Electronic copy available at: http://ssrn.com/abstract=2472571

Are Cash Flows Better Stock Return Predictors Than Profits?

Stephen Foerster, Ivey Business School, Western University*John Tsagarelis, Primes Corp. Grant Wang,

Highstreet Asset Management Inc.

August 6, 2014

Abstract

Novy-Marx (2013) shows that profitability, measured by gross profits-to-assets, predictsthe cross-section of average returns just as well as book-to-market ratios do. We findthat, in our 1994-2013 sample of S&P 1500 stocks, cash flow measures are even betterpredictors of stock returns than various income statement-based measures. We presenta procedure for transforming indirect cash flow method statements into disaggregatedand more direct estimates of cash flows from operations and other sources. We thenderive ‘direct method’ cash flow measures and form portfolio deciles based on thesemeasures. Stocks in the highest cash flow decile outperform those in the lowest cashflow decile by over 10% annually after controlling for well-known risk factors. Ourresults are robust to the investment horizon, and controlling for sector differences. Wealso show that, in addition to operating cash flow information, cash taxes and capitalexpenditures provide incremental predictive power.

JEL Codes: G10, G14Keywords: direct cash flow method; cash flows; stock return predictability

————————————————————————–* Corresponding author. Ivey Business School, Western University, 1255 WesternRoad, London, ON N6G 0N1, (519) 661-3726, sfoerster@ivey.ca. We wish to thankMatthias Hanauer, Darren Henderson, Rick Robertson and seminar participants atWestern University for helpful comments and suggestions. All errors remain our ownresponsibility.

Electronic copy available at: http://ssrn.com/abstract=2472571

1 Introduction

Investors rely on financial information, such as profitability and cash-related measures, to

assess a firm’s intrinsic equity value and predict the cross-section of average returns. Fama

and French (2006) find that more profitable firms have higher expected returns. Novy-

Marx (2013) shows that profitability, measured by gross profits-to-assets, predicts the cross-

section of average returns just as well as book-to-market ratios do. Fama and French (2013)

incorporate the profitability measure as a new factor, extending their well-known three-factor

model. Ball et al. (2014) show that an operating profitability measure that better matches

current expenses and revenues is an even better predictor of returns and also show that

results depend on whether the denominator is total assets or the market value of equity.

Thus the search continues for financial information that better predicts stock returns.

While the income statement has long been at the center of financial statement analysis,

well-documented shortcomings1 and the recent studies such as Ball et al. (2014) show that

not all components of the income statement have the same predictive power. Notorious

bankruptcies including Enron and WorldCom, graphically illustrate that profitable GAAP

income statements can simultaneously co-exist with negative operating or free cash flow for

the same company (see Appendix A for an example). Novy-Marx’s (2013) intuition that

the farther down the income statement one goes, the more “polluted” profitability measures

become and less related to “true” or economic profitability certainly rings true. Yet it is not

obvious that any accounting profit measures, regardless of where on the income statement

it appears, should be superior to cash-based measures of profitability. This friction between

accrual earnings and cash flows has spawned considerable academic interest in the field of

“quality of earnings”2 and has contributed to investors renewed interest in cash flow related

1See Sloan (1996) and Markham (2006). See also more generally, American Institute of Certified PublicAccountants (1973). The committee, chaired by Robert Trueblood, urged the accounting profession tobe more responsive to investors and creditors in providing information about the companies they audited.This was in reaction to the Securities and Exchange Committee’s (SEC) growing pressure on accountantsto forsake management and serve new constituencies of investors, creditors, rating agencies, analysts andanyone else with an interest in a company’s financial statements.

2See O’Glove (1987), Kellogg and Kellogg (1991) and Siegel (1991).

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measures. However, it is unclear how the common indirect cash flow statement3 should be

used: should investors focus on operating cash flows? Free cash flows? Pre-tax or post-

interest costs? Investors’ frustration with both the income statement and the indirect cash

flow statement has inspired periodic reviews by the International Accounting Standards

Board (IASB) and the U.S. Financial Accounting Standard Boards (FASB).4 While the

FASB (2008b) study provides a series of far reaching insights and highlighted the potential

benefits of disaggregating financial information using a direct cash flow methodology, it fails

to outline how these techniques can be used today and provides no evidence of their efficacy.

Our study attempts to fill that void.

Our main finding is that ‘direct cash flow’ measures are generally better stock return

predictors than indirect cash flow measures, which in turn tend to be better than various

income statement profitability measures that focus on either gross profits, operating profits,

or net income. We first outline a systematic process for combining existing income and

cash flow statements to generate a ‘direct cash flow’ approximation. That is, we create a

new statement that disaggregates operating, financing, tax, and non-operating cash flows

to isolate recurring value creation activities. We then create a series of cash-based financial

ratios and compare them to Ball et al.’s (2014) operating profitability measure, Novy-Marx’s

(2013) gross profitability measure, as well as the traditional return on assets (ROA)5 measure,

all of which have total assets in the denominator. Consistent with Ball et al. (2014), we

repeat our analysis using market value of equity as the denominator. We show that our

new measures generate greater risk-adjusted returns than those based on standard income

statement information. Our results are robust across investment horizons and risk factors

3See Goyal (2004), who estimates that 97% of firms use the indirect rather than direct method. Anecdo-tally, the current number is probably close to 100%.

4The accounting standard for presenting the statement of cash flows is documented in the FinancialAccounting Standards Board (FASB) (1987), known simply as FASB 95 or FAS 95. In particular, see page6 that states “The information provided in a statement of cash flows...should help investors...to assess theenterprise’s ability to generate positive future net cash flows.” See also FASB (2008b) for a discussion paperon proposed changes in the standard. For an overview of the proposed changes see Reilly (2007).

5In unreported results we repeat our analysis with return on capital measures as well as the traditionalreturn on equity measure. The results are qualitatively the same.

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including controlling for sector differences.

While the intrinsic value of a firm is the present value of future cash flows, there are many

proxies for future cash flows.6 Variation and inconsistency in financial statement presentation

formats creates difficulties for investors who wish to understand what to extrapolate and to

analyze how sustainable the value creating activities are. While FASB specifically endorses

the accrual accounting system, we believe accrual accounting7 also creates many potential

forecasting problems for investors. A key aspect of the accrual system is its ability to smooth

out ‘temporary’ fluctuations in cash flows (e.g., see Dechow (1994) and Dechow, Kothari and

Watts (1998)). The contrast between cash basis reporting and earnings reported under the

accrual system is highlighted in FASB (2008b, p. 9): “[Accrual accounting recognizes that]

the buying, producing, selling, and other operations of an entity during a period, as well as

changes in fair value and other events that affect its economic resources and claims to them,

often do not coincide with the cash receipts and payments of that period.” The accrual

system, therefore, implicitly assumes that by recognizing economic events independent of

the timing of the underlying cash flows, users can rely on the income statement as an

unbiased decision predictor. For example, an increase in accounts receivable stemming from a

customer’s delayed payment (which simultaneously reduces cash flows and increases accruals

by the same amount) leaves both revenues and net income unchanged as if full payment

was received. While FASB sees accruals as improving the ability of earnings to measure firm

performance,8 we can also view the lack of cash inflows from the tardy customer and the

resulting differences in accrual and cash statements as creating the potential for securities

mispricings. More generally, research suggests earnings targets influence accounting decisions

6As FASB (2008b, p. 3) outlines in their discussion paper, “Transactions or events recognized in financialstatements today are not described or classified in the same way in each of the statements...That makes itdifficult...for users who want to assess the quality of an entity’s earnings by comparing operating incomewith operating cash flows.”

7The term “accrual” is used in a general sense and includes both accruals and deferrals.8See FASB (2008a) at paragraph 44: “Information about enterprise earnings and its components measured

by accrual accounting generally provides a better indication of enterprise performance that does informationabout current cash receipts and payments.”

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thereby creating strong incentives to bias accruals either upward or downward.9 This pattern

of persistent “timing” differences between the two statements that fails to converge is not

uncommon.10

In an effort to improve the usefulness of information both the IASB and FASB undertook

a detailed study to explore how the presentation of financial information can be modified

to meet investors needs. While FASB “encourages” firms to report cash flows using the

direct cash flow method (DCFM), very few firms actually do. In the FASB (2008b) exposure

draft, the IASB and FASB examined using the DCFM to measure a company’s operating

performance rather than the commonly used indirect method that starts with net income

and makes adjustments. According to FASB (2008b, p. 45) the indirect approach includes a

major deficiency: “it derives the net cash flow from operating activities without separately

presenting any of the operating cash receipts and payments.” Additionally, it merely recon-

ciles information and is not a valid substitute for operating cash inflows and outflows. Under

the DCFM, cash flows are segregated between cash receipts from customers less cash pay-

ments to suppliers, financing activities, tax impacts and other non-recurring, non-operational

activities (see Appendix A for an example of the indirect and direct cash flow methods). The

motivation for this compartmentalization is to isolate recurring cash flows from business ac-

tivities that are additive to net present value, segregate financing activities that are not and

flag on-off gains or losses.11 As Lee (2014) notes, information that is useful in forecasting

future “residual income” (i.e., above a normal rate of return) is the type of information

that directs investor attention to the sustainability of future cash flows, which requires more

normalized measures of performance.

It is our belief that segregating cash flows into their common underlying economic factors

9See Graham, Harvey and Rajgopal (2005) and regarding abnormal accruals see Healy and Wahlen (1999).10For example, we investigated the U.S. railway industry. Between 1993 and 2012, reported trailing four-

quarter net income consistently and persistently exceeded trailing four-quarter free cash flows for each ofCSX Corp., Norfolk Southern Corp. and Union Pacific Corp.

11See Esplin et al. (2014). See also, Nissim and Penman (2001), Fairfield and Yohn (2001), Soliman (2008),and Fairfield, Kitching and Tang (2009). Also, many financial statement analysis textbooks recommendthe operating/financial disaggregation for profitability forecasting and firm valuation: see Penman (2013),Lundholm and Sloan (2006).

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permits investors to better understand and model the magnitude, frequency, timing and

volatility of recurring cash flows. Unfortunately, the cost of preparing cash flow statements

under the DCFM and the hesitancy by firms to reveal the information provided by them has

resulted in very few companies adopting this approach, despite the FASB’s encouragement.

Accordingly, there is very little research linking the DCFM and future stock returns.12 Our

study attempts to fill this gap by creating a proxy for DCFM statements from existing

financial statements and testing their efficacy as it relates to predicting future stock returns.

Our study lends support to the IASB’s and FASB’s initiatives to require a direct method

financial reporting regime.

Our research is most closely related to and extends the work of Novy-Marx (2013) and

Ball et al. (2014). While Novy-Marx (2013) argues that gross profitability is the cleanest

accounting measure of true economic profitability, and Ball et al. (2014) derive an improved

operating profitability measure, we argue that even “cleaner” measures can be derived by

focusing on the cash flow statement and cash-related items: we focus on cash-related mea-

sures that better reflect cash that is available to equity holders.13 We empirically test the

relationship between DCFM-based cash flow estimates and future stock returns in the U.S.

equity market in order to test our conjecture that disaggregated direct cash flows permit

investors to isolate recurring from non-recurring value adding activities, leading to superior

return predictions. First, we provide a template to show how DCFM cash flows can be esti-

mated from income statements and indirect cash flow statements by segregating cash flows

from operating activities, financing activities, taxes, and other non-operating activities. Sec-

12Krishnan and Largay (2000) examine the usefulness of direct method cash flow information and theextent to which this information is more beneficial in predicting future operating cash flows compared withthe indirect method. Orpurt and Zang (2009) report evidence that suggests market participants utilize directmethod disclosures to better forecast future operating performance. Farshadfar and Monem (2013) examinedata from Australia from 1992 to 2004 and find that the cash flow components as reported in the DCFMhave a greater ability to predict future cash flows as compared with an aggregate cash flow measure.

