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CASH CONVERSION CYCLE AND THE FINANCIAL PERFORMANCE OF PHILIPPINE FIRMS1

Francis Adrian H. Viernes

The importance of efficient working capital management has been gaining attention because of the 2008

Global Financial Crisis. In addition, it is equally important for the profitability and growth of the company.

Findings regarding the relationship of CCC and financial performance are mixed and the applicability to

other countries, particularly the Philippines, is an interesting empirical question. This study will contribute

by adding to the limited study on the topic for developing countries like the Philippines.

INTRODUCTION

The importance of efficient working capital management has been gaining attention because of the 2008

Global Financial Crisis. During the 2008 Global Financial Crisis, the “Big Three” carmakers in the US,

namely, General Motors (GM), Ford and Chrysler, faced strong pressures to undergo bankruptcy and have

therefore asked the government for financial assistance amounting to $13.4 Billion from the Wall Street

Bailout fund (Isidore, 2008). The letter addressed to the Senate says that the Big Three “face rapidly

evaporating operational liquidity” and “soon may not be able to continue to operate” (CNN Politics, 2008).

Ironically, just before the crisis, these automakers were holding $7.6 Billion Dollars in the form of excess

working capital, tied up in invoices paid by late customers, suppliers being paid too early and inventory

being unsold and kept in warehouses. (REL Research, 2006). It is then interesting to ask, had these

automakers managed their working capital more efficiently, would these “operational liquidity” problems

have been avoided (Luo, 2009)?

REL Consultancy Group estimates that $863 Billion have been tied-up in working capital, in the form of

unpaid bills and excess inventory. These are considered unproductive, as these are funds that could have

been used in ventures that are more profitable. Yet, we need not look far from our own country as the

failure of the once dominant industry, the preneed industry, may also be traced to liquidity problems.

Before the 1997 Asian Financial Crisis, there were 200 preneed firms in the Philippines. Currently, only 20

remains (Remo, 2013). A post-mortem of this phenomenon claims that the fundamental error for the

preneed companies is its business model (The Manila Times, 2013). However, liquidity was equally

problematic for these as these companies have enough assets to cover liabilities and are therefore

solvent. The problem was meeting claims as they arise; they were simply illiquid (Delavin, 2016).

In addition to its importance to the survival of the firm, efficient working capital management is important

for profitability and growth. For profitability, efficient cash flows ensure that companies rely less on

external financing, which may be more expensive than internal funds. This is more important for small

firms, which due to their size, cannot access the financial markets as easily as large firms can (Majeed,

Makki, Saleem, & Aziz, 2013). Following this, emerging markets have many small firms and less developed

financial markets making liquidity management more crucial for these markets. If working capital is

efficient, funds are easily released and reinvested to the firm causing an improvement in long-term

1 Prepared under the supervision of Dr. Aliza Racelis and Dr. Joel Yu for a graduate course in Finance

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earnings. This leads us to ask, if the efficient management of working capital leads to improved earnings,

do investors understand this and incorporate this fact in their valuation of stock prices?

In an article published by Forbes (YCharts, 2012), US investors reward retail stocks with lower cash

conversion cycle through higher prices. The highest returns were consistently found on the stocks of

companies with the lower cash conversion cycles. Conversely, the lowest stock performances are found

on the stock of companies having the longer cash conversion cycles. Investors, at least in the US,

understand the complexity of the company’s daily operations and how working capital efficiency is

translated into future earnings (Luo, 2009). Can this be true for the Philippines? We begin listing down the

questions we want to resolve.

In this study, we are interested in determining if there is a relationship between financial performance

and working capital management. Particularly, we like to answer the following empirical questions:

1.) What is the relationship between CCC and profitability?

2.) What relationship do the components of CCC have with profitability?

3.) Can CCC be a good predictor of future financial profitability?

4.) Can improvements in CCC, relative to industry average, lead to excess returns relative to industry

average?

We use the cash conversion cycle (CCC) to measure the efficiency of working capital management. It is a

comprehensive measure because it measures both, the efficiency of current assets as represented by

receivables and inventories through Days of Sales Outstanding (DSO) and Days of Inventory Outstanding

(DIO), and the efficient handling of current liabilities through the Days of Payables Outstanding (DPO). In

fact, CCC goes so far to tell us the investment and credit decisions in suppliers, inventory and customers

(Gitman, 1974).

The answers to the above questions will be valuable to both managers and academics, as this topic has

not yet been studied, to the best of our knowledge, for the Philippines. This will also shed some light into

the working capital management practices, represented by the cash conversion cycle for Philippine firms.

REVIEW OF RELATED LITERATURE

A huge part of written finance, both in academic and empirical literatures, focuses on the importance of

long-term financing decisions, particularly on how capital budgeting and capital structure affect the

financial performance of the firm (Majeed, Makki, Saleem, & Aziz, 2013). Studies dealing with liquidity or

working capital management and its similar effect to financial performance are relatively new and

empirical works are scarce.

Over the past two decades, studies regarding the effect of short-term financing decisions on profitability

have been increasing (Lazaridis & Lyroudi, 2000).

The importance of liquidity management and the fundamental basis of the Working Capital Management

(WCM) theory was based on the idea developed by Richards and Laughlin in the 1980s known as the Cash

Conversion Cycle (CCC). WCM is assessed using two methods: the static and dynamic methods. (Majeed,

Makki, Saleem, & Aziz, 2013). The static measures of liquidity are the point-in-time ratios such as the cash

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ratio, quick ratio and current asset ratio. On the other hand, the dynamic method focuses on the

fundamentals of the firm’s daily operations such as the cash conversion cycle. The CCC, as a dynamic

measure of liquidity, combines both the balance sheet and income statement, incorporating a time

dimension in its approach (Jose, Lancaster, & Stevens, 1996).

Working capital management is efficient if receipts are collected, as early as possible, and disbursements

are held-off for as long as possible (AlHajjar, 2011). An efficient cash conversion cycle is one that is short.

Most studies conducted on the empirical relationship between CCC and profitability point to a significant

and negative relationship. We begin reviewing some of these below.

As an example, Shin and Soenen found out that by lengthening CCC, profitability is lowered, all things held

constant. They showed that in 1994, Walmart and K-mart were comparable companies when it comes to

their debt-equity ratio but the CCC of K-mart is around 61 days while Walmart has around 40 days. This is

showed to increase annual financing by around $198.3 Million, subsequently contributing to the

bankruptcy of K-mart.

Incorporating an industry approach, Eljelly (2004) examined the effect of liquidity using both current ratio

and “cash gap” (his coined term for cash conversion cycle. He analyzed 29 joint-stock companies listed in

Saudi Arabia for 1996-2004. He found out that using correlation and regression analysis, a significant

negative relationship exist between the firm’s profitability and its current ratio. At the industry level,

however, the cash conversion cycle is of more importance as a liquidity measure that affects profitability.

The size variable (size or log of assets) is significant and having the expected sign.

Similarly, Raheman and Nasr (2007) conducted a study in Pakistan for 94 listed companies and found

significant, negative relationship with the components of CCC. The dependent variable was Net Operating

Profitability and all the variables, has a negative relationship with CCC. The control variables, firm size and

debt ratio, are all in accordance to theory.

Luo (2009) made a research for whole supply chain of physical goods referring to manufacturing, retail

and wholesale companies in an attempt to determine the relationship of CCC, profitability and stock value.

They found a significant negative relationship between CCC and profitability. He also found out that Days

of Payables Outstanding and Days of Inventory Outstanding is in accordance with theory, but Days of Sales

Outstanding is not. This is also the first work, to the best of our knowledge, which tried to assess the long-

term effect of CCC in terms or profitability. We will explain more of this later including his findings on the

effect to firm value. This work is a major influence in our research paper.

