in a broad spectrum of analyses. Whereas in

the context of the bullwhip effect, relative volatility measures are quite commonly

employed, we hope to encourage scholars to also consider such relative volatil-

ity measures to a greater extent at the firm-level. Furthermore, as it is not in the

5.5. Conclusion 171

scope of this study to identify the multitude of potential causes for DSMs, future

research may benefit from a careful analysis of the various factors and events that

drive DSMs.

Our paper has a major limitation: we restrict our analysis to firms in the retailing

sector because inventory investments account for large fractions of these retailers’

current assets and are thus a key management item. Although industry-specific

analyses have several advantages, they naturally limit the generalizability of find-

ings. Therefore, it might be interesting for future research to analyze whether our

findings also apply to other sectors.


5.A Appendix A: The Effect of Ordering Frequency on

DSM

Consider a responsive retailer (A) that places twice as many orders per quarter as a

less responsive retailer (B), ceteris paribus. Suppose A and B face identical quarterly

COGS of ψ in quarter q “ 1 of a year t. Given that B has an inventory level in q of

Iq“1, A’s quarterly inventory is only half of B’s, i.e., Iq“1

2 . Accordingly, B’s inventory

turnover in q “ 1 is ITBq“1 “2ψIq“1

, and A’s inventory turnover is ITAq“1 “ψIq“1

. As-

sume further that over the next three quarters, the inventory turnover of A and B

increases by 20% per quarter, such that ITBq“2 “ 1.2`

ITBq“1

˘

, ITBq“3 “ p1.2q2`

ITBq“1

˘

,

ITBq“4 “ p1.2q3`

ITBq“1

˘

, and for ITAq for q “ 1, ..., 4 analogously. The average in-

ventory turnover is then ¯IT it “IT iq“1

4

ř3k“0p1.2q

k for i “ A,B, and the standard

deviation of the inventory turnover over the four quarters for retailer i “ A,B

is

d

14

4ř

q“1

´

IT iq ´¯IT it

¯2

“ IT iq“1

d

14

4ř

q“1

´

p1.2qq´1 ´ 14

ř3k“0p1.2q

k¯2

. Therefore, the

CV of the inventory turnover of i over the four quarters is

CV pIT qit “

d

14

4ř

q“1

´

p1.2qq´1 ´ 14

ř3k“0p1.2q

k¯2

IT iq“1

4

ř3k“0p1.2q

k,

which is identical for both retailers. Table 5.12 presents an intuitive example that

shows that the CV of IT of retailer A is equal to that of retailer B, although retailer A

has a higher ordering frequency (i.e., is more responsive).

5.B Appendix B: Computation of AIG and AIT

We compute abnormal inventory growth (AIG) as proposed by Kesavan and Mani

(2013). The authors derive the measure from the expectation model of growth of

inventory per store (Kesavan et al., 2010), which incorporates the dependence be-

tween inventory per store and the previous fiscal year’s inventory per store, con-

5.B. Computation of AIG and AIT 173

Table 5.12: Responsiveness and the coefficient of variation of inventory turnover

Firm B

Period ITB SDpITBq MeanpITBq CV pITBq

q=1 2.00 0.54 2.68 0.20q=2 2.40 0.54 2.68 0.20q=3 2.88 0.54 2.68 0.20q=4 3.46 0.54 2.68 0.20

Firm A

Period ITA SDpITAq MeanpITAq CV pITAq

q=1 4.00 1.08 5.37 0.20q=2 4.80 1.08 5.37 0.20q=3 5.76 1.08 5.37 0.20q=4 6.91 1.08 5.37 0.20

Note. SD=standard deviation.

temporaneous and lagged COGS per store, gross margin, lagged accounts payable

to inventory ratio, store growth and lagged capital investment per store, all of which

are suggested to affect inventory investments. For firm i in fiscal year t and quar-

ter q, Kesavan and Mani (2013) define the average annual inventory per store as

isAIGit ” ln´”

14

ř4q“1 INVitq ` LIFOit

ı

{Nit

¯

, with Nit denoting firm i’s total num-

ber of stores open by the end of fiscal year t, cost of goods sold per store as csAIGit ”

ln prCOGSit ´ LIFOit ` LIFOi,t´1s {Nitq , gross margin as gmAIGit ” lnpSALit{

rCOGSit ´ LIFOit ` LIFOi,t´1sq, accounts payable (AP ) to inventory ratio as

piAIGit ” ln

ˆ

ř4q“1 APitq

ř4q“1 INVitq`4LIFOit

˙

, store growth as gAIGit ” ln´

NitNi,t´1

¯

, and the cap-

ital investment per store as capsAIGit ” ln´”

14

ř4q“1 PPEitq `

ř5τ“1

RENTitτp1`dqτ

ı

{Nit

¯

,

with PPE denoting the plant, property, and equipment, RENTit1, RENTit2, ...

RENTit5, denoting retailers’ rental commitments for the next five years, and d de-

noting the discount rate, which is assumed to be d “ 8%.