13Our work also complements previous studies that investigate cash flows and investments (e.g., Lakon-ishok, Shleifer, and Vishny (1994), Hackel, Livnat, and Ra (1994), Hackel and Livnat (1991)); accruals andinvestments (e.g., Sloan (1996), Xie (2001), DeFond and Park (2001), Thomas and Zhang (2002), Richard-son, Sloan, Soliman, and Tuna (2005), and Livnat and Santicchia (2006)); and cash flows, accruals, andinvestments (e.g., Houge and Loughran (2000), and Livnat and Lopez-Espinosa (2008)).

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ond, we construct new profitability measures based on various estimates of cash flows from

operations less capital expenditures, adjusted for financing and taxes. Consistent with Ball

at al. (2014), we use both total assets and market value of equity in the denominator of these

ratios. These ratios are compared with Novy-Marx’s (2013) gross profitability measure, Ball

et al.’s (2014) operating profitability measure, and traditional return on assets as well as

earnings-to-price (EP) and related market value of equity-based measures. We find a signif-

icant positive relationship between the derived cash-based ratios and future stock returns as

measured by high-low portfolio returns, information ratios, and risk-adjusted alphas. Stocks

in the highest cash flow decile outperform those in the lowest cash flow decile by over 10%

annually after controlling for well-known risk factors. In contrast, other profitability and

earnings-based ratios generally have relatively weaker relationships with future returns in

our sample of S&P 1500 stocks in the 1994 to 2013 period. We also show that, in addition

to operating cash flow information, cash taxes and capital expenditures information provide

incremental predictive power.

The remainder of the paper is organized as follows. Section 2 presents our direct method

cash flow template that segregates cash from operations, financing, taxes, and other. Section

3 describes our data and methodology. Sections 4 and 5 present our main results while section

6 presents robustness checks. Section 7 investigates cash flow components and section 8

concludes.

2 The Direct Cash Flow Template

As an alternative to the traditional income and cash flow statements we propose the following

direct cash flow template and an alternative set of cash-based capital efficiency and valuation

ratios. Unlike the indirect approach that begins with net income and reconciles to operating

cash flows by reversing non-cash activities, the direct cash flow template adheres to the

culinary principle of mise en place. That is, we organize and sort financial information

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into clusters of homogeneous business activities thereby permitting the reader to understand

how value is being created and what can be extrapolated. In this way, operating activities

associated with a competitive advantage are value enhancing and are likely to repeat, while

financing activities are not. Tax structures and other non-operating activities may be value

enhancing attributes but are likely unsustainable. This overview is consistent with earlier

studies that demonstrated that cash-based component of earnings are more persistent and

therefore are of “higher quality” than accrual based components (Sloan (1996)).

While we recognize that this template does not generate the ‘actual’ direct cash flows

as envisioned by FASB (2008b) (since corporations are not required to publish segregated

operating from non-operating cash flows) we nevertheless feel it provides an improved method

over non-transformed accounting data for estimating intrinsic values. While all the individual

components of the cash flows are publicly available, we conjecture that their availability

alone does not ensure it is integrated in a timely manner and used by investors to estimate

intrinsic values. Patterns must be isolated, revealed and applied before stock prices reflect

this information. Our analysis also highlights what types of information are most informative.

The direct cash flow template we use is presented in Table 1 and is summarized as follows.

We first directly estimate net cash flows from operating activities. We begin with the main

cash inflow of sales, adjusted for changes in accounts receivable, deferred revenues, and other

cash inflows from operations. We subtract cost of goods sold as well as selling, general, and

administrative expenses, then adjust for changes in accounts payable from operations and

changes in inventories. The result is our estimate of Net Cash Flows from Operations (A).

Next we estimate Net Cash Flows from Operations After Financing Activities (B) by

subtracting interest expenses and adjusting for other financing income or expenses. We

then estimate Net Cash Flows from Operations After Financing and Tax Activities (C) by

subtracting cash flows related to tax activities—including taxes on the income statement,

adjusted for changes in account payable taxes and deferred taxes. Finally, we estimate

Net Cash Flows from Operations After Financing, Tax, and Extraordinary Activities (D)

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by accounting for other non-operating activities, including discontinued operations, foreign

exchange, and pension-related items. For completeness, in order to reconcile this cash flow

estimate with the traditional Free Cash Flows to Equity Holders, we account for cash flows

from investing activities. Here we simply subtract the cash outflows associated with capital

expenditures.

3 Data and Methodology

The fundamental and pricing data come from Standard & Poor’s Xpressfeed North American

database (Xpressfeed). The investment universe is the S&P 1500 which consists of the largest

500 stocks by market capitalization, the mid-cap 400, and small-cap 600. The choice of S&P

1500 ensures that these stocks are truly investable and the tested investment strategies

are more relevant to practitioners. The historical S&P 1500 constituents also comes from

Xpressfeed. Each month we obtain a list of stocks included in S&P 1500 at that time, thus

preventing any survivorship bias. As is common in studies that rely on accounting metrics,

and given some major structural differences between financial and non-financial firms, for

most of our reported analysis we exclude financial firms from our sample (including banks,

insurance firms, and REITs), on average, about 15% of the sample. We use data from

October 1994 to December 2013 as cash flow statements only became mandatory in the

U.S. since 1987, and actual index constituents for the S&P 1500 only become available since

October 1994.

The major fundamental variables used in this study are: operating activities-net cash flow

(Xpressfeed mnemonic OANCF), net sales (SALE), accounts receivable decrease/increase

(RECCH), cost of goods sold (COGS), selling, general and administrative expense (XSGA),

depreciation and amortization income statement (DP), depreciation and amortization cash

flow statement (DPC), funds from operations – other (FOPO), accounts payable and accrued

liabilities - increase/decrease (APALCH), inventory – decrease/increase (INVCH), assets

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and liabilities – other – net change (AOLOCH), sale of property, plant and equipment and

investments – gain/loss (SPPIV), interest and related expense – total (XINT), income taxes

— total (TXT), income taxes – accrued – increase/decrease (TXACH), deferred taxes – cash

flow (TXDC), special items (SPI), discontinued operations (DO), extraordinary items (XI),

capital expenditure (CAPX) and income before extraordinary items – available for common

(IBCOM). Following industry practice, we create trailing twelve month (TTM) values for the

fundamental variables. These TTM values are updated with each quarter’s new information

and are combined with monthly returns data to conduct the tests. Despite having quarterly

updates and to avoid any look-ahead biases, we only use accounting data that has been

lagged by four months.14

Table 2 presents summary statistics for the fundamental variables for companies that are

in the S&P 1500 at any time during our sample period. There are 164,385 company-quarter

total observations from 1992 to 2013. Mean (median) net sales are $4,352 million ($914m);

net cash flows from operating activities are $513m ($81m); capital expenditures are $287m

($40m); and income before extraordinary items available for common shareholders are $237m

($36m).

Based on our template definitions in Table 1, we create various direct and indirect free

cash flow measures. FCF AFAT represents net cash flows from operations after financing

and tax activities (C) - capex; FCF AF represents net cash flows from operations after

financing activities (B) - capex; and FCF O represents net cash flows from operations (A) -

capex. These direct cash flow measures (DCFM) are compared with the indirect cash flow

measure FCF IM defined as operating activities net cash flow (OANCF) - capex. Note that

this indirect measure, OANCF, results in the same number as net cash flows from operations

after financing, tax, and extraordinary activities, (D) in Table 1.

These free cash flow definitions are then used to construct various cash returns on assets

and free cash flow yield metrics. The variable CFAFAT/TA is the cash return on assets

14The four month lag is conservative. In our un-tabulated analysis of S&P 1500 firms between 1993 and2013, 99% of these firms filed 10-Q statements within three months after the quarter end.

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measured as the free cash flow direct method measure FCF AFAT divided by total assets.

The variables CFAT/TA, CFO/TA, and CFIM/TA are alternative return on assets measures

that have as the numerator FCF AT, FCF O, and FCF IM, respectively. We compare these

measures to a number of accounting-based measures. The operating profit measure, OP/TA,

has as the numerator operating profits (as estimated by Ball et al., 2014, subtracting from

sales the cost of goods sold and selling, general & administrative costs excluding research &

development) and total assets as the denominator. Novy-Marx’s (2013) gross profit to total

assets, GP/TA, has as the numerator gross profit as measured as sales less cost of goods sold.

The traditional return on assets ratio is measured as IBCOM (income before extraordinary

items available for common shareholders) divided by total assets. The price yield measures

are similar to the measures just described expect total assets are replaced by the market

value of equity (MVE) in the denominator. Note that the traditional earnings yield, or the

inverse of the price-to-earnings ratio, EP, is measured as IBCOM or net income divided by

the market value of equity, and is noted simply as NI/MVE.

Each month, we rank the S&P 1500 firms by a particular return on asset or yield measure

and divide the universe into deciles from low (P1) to high (P10). One-month-ahead portfolio

returns are calculated on a value-weighted basis and equal-weighted basis (we also consider

other horizons). In standard fashion, a long-short portfolio return is estimated as the spread

return difference between the highest (P10) and lowest (P1) portfolio returns. We then test

for the significance of the return differences.

4 Cash Flow and Profitability Return and Yield Mea-

sures and One-Month-Ahead Returns

Table 3 presents results comparing the one-month-ahead returns for the various portfolios

(P1 through P10), as well as the P10-P1 high-low portfolio return spread, sorted on the

basis of the previous month’s return on assets or yield measure, for the entire October

1994 to December 2013 period. We also show the standard deviation of the P10-P1 return

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measure, the t-statistic of the significance of the P10-P1 return, the minimum and maximum

monthly P10-P1 observations, and the information ratio (IR) measured as the annualized

P10-P1 return divided by the annualized standard deviation of the return difference. Panel A

presents results measured by value-weighted returns.15 Not surprisingly, across all measures

there is a general (but not perfect) monotonic relationship, with higher measures tending to

exhibit higher one-month-ahead returns than lower measures.

Panel A presents results based on value-weighted returns. The upper part of the panel

presents measures with total assets in the denominator. Returns generally increase somewhat

monotonically across the low to high portfolios for the various measures. We see that the

Novey-Marx (2013) GP/TA variable has the largest P10 return among the return on asset

variables but not the biggest P10-P1 spread since many of the cash flow measures have

more cross-sectional variation. For the portfolios sorted on the basis of our three DCFM

efficiency measures (CFAFAT/TA, CFAF/TA and CFO/TA) the high-low (P10-P1) return

differences are significantly positive (t-statistics ranging from 2.14 to 2.73), with monthly

return differences ranging from 0.64% to 0.82% (or equivalently 8.0% to 10.3% annually).