In the same year and having some similar aspects, Uyar (2009) examined the benchmark for cash

conversion cycle for merchandising and manufacturing companies in Turkey. He found out that the

merchandising industry has a shorter CCC compared to manufacturing industries, primarily because

merchandising companies do not produce their own goods. He further examined the relationship

between the length of CCC and the size of the firm using total assets and net sales and found a negative

correlation. Furthermore, he found a negative correlation between the length of CCC and profitability.

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Zariyawati et al. (2009) studied the same topic using Pooled OLS for six sectors listed in Bursa Malaysia.

They found a strong negative and significant relationship between the length of the cash conversion cycle

and profitability as measured by operating income plus depreciation as a percentage of total assets.

Taking on a much shorter sample, Nuru (2011) studied 11 manufacturing private limited companies in

Tigray region, Ethiopia for the period of 2005-2009. Using both correlation and pooled regression analysis,

a significant negative relationship between CCC, including its individual components, and profitability

exists.

Using a different dependent variable than most, AlHajjar et al. (2011) found a significant negative

relationship between the cash conversion cycle and return on investment (ROI) for the Japanese firms for

small, medium, and large companies, and for all industries except for consumer goods and services. While

this confirms the negative relationship of the cash conversion cycle and profitability, a similar study using

a comparable metric – ROIC, was done by Mengesha (2014) and found no relationship.

Akinlo (2012) studied 66 Nigerian non-financial firms for the period 1997-2007. Similar to the previous

studies, the CCC and its individual components, when regressed against profitability (return on assets),

have significant and negative relationships.

Anser & Malik (2013) studied listed manufacturing companies in the Karachi Stock Exchange for 2007 to

2011. The study indicated that the cash conversion cycle is significantly but inversely related to the length

of CCC. The control variables, debt ratio and size, did not have the expected signs and were not significant.

One of the comprehensive study done on SMEs was the work of Yazdanfar and Ohman (2014). They used

seemingly unrelated regression (SURE) to analyze cross-sectional panel data covering 13,797 SMEs

operating in four industries. They found out that return on assets, as a proxy for profitability, has a

significant, negative correlation with the length of CCC. The control variable, which is firm size, is positively

related to profitability.

Similarly and on the same year, Mathuva (2014) wanted to find out if the 33 publicly listed firms in the

Nairobi Securities Exchange (NSE), for the years 1993 to 2008, exhibit a target CCC. The findings reveal

that companies in NSE maintains a target CCC and that CCC has a significant, negative relationship with

return on assets, investment in capital expenditures and growth opportunities.

Lastly, Mengesha (2014) studied 11 manufacturing private, limited companies for Ethiopia, 2008-2012.

The study reveals that CCC, Days of Sales Outstanding and Days of Payable Outstanding have negative

relationships with profitability as measured by Return on Assets but have insignificant relationships when

it comes to Return on Invested Capital (ROIC).

Despite the number of studies that point to a significant and negative relation, some studies return a

different result. In some studies, the relationship of the CCC with profitability is positive. We discuss some

of these in the succeeding paragraphs.

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Lyroudi and Lazaridis (2000) used the food industry in Greece to examine CCC and determine its

relationship with the static ratios. In addition, they investigated the relationship of the cash conversion

cycle and profitability. They found a significant and positive relationship between the cash conversion

cycle and the traditional liquidity measures, most notably the current and quick ratios. There also exists a

positive relationship between profitability (Return on Assets and Net Profit Margin) CCC although there

are no linear relationships with financial leverage.

Later, Abuzayed (2009) analyzed data from a sample of 52 listed firms for the period from 2000 to 2008.

These firms are listed in the Amman Stock Exchange and is considered a less developed financial market.

The dependent variable for profitability is Gross Operating Income. The study found a significant, positive

relationship between CCC and Gross Operating Income. Abuzayed argues that this is due to the inherent

inefficiency for less developed financial markets as they failed to penalize inefficient working capital

management. Thus, there is no incentive for profitable firms to manage their cash conversion cycle. We

will cite this research again in the studies found for firm valuation.

Attari (2012) studied the relationship of the cash conversion cycle with the size and profitability of the

firms in the four specific manufacturing sectors listed at Karachi Stock. The study indicated a positive

relationship between Return on Assets (ROA) and CCC and a significant but negative relationship between

CCC and using Return on Equity (ROE).

Taking on a much smaller sample size, Panigrahi (2013) studied five cement companies listed in the

Bombay Stock Exchange for the period of 2001-2010. The dependent variables are Return on Assets (ROA)

and Return on Equity (ROE). The study indicated that there exists a positive relationship between

profitability and the length of the cash conversion cycle. The control variables, firm size and leverage ratio,

has the expected sign and are significant.

Lastly, Muscetolla (2014) studied 4,226 manufacturing companies in Italy classified as SMEs for the years

2007-201. Using Ordinal Logistic Regression, he found a positive relationship between the length of CCC

and profitability, measured by EBITDA/Net Sales.

We have discussed some prominent studies on the topic and while majority points to a negative

relationship and some to a positive relationship, a few studies point to neither of the two. Some studies

return no significant relationship as well for both profitability and the length of the cash conversion cycle.

As an example, Abbasi & Bosra (2012) studied the relationship of the cash conversion cycle for 112 firms

listed in the Tehran Stock Exchange for 1998 – 2009. The dependent variable used is the Gross Profit to

Total Assets (GP) Ratio. The net cash conversion cycle did not have a significant relationship with the GP

ratio. However, when regressed using the individual components, each are having a significant, negative

relationship with the GP ratio. The control variables financial debt ratio and fixed asset ratio, sales growth

all have the expected sign but the size, log of assets, do not.

Laslty, Al-Shubiri & Aburumman (2013) studied 11 industrial sectors with periods from 2005-2011. They

found no significant relationship between the profitability index and the cash conversion cycle.

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Profitability is likewise having a significant positive relationship between control variables (Debt, market,

Productivity, Market, Liquidity and Dividends Indicator).

We have now discussed studies detailing the relationship of profitability and the cash conversion cycle.

Nonetheless, despite all these, studies regarding the effect on shareholder value are surprisingly scarce.

We will discuss below some of these studies and their findings.

Abuzayed (2009), in addition to the one discussed above, wanted to find out if efficient working capital

management is incorporated in the firm value. It was later concluded that there is not relationship

between firm value (measured by Tobin-Q ratio) and CCC.

Luo (2009) did a similar study on the same year. Luo uses the excess return or portfolio approach using

the 25 Fama and French formed on book to market. This approach produced significant results detailing

that improvements in CCC increases firm value but the incorporation of this fact is not perfect.

Only Abuzayed (2009) and Luo (2009), to the best of our knowledge, simultaneously studied the effect of

CCC in profitability and firm value.

SIGNIFICANCE OF THE STUDY

Findings regarding the relationship of CCC and profitability are mixed. While majority of are pointing to a

negative relationship, some studies, including emerging market studies, point to a positive relationship.

Thus, the relationship between these two variables, particularly in the Philippines, is an interesting

empirical question.

This research will contribute to the existing literatures in two ways. First, it will initiate the study of

working capital management, particularly CCC and its effect to profitability for the Philippines. To the best

of our knowledge, no research on the Philippines has been done on this topic. The research findings will

help decision-makers in the Philippines in setting up policies in working capital management, particularly

the credit period granted to customers and inventory holding period for production.