Then, the expectation model of growth of inventory per store is estimated in

first differences (∆) and given by the following log-log specification: ∆isAIGit “

∆x1itβ2 `∆ηit. The column vector, x1it, comprises the explanatory variables csAIGit ,


gmAIGit , csAIGi,t´1, isAIGi,t´1, piAIGi,t´1, gAIGit , and capsAIGi,t´1, and β2 is the row vector of the

corresponding coefficients, with β2 “ pβ21, β22, β23, β24, β25, β26, β27q1. Kesavan and

Mani (2013) assume that firms in a given segment (i.e., two-digit SIC codes) are

homogeneous, which implies that the coefficients of β2 are identical for firms in

a given segment, and hence reduces the above estimation equation to ∆isAIGit “

∆x1itβ2,spiq ` ∆ηit, where spiq denotes the two-digit SIC code segment specific co-

efficients to which firm i belongs. Based on the coefficients that the authors ob-

tain by using a generalized least squares method, which handles heteroscedasticity

and panel-specific autocorrelation, Kesavan and Mani (2013) predict the expected

logged inventory growth per store, Ep∆isAIGit q. The abnormal inventory growth

per store for firm i in fiscal year t is then computed as AIGit “´

ISAIGit

ISAIGi,t´1´ 1

¯

´

pexppEr∆isAIGit sq ´ 1q, with ISAIGit “ exppisAIGit q.

We obtain the estimates of abnormal inventory growth analogously to the above-

described procedure, with the slight adaptation that we do not deflate inventory,

COGS, and capital investment by the number of stores and that we do not include

store growth as additional control variable in the regression equation. We follow

this approach because information regarding the number of stores per retailer is not

available for many firms during our sampling period and would thus significantly

decrease the sample size, potentially rendering the sample unrepresentative. That

is why Alan et al. (2014) make a similar adjustment.

In line with Alan et al. (2014), we compute adjusted inventory turnover (AIT )

by fitting cross-sectional regression models for each firm i and year t that control for

segment-specific fixed effects (two-digit SIC code), gross margins, capital intensity,

and sales surprise. Controlling for gross margin (GM ) is important because GM is

positively correlated with a firm’s service level, product variety, and the quality of

products, each of which may lead to lower inventory turnover. Capital intensity

(CI) serves as proxy for firms’ supply chain and information technology infras-

tructure, which may facilitate the reduction of safety stocks. Therefore, inventory

turnover should be adjusted for CI . Moreover, sales surprise (SS) may affect inven-

tory turnover because high sales realizations (compared to the previous year) may

5.C. Estimation Results After Shifting of the Portfolio Formation Date 175

cause an increase in inventory turnover and serves further as proxy for economic

shocks. To computeAIT , we operationalize these variables as follows: for firm i and

fiscal year t, inventory turnover is defined as ITAITit ”COGSit´LIFOit`LIFOi,t´1

INVit`LIFOit,

GM as GMAITit ” SALit

COGSit´LIFOit`LIFOi,t´1, CI as CIAITit ”

pGFAit `ř4τ“1

RENTitτp1`dqτ ` RENTit5

dp1`dq4 q{pTAit ` LIFOit `ř4τ“1

RENTitτp1`dqτ ` RENTit5

dp1`dq4 q,

with GFA denoting gross fixed assets, and the discount rate d is again assumed to

be d “ 8%, and SS as SSAITit ” SALitSALi,t´1

. Given these variables, we fit the following

regression model for each firm i and year t: lnpITAITit q “ Fjris ` b1lnpGMAITit q `

b2lnpCIAITit q` b3lnpSS

AITit q` εit, with Fjris being the segment-specific intercept j to

which firm i belongs. AIT for firm i in fiscal year t is then given by the residual (εit)

of that regression equation.

5.C Appendix C: Estimation Results After Shifting of

the Portfolio Formation Date

Table 5.13 summarizes the estimation results for the zero-cost and quintile portfo-

lios from the Fama and French (1993) three-factor plus momentum (Carhart, 1997)

model for DSM ITSAL, DSM IDSAL, and DSMPURSAL, after the shifting of the

portfolio formation date from July 31 of year t to June 30 of year t.


Table 5.13: Fama-French-Carhart four factor regression results for zero-cost andquintile portfolios using December 31 of t´ 1 as cutoff date for portfolio formation

Portfoliorank

α RmRf SMB HML UMD R2

DSMITSAL

1 (low) 0.82%*** 0.97*** 0.61*** 0.17*** -0.21*** 60.76%2 0.74%*** 1.20*** 0.56*** 0.37*** -0.25*** 62.45%3 0.12%** 0.99*** 0.64*** 0.37*** -0.44*** 59.61%4 -0.11% 1.13*** 0.75*** 0.46*** -0.33*** 63.16%5 (high) -0.35%*** 1.01*** 1.02*** 0.39*** -0.44*** 59.87%

Zero-cost 0.97%*** 0.01*** -0.29*** -0.16*** 0.15*** 68.51%

DSMIDSAL

1 (low) 0.84%*** 1.04*** 0.62*** 0.26*** -0.22*** 61.11%2 0.56%*** 1.07*** 0.52*** 0.29*** -0.31*** 60.15%3 0.35%*** 1.07*** 0.65*** 0.35*** -0.34*** 62.59%4 -0.27%*** 1.08*** 0.68*** 0.41*** -0.34*** 61.79%5 (high) -0.21%** 1.05*** 1.10*** 0.42*** -0.44*** 61.22%

Zero-cost 0.93%*** 0.05*** -0.31*** -0.14*** 0.12*** 67.65%

DSMPURSAL

1 (low) 0.35%*** 1.03*** 0.53*** 0.28*** -0.25*** 58.87%2 0.33%*** 1.04*** 0.57*** 0.21*** -0.34*** 65.85%3 0.12% 1.10*** 0.68*** 0.38*** -0.34*** 65.92%4 0.04% 1.03*** 0.72*** 0.42*** -0.34*** 65.61%5 (high) -0.35%*** 0.96*** 0.84*** 0.31*** -0.16*** 64.88%

Zero-cost 0.51%*** 0.04*** -0.22*** -0.12*** -0.09*** 69.96%Note. *p ă .1, **p ă .05, ***p ă .01.

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