Information ratios range from 0.489 to 0.623. For the indirect method cash flow return

measure (CFIM/TA), the high-low monthly return difference drops to 0.49% (6.0% annually)

but not significant, and the information ratio drops to 0.356. The return difference for the

portfolios sorted on the basis of operating profits-to-total assets (OP/TA) is very similar to

that based on the CFIM/TA variable at 0.49% (6.0% annually) and not significant and with

a similar information ratio of 0.356. The high-low portfolio returns based on the gross profit-

to-total assets (GP/TA) measure is also similar at 0.49% (6.1% annually), but given the lower

standard deviation of returns is significant (t-statistic of 2.10); additionally, the information

ratio is higher at 0.480. Finally, for the return on assets (NI/TA) measure, the monthly high-

low return difference is only 0.06% (0.7% annually) and is not significantly different from

zero and the information ratio drops to 0.040. Among these cash flow return measures, the

15We also repeat our analysis including financial firms and results are substantively similar.

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best performing high-low portfolio is based on the sorting using the FCF DM AF measured

(net cash flows from operations after financing activities (B in Table 1) less capex). This

same measure also has the highest information ratio.

The lower part of the panel presents measures with market value of equity in the de-

nominator. We also see that all of the return differences for the portfolios sorted on the

basis of our three direct method cash flow yield measures (CFAFAT/MVE, CFAT/MVE

and CFO/MVE) as well as the indirect method cash flow return measure (CFIM/MVE)

are significantly positive (t-statistics ranging from 1.91 to 3.03), with monthly return differ-

ences ranging from 0.55% to 0.77% (or 6.9% to 9.6% annually). Information ratios range

from 0.436 to 0.692. The return difference for the portfolios sorted on operating profit yield

(OP/MVE) is 0.70% (8.7% annually) and marginally significant, while the information ratio

is lower at 0.380. For the gross profit yield (GP/MVE), the high-low return difference is

0.58% (7.2% annually) but not significant and the information ratio drops to 0.313. Finally,

for the earnings yield measure (NI/MVE), the monthly high-low return difference is 0.43%

(5.2% annually) and is not significant, and the information ratio is only 0.271. Among these

cash flow return measures, the best performing long-short portfolio is again based on the

sorting using CFAF/MVE (net cash flows from operations after financing (B in Table 1)

less capex) and generates the highest information ratio. The AFAT and AF DCFM measure

sortings result in higher information ratios than for the indirect cash flow measure sorting

(CFIM/MVE).

Table 3, Panel B presents results based on equal-weighted returns. Recall that since

our sample includes the largest and generally most liquid U.S. stocks (i.e., the top 1500,

less financial firms), it makes sense to consider equal-weighted returns in addition to the

previously reported value-weighted return results. In general the results are similar to those

in Panel A with the cash flow measures stronger than profitability-related measures. For the

return on asset measures in the upper part of the panel, based on high-low return differences,

we note a monotonic relationship, with all three of the DCFM measures dominating the

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indirect method measure and all of the cash flow measures dominating the OP and GP

measures and the net income measure. The largest high-low monthly return difference of

0.69% (8.6% annually) is for the CFAFAT/TA variable. All of the cash flow measures are

significant (t-statistics from 2.47 to 3.65] while all of the profitability measures are not

statistically significant. We note the same monotonic relationship based on the information

ratio.

For the cash flow yield measures in the lower part of Panel B, the story is again very

similar. The CFAFAT/MVE and CFAF/MVE dominate the indirect method measure and

all dominate the OP and GP measures and the traditional earnings yield measure, although

the operating profit high-low return difference is significant while return differences based

on the gross profit measure is marginally significant. Again we note the same monotonic

relationship based on the information ratio.

Thus for our sample, before we adjust for risk factors (i.e., in our regression analysis

below), we don’t see the differences within the panels highlighted by Ball et al. (2014):

the total asset versus market value of equity denominator or deflator doesn’t appear to

substantively impact the results.

In un-tabulated results, we also perform difference of means t-tests of the P10-P1 high-low

return differences for various DCFM measures compared with indirect method measures and

profitability measures. For the value-weighted return results, the CFAFAT/TA sorted return

differences are significantly greater than the NI/TA returns; and both the CFAT/TA and

CFO/TA returns are significantly greater than both the CFIM/TA and NI/TA returns. For

the equal-weighted return results, each of the CFAFAT/TA, CFAT/TA and CFO/TA sorted

return differences are significantly greater than the both the GP/TA and NI/TA returns; and

each of the CFAFAT/MVE, CFAT/MVE and CFO/MVE returns are significantly greater

than the NI/MVE returns.

We also divide the test periods into four sub-periods, chosen as follows: sub-period 1,

October 1994 to February 2000 (65 months), coincides with the technology bubble period;

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sub-period 2, March 2000 to July 2007 (89 months), coincides with the post-technology

bubble and pre-financial crisis period; sub-period 3, August 2007 to May 2009 (22 months),

coincides with the financial crisis period; and finally sub-period 4, June 2009 to December

2013 (55 months), coincides with the post-financial crisis period. We report these sub-period

results in Table 4 Panel A, B, C, and D, respectively.

In Panel A, the technology bubble, results are generally consistent with the overall results,

particularly for the cash flow return measures where the most significant measure is based

on the CFAFAT/TA measure followed by the CFAF/TA measure and then the third direct

method CFO/TA measure. The indirect method measure, CFIM/TA, is also significant but

has a lower information ratio, as do the OP/TA and GP/TA measures. The NI/TA measure

is not significant. None of the yield measure return differences are significantly positive but

the cash flow yields are positive while the OP/MVE is negative but not significant and the

GP/MVE and NI/MVE measures are significantly negative. In retrospect it is not surprising

that the yield measures, with stock price as the denominator, did not appear to be related

to future stock returns given the high valuations during this period.

In Panel B the results for the period between the technology bubble and the financial

crisis, while the CFAFAT/TA measure information ratio is not significant, the other two

direct method cash flow return measures, CFAF/TA and CFO/TA are significant. None

of the other return on asset measure return differences are significantly different from zero.

The yield measure return differences are all significant and the CFAF/TA measure slightly

dominates all of the other measures in terms of the information ratio.

In Panel C we see the results for the financial crisis period. For the cash flow return mea-

sures, CFAFAT/TA and CFAF/TA return differences are marginally significant although the

OP/TA and GP/TA measures dominate. The NI/TA measure is negative but not signifi-

cant. For the yield measures the CFAFAT/MVE measure has the largest information ratio

measure and is the only one that is significantly positive. What may be partially driving

some of the weaker results is the major failure of quantitative models in August 2007, as

14

described and explained in Khandani and Lo (2007). In addition, in retrospect given the

unprecedented events including the failure of Lehman Brothers and near-failures of other

major financial institutions, market participants may have overreacted and prices may have

diverged from true intrinsic values.

Finally, in Panel D, in the post-financial crisis period, we seemed to have returned to more

“normal” times. The CFAFAT/TA cash flow return measure has the highest information

ratio and the return difference is significant. Most of the other measure return differences are

not significant, perhaps due to the relative shortness of the sub-period, while the traditional

NI/TA measure is significantly negative. All of the direct method cash flow yield measures

as well as the indirect method measure return differences are statistically significant. The

OP/TA the GP/TA measure return differences are not significant, while the traditional

NI/TA measure return difference is negative and significant.

Overall we note that the yield measures, based on market values in the denominator, tend

to do better in “normal” markets such as sub-periods 2 and 4, while the return measures,

with book values in the denominator tend to do better in “abnormal” markets, particularly in

the frothy sub-period 1 technology bubble period. We also conclude that relying on cash flow

related metrics is a worthwhile endeavor and direct method measures, particularly AFAT,

are generally superior to the indirect method and the traditional measures generally have

very little predictive power.

It is worthwhile to highlight the differences, so far, between our results and Novy-Marx

(2013) who concludes that profitability measures are superior to cash flow measures as pre-

dictors of stock returns (we compare our regression results to his in the next section). First,

given our data limitations, we focus on a shorter and more recent period, 1994 to 2013, com-

pared to his 1963 to 2010 study. It is possible that the profitability measure has weakened

more recently. Second, our sample of stocks are different. He uses the universe of Compus-

tat stocks (excluding financials) while we focus on the largest 1500 stocks, which is a more

liquid and tradable sample. Third, our focus is on portfolio performance while he initially

15

emphasizes Fama and MacBeth (1973) regression results (his Table 1) with winsorized in-

dependent variables. As he points out, the methodology weights each observation equally

and thus puts tremendous weight on small or “nano/micro” stocks that make up two-thirds

of the sample but less than 6% by market capitalization weight (see also Fama and French

(2006)).16 Fourth, he utilizes an estimate of free cash flows that differs from our approach.

He defines free cash flow as net income plus depreciation less working capital change less

capital expenditures. Interestingly, in Appendix Table A2, his EBITDA-to-assets variable-

most similar to some of our direct cash flow method measures except it does not account for

capital expendituresperforms well and better than his free cash flow measure.

5 Portfolio Sorted Regressions

We adjust for risk in a more systematic manner by using the P1 through P10 portfolio returns

as well as the P10-P1 return spread (i.e., the portfolios sorted on either the return on asset

or yield variables) to run regressions against well-known risk factors. We use monthly data

for the entire October 1994 to December 2013 sample period. Our regression model is:

Pi,t+1 = αi+β1iMKTRFt+β2iSMBt+β3iHMLt+β4iUMDt+β5iLIQt+εi,t+1, t = 1, ..., N (1)

The dependent variables are the individual decile portfolios, P1 (low) through P10 (high),

as well as the P10-P1 portfolio return difference for the various return on asset and yield

sorted portfolio measures, i, in Table 3, for each monthly time series. Most of the independent

variables are based on Carhart’s (1997) extension of the Fama-French (1993) three-factor

model where MKTRF is the market risk premium (market return in excess of the risk-

free rate), SMB is the small minus big (size) factor, HML is the high minus low (book-

to-market) factor, and UMD is the up minus down (price momentum) factor. We also

16See also Fama and French (2008) for a discussion of the advantages and disadvantages of the Fama-MacBeth approach compared to sorting returns.

16

include LIQ, Pastor and Stambaugh’s (2003) traded liquidity factor, derived from dividing

U.S. common stocks into ten portfolios based on each stock’s sensitivity to the ‘non-traded’

liquidity innovation factor—the return on the portfolio of high liquidity betas less the return

on the portfolio of low liquidity betas.17 Once we control for all of these risk factors, the

intercept, α, is interpreted as the alpha or abnormal return. If alpha is positive and significant

it implies the other factors can’t be combined to form a mean-variance efficient portfolio and

it suggests how an investor can tilt their portfolio toward a more profitability strategy,

such as investing in portfolios that have recently generated high cash flows relative to their

total assets or market value of equity. We use White’s (1980) heteroscedasticity-consistent

standard errors. For comparison we present the average monthly return measured in excess

of the monthly Treasury bill (from Ken French’s website).

Table 5 summarizes the regression results. Panel A presents the regression results, with

value-weighted returns, for the P1 (low) portfolio sorted on the various return and yield

measures. Panel B presents regression results, with value-weighted returns, alphas-only, for

portfolios 2 through 9. Panel C presents the regression results, with value-weighted returns,

for the P10 (high) portfolio sorted on the various return and yield measures. Panel D presents

the regression results, with value-weighted returns, for the P10-P1 portfolio return difference

sorted on the various return and yield measures. Finally, Panel D presents the regression

results, with equal-weighted returns, for the P10-P1 portfolio return difference sorted on the

various return and yield measures.