Second, not only in the Philippines, this research will increase the availability of studies on firm value and

working capital management particularly for the developing markets.

THEORETICAL FRAMEWORK

Tradeoff between Liquidity and Profitability

Smith (1980) first highlighted the tradeoff between profitability and liquidity. He noted that decisions that

maximize profitability tend not to take into considerations liquidity. Conversely, decisions that make the

firm liquid do not maximize profitability. The long-run survival of the firm, however, requires that business

must be run both efficiently (in terms of working capital) and profitably. (Mohamad & Noriza, 2010)

The main objective of working capital management is to reach an optimal balance between WCM

components and profitability. The importance of cash, as an indicator of continuing financial health,

should not be surprising in view of its crucial role within the business (Abuzayed, 2012).

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Shortening CCC is profitable because longer CCC increases the potential for companies to resort to

external borrowings for liquidity needs. (Majeed, Makki, Saleem, & Aziz, 2013).

Thus, along with other variables that are affecting the firm’s profitability, such as firm size and financial

leverage, the expected relationship of this with the firm’s financial performance is negative. This

relationship is expressed in the framework below:

𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 (𝑃𝑟𝑜𝑓𝑖𝑡𝑎𝑏𝑖𝑙𝑖𝑡𝑦, 𝑆𝑡𝑜𝑐𝑘 𝑃𝑟𝑖𝑐𝑒)

= 𝛽0 + 𝛽1𝑆𝑖𝑧𝑒 − 𝛽2𝐹𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝛽3𝐶𝐶𝐶 + 𝜇𝑖(𝑂𝑡ℎ𝑒𝑟 𝑓𝑎𝑐𝑡𝑜𝑟𝑠

However, as seen in the studies of Lyroudi and Lazaridis (2000), Abuzayed (2009) and Attari (2012), can a

positive relationship between CCC and financial performance be explained theoetically?

Efficient management of liquidity has costs and benefits. It is depending on the net effect of these costs

and benefits, which determines whether CCC will be positively or negatively related to profitability. Thus,

the answer to our theoretical question is yes; that it is possible for the CCC and profitability to have a

positive relationship, if the costs to managing WCM outweighs its benefits.

If this tradeoff of costs and benefits exists between the overall profitability and liquidity, then this must

exist as well in the individual components of liquidity. We will examine these components along with their

costs and benefits closely.

Cash Conversion Cycle (CCC)

The CCC, as expressed by Eljelly, is better understood as the “cash gap” (Eljelly, 2004). This represents the

period from when cash is paid to suppliers and cash is received from customers.

The Cash Conversion Cycle (CCC) is computed as:

𝐶𝐶𝐶 = 𝐷𝑎𝑦𝑠 𝑜𝑓 𝑆𝑎𝑙𝑒𝑠 𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔 (𝐷𝑆𝑂) + 𝐷𝑎𝑦𝑠 𝑜𝑓 𝐼𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦 𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔(𝐷𝐼𝑂) −

𝐷𝑎𝑦𝑠 𝑜𝑓 𝑃𝑎𝑦𝑎𝑏𝑙𝑒𝑠 𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔 (𝐷𝑃𝑂)

where:

𝐷𝑎𝑦𝑠 𝑜𝑓 𝑆𝑎𝑙𝑒𝑠 𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔 = 𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑠 𝑅𝑒𝑐𝑒𝑖𝑣𝑎𝑏𝑙𝑒𝑠

𝑆𝑎𝑙𝑒𝑠 × 365

𝐷𝑎𝑦𝑠 𝑜𝑓 𝐼𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦 𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔 = 𝐼𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑖𝑒𝑠

𝐶𝑜𝑠𝑡 𝑜𝑓 𝐺𝑜𝑜𝑑𝑠 𝑆𝑜𝑙𝑑 × 365

𝐷𝑎𝑦𝑠 𝑜𝑓 𝑃𝑎𝑦𝑎𝑏𝑙𝑒𝑠 𝑂𝑢𝑡𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔 = 𝐴𝑐𝑐𝑜𝑢𝑛𝑡𝑠 𝑃𝑎𝑦𝑎𝑏𝑙𝑒

𝐶𝑜𝑠𝑡 𝑜𝑓 𝐺𝑜𝑜𝑑𝑠 𝑆𝑜𝑙𝑑 × 365

Days Inventory Outstanding (DIO)

Days Inventory Outstanding (DIO) tells us, how long it takes the company to sell their inventory. A lower

DIO is, thus, more desirable for a company. A high and increasing trend in the DIO may be indicative of

slowing demand for the company’s good and services. In addition, holding inventories incur storage costs

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(Al-Shubiri & Aburumman, 2013) and are prone to obsolescence. Thus, the company benefits if a short

inventory holding policy is enforced.

On the other hand, if the company keeps little stock and is too efficient, it foregoes the opportunity to sell

these goods when there is unexpected demand in the market.

Thus, the relationship of DIO and profitability depends on the net effect of these costs and benefit of

holding high inventories.

In most cases, this has been shown empirically to have a negative relationship with profitability, implying

that the holding costs outweigh the benefits of a high level of inventories. However, Mathuva (2014)

found the DIO to be positively related to profitability. He argued that high inventory levels reduce costs

of possible interruptions in the production process and loss of business due to scarcity of products

Days Sales Outstanding (DSO)

Days of Sales Outstanding (DSO) tells us how long the company can collect the receivables from the

customers’ credit sales. The sooner the company can collect its cash or liquidate its receivables, the sooner

it can reinvest these for interest income. It can also use these cash to pay dues saving the company huge

borrowing costs. Thus, the company benefits if a short trade credit period is implemented.

On the other hand, if the trade credit period is too short or conservative, the company might be losing

sales. Longer trade credit period usually stimulates sales as it allows more time for the customer to pay.

In addition, companies usually give discounts for early settlement so the company does not receive the

full price for early-paying customers.

Thus, the relationship of DSO and profitability depends on the net effect of these costs and benefit of the

established receivable collection policy. Most studies find a negative relationship between DSO and

profitability. However, DSO may be positive when extending credit period is used as an aggressive to

compete for market share or to nurture long-term relationships with profitable customers (Rajan &

Peterson, 1997). Muscettola (2014) explained the same way regarding the positive relationship of days

of accounts receivables and profitability.

Days Payable Outstanding

Days of Payable Outstanding (DPO) tells us how long it takes the company to pay its obligations to its

suppliers. Delaying payment to suppliers is beneficial to the company as supplier financing is one of the

cheapest way to finance operations. The company can then use the cash for reinvestment or to other

more urgent need instead of borrowing. Thus, a longer payment period is deemed beneficial for the

company.

On the other hand, stretching payment period too might cause the company to lose discount for early

payments and worse, the supplier might factor this into the pricing and might even increase prices for the

company (Al-Shubiri & Aburumman, 2013). On top of that, the company might damage its reputation that

would make it harder to do business in the future.

Thus, the relationship of DPO and profitability depends on the net effect of these costs and benefit of the

established payable payment policy. Studies are mixed on this aspect. While theory favors a positive

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relationship, most studies show a negative relationship despite the fact that payables are presumed to

reduce the cash gap (Mengesha, 2014). DPO can be negative on the idea that firms that are less profitable

pay longer than profitable ones and thus a case of negative causality (Raheman & Nasr, 2007). The

negative relationship was found in the study of Nuru (2011), Akinlo (2012), Abbasi & Bosra (2012),

Mengesha (2014) as well.

A negative relationship is thus consistent with the view that highly profitable firms usually afford their

suppliers shorter payment periods, as they have the financial resources to do so (Mengesha, 2014).

RESEARCH DESIGN AND METHODOLOGY

Data Source

Using FactSet Fundamentals Database, we screened on all the companies currently listed with calculated

CCC for 2010-2015. We chose listed companies because these firms have reliability and availability of

financial statements because of regulations. (Abuzayed, 2012). Additionally, as argued by Lazaridis and

Tryfonidis (2006), hiding profits to avoid corporate tax is a common motive for non-listed firms, making

their financial data less reliable for academic studies.

This period has been chosen because trend analysis reveals that the Philippine Stock Exchange Index (PSEi)

demonstrated a huge jump in price (return), 63% increase from 2008 to 2009. We believe that this set of

years will rule out abnormal growth or any effect to our variables that may be caused by the 2008 Global

Financial Crisis.

We eliminated companies that did not have consistent CCC data from 2010 to 2015. As a result, financial

firms are eliminated because these do not have inventories and therefore Days of Inventory Outstanding.

This type of sampling is common for working capital management studies. (Avvasi & Bosra, 2012; Majeed,

Makki, Saleem, & Aziz, 2013; Mathuva, 2014; ). We ended up with 89 companies. Testing the market

capitalization of the samples over the total market capitalization of all the listed companies in the

Philippine Stock Exchange, we got 55% which means that this sample size is representative enough of the

Philippine equity market as a whole.

Research Design and Methodology

We use Return on Assets (ROA) as proxy for profitability. Aside from the fact that this is the most used

profitability proxy in CCC study (Lyroudi & Lazaridis, 2000; Nuru, 2011; Akinlo, 2012; Attari, 2012;

Panigrahi, 2013; Yazdanfar and Ohman, 2014; Mathuva, 2014, Mengesha, 2014), this is most appropriate

as this measure the returns from efficient use of assets. Thus, CCC as an efficiency measure, is better

regressed against a variable which measures efficiency as well. We will use OLS regression to find CCC’s

effect on the financial performance.

To answer our first research question, the dependent variable will be CCC. This is measured in days. We

used other control factors that are identified in the previous researches as determinants of ROA. These

includes:

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Control Variable Definition Expected

Sign Explanation

Leverage Total Assets / Total

Equity -

Borrowings increase interest expense, which

drives down earnings. External borrowings

are more costly than retained earnings.

Size Natural Lograrithm

of Sales (LN(Sales)) +

Larger companies have greater bargaining

power to lower costs. Also, larger companies

in most industries benefit from economies of

scale.

Profit Margin (PM)/ Δ

in PM (Models 5 & 6)

Net Income / Total

Sales +

This refers to cost efficiency; how much net

income is left per unit of sales left. The

higher the number, the more cost efficient

the company in generating net income.

Asset Turnover Ratio

(AT)/ Δ in PM

(Models 5 & 6)

Net Sales / Average

Total Assets +

This refers to the efficiency of assets; how

much a per unit of assets generate per unit

of sales. The higher the figure, the more

efficient the company is in its assets use.

Δ in Sales (Models 5 &

6) Change in Sales +

Change in sales represent the growth of the

company’s operations and is expected to be

viewed favorably by investors.

Model 1, therefore, becomes:

𝑅𝑂𝐴 = 𝛽0 + 𝛽1𝐶𝐶𝐶 + 𝛽2𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝛽3𝑆𝑖𝑧𝑒 + 𝛽4𝑃𝑀 + 𝛽5𝐴𝑇 + 𝜇𝑖 (1)

The second question has two parts: first, CCC as the sum of the ratios, DIO, DSO and DPO and lastly, the

prior CCC and change in CCC.

To answer the first part of the second research question, we break the components of CCC into its

components, Days of Inventory Outstanding (DIO), Days of Sales Outstanding (DSO) and Days of Payable

Outstanding (DPO).

Model 2, therefore, becomes:

𝑅𝑂𝐴 = 𝛽0 + 𝛽1𝐷𝐼𝑂 + 𝛽2𝐷𝑆𝑂 + 𝛽3𝐷𝑃𝑂 + 𝛽4𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝛽5𝑆𝑖𝑧𝑒 + 𝛽6𝑃𝑀 + 𝛽7𝐴𝑇 + 𝜇𝑖 (2)

The second part of the research question explores how past CCC affects current profitability. Most

research on this topic usually regresses CCC and ROA for the same period. However, we believe that the

benefits of working capital efficiency for the current period may be realized in the next period. For

example, the interest expense or interest income gained by successfully managing cash may only be

realized/received after one-year.

11

Model 3, therefore, becomes:

𝑅𝑂𝐴 = 𝛽0 + 𝛽1𝐶𝐶𝐶𝑡−1 + 𝛽2∆𝐶𝐶𝐶 + 𝛽3𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝛽4𝑆𝑖𝑧𝑒 + 𝛽5𝑃𝑀 + 𝛽6𝐴𝑇 + 𝜇𝑖 (3)

To assess the effect of individual changes, we break down the change in CCC to its components.

Model 4, therefore, becomes:

𝑅𝑂𝐴 = 𝛽0 + 𝛽1𝐶𝐶𝐶𝑡−1 + 𝛽2∆𝐷𝐼𝑂 + 𝛽3∆𝐷𝑆𝑂 + 𝛽4∆𝐷𝑃𝑂 + 𝛽5𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝛽6𝑆𝑖𝑧𝑒 + 𝛽7𝑃𝑀 +

𝛽8𝐴𝑇 + 𝜇𝑖 (4)

In answering the third research question, we need to regress the past determinants of CCC with a future

value. Luo (2009) suggested using two-time periods lag to test this.

Model 5, therefore, becomes:

𝑅𝑂𝐴𝑡+1 = 𝛽0 + 𝛽1𝑅𝑂𝐴𝑡−1 + 𝛽2𝐶𝐶𝐶𝑡−1 + 𝛽3∆𝐶𝐶𝐶 + 𝛽4∆𝑆𝑎𝑙𝑒𝑠 + 𝛽5𝑆𝑖𝑧𝑒𝑡 + 𝛽6∆𝑃𝑀𝑡 + 𝛽7∆𝐴𝑇𝑡 + 𝜇𝑖

(5)

Regressing ROA with the prior CCC and increase in CCC makes Model 5 less susceptible to endogeneity

problems (Luo, 2009).

To assess the individual effect, we break down the change in CCC according to its component.

Model 6, therefore, becomes:

𝑅𝑂𝐴𝑡+1 = 𝛽0 + 𝛽1𝑅𝑂𝐴𝑡−1 + 𝛽2𝐶𝐶𝐶𝑡−1 + 𝛽3∆𝐷𝐼𝑂𝑡−1 + 𝛽4∆𝐷𝑆𝑂𝑡−1 + 𝛽5∆𝐷𝑃𝑂𝑡−1 + 𝛽6∆𝑆𝑎𝑙𝑒𝑠 +

𝛽7𝑆𝑖𝑧𝑒𝑡 + 𝛽8∆𝑃𝑀𝑡 + 𝛽9∆𝐴𝑇𝑡 + 𝜇𝑖 (6)

Finally, to answer the last question, we grouped the samples according to industry and got the mean

return. We then subtracted the mean industry return from the individual returns to get the “excess

returns”. The excess change in CCC was likewise derived by subtracting the mean change in industry CCC

from the individual change in CCC.

The excess industry return intends to measure change in market value. Studies concerning market value

often use Tobin-Q (Nazir, 2009; Mohamad & Nariza, 2010; Abuzayed, 2012). The Tobin-Q, however, is

both harder to get and more difficult to interpret. (Luo, 2009)

The primary independent variable is the excess CCC as compared to industry average. This is ideal as it

tells you how the company is doing against its peers. (Hutchison, Farris II, & Anders, 2007). The average

CCC differs per industry because the nature of how cash is managed differs. Therefore, the correct way to

analyze improvements in CCC of the firm is to compare it against the industry (Uyar, 2009).