Across the panels we see that the portfolio alphas (as well as the average returns) generally

tend to increase as we move from P1 through P10 for the various return on asset and

yield measures. In Panel A for the P1 (value-weighted return) results, most of the alphas

are negative but not significant; in Panel B (value-weighted return) all of the alphas are

significantly positive for portfolios P7 through P9; while in Panel C for the P10 (value-

17Data for the first four factors are available from Ken French’s website http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data library.html while the liquidity factor is available fromLubos Pastor’s website http://faculty.chicagobooth.edu/lubos.pastor/research/liq data 1962 2011.text.

17

weighted return) results the alphas are also all positive and significant. Consistent with our

results in Table 3, in both Panel D (value-weighted returns) and Panel E (equal-weighted

returns), all of the three DCFM cash flow based return and yield measure P10-P1 return

differences are significantly positive; the indirect measure return differences are positive and

in most cases significant; the OP return differences are significantly positive only for the yield

measure; and in most cases the cash flow based measures have higher alphas than for the

corresponding OP measure. The GP measure is significant with TA as the denominator and

with value-weighted returns but not equal-weighted returns, and is not significant with MVE

as the denominator and with value-weighted returns, but significant with equal-weighted

returns; in all cases with a lower alpha than for the corresponding OP. For the net income

measure, only with TA as the denominator and value-weighted returns are results significant.

For value-weighted return results, at least one of the DCFM variables result in larger alphas

than the indirect method variable. Among the DCFM variables, it is difficult to conclude

unequivocally which one is best. Based on value-weighted returns (Panel D) and TA in the

denominator, the alpha for CFO is 1.10%, which translates into an annual return difference

of 14.0%, even stronger than the Table 3 results. With MVE as the denominator, CFAF

has an alpha of 0.84%, which translates to an annual difference of 10.6%. Based on equal-

weighted returns (Panel E) and TA as the denominator, the CFAFAT alpha is 0.85%, which

translates to an annual difference of 10.7%. With MVE as the denominator, CFAF has an

alpha of 0.86%, which translates to an annual difference of 10.8%. (In section 7 we explore

what incremental information may lead to different results among the DCFM variables.)

Thus even controlling for well-known risk factors we find significant value from utilizing

information available from our “direct cash flow” statements.

We again contrast our P10-P1 results to those of Novy-Marx (2013) as reported in his

Table 2. Despite differences in methodologies described in the previous section as well as his

use of the traditional Fama-French three-factor model that excluded the liquidity factor in

our study, it is comforting to find that our alpha estimates for the gross profit-to-total assets

18

measure, 0.69% based on value-weighted returns and 0.33% based on equal-weighted returns,

bracket his estimate of 0.52%. (Curiously, for the gross profit-to-market value of equity

measure, we uncover an alpha of 0.48% based on value-weighted returns and 0.71% based on

equal-weighted returns, while Ball et al. (2014) find a negative but not significant alpha for

their longer sample period.) Thus we are not concluding that his measure performs poorly

given our different sample and design, but rather we conclude that cash-based measures

perform even better.

As noted by Fama and French (1992) and Ball et al. (2014), the relationship between

the various return on asset and yield measures and future stock returns can be explained

by either rational or irrational asset pricing stories. Mispricing may be explained by the

behaviors and biases of irrational investors or short-term trading frictions, or alternatively

there may be risk factors not captured by the five factors included in our regression model.

Persistence of returns, as examined in the next section, may be consistent with a rational

explanation if we assume that trading frictions shouldn’t persist over longer periods.

6 Robustness Checks

6.1 Extended Horizon Returns

In Table 6 we re-examine our earlier one-month-ahead value-weighted return results by ex-

amining a variety of extended horizon holding periods, including three months (Panel A), six

months (Panel B), and twelve months (Panel C). The results are generally consistent with the

one-month return results. In Panel A, the return differences are significantly positive in all of

the cash flow return measure cases as well as the operating profit, OP/TA, and gross profit,

GP/TA measures while the traditional return on asset measure, NI/TA, is not significant.

Return differences are highest for the DCFM measures followed by the GP/TA measure

and then the indirect method cash flow measures. The three-month return difference for

the CFAF/TA measure is 2.1% (annualized 8.7%). For the yield measures all of the return

19

differences are significantly positive with a similar order of magnitude for the CFAF/MVE,

CFIM/MVE, OP/MVE and GP/TA measures, while the earnings yield, NI/MVE, is only

marginally significant. The three-month return difference for the CFAF/MVE measure is

2.1% (annualized 8.6%). Among the return on asset measures, the information ratio is high-

est for the GP/TA measure given the much lower standard deviation of returns. Among the

yield measures the information ratio is much higher for the cash flow measures compared

with the profitability measures.

In Panel B, for the six-month horizon, the return differences for the return on asset

measures are again significantly positive in all cases except for the NI/TA measure. The

highest return difference is again for the CFAF/TA variable, with a six-month return of

3.4% (annualized 6.9%). Among the yield measures the CFIM/MVE and GP/MVE six-

month returns are almost identical, at around 3.8% (7.7% annualized). For the return on

asset measures, the information ratio is highest for the GP/TA measure followed by the

various cash flow measures, while for the yield measures the cash flow measures dominate

based on the information ratio.

Results in Panel C are similar to those in Panel B. For both the return on asset measures

and for the yield measures the DCFM measures resulted in superior information ratios

compared to the indirect measure. Most of the DCFM and indirect method return on asset

return differences are generally superior to the profitability measures, which in turn are

superior to the NI/TA and NI/MVE measures. Even for the 12-month horizon, the high-

low CFAF/MVE portfolio offered returns of almost 7.5%. Overall our Table 6 results are

consistent with our earlier results and also suggest, while still positive and significant, only

a small tapering of return differences for longer horizons.

6.2 Sector Analysis

We check to see whether our results are sector-specific. We perform a number of tests. First

we segregate our sample among the ten Global Industry Classification Standard (GICS) sec-

20

tors: energy, materials, industrials, consumer discretionary, consumer staples, health care,

financials, information technology, telecommunication services, and utilities (recall that fi-

nancials are not included in previous results but are included here since results are segmented

by industry). We estimate information ratios for each of our measures within each sector.

Based on un-tabulated results, while there is no consistent pattern across all sectors, we

can summarize our results as follows. For the measures with total assets as the denom-

inator, cash flow measures tend to do better than the various profitability and earnings

measures particularly in the energy and industrial sectors, but also in materials, consumer

discretionary, health care, and telecom, as well as financials. Information ratios are strong

across all measures for telecom firms, with the operating profitability measure doing the

best. Information ratios are low across all measures in consumer staples (except for NI/TA)

and utilities. For the measures with market value of equity as the denominator, cash flow

measures tend to do better than the various profitability and earnings measures particularly

in the industrial, consumer discretion, and technology sectors, but also in consumer staples

and telecom. Profitability measures tend to do better in the energy, materials, and health

care. Information ratios are low across all measures in utilities, and only NI/MVE does well

in the financial sector.

In Table 7, we present another set of robustness checks related to the nature of the sectors

in which stocks in the long-short portfolio are invested. It is possible that the composition

of the lowest return portfolio based on sorting, P1, might be very different from the com-

position of the highest return portfolio based on sorting, P10, in terms of the sectors in

which the stocks are invested. To control for this, we create sector neutral portfolios. We

repeat our sorting procedure within each of 20 of the 24 GICS industry groups (recall that

financials—banks, insurance companies, diversified financials, and REITs—are eliminated

from our sample). P1 (P10) is populated by the lowest (highest) performing stocks by decile

within each sector.

Table 7, Panel A presents the one-month-ahead value-weighted return results. The results

21

are generally consistent with those in Table 3, with some notable exceptions. For the return

on asset results, we see the pattern that cash flow measures tend to have higher return

differences and information ratios than profitability measures, except the NI/TA measures

does very well. Interestingly, neither of the OP/TA and GP/TA return differences are

significant, suggesting results in other studies that highlight these variables may be benefiting

from a long position in one particular industry and a short position in stocks in a different

industry. For the yield measures, the only marginally-significant results are for the GP/MVE

measure, while all of the other return differences are significant.

Table 7, Panel B presents a final sector robustness check. We present sector neutral

portfolio results for the regression analysis. The results are similar to those in Table 5,

with similar exceptions to those uncovered in Panel A. Neither of the OP/TA and GP/TA

alphas are significantly different from zero. For the yield measures the GP/MVE alpha is

also not significant. Thus it appears that our results are not being driven by sector specific

performance, with the exception of the gross profit results.

6.3 Growth, Size, Momentum, and Profitability

In this section of analysis we investigate the extent to which our results may be related to

four well-known empirical regularities whereby 1) value or high book-to-price stocks tend to

outperform growth or low book-to-price stocks; 2) small market capitalization stocks tend

to outperform large cap stocks; 3) high price momentum stocks tend to outperform (in

the short-run) low price momentum stocks; and most recently, as uncovered by Novy-Marx

(2013), 4) high gross profit-to-total asset stocks tend to outperform low gross profit-to-total

assets stocks. We perform four separate sets of analysis examining one-month-ahead returns.

We start by forming five portfolio quintiles sorted by their beginning-of-month book-to-price

(B/P) ratio. Then within each of those portfolios we form five sub-portfolios sorted on the

basis of the beginning-of-month cash flow metric, either a cash flow return metric or cash flow

yield metric. Consequently we have 25 portfolios. If our results are simply a manifestation

22

of the value-growth phenomenon then we would not expect to find any significant differences

in returns comparing the high (quintile 5) and low (quintile 1) cash flow metrics within a

particular B/P category. We perform similar analysis for size, momentum, and gross profit.

Table 8 compares various two-way sorts for one of our arbitrarily-chosen cash flow return

on asset measures, CFAFAT/TA, in Panel A, and one of our cash flow yield measures,

CFAFAT/MVE, in Panel B.

For the cash flow return on asset metric, Panel A, the high-low returns are significant

across most of the book-to-price quintiles except the third and fourth ones; across most of

the size quintiles expect the smallest one; across most of the momentum quintiles except the

fourth one; and across the second lowest GP/TA quintile. For the cash flow yield metric,

Panel B, the high-low returns are significant across most of the book-to-price quintiles except

the third and fourth ones; across all of the size quintiles; across most of the momentum

quintiles except the highest one; and across most of the GP/TA quintiles except the lowest

one. Consistent across both panels, results appear to be strongest for low momentum stocks.

Thus our analysis suggests that our results are not simply a confounding effect of other well-

known effects.

7 Cash Flow Components

In previous analyses we showed the superiority of various cash flow measures in predicting

the cross-section of future returns, but did not conclude whether one of the cash flow mea-

sures was better than others. In this final section of analysis we investigate the impact of

segregating various cash flow components in order to attempt to uncover why one cash flow

measure might be better than another at predicting stock returns.