The controls for this model, following Faulkender and Wang (2006), are all divided by the lagged market

value of Equity. The independent variable is excess industry return. Model 7 then becomes:

𝐸𝑥𝑐𝑒𝑠𝑠 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑅𝑒𝑡𝑢𝑟𝑛𝑠 = 𝛽0 + 𝛽1𝐸𝑥𝑐𝑒𝑠𝑠∆𝐶𝐶𝐶 + 𝛽𝑖𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝜇𝑖 (7)

The controlled variables are the following:

12

Definition Expected

Sign Explanation

Δ in Earnings Before Non-

recurring Items, Interest and

Taxes

+

Higher core earnings indicate stability and

growth so this is expected to be positively

related to excess industry earnings.

Δ in Cash Dividends Paid for

Common Stock +

Dividend payout signals excess cash and is

therefore associated with profitability

Δ in Holding of Non-Cash Assets +

Non-cash assets are still considered assets for

the company and benefits are still expected to

be received from these.

Δ in Cash Holdings +

Cash is viewed positively among other forms of

assets as this is something which value is certain

unlike intangible assets and can therefore be

distributed to shareholders.

Δ in Research and Development -

This represents growth opportunities or

Intangible Assets, which values are uncertain.

Higher values imply that more money are

allocated to projects which have uncertain cash

flows.

Δ in Interest Expense -

This represents the interest expense for the

period. Higher interest leads to lower earnings

and therefore lower stock performance.

All control variables are standardized by dividing with the market value of equity at the beginning of the

year. Since the independent variable is the spread of the current and past market values, divided by the

market value, this standardization allows us to interpret the estimated coefficients as the “peso change

in value for one dollar change in the corresponding independent variable” (Faulkender & Wang, 2006).

To help us refine the source of excess CCC’s effect, we further break it down into its components.

Model 8, therefore, becomes:

𝐸𝑥𝑐𝑒𝑠𝑠 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝑅𝑒𝑡𝑢𝑟𝑛𝑠 = 𝛽0 + 𝛽1𝐸𝑥𝑐𝑒𝑠𝑠∆𝐷𝐼𝑂 + 𝛽2𝐸𝑥𝑐𝑒𝑠𝑠∆𝐷𝑆𝑂 + 𝛽3𝐸𝑥𝑐𝑒𝑠𝑠∆𝐷𝑃𝑂 +

𝛽𝑖𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝜇𝑖 (8)

For Models 7 & 8, we have trimmed the dataset to exclude sectors that only have one or two companies.

Some sectors have three or more companies but do not have consistent data for either the CCC or stock

price returns. These sectors were likewise excluded.

13

Hypothesis

Following the intuition behind the variables and our theoretical framework, the following are our

hypotheses. The number in the parenthesis, after each hypothesis, indicates the model/s to which it

pertains.

H1 = The conversion cycle (CCC) is inversely related to profitability (ROA). (1)

H2 = The Days of Inventory Outstanding (DIO) is inversely related to profitability (ROA). (2)

H3 = The Days of Sales Outstanding (DSO) is inversely related to profitability (ROA). (2)

H4 = The Days of Payables Outstanding (DPO) is positively related to profitability (ROA). (2)

H5 = Prior CCC (CCC t-1) is negatively related to current profitability (ROA). (3) (4)

H6 = The change in CCC is negatively related to profitability (ROA). (3)

H7 = The change in Days of Inventory Outstanding (DIO) is inversely related to profitability (ROA). (4)

H8 = The change in Days of Sales Outstanding (DSO) is inversely related to profitability (ROA). (4)

H9 = The change in Days of Payables Outstanding (DPO) is positively related to profitability (ROA). (4)

H10 = Past profitability (ROAt-1) is positively related to future profitability (ROA t+1). (5)

H11 = Prior CCC (CCC t-1) is negatively related to future profitability (ROA t+1). (5) (6)

H12 = The change in CCC is negatively related to future profitability (ROA t+1). (5)

H13 = The change in DIO is negatively related to future profitability (ROA t+1). (6)

H14 = The change in DSO is negatively related to future profitability (ROA t+1). (6)

H15 = The change in DPO is positively related to future profitability (ROA t+1). (6)

H16 = The excess change in CCC is inversely related to shareholder value. (7)

H17 = The excess change in DIO is inversely related to shareholder value. (8)

H18 = The excess change in DSO is inversely related to shareholder value. (8)

H19 = The excess change in DPO is positively related to shareholder value. (8)

RESULTS AND DISCUSSIONS

Descriptive Statistics and Correlation Analysis

The mean, minimum, maximum and standard deviation for each variables are computed and presented

in Table 1 of the Appendix. The mean return for the ROA is 4.91% with the standard error being 8.5%. The

mean CCC is 181 days. This is broken down into mean DIO of 128 days, DSO of 150 days and mean DPO of

97 days.

This means that Filipino firms pay earlier than they received their receivables, with 31-day (or a full month)

gap.

The correlation of each variables are generated and presented in Tables 2 and 3 of the Appendix. The

correlation analysis in Table 2 reveals that CCC is inversely related to ROA, Size, Leverage, PM and AT. This

confirms that the more profitable companies have lower CCC. The correlation to size implies that the

larger companies are better in managing CCC than smaller firms. When it comes to leverage, a negative

correlation implies that companies that have more debt, have more incentive to manage CCC. This means

that debt is effective in disciplining managers to efficiently manage cash. CCC is negatively correlated to

the efficiency ratios, as expected.

14

As expected, excess industry returns are negatively correlated with the excess CCC change. Oddly, excess

industry returns are negatively correlated change in standardized earnings, dividends, cash, noncash,

excess change in DPO, where the expected signs are positive. The same goes for standardized change in

interest expense which is expected to be negative but came out positive. We do not have an explanation

for this yet.

Residual and Multi-collinearity tests

Since the Durbin-Watson statistic computed for each model are around 2.0, there model does not suffer

from autocorrelation. The regression models are corrected for heteroscedasticity by choosing the White

heteroscedasticity-consistent standard errors and covariance option. The VIF of each variables in all

models, are less than 10, indicating the multi-collinearity is acceptable. (Abbasi & Bosra, 2012).

CCC and Profitability

We ran Model 1 and the results are presented in Table 4 of the Appendix. CCC has a negative and

significant relationship with ROA. We, therefore, accept H1. This in accordance with the majority of

findings regarding the topic. Interpreting the coefficient of CCC, we say that a decrease of 1.99 or 2 days

to CC, improves ROA by one basis points.

To find out which component of CCC explains variations in the profitability of the company, we ran Model

2 and the results are presented in Table 4 of the Appendix. The components of CCC all have the expected

signs however; only the Days of Sales Outstanding is significant at 5% level and Days of Payable

Outstanding at 10% level. We, therefore, reject H2 and accept H3 and H4. ROA will increase by one basis

point for a decrease of 0.93 days or one day in the DSO. On the other hand, ROA would increase by one

basis point for an increase of 1.69 DPO.

The second part of question 2 involves asking if past CCC affects current profitability. We ran Model 3 and

the results are presented in Table 4 of the Appendix. As expected, both the level of prior CCC and the

change in CCC have significant, negative relationships with current profitability. We, therefore, accept H5

and H6. Decreasing CCC by 2.47 or around three days would lead to an increase of ROA for the next year

by one basis point. Decreasing the change in CCC for the current year by 1.04 days leads to an increase of

ROA by one basis point. This is particularly important, not only because it is more economically significant

than the past or current level of CCC, but because this variable deals with the incremental effect which is

relevant for any policy review.

To be more specific about the effects of CCC change, we ran model 4. The results are presented in Table

4 of the Appendix. The level of past CCC, change in DSO, change in DIO and change in DPO, all have the

expected signs and have significant relationships with ROA. We, therefore, accept H7, H8 and H9. Similar to

Model 2, the highest economic significance is given by the change associated with DSO, with 0.75 days

decrease leading to a one basis point increase in ROA. Decreasing the change in DIO and change in DPO

by 3.39 days and 0.97 days respectively will lead to a similar one basis point increase in ROA.

In addition to that, Leverage, Size and Asset Turnover are significant. The signs of the significant control

variables are as expected and is in accordance with with previous studies (Eljelly, 2004; Panigrahi, 2013;

Al-Shubiri & Aburumman, 2013; Öhman, 2014). According to Models 1,2,3 and 4, ROA will increase by one

15

basis point, for a 0.344, 0.335, 0.323 and 0.321 decrease, respectively, in the Debt-to-Asset ratio. Similarly,

ROA will increase by one basis point for a 1.63%, 2.0%, 1.56% and 1.59% increase, respectively, in the

Total Assets. Lastly, ROA will increase by one basis point for a 0.004, 0.005, 0.004 and 0.004 increase in

the Asset Turnover Ratio.

CCC and Future Profitability

The primary reason why working capital management is crucial for the firms’ survival is that it ensures

that cash tied to working capital is freed and invested in projects that generate long-term cash flows. It

should therefore follow that how managers manage working capital has long-term implications.

Model 5 was ran and the results are presented in Table 3 of the Appendix.

The current level of CCC is statistically significant and negatively related to future ROA. We, therefore,

accept H11 and reject H10 and H12. The model predicts that decreasing the cash conversion cycle by 2.36

days will lead to an increase of one basis point in future ROA.

Breaking down the components of the change in CCC, we ran Model 6. The results are presented in Table

3 of the Appendix. Similar to Model 5, the level of past CCC affects future earnings in a negative but

significant statistical relationship. The model predicts that decreasing the net cash conversion cycle by

2.34 days will lead to an increase of one basis point for future ROA.

For model 6, the change in DIO is significant and has the expected sign. Decreasing the DIO by 2.21 days

lead to an increase of one basis point for future ROA according to the model. We, therefore, accept H13

and reject H14 and H15.

It is interesting to link this to the previous findings generated by Models 2 and 4. For both these models,

the variable related to the DIO always received the lowest explanatory power among the components of

CCC and change in CCC. In fact, in Model 2, the DIO is statistically insignificant. In Model 4, the change in

DIO has the lowest explanatory power among those considered significant. Model 6 brings an intuitive

explanation to this. It seems that the change in DIO is affecting, not the current profitability, but the future

profitability. This is probably because, unlike collections and payment policies, efficient handling of

inventories more or less depends on innovation, through either technological advances or process

improvement. This was discussed by Boisjoly (2009) in his research stating that improvements in inventory

outstanding comes from successful application of manufacturing managements such as just-in-time

procedures, make-to-order procedures, lean manufacturing initiatives to improve their operating

processes, quality programs to reduce parts and supplier rationalization (Boisjoly, 2009).

The implications of this are huge as it confirms our intuition regarding the relationship of CCC and future

earnings. Decisions regarding working capital management have long-term implications and managers

should incorporate this fact when making or revising current working capital policies.

The remaining variables that are statistically significant are change in asset turnover, size and change in

sales. All the other variables, including past ROA are insignificant.

The sign of the sales growth, which is our proxy for growth opportunities, is significant, negative and not

in accordance with theory. This may be due to the fact that sales growth are expensive to attain. The

16

market for listed companies maybe is saturated and that cutting of margins are being done to generate

these sales. According to Models 5 and 6, increasing sales by PhP 285.71 and PhP 289.86 Million,

respectively, will lead to a one basis point decrease in ROA.

Similarly, ROA will increase by one basis point for a 1.56% and 1.60% increase, respectively, in the Total

Assets, according to Models 5 and 6. We will not interpret the coefficient of Asset Turnover as it does not

make any economic sense.

CCC and Stock Price Performance

Working capital policies affect the short-term dimension and deal primarily with day-to-day operations.

Some may, therefore, argue that the benefits of an improved working capital management are overlooked

by investors who are more focused on the long-term performance of the firm (Luo, 2009).

To confirm, we ran models 7 and 8 and the results are found in Table 4 of the Appendix. For Model 7,

there is a weak but statistically significant and negative relationship between excess industry returns and

excess CCC change. We, therefore, accept H16. The change in standardized cash holdings is likewise having

a weak but significant, positive relationship with excess industry returns.

A one standard deviation increase in the excess change in CCC leads to a 5.4 (0.54%) basis points excess

industry return. The cash holdings variable is significant at 10% but its interpretation does not make any

economic sense.

Further examining the source of CCC’s explanatory power, we ran Model 8. Excess Change in DSO is

significantly and positively related to excess industry returns. This is not the expected sign for this variable.

We, therefore, reject H17, H18, and H19. This, however, is supported by theories brought forward by Peterson

and Rajan (1997) saying that DSO (or change in DSO) may be positive when companies lengthen the credit

period as a strategy to gain market share. The same explanation is offered by Muscettolla (2014) for SMEs.

A one standard deviation increase in the excess change in DSO leads to a 9.9 (0.987%) basis points excess

industry return. This result may be supported by the fact that sales growth has a significant but negative

relationship with earnings. Thus, this type of aggressive sales strategy is viewed favorably and rewarded

by the market through excess stock price returns.

Both the cash holdings and non-cash holdings variables are significant at 10% but their interpretations do

not make any economic sense. Also, their signs are opposing with cash holdings being positively

significant, while the non-cash holdings being negatively significant. The explanation may be found from

the previous work of AlHajjar (2011). His results showed that the working capital competes with

investment in fixed assets for the funds of the firms when it has financial constraints, thus the negative

relationship (AlHajjar, 2011). The study of Abbasi and Bosra (2012) implied similar results as the control

variable, fixed asset ratio had a negative relationship with the financial performance of the firm.

Summary of Results

A table of summary is prepared in Table 7. Summary of Results - Hypothesis and Models of the

Appendix.

17

CONCLUSIONS, IMPLICATIONS AND FUTURE DIRECTION

Conclusion

We now begin to resolve the questions posted at the beginning of this research:

The length of CCC and profitability for the nonfinancial firms in the Philippines is inversely related. This

finding is in accordance with that of Shin and Soenen (1998), Raheman and Nasr (2007), Zariyawati et.al

(2009), Uyar (2009), AlHajjar et al. (2011), Nuru (2011), Akinlo (2012), Yazdanfar and Ohman (2013),

Mengesha (2014), Mathuva (2014) .

The components of CCC and its relationship with profitability are as expected. The Days of Receivables

Oustanding and Days of Inventory Outstanding are both having a negative effect on profitability while the

Days of Payable Outstanding has a positive effect on profitability. The results are confirmed by the same

studies cited in the previous paragraph.

Improvements in CCC may have current implications in current profitability, but more often than not, this

will have an effect on later years. This is actually confirmed by two regression models used wherein the

current length of cash conversion cycle affects future earnings. Thus, working capital policy have long-

term implications.

Improvements in CCC, in excess of industry average, are rewarded positively through excess returns.