As a preliminary investigation, we examine the correlations of the time-series of the

averages across firms of various cash flow measures and components: our direct method

cash flow from operations measure, CFODM; our indirect method cash flow from operations

23

measure, CFOIM; Novey-Marx’s (2013) free cash flow measure of net income plus deprecia-

tion/amortization, less working capital change, less capital expenditures, CFONM; financing

activities, FinAct (see Table 1 for definitions of this and other “activities” measures); tax

activities, TaxAct; other activities, OthAct; and capital expenditures, Capex. Each of the

variables is deflated by either total assets, TA or market value of equity, MVE. Results are

presented in Table 9, with Panel A showing the Pearson correlations and Panel B showing

the Spearman correlations. The correlation patterns are similar in both panels and so our

discussion below focuses on the Panel A results.

Not surprisingly, there is a high correlation among the CFODM/TA, CFOIM/TA and

CFONM/TA measures: 0.81 comparing the first two, dropping to 0.45 comparing the first

and third. Consistent with Ball et al. (2014) the correlations across variables with the same

numerator but MVE instead of TA in the denominator are quite low in many instances: for

CFODM 0.28, for CFOIM 0.32, and for CFONM 0.47. These results show the impact of the

book value of total assets that changes slowly over time compared to the market value of

equity that changes more frequently and generally by larger magnitudes.

Across the three cash flow measures that are deflated by TA, we see a consistent pattern

of correlations compared to FinAct (negative), TaxAct (positive), and Capex (positive).

The absolute value of the correlations with FinAct are generally low, below 0.23, while those

with Capex are generally slightly higher and those with TaxAct are higher still, above 0.48.

The absolute value of the correlations with OthAct is low compared to the CFODM and

CFOIM variables, but surprisingly high at 0.55 with the CFONM variable. Not surprisingly

the correlations tend to be lower with MVE in the denominator. These preliminary results

suggest a more rigorous investigation of the components of cash flows is warranted.

As such, in the spirit of Ball et al. (2014, Table 6) we perform Fama and MacBeth (1973)

cross-section regressions. For each month of our sample we regress the one-month ahead

returns for each stock on our net cash flow from operations measure, Net CF Ops (“A” in our

Table 1 template); Financing Activities as measured by interest expenses +/- other financing

24

income/expenses; Tax Activities as measured by taxes on the income statement +/- changes

in accounts payable taxes +/- changes in deferred taxes; other non-operating activities, Non-

Op Activities, as measured as discontinued operations/special charges +/- foreign exchange

gains/losses +/- pension gains/losses/contributions; and capital expenditures, Capex. We

deflate all of these variables by the book value of total assets. Similar to Ball et al. (2014) we

include a number of control variables: log(BVE/MVE) is the natural logarithm of the ratio

of the book value of equity to market value of equity; log(ME) is a size variable measured

as the natural logarithm of the market value of equity; r1,1 is the stock’s one-month-prior

return; and r12,2 is the stock’s prior year’s return skipping a month. We winsorize all

independent variables based on the first and 99th percentiles. We also perform separate

regressions that replace the Net CF Ops variable with either our measure of cash flows from

operations based on the indirect cash flow method, CFOIM, or Novey-Marx’s (2013) free cash

flow measure of net income plus depreciation/amortization, less working capital change, less

capital expenditures.

In Table 10 we present our results that include averages of the Fama and MacBeth (1973)

slope coefficients along with their t-values. Column (1) presents the regression of Net CF

Ops alone with the control variables. We see that the Net CF Ops coefficient estimate is

significant, with a t-statistic of 3.22. In the next four regressions we add one additional vari-

able that represents a segmentation of cash flows: column (2) presents Financing Activities

and control variables; column (3) presents Tax Activities and control variables; column (4)

presents Non-Op Activities and control variables; and column (5) presents Capex and control

variables. When we add these variables individually, the Net CF Ops coefficient estimate

remains significant but only the Capex variable is marginally significant, with a t-statistic

of -1.89. The next two regressions add combinations of variables: column (6) presents Fi-

nancing Activities, Tax Activities and control variables; and column (7) presents Financing

Activities, Tax Activities, Non-Op Activities and control variables. In these regressions the

Net CF Ops variable remains significant but none of the added variables are significant. Col-

25

umn (8) presents all variables. In addition to the Net CF Ops variable, the Tax Activities

coefficient estimate is negative and marginally significant with a t-statistic of -1.91, while

the Capex coefficient estimate is also negative and significant, with a t-statistic of -2.03.

Our finding no significance in the Financing Activities coefficient estimate is consistent with

Ball et al. (2014) who find no significance with their Interest coefficient estimate for their

“all-but-microcaps” sample. However, unlike Ball et al. (2014) we find our Tax Activities

coefficient estimate is significant while theirs is not. We would expect that as cash taxes

increase, there should be a negative correlation with the intrinsic value of equity holders.

Column (9) presents the regression of CFOIM and all variables. The CFOIM coefficient

estimate is significant as expected, with a t-statistic of 3.71. Consistent with our column (8)

results, the Capex coefficient estimate is significantly negative. The Tax Activities coefficient

estimate is still negative but not significant, while the Non-Op Activities coefficient estimate

is negative and marginally significant. Finally, column (10) presents free cash flows, FCF,

measured as in Novy-Marx (2013) as net income plus depreciation less change in working

capital less capital expenditures. Only the Non-Op Activities variables is significant. Overall

our results suggest that the best information for predicting the cross-section of returns for our

sample comes from the disaggregation of cash flows in a manner that captures the direct cash

flow method. In particular, there is incremental information in disaggregating tax activities

and capital expenditures.

8 Conclusions

Inspired by Novy-Marx (2013), much recent attention has been focused on the importance

of income statement related measures such as gross profit-to-total assets as predictors of the

cross-section of returns. We show that commonly used income statement related metrics

including return on assets and earnings yields are poor return predictors, and cash-based

measures are superior to operating profitability and gross profitability-to-total asset mea-

26

sures. We examine strategies that suggest superior risk-adjusted returns based on utilizing

transformed financial statements using our DCFM to estimate free cash flows. We create a

direct cash flow template by segregating operating cash flows from activities related to fi-

nancing, tax, non-operations, and investing. We argue that this segregation assists investors

to understand how value is being created and hence better estimate what is the true intrinsic

value of the firms equity. We find that DCFM return on asset and DCFM yield measures are

generally superior to the indirect cash flow method at predicting stock returns, which in turn

are generally superior to income statement measures based on profitability. When we con-

trol for well-known risk factors related to the market, market capitalization, book-to-market

ratios, price momentum, and liquidity using regression models, we find similar results. We

also test different return holding periods and we perform robustness checks that control for

sector performance, and find similar results. Through a double-sorting procedure we show

that our results are not simply manifestations of other well-known phenomena related to

value/growth, size, momentum, and profitability. Our Fama-MacBeth regressions suggest

the incremental importance, in particular, of information related to taxes and capital ex-

penditures, in addition to information on operating cash flows. Future research may provide

further explanations as to why those variables are important to forecasting stock returns.

Our conclusion—that there is incrementally better information by segregating cash flows

to account for both financing activities and taxes—lends support to the International Ac-

counting Standards Board (IASB) and the U.S. Financial Accounting Standard Boards

(FASB) initiatives to require reconstructed financial statements. From an investment per-

spective, investors may be able to derive better information about the future prospects—and

hence future stock returns—by relying on cash flows that disaggregate operating cash flows

from financing, tax, non-operating and non-recurring activities.

27

A Comparison between the Income Statement, the In-

direct and Direct Cash Flow Statements

Corporations can present their net cash flow from operating activities by using either the

direct or indirect method. Under each, net cash flows from operating activities will be the

same. However, the direct method presents the specific amounts of cash received and paid

for each significant business activity and the resulting net cash flow arising from operating

activities. We argue that using the structure of the income statement and the informational

content of the cash flow statement, investors are better able to isolate recurring cash flows

that are additive to net present value, segregate financing activities that are not and flag

one-off gains/losses.

The indirect method, on the other hand, derives net cash from operating activities with-

out separately presenting any of the operating cash receipts and payments. The indirect

approach merely reconciles information between the income statement and actual cash flows.

In this way, the indirect statement is not as intuitive as the direct method. In addition, it

is difficult to forecast corporate results that are presented ‘net’ of operating, financing and

tax effects.

To highlight some of the key concepts and illustrate how operating cash flows are reported

using both the direct and indirect methods, we begin with XYZ Corp.’s income statement

and balance sheet amounts:

Income Statement Amounts Balance Sheet AmountsSales $1000 Change in Accounts Receivable (A/R) +$200-Cost of Goods Sold 700 Change in Inventory +100-Operating Expenses 100 Change in Accounts Payable (A/P) Operations -50-Depreciation 10 Change in Accounts Payable (A/P) Taxes +90-Interest Expense 10+Interest Income 3-Corporate Taxes 35Net Income $148

Using the direct method, net operating cash flows are calculated using specific cash inflows

and outflows related to the income statement and balance sheet working capital accounts.

28

For example,

Cash Inflows:Sales $1000Change in A/R -200 (where A/R increases, cash has yet to be received)Gross Cash Inflows $800

Cash Outflows:Cost of Goods Sold -$700Operating Expenses -100Change in Inventory -100Change in A/P Operations -50 (where A/P decreases, cash was paid)Gross Cash Outflows -950Direct Business Cash Flows -$150

Financing:Interest Expense -$10Interest Income +3Financing Outflows -$7

Corporate Taxes:Income Statement Taxes -$35Changes in A/P Taxes +90Net Tax Cash Flows +$55

Net Cash From Operating Activities -$102

The information presented here depicts gross cash inflows and outflows in a straightforward

manner, along with an undistorted view of accrual revenues and costs versus cash inflows

and outflows. For example, while XYZ booked $1000 in sales, only $800 was received in cash

for the period, and the balance was sold on credit. Cost of goods sold, operating expenses

and working capital needs totaled $850. On an operating cash flow basis the firm suffered a

-$150 loss for the period. Recall that net income is reported as +$148. As a result of interest

expenses and a favourable tax affect, net operating cash flows were only -$102.

Using the indirect method, net operating cash flows are calculated using the differences

between net income and working capital changes. For example,

29

Net Income $148Add/Subtract Non-Cash Adjustments:

Depreciation +10Change in A/R -200Change in Inventory -100Change in A/P Operations -50Change in A/P Taxes +90

Net Cash from Operating Activities -$102

Both methods conclude that net cash from operating activities is -$102. However, the

direct method shows specific amounts of cash receipts and payments segregated by major

business activities. The indirect method merely reconciles net income to the operating cash

flows. Users of the indirect statement may not fully appreciate that business activities

(pre financing, pre-tax) generated -$150, while a tax benefit of $90 minimized the overall

cash outflows. Also, while the direct statements links working capital gains/losses to their

respective source accounts (accounts receivable to sales, accounts payables to cost of goods

sold) the indirect statement relates these working capital changes to net income. It leaves

the user with the cumbersome task of sorting and rearranging. Thus investors have three

different measures to extrapolate: net income $148, direct business cash flows of $-150, or

net operating cash flows of -$102.

30

References

American Institute of Certified Public Accountants, 1973, “Objectives of Financial

Statements,” Report of the Study Group on the Objectives of Financial Statements.

Ball, Ray, Joseph Gerakos, Juhani Linnainmaa, and Valeri Nikolaev, 2014, “Deflating

Gross Profitability,” Unpublished Working Paper, University of Chicago.