When it comes to the individual components of CCC, however, it is the increase in DSO, relative to industry

average, which is significantly associated with excess returns suggesting that aggressive trade credit

policies are rewarded by the Filipino investors.

Managerial Implications

The findings of this research suggest firms can be profitable by bringing the overall level of CCC down.

Both the credit policy and payment policy are within the control of the firm’s financial executives. The

research suggest, therefore, the setting of an appropriate collection period that is shorter than the

payment period to suppliers.

Based on the study we have on Days of Inventory Outstanding, the company should pursue technological

advancements or the adoption of inventory utilization models to lower costs associated with it. This is the

only working capital policy that can have long-term effect on profitability.

The research, however, do not suggest to adjust these policies indefinitely. It is only saying that there are

costs and benefits to changing these policies and in the case of the Philippines, there is a net benefit in

decrease of CCC implying that the costs outweighs the benefits.

FUTURE RESEARCH DIRECTION

The implications arising from this research are general in nature. However, the CCC can be better assessed

on industry level. Applying the research and methodology on certain industries, may reveal results which

are specific to that industry, such as the relationship of Days Inventory Outstanding or Days Sales

Outstanding with the profitability. We believe the relationship of these variables may change depending

on the industry as credit and collection policies are highly dependent on industry practice. In addition, a

18

target CCC or determinant of CCC might be a good industry-specific topic expanding the results of this

one.

In addition to that, and as previous studies have revealed, the general relationship of WCM with financial

performance generally differs, not only with emerging markets and developed markets, but also within

countries from emerging markets. It might be worthwhile to examine this in the context of other markets

in Southeast Asia, as there are very little studies made, if any, in these countries.

Non-listed companies are also interesting to be researched on as being unlisted implies that asymmetric

information is greater. Because of this, access to funds are limited and that requires these companies to

be self-financing. Thus, efficiency of WCM is relevant for these firms.

Other interesting topics to examine in the Philippine settings likewise include the question: “Do firms have

a target (optimal) CCC to maintain, as explored by Mathuva (2012) for the Kenyan market? Or is this simply

“accidental”?” Concerning the firm value, after finding out that investors incorporate this into their

valuation of stocks, do shareholders underreact or overreact? Are incorporations in the stock price

perfect? These are the questions we want to explore next.

19

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22

APPENDIX

CCC AND PROFITABILITY

Mean Median Minimum Maximum Std. error

CCC 181.1294 94.0555 -1005.9429 2623.8050 347.4302

DIO 127.9454 57.7589 0.3201 2140.3136 242.7150

DSO 149.7803 80.1662 5.3299 2980.7315 262.4395

DPO 96.5963 50.1575 3.9970 3072.3906 192.1285

Δ in CCC 6.5281 0.2725 -2117.6000 1950.9141 198.0400

Δ in DIO 3.3704 0.3748 -1365.8766 1226.5759 122.3316

Δ in DSO 3.5722 0.5515 -2296.6569 2278.5170 198.5980

Δ in DPO 0.4145 1.4979 -2712.8201 1196.5094 157.7632

ROA 4.9180 4.3243 -76.5638 62.5603 8.5006

CCC(-1) 174.6014 95.5813 -1005.9429 2623.8050 343.3111

Leverage 4.2765 2.1318 -33.7708 448.3800 25.9127

LN(Assets) 9.6019 9.4151 3.6255 14.0355 1.9894

Profit Margin 13.7165 6.8982 -664.7136 2637.8869 123.0573

Asset Turnover Ratio 0.6625 0.4965 0.0061 3.1288 0.5498

CCC and Excess Industry Returns

Excess Industry Returns -2.8046 0.0000 -2954.6336 473.8175 183.5456

Δ in CCC 3.1694 0.0258 -397.1684 1120.5787 99.1865

Δ in STD earnings -0.0464 -0.0051 -2.5776 1.5386 0.3811

Δ in STD dividends -0.0023 0.0000 -0.2482 0.2387 0.0355

Δ in STD noncash -0.6236 -0.0432 -33.0634 22.8786 3.7810

Δ in STD cash -0.0346 -0.0040 -7.6442 8.8620 0.7354

Δ in STD RDexp 0.0001 0.0000 -0.0621 0.0735 0.0086

Δ in STD intexp -0.0214 -0.0004 -2.5760 2.0764 0.2195

Δ in DIO 2.5057 -0.0990 -208.6128 313.6576 31.7531

Δ in DSO 0.5751 0.1327 -398.6722 328.0326 59.6974

Δ in DPO -0.0886 0.8179 -1090.4314 717.4740 88.3963

Excess CCC Change 3.1694 -0.8355 -131.5983 288.7768 53.2356

Table 1. Descriptive Statistics for Variables in the CCC and Profitability Regression

23

CCC Δ in CCC CCC(-1) DIO DSO DPO

Δ in DPO

Δ in DIO

Δ in DSO ROA

ROA(-1)

LN(Assets)

Leverage PM AT

CCC 1

Δ in CCC 0.3057 1

CCC(-1) 0.8357 -0.2675 1

DIO 0.7880 0.1296 0.7226 1

DSO 0.5741 0.3185 0.3973 0.2041 1

DPO -0.0287 0.0460 -0.0556 0.1172 0.5857 1

Δ in DPO 0.0334 -0.0942 0.0881 0.0769 0.1583 0.2530 1

Δ in DIO 0.1534 0.5288 -0.1498 0.2687 0.0178 0.0864 0.2105 1

Δ in DSO 0.2368 0.5967 -0.1045 0.0248 0.4323 0.1936 0.5708 0.0786 1

ROA -0.3001 -0.2271 -0.1728 -0.1307 -0.3690 -0.1265 -0.0256 -0.0027 -0.2450 1

ROA (-1) -0.0444 -0.0154 -0.0360 -0.0413 -0.0612 -0.0555 0.0327 -0.0052 0.0138 0.0562 1

LN(Assets) -0.1181 0.0024 -0.1209 -0.0641 -0.1985 -0.1386 0.0128 0.0261 -0.0035 0.1652 0.0565 1

Leverage -0.0835 -0.0621 -0.0487 -0.0188 -0.0066 0.1183 0.0844 0.0029 0.0034 -0.0768 0.0186 -0.0778 1

PM -0.1262 -0.1475 -0.0426 -0.0227 -0.1571 -0.0151 -0.0323 -0.0034 -0.1707 0.3565 0.0188 0.0295 -0.0223 1

AT -0.2988 -0.0497 -0.2737 -0.2915 -0.3086 -0.2495 -0.0168 -0.0232 -0.0486 0.1752 0.0398 -0.0506 0.0844 -0.0617 1

Table 2. Correlation Analysis for Variables Used in the ROA/Future ROA- Dependent Variable Models

24

Excess Industry Returns

Δ in STD earnings

Δ in STD dividends

Δ in STD noncash

Δ in STD cash

Δ in STD Rdexp

Δ in STD intexp

Excess CCC Δ

Excess Δ in DIO

Excess Δ in DSO

Excess Δ in DPO

Excess Industry Returns

1

Δ in STD earnings

-0.00614 1

Δ in STD dividends

-0.07906 0.099004 1

Δ in STD noncash

-0.00272 0.12455 0.043467 1

Δ in STD cash -0.0042 0.253515 0.010127 0.655579 1

Δ in STD Rdexp

-0.0005 -0.10237 -0.036 -0.05678 -0.02966 1

Δ in STD intexp

0.010392 -0.0492 -0.00533 0.605924 0.115809 -0.02898 1

Excess CCC Δ -0.01439 0.017519 0.135286 0.004942 0.002859 -0.0114 -0.00702 1

Excess Δ in DIO

-0.00286 0.062422 -0.01531 0.033711 0.032755 -0.00523 -0.01436 0.157817 1

Excess Δ in DSO

-0.18818 0.073652 0.043399 0.041853 0.034916 -0.01998 0.006921 0.206599 0.013834 1

Excess Δ in DPO

-0.00044 0.036711 -0.06034 0.027563 0.011462 -0.00799 -0.04895 -0.38628 -0.05173 0.187033 1

Table 3. Correlation Analysis for Variables Used in the Excess Industry Returns- Dependent Variable Mode

25

Dependent Variable: Return on Assets in percentage (1.0 = 1%)

Explanatory Variables Model 1 Model 2 Model 3 Model 4

Constant -1.748948 (1.99672)

-0.195199 (1.945794)

-2.171886 (2.054557)

-2.048048 (2.013959)

CCC (in days) -0.005006**

(.001981)

Days of Inventory Outstanding (DIO)

-0.000995 (0.001019)

Days of Sales Outstanding (DSO)

-0.010744** (0.004221)

Days Payable Outstanding (DPO)

0.005934* (0.003371)

CCC(-1) in days -0.004043***

(0.001316) -0.003976***

(0.001173)

Change in CCC in days -0.009561** (0.004636)

Change in DIO -0.002947* (0.001773)

Change in DSO -0.013178** (0.006208)

Change in DPO 0.010330** (0.004368)

Leverage (Debt/Assets) -0.029036** (0.008949)

-0.029847** (0.010064)

-0.030680*** (0.009864)

-0.031143*** (0.010171)

Size (LN Assets) 0.613643*** (0.198129)

0.501004*** (0.171008)

0.640136*** (0.198006)

0.628314*** (0.191271)

Growth Opportunity -0.0000175 (.0000175)

-0.0000159 (0.0000112)

-0.0000190 (0.0000122)

-0.0000185 (0.0000120)

Profit Margin (PM) 0.023046

(0.014587) 0.021296

(0.013297) 0.022081

(0.013763) 0.021143

(0.013143)

Asset Turnover (AT) 2.358232*** (0.468922)

2.064256*** (0.644011)

2.44373*** (0.465054)

2.414700*** (0.467631)

R-Square 0.23672 0.268011 0.251418 0.265542

Adjusted R-Square 0.22803 0.2586857 0.241456 0.25928

Sample Standard Deviation 7.468765 7.327989 7.403534 7.347338

Durbin Watson Stat 1.93245 1.938824 1.930403 1.92917

White-test for heteroscedasticity

311.7545 287.5424 277.1351 332.3285

Table 4. Regression Output for ROA as Dependent Variable Models (1, 2, 3 & 4)

Note: *Significant at 10%, ** Significant at 5%, *** Significant at 1%. The figure in the parenthesis below the coefficient is its standard error.

26

Dependent Variable: Future ROA in percentage (1.0 = 1%)

Explanatory Variables Model 5 Model 6

Constant -0.488926 (2.633223)

-0.281429 (2.667081)

ROA (t-1) or past ROA 0.030152

(0.035740) 0.027197

(0.038823)

CCC(-1) in days -0.004233***

(0.001356) -0.004271***

(0.001360)

Δ in CCC in days -0.002823 (0.002310)

Δ in DIO -0.004525** (0.001809)

Δ in DSO -0.002030 (0.003623)

Δ in DPO 0.003190

(0.003190)

Δ in Sales -0.000035***

(0.000126)

-0.0000345***

(0.000124)

Size (LN Assets) 0.642021*** (0.265449)

0.624209** (0.270722)

Δ in Profit Margin (PM) 0.000889

(0.001488) 0.000962

(0.001533)

Δ in Asset Turnover (AT) 0.0000856*** (0.0000309)

0.0000875*** (0.0000304)

R-Square 0.054827 0.057885

Adjusted R-Square 0.042005 0.041388

Sample Standard Deviation 8.348602 8.351287

Durbin Watson Stat 2.028825 2.029865

White-test for heteroscedasticity

106.6659 185.6005

Table 5. Regression Output for Future ROA as Dependent Variable Models (5&6)

Note: *Significant at 10%, ** Significant at 5%, *** Significant at 1% Note: The figure in the parenthesis below the coefficient is its standard error.

27

Dependent Variable: Excess Industry Return (ACTUAL)

Explanatory Variables Model 7 Model 8

Constant 4.656032* (2.693804)

4.373384 (3.192440)

Excess Δ CCC -0.097002* (0.055400)

Excess Δ DIO -0.006589 (0.024035

Excess Δ DSO 0.161957***

(0.060902)

Excess Δ DPO 0.001083

(0.009039)

Δ in Interest Expense -59.69009 (74.99168)

60.25661 (75.37783)

Δ in R&D Expense -181.1207 (127.2759)

-218.6609 (140.0399)

Δ in Cash Holdings 21.23346* (11.77104)

21.79026* (11.57298)

Δ in Non-Cash Assets -6.635958 (4.098997)

-6.939643* (4.040218)

Δ in Dividend Payout 6.133575

(60.65782) -22.99455 (65.92709)

Δ in Earnings -10.00671 (14.75047)

-12.25281 (14.74666)

R-Square 0.213866 0.227946

Adjusted R-Square 0.193332 0.201823

Sample Standard Deviation 48.68801 48.43108

Durbin Watson Stat 1.792182 1.709827

White-test for heteroscedasticity

52.74377 53.20627

Table 6. Regression Output for Excess Industry Returns as Dependent Variable Models (7&8)

Note: *Significant at 10%, ** Significant at 5%, *** Significant at 1% Note: The figure in the parenthesis below the coefficient is its standard error.

28

# Hypothesis Model/s Decision/s

H1 The conversion cycle (CCC) is inversely related to profitability (ROA).

1 Accepted

H2 The Days of Inventory Outstanding (DIO) is inversely related to profitability (ROA).

2 Rejected

H3 The Days of Sales Outstanding (DSO) is inversely related to profitability (ROA).

2 Accepted

H4 The Days of Payables Outstanding (DPO) is positively related to profitability (ROA).

2 Accepted

H5 Prior CCC (CCC t-1) is negatively related to current profitability (ROA).

3 & 4 Accepted on both

models

H6 The change in CCC is negatively related to profitability (ROA). 3 Accepted

H7 The change in Days of Inventory Outstanding (DIO) is inversely related to profitability (ROA).

4 Accepted

H8 The change in Days of Sales Outstanding (DSO) is inversely related to profitability (ROA).

4 Accepted

H9 The change in Days of Payables Outstanding (DPO) is positively related to profitability (ROA).

4 Accepted

H10 Past profitability (ROAt-1) is positively related to future profitability (ROA t+1).

5 Rejected

H11 Prior CCC (CCC t-1) is negatively related to future profitability (ROA t+1).

5 & 6 Accepted on both

models

H12 The change in CCC is negatively related to future profitability (ROA t+1).

5 Rejected

H13 The change in DIO is negatively related to future profitability (ROA t+1).

6 Accepted

H14 The change in DSO is negatively related to future profitability (ROA t+1).

6 Rejected

H15 The change in DPO is positively related to future profitability (ROA t+1).

6 Rejected

H16 The excess change in CCC is inversely related to shareholder value.

7 Accepted

H17 The excess change in DIO is inversely related to shareholder value.

8 Rejected

H18 The excess change in DSO is inversely related to shareholder value.

8 Rejected;

difference in sign

H19 The excess change in DPO is positively related to shareholder value.

8 Rejected

Table 7. Summary of Results - Hypothesis and Models