Beneish, Messod and Mark Vargus, 2002, “Insider Trading, Earnings Quality and Ac-

crual Mispricing,” Accounting Review 77:4, pp. 755-791.

Carhart, Mark, 1997, “On Persistence in Mutual Fund Performance,” Journal of Finance

52:1, pp. 5782.

Chan, Louis, Jason Karceski and Josef Lakonishok, 2003, “The Level and Persistence

of Growth Rates,” Journal of Finance 58:2, pp. 643-684.

DeFond, Mark and Chul Park, 2001, “The Reversal of Abnormal Accruals and the

Market Valuation of Earnings Surprises,” Accounting Review 76:3, pp. 375-404.

Esplin Adam, Max Hewitt, Marlene Plumlee, and Terri Lombardi Yohn, 2014, “Dis-

aggregating Operating and Financial Activities: Implications for Forecasts of Prof-

itability,” Review of Accounting Studies 19:1, pp. 328-362.

Fama, Eugene and Kenneth French, 1992, “The Cross-Section of Expected Returns,”

Journal of Finance 47:2, pp. 427-465.

Fama, Eugene and Kenneth French, 1993, “Common Risk Factors in the Returns on

Stocks and Bonds,” Journal of Financial Economics 33: 3, pp. 3-56.

Fama, Eugene and Kenneth French, 2006, “Profitability, Investment and Average Re-

turns,” Journal of Financial Economics 82, pp. 491-518.

31

Fama, Eugene and Kenneth French, 2008, “Dissecting Anomalies,” Journal of Finance

63:4, pp. 1653-1678.

Fama, Eugene and Kenneth French, 2013, “A Five-Factor Asset Pricing Model,” Un-

published Working Paper, University of Chicago.

Fama, Eugene and James MacBeth, 1973, “Risk, Return, and Equilibrium: Empirical

Test,” Journal of Political Economy 81:3, pp. 607-636.

Fairfield, Patricia and Terri Yohn, 2001, “Using Asset Turnover and Profit Margin to

Forecast Changes in Profitability,” Review of Accounting Studies 6, pp. 372-386.

Fairfield, Patricia, Karen Kitching, and Vicki Tang, V.W., 2009, “Are Special Items

Informative about Future Profit Margins?” Review of Accounting Studies 14, pp.

204-236.

Farshadfar, Shadi and Reza Monem, 2013, Further Evidence on the Usefulness of Direct

Method Cash Flow Components for Forecasting Future Cash Flows, The International

Journal of Accounting 48, pp. 111-133.

Financial Accounting Standards Board, 1987, “Statement of Financial Accounting Stan-

dards No. 95 – Statement of Cash Flows,” (November 1987).

Financial Accounting Standards Board, 2008a, “Original Pronouncements as Amended,

Statement of Financial Accounting Concepts No. 1, Objectives of Financial Reporting

by Business Enterprises.”

Financial Accounting Standards Board, 2008b, “Preliminary Views on Financial State-

ment Presentation,” Financial Accounting Series Discussion Paper No. 1630-100 (Oc-

tober 16, 2008).

Goyal, Mahendra, 2004, “A Survey of Popularity of The Direct Method of Cashflow

Reporting,” Journal of Applied Management Accounting Research 2:2, pp. 41-52.

32

Graham, John, Campbell Harvey and Shiva Rajgopal, 2005, “The Economic Implica-

tions of Corporate Financial Reporting,” Journal of Accounting and Economics 40:1,

pp. 3-73.

Hackel, Kenneth, Joshua Livnat, and AtuI Ra, 1991, Cash Flow and Security Analysis,

Homewood, Ill.: Business One-Irwin.

Hackel, Kenneth, Joshua Livnat, and AtuI Ra, 1994, “The Free Cash Flow/Small-Cap

Anomaly,” Financial Analysts Journal 50:5, pp. 33-42.

Healy, Paul and James Wahlen, 1999, “A Review of the Earnings Management Lit-

erature and Its Implications for Standard Setting,” Accounting Horizons 13:4 pp.

365-383.

Houge, Todd and Tim Loughran, 2000, ”Cash Flow Is King? Cognitive Errors by

Investors,” The Journal of Psychology and Financial Markets 1:3&4: pp. 161-175.

Kellogg, Irving, and Loren Kellogg, 1991, “Fraud, Window Dressing, and Negligence in

Financial Statements,” New York: McGraw-Hill.

Khandani, Amir and Andrew Lo, 2007, “What Happened to the Quants in August

2007?,” Journal of Investment Management 5:4, pp. 5-54.

Krishnan, Gopal and James Largay III, 2000, “The Predictive Ability of Direct Method

Cash Flow Information,” Journal of Business Finance & Accounting 27:1&2, pp. 215-

245.

Lakonishok, Josef, Andrei Shleifer, and Robert Vishny, 1994, “Contrarian Investment,

Extrapolation, and Risk,” Journal of Finance 49, pp. 1541-1579.

Lee, Charles, 2014, “Performance Measurement: An Investors Perspective,” Accounting

and Business Research 44:4, pp. 383-406.

33

Livnat, Joshua and Massimo Santicchia, 2006, “Cash Flows, Accruals, and Future Re-

turns,” Financial Analysts Journal 62:4, pp. 48-61.

Livnat, Joshua and German Lopez-Espinosa, 2008, “Quarterly Accruals or Cash Flows

in Portfolio Construction?” Financial Analysts Journal 64:3, pp. 67-79.

Lundholm Russell and Richard Sloan, 2006, Equity Valuation and Analysis with eVal

(2nd ed.), Boston, MA: McGraw-Hill Irwin.

Markham, Jerry, 2006, A Financial History of Modern U.S. Corporate Scandals: From

Enron to Reform, Armonk, NY: M.E. Sharpe Inc.

Nissim, Doron and Stephen Penman, 2001, “Ratio Analysis and Equity Valuation: From

Research to Practice,” Review of Accounting Studies 6, pp. 109-154.

Novy-Marx, Robert, 2013, “The Other Side of Value: The Gross Profitability Premium,”

Journal of Financial Economics 108:1, pp. 1-28.

OGlove, Thornton, 1987, T.L., 1987, “Quality of Earnings,” New York: The Free Press.

Orpurt, Steven and Yoonseok Zang, 2009, “Do Direct Cash Flow Disclosures Help

Predict Future Operating Cash Flows and Earnings?,” Accounting Review 84:3, pp.

893-935.

Pastor, Lubos and Robert Stambaugh, 2003. “Liquidity Risk and Expected Stock Re-

turns.” Journal of Political Economy 111:3, pp. 642-685.

Penman Stephen, 2013, Financial Statement Analysis & Security Valuation (5th ed.),

Boston, MA: McGraw-Hill Irwin.

Reilly, David, 2007, “Profit as We Know It could Be Lost with New Account-

ing Statements,” Wall Street Journal, May 12, 2007 http://online.wsj.com/article/

SB117893520139500814.html (accessed May 31, 2013).

34

Richardson, Scott, Richard Sloan, Mark Soliman, and Irem Tuna, 2005, “Accrual Relia-

bility, Earnings Persistence and Stock Prices,” Journal of Accounting and Economics

39:3, pp. 437485.

Siegel, Joel, 1991, “How to Analyze Businesses, Financial Statements, and the Quality

of Earnings,” 2nd Edition, Englewood Cliffs, NJ: Prentice-Hall.

Soliman, Mark, 2008, “The Use of DuPont Analysis by Market Participants,” The

Accounting Review 83:3, pp. 823-853.

Sloan, Richard, 1996, “Do Stock Prices Fully Reflect Information in Accruals and Cash

Flows about Future Earnings?” Accounting Review 71:3, pp. 289-315.

Thomas, Jacob, and Huai Zhang, 2002, “Inventory Changes and Future Returns.” Re-

view of Accounting Studies, vol. 7, nos. 2-3 (June-September): 163-187.

White, Halbert, 1980, “A Heteroskedasticity-Consistent Covariance Matrix Estimator

and a Direct Test for Heteroskedasticity,” Econometrica 48:4, pp. 817838.

Xie, Hong, 2001, “The Mispricing of Abnormal Accruals,” Accounting Review 76:3, pp.

357-373.

35

Table 1: Direct Cash Flow Template

This table presents a template for the estimation of various free cash flow direct measurespresented in the paper. FCF AFTA is measured as C - capex, FCF AF is measured as B -capex, and FCF O is measured as A - capex.

Operating ActivitiesCash Inflows:

Sales+/- change in accounts receivable+/- change in deferred revenues+/- change in other cash inflows from operations= Cash inflows to the firm

Cash Outflows:less: cost of goods soldless: selling general & administrative expenses+/- change in accounts payable from operations+/- change in inventories

= Net Cash Flows from Operations (A)

Financing Activitiesless: interest expense+/- other financing income/expenses

Net Cash Flows from Operations After Financing Activities (B)

Tax Activitiesless: taxes on income statement+/- change in accounts payable taxes+/- change in deferred taxes

= Net Cash Flows from Operations After Financing and Tax Activities (C)

Other Non-Operating Activities (Extraordinary Activities)

+/- discontinued operations/special charges+/- foreign exchange gains/losses+/- pension gains/losses/contributions

= Net Cash Flows from Operations After Financing, Tax, andExtraordinary Activities (D)

Investing Activitiesless capital expenditures (capex)

= Free Cash Flows to Equity Holders (FCF)

36

Table 2: Descriptive Statistics

This table presents summary statistics for variables used to create free cash flow estimates fornon-financial S&P 1500 stocks, quarterly from October 1992 to December 2013. The mean(Mean), minimum (Min) and maximum (Max) observations are presented, in $millions, alongwith various percentile cutoffs (P1, P50, and P99). Variables include operating activities-net cash flow (Xpressfeed mnemonic OANCF), net sales (SALE), accounts receivable - de-crease/increase (RECCH), cost of goods sold (COGS), selling, general and administrativeexpense (XSGA), depreciation and amortization - income statement (DP), depreciation andamortization - cash flow statement (DPC), funds from operations - other (FOPO), accountspayable and accrued liabilities - increase /decrease (APALCH), inventory - decrease/increase(INVCH), assets and liabilities - other - net change (AOLOCH), sale of property, plant andequipment and investments - gain/loss (SPPIV), interest and related expense - total (XINT),income taxes - total (TXT), income taxes - accrued - increase/decrease (TXACH), deferredtaxes - cash flow (TXDC), special items (SPI), discontinued operations (DO), extraordinaryitems (XI), capital expenditure (CAPX) and income before extraordinary items - availablefor common (IBCOM).

Variable Mean Min P1 P50 P99 MaxOANCF 513 -22,365 -133 81 7,609 61,236SALE 4,352 -219 6 914 58,689 476,294RECCH -30 -10,766 -767 -4 342 13,651COGS 2,992 -1 3 553 41,551 358,069XSGA 767 -283 4 160 10,782 91,129DP 216 -4 0 37 2,832 21,577DPC 229 -941 0 39 2,970 44,917FOPO 79 -112,954 -338 6 1,674 52,885APALCH 22 -5,797 -374 3 687 7,951INVCH -22 -55,024 -549 -1 263 58,188AOLOCH 0 -55,521 -635 0 634 38,540SPPIV -14 -14,535 -309 0 29 7,276XINT 98 -1 0 14 1,086 26,586TXT 129 -34,831 -161 19 2,092 39,217TXACH 3 -2,110 -157 0 180 9,454TXDC 8 -35,561 -314 0 475 7,895SPI -53 -52,740 -1,130 0 195 125,703DO 5 -14,316 -121 0 207 25,896XI -6 -54,235 -59 0 3 5,045CAPX 287 -1,053 0 40 4,018 37,985IBCOM 237 -62,834 -867 36 4,551 125,769

37

Tab

le3:

Ret

urn

san

dY

ield

Mea

sure

san

dO

ne-

Mon

th-A

hea

dR

eturn

s

This

table

mea

sure

sth

eav

erag

eon

e-m

onth

-ahea

dre

turn

(%)

for

the

enti

reO

ctob

er19

94to

Dec

emb

er20

13p

erio

don

por

tfol

ios

sort

edon

the

bas

isof

vari

ous

retu

rnon

asse

tan

dre

turn

onth

em

arke

tva

lue

ofeq

uit

yca

shflow

-bas

edan

dac

counti

ng-

bas

edm

easu

res

wit

hP

1th

elo

wes

tdec

ile

S&

P15

00st

ock

san

dP

10th

ehig

hes

tdec

ile

stock

s,an

dP

10-P

1is

the

retu

rndiff

eren

ce.

Std

Dev

isth

est

andar

ddev

iati

on,

T-s

tat

isth

et-

stat

isti

cof

the

sign

ifica

nce

ofP

1-P

10,

Min

isth

em

inim

um

retu

rn,

Max

isth

em

axim

um

retu

rn,

and

IRis

the

info

rmat

ion

rati

om

easu

red

asth

ean

nual

ized

P10

-P1

retu

rndiv

ided

by

the

annual

ized

stan

dar

ddev

iati

onof

the

retu

rndiff

eren

ce.

FC

FD

MA

FA

Tis

mea

sure

das

net

cash

flow

sfr

omop

erat

ions

afte

rfinan

cing

and

tax,

-ca

pex

;F

CF

DM

AF

ism

easu

red

asnet

cash

flow

sfr

omop

erat

ions

afte

rfinan

cing

acti

vit

ies

-ca

pex

;an

dF

CF

DM

Ois

mea

sure

das

net

cash

flow

sfr

omop

erat

ions

-ca

pex

.T

hes

edir

ect

met

hod

(DM

)ca

shflow

sar

eco

mpar

edw

ith

indir

ect

met

hod

(IM

)ca

shflow

s,F

CF

IMm

easu

red

asop

erat

ing

acti

vit

ies

net

cash

flow

(OA

NC

F)

-ca

pex

.F

orth

ere

turn

onas

set

mea

sure

s,th

eva

riab

leC

FA

FA

T/T

Ais

mea

sure

das

the

free

cash

flow

dir

ect

met

hod

mea

sure

FC

FD

MA

FA

Tdiv

ided

by

tota

las

sets

.T

he

vari

able

sC

FA

F/T

A,

CF

O/T

A,

and

CF

IM/T

Ahav

eas

the

num

erat

or,

FC

FD

MA

F,

FC

FD

MO

,an

dF

CF

IM,

resp

ecti

vely

.W

eco

mpar

eth

ese

mea

sure

sto

OP

/TA

,w

ith

the

num

erat

oras

oper

atin

gpro

fits

(as

esti

mat

edby

Bal

let

al.

(201

4),

subtr

acti

ng

from

reve

nue

cost

ofgo

ods

sold

and

sellin

g,ge

ner

al&

adm

inis

trat

ive

cost

sex

cludin

gre

sear

ch&

dev

elop

men

t);

GP

/TA

,th

egr

oss

pro

fit-

to-t

otal

asse

tsm

easu

re;

asw

ell

asth

etr

adit

ional

retu

rnon

asse

tsm

easu

reN

I/T

A,

whic

his

mea

sure

das

net

inco

me

orIB

CO

Mdiv

ided

by

tota

las

sets

.F

orth

em

arke

tva

lue

ofeq

uit

y-b

ased

mea

sure

s,th

eva

riab

les

hav

eth

esa

me

num

erat

ors

asth

ose

abov

ew

hile

the

den

omin

ator

isth

em

arke

tva

lue

ofeq

uit

y,M

VE

(inst

ead

ofto

tal

asse

ts).

Pan

elA

resu

lts

are

bas

edon

valu

e-w

eigh

ted

retu

rns

(for

non

-finan

cial

firm

son

ly);

Pan

elB

resu

lts

are

bas

edon

equal

-wei

ghte

dre

turn

s.

38

Panel

A:Value-W

eightedReturn

s

Measu

re

P1

P2

P3

P4

P5

P6

P7

P8

P9

P10

P10-P

1Std

Dev

T-sta

tM

inM

ax

IR(low)

(high)

CFAFAT/TA

0.517

0.547

0.688

0.745

0.931

0.759

1.079

1.135

1.058

1.159

0.642

4.555

[2.14]

-11.740

14.952

0.489

CFAF/TA

0.413

0.679

0.417

1.040

0.824

0.859

1.063

1.207

0.890

1.232

0.819

4.554

[2.73]

-12.906

15.300

0.623

CFO/TA

0.435

0.633

0.694

0.872

0.787

0.859

1.031

1.168

0.947

1.221

0.786

4.572

[2.61]

-12.089

16.011

0.595

CFIM

/TA

0.672

0.494

0.672

0.939

0.801

0.865

0.932

1.159

1.034

1.161

0.489

4.757

[1.56]

-15.334

13.541

0.356

OP/TA

0.630

0.816

0.681

1.058

0.770

0.786

0.885

0.878

0.995

1.118

0.488

4.743

[1.56]

-18.139

14.284

0.356

GP/TA

0.785

0.781

0.796

0.690

0.958

0.847

1.009

0.733

1.057

1.276

0.491

3.547

[2.10]

-8.589

11.545

0.480

NI/TA

1.055

0.710

0.918

0.638

0.976

0.940

0.942

0.858

0.781

1.110

0.056

4.847

[0.17]

-23.592

15.464

0.04

CFAFAT/MVE

0.760

0.556

0.497

0.560

0.691

1.223

1.061

1.122

1.311

1.476

0.716

3.935

[2.77]

-11.336

20.771

0.630

CFAF/MVE

0.728

0.354

0.532

0.437

0.909

1.129

1.170

1.090

1.437

1.494

0.766

3.837

[3.03]

-11.875

13.083

0.692

CFO/MVE

0.740

0.376

0.341

0.704

1.150

1.056

1.019

1.257

1.348

1.294

0.554

4.401

[1.91]

-13.778

20.451

0.436

CFIM

/MVE

0.762

0.606

0.410

0.640

0.597

0.976

1.146

1.407

1.213

1.490

0.729

4.086

[2.71]

-12.829

12.088

0.618

OP/MVE

0.611

0.833

0.807

0.891

0.816

0.931

1.127

1.118

1.474

1.310

0.699

6.381

[1.67]

-22.587

38.757

0.380

GP/MVE

0.792

0.646

0.829

0.974

1.068

1.023

1.151

1.296

1.268

1.373

0.581

6.427

[1.37]

-20.541

43.622

0.313

NI/MVE

0.916

0.663

0.804

0.870

0.846

0.870

0.909

1.015

1.248

1.341

0.425

5.437

[1.19]

-26.958

24.932

0.271

Panel

B:Equal-W

eightedReturn

s

Measu

re

P1

P2

P3

P4

P5

P6

P7

P8

P9

P10

P10-P

1Std

Dev

T-sta

tM

inM

ax

IR(low)

(high)

CFAFAT/TA

0.871

0.917

1.085

1.130

1.142

1.100

1.256

1.229

1.389

1.558

0.687

2.863

[3.65]

-10.284

10.230

0.831

CFAF/TA

0.867

0.998

1.069

1.170

1.031

1.126

1.262

1.340

1.329

1.485

0.618

3.168

[2.97]

-10.774

9.431

0.676

CFO/TA

0.874

1.007

1.097

1.135

1.038

1.179

1.293

1.225

1.374

1.454

0.579

3.276

[2.69]

-11.640

9.185

0.612

CFIM

/TA

0.825

1.053

1.113

1.171

1.077

1.201

1.246

1.293

1.289

1.419

0.594

3.647

[2.47]

-12.576

10.255

0.564

OP/TA

0.907

1.267

1.126

1.184

1.153

1.057

1.227

1.178

1.256

1.344

0.437

4.375

[1.52]

-26.002

11.593

0.346

GP/TA

1.045

0.996

1.060

1.099

1.227

1.092

1.186

1.294

1.343

1.301

0.257

3.340

[1.17]

-11.491

9.107

0.266

NI/TA

1.310

1.220

1.253

1.100

1.129

1.081

1.093

1.108

1.113

1.237

-0.073

4.710

[-0.23]

-21.944

12.430

-0.054

CFAFAT/MVE

0.938

0.868

0.912

0.916

1.005

1.167

1.244

1.340

1.464

1.814

0.876

3.076

[4.33]

-7.647

17.271

0.986

CFAF/MVE

0.929

0.855

0.847

0.867

1.057

1.167

1.273

1.319

1.523

1.832

0.903

3.311

[4.15]

-9.425

20.098

0.945

CFO/MVE

0.919

0.769

0.845

0.937

1.129

1.240

1.165

1.402

1.501

1.761

0.842

3.781

[3.38]

-11.021

22.062

0.771

CFIM

/MVE

0.919

0.953

0.868

0.891

1.004

1.057

1.285

1.499

1.397

1.804

0.885

3.483

[3.86]

-9.858

13.807

0.880

OP/MVE

0.778

0.876

0.880

1.062

1.026

1.172

1.230

1.474

1.604

1.594

0.816

6.054

[2.05]

-33.328

40.424

0.467

GP/MVE

0.804

0.725

0.960

0.973

1.222

1.181

1.279

1.375

1.490

1.631

0.827

6.767

[1.86]

-34.558

48.391

0.424

NI/MVE

1.325

1.063

0.960

1.024

0.986

1.068

1.087

1.120

1.483

1.523

0.198

4.358

[0.69]

-20.796

11.879

0.158

39

Table 4: Returns and Yield Measures and One-Month-Ahead Returns: Sub-Period Results

This table measures the average one-month-ahead value-weighted return (%) on portfolios sortedon the basis of various cash flow and yield measures with P1 the lowest decile S&P 1500 stocksand P10 the highest decile stocks, and P10-P1 is the return difference. The results are basedon value-weighted returns for non-financial firms only (see Table 3, Panel A for overall results).The various measures are defined in Table 3. Panel A covers October 1994 to February 2000, thetechnology bubble period; Panel B covers March 2000 to July 2007, the post-technology bubbleand pre-financial crisis period; Panel C covers August 2007 to May 2009, the financial crisis period;and Panel D covers June 2009 to December 2013, the post-financial crisis.

Panel A: Sub-period 1, October 1994 to February 2000 (65 months)

Measure P1 P10 P10-P1 Std Dev T-stat Min Max IR

CFAFAT/TA 1.398 3.066 1.669 4.788 [5.30] -10.505 12.545 1.207CFAF/TA 1.433 3.041 1.608 4.769 [5.12] -10.048 12.824 1.168CFO/TA 1.503 3.001 1.498 4.897 [4.65] -10.233 12.28 1.060CFIM/TA 1.627 3.039 1.412 4.766 [4.50] -8.919 13.376 1.026OP/TA 2.046 3.352 1.306 4.903 [4.05] -8.071 14.284 0.923GP/TA 2.049 3.062 1.013 4.138 [3.72] -8.589 11.545 0.848NI/TA 2.790 3.123 0.332 4.064 [1.24] -8.956 10.686 0.283

CFAFAT/MVE 1.442 1.81 0.368 3.877 [1.44] -11.336 9.193 0.329CFAF/MVE 1.546 1.805 0.259 3.895 [1.01] -11.875 8.992 0.231CFO/MVE 1.663 1.758 0.095 4.122 [0.35] -10.901 11.031 0.080CFIM/MVE 1.459 1.651 0.192 3.756 [0.78] -7.828 8.661 0.177OP/MVE 2.412 2.091 -0.321 5.938 [-0.82] -20.691 17.852 -0.187GP/MVE 2.711 1.701 -1.010 5.851 [-2.62] -20.541 12.533 -0.598NI/MVE 2.375 1.698 -0.677 4.609 [-2.23] -12.473 11.92 -0.509

Panel B: Sub-period 2, March 2000 to July 2007 (89 months)

Measure P1 P10 P10-P1 Std Dev T-stat Min Max IR

CFAFAT/TA -0.004 -0.043 -0.039 4.514 [-0.13] -11.740 14.953 -0.030CFAF/TA -0.609 0.117 0.726 4.737 [2.33] -12.906 15.300 0.531CFO/TA -0.543 0.142 0.685 4.523 [2.30] -8.031 16.011 0.525CFIM/TA 0.034 -0.018 -0.052 4.755 [-0.17] -15.334 13.541 -0.038OP/TA -0.216 -0.273 -0.057 4.485 [-0.19] -9.939 12.109 -0.044GP/TA 0.105 0.195 0.090 3.378 [0.40] -7.387 10.049 0.092NI/TA -0.399 -0.037 0.362 5.711 [0.96] -23.592 15.464 0.220

CFAFAT/MVE 0.385 1.078 0.693 3.497 [3.01] -8.402 11.313 0.687CFAF/MVE 0.156 1.300 1.144 3.547 [4.90] -6.733 12.210 1.117CFO/MVE 0.031 1.177 1.146 4.376 [3.98] -10.092 20.451 0.908CFIM/MVE 0.411 1.548 1.137 4.041 [4.28] -8.540 12.088 0.975OP/MVE -0.860 0.923 1.783 6.881 [3.94] -22.587 30.258 0.898GP/MVE -0.735 1.267 2.003 6.217 [4.90] -15.105 27.587 1.116NI/MVE -0.445 1.554 1.999 6.941 [4.38] -26.958 24.932 0.998

40

Panel C: Sub-period 3, August 2007 to May 2009 (22 months)

Measure P1 P10 P10-P1 Std Dev T-stat Min Max IR

CFAFAT/TA -0.622 -1.357 0.735 6.390 [1.75] -10.997 14.247 0.398CFAF/TA -0.594 -1.235 0.641 5.486 [1.78] -9.876 12.359 0.405CFO/TA -0.745 -1.175 0.430 6.044 [1.08] -12.089 12.361 0.247CFIM/TA -0.889 -1.477 0.589 6.361 [1.41] -11.750 12.467 0.321OP/TA -1.003 -2.543 1.540 7.097 [3.30] -18.139 11.874 0.752GP/TA -0.686 -1.842 1.155 3.333 [5.27] -3.054 10.506 1.201NI/TA -0.932 -0.652 -0.280 5.181 [-0.82] -12.697 12.461 -0.187

CFAFAT/MVE -0.271 -1.278 1.006 7.058 [2.17] -9.806 20.771 0.494CFAF/MVE -0.823 -1.242 0.419 6.247 [1.02] -10.851 13.083 0.232CFO/MVE -1.821 -0.973 -0.848 6.953 [-1.85] -13.778 13.532 -0.422CFIM/MVE -1.282 -1.270 -0.012 6.541 [-0.03] -12.829 11.692 -0.006OP/MVE -0.974 -1.567 0.594 10.178 [0.89] -10.228 38.757 0.202GP/MVE -0.913 -1.276 0.362 11.980 [0.46] -16.354 43.622 0.105NI/MVE -0.942 -0.758 -0.184 3.941 [-0.71] -7.057 5.961 -0.162

Panel D: Sub-period 4, June 2009 to December 2013 (55 months)

Measure P1 P10 P10-P1 Std Dev T-stat Min Max IR

CFAFAT/TA 1.565 1.069 0.496 3.172 [2.37] -6.604 7.676 0.541CFAF/TA 1.629 1.522 0.107 3.441 [0.47] -8.074 7.777 0.108CFO/TA 1.648 1.399 0.249 3.474 [1.09] -8.301 8.712 0.248CFIM/TA 1.668 1.436 0.232 3.912 [0.90] -7.219 9.160 0.206OP/TA 1.576 1.595 -0.018 3.600 [-0.08] -7.706 8.644 -0.018GP/TA 1.698 1.441 0.258 3.088 [1.27] -5.369 7.629 0.289NI/TA 1.405 2.039 -0.634 4.017 [-2.40] -9.750 9.287 -0.547

CFAFAT/MVE 2.425 1.378 1.047 2.921 [5.45] -5.848 5.998 1.242CFAF/MVE 2.366 1.474 0.892 2.904 [4.67] -6.822 8.478 1.064CFO/MVE 2.181 1.481 0.700 3.270 [3.26] -6.327 7.153 0.742CFIM/MVE 2.315 1.316 0.999 3.198 [4.75] -6.608 8.478 1.082OP/MVE 1.925 1.733 0.193 3.278 [0.89] -7.719 10.800 0.203GP/MVE 2.072 1.824 0.248 3.169 [1.19] -6.453 9.147 0.272NI/MVE 1.487 2.065 -0.577 3.077 [-2.85] -6.924 7.047 -0.650

41

Table 5: Returns and Yield Measures Regression Results

This table presents regression results for monthly data from October 1994 to December2013. Average returns are in excess of the one-month Treasury bill rate (from KenFrench’s website). The dependent variables are the P1 (lowest) to P10 (highest) portfoliovalue-weighted returns (for Panels A through D) or equal-weighted returns (Panel E) fornon-financial firms for the portfolios sorted on the basis of the various measures describedin Table 3. The independent variables are based on Carhart’s (1997) extension of theFama-French (1993) three-factor model where MKTRF is the market risk premium (marketreturn in excess of the risk-free rate), SMB is the small minus big (size) factor, HML isthe high minus low (market-to-book) factor, and UMD is the up minus down (momentum)factor; as well as LIQ, Pastor and Stambaugh’s (2003) traded liquidity factor—see equation(1). R2 is the adjusted R-square. T-statistics, reported in parentheses, are based on White’s(1980) heteroscedasticity-consistent standard errors are reported below estimates. Theintercept, α, is interpreted as the alpha or abnormal return.

Panel A: P1 (value-weighted returns)

Dependent AverageVariable Return α MKTRF SMB HML UMD LIQ R2

CFAFAT/TA 0.285 -0.0023 1.200 0.062 -0.221 -0.093 0.138 0.776[0.63] [-1.05] [22.66] [0.79] [-2.17] [-1.94] [2.32]

CFAF/TA 0.181 -0.0032 1.174 0.075 -0.246 -0.095 0.131 0.767[0.41] [-1.46] [22.46] [0.94] [-2.31] [-1.94] [2.21]

CFO/TA 0.203 -0.0029 1.188 0.059 -0.280 -0.105 0.13 0.774[0.45] [-1.28] [22.40] [0.74] [-2.73] [-2.23] [2.19]

CFIM/TA 0.440 -0.0023 1.285 0.167 -0.133 -0.048 0.179 0.781[0.93] [-0.97] [21.98] [2.07] [-1.74] [-1.05] [3.10]

OP/TA 0.398 -0.0003 1.272 -0.004 0.118 -0.164 -0.152 0.830[0.91] [-0.14] [25.64] [-0.08] [1.78] [-3.63] [-3.14]

GP/TA 0.553 0.002 0.968 -0.179 0.114 0.013 -0.041 0.806[1.79] [1.45] [26.37] [-4.77] [2.01] [0.41] [-1.05]

NI/TA 0.823 0.0038 1.265 0.293 -0.357 -0.235 -0.006 0.838[1.65] [1.73] [20.29] [3.72] [-4.50] [-4.11] [-0.10]

CFAFAT/MVE 0.528 -0.0002 1.068 0.016 0.190 -0.122 0.209 0.790[1.38] [-0.08] [22.47] [0.22] [2.83] [-3.18] [4.80]

CFAF/MVE 0.496 -0.0008 1.078 0.030 0.157 -0.109 0.226 0.790[1.28] [-0.39] [22.94] [0.40] [2.41] [-3.14] [5.13]

CFO/MVE 0.508 -0.0003 1.111 0.020 0.012 -0.105 0.194 0.769[1.25] [-0.14] [21.72] [0.25] [0.15] [-2.46] [3.88]

CFIM/MVE 0.530 -0.0013 1.189 0.060 0.232 -0.164 0.253 0.778[1.21] [-0.57] [20.15] [0.66] [3.35] [-3.85] [4.63]

OP/MVE 0.379 -0.0004 1.205 -0.026 -0.618 0.09 -0.009 0.846[0.85] [-0.25] [24.07] [-0.44] [-7.39] [2.06] [-0.17]

GP/MVE 0.560 0.0016 1.151 -0.126 -0.58 0.125 0.006 0.866[1.37] [1.07] [26.59] [-2.12] [-8.13] [3.35] [0.13]

NI/MVE 0.684 0.0024 1.265 0.305 -0.189 -0.336 0.010 0.830[1.35] [1.04] [20.76] [3.50] [-2.22] [-4.64] [0.16]

42

Panel B: P2 through P9 s (value-weighted returns)

DependentVariable P2 α P3 α P4 α P5 α P6 α P7 α P8 α P9 α

CFAFAT/TA -0.0018 0.0008 0.0009 0.0037 0.0012 0.0056 0.0053 0.0054[-1.18] [0.49] [0.65] [2.78] [1.01] [3.92] [3.68] [4.21]

CFAF/TA -0.0001 -0.0031 0.0044 0.0026 0.0022 0.0046 0.0063 0.0036[-0.07] [-2.33] [3.06] [1.97] [1.56] [3.78] [4.04] [2.87]

CFO/TA -0.0007 0.0002 0.0028 0.0021 0.0025 0.0043 0.0057 0.0046[-0.42] [0.11] [1.90] [1.85] [1.96] [3.67] [3.84] [3.77]

CFIM/TA -0.0019 0.0002 0.0026 0.0021 0.0022 0.0036 0.0062 0.0053[-1.14] [0.16] [2.00] [1.48] [1.36] [2.67] [4.77] [3.65]

OP/TA 0.0015 -0.0003 0.0038 0.001 0.0017 0.0029 0.003 0.0049[0.91] [-0.24] [2.42] [0.67] [1.26] [2.18] [2.62] [3.67]

GP/TA 0.0001 0.0005 0.0001 0.0039 0.0035 0.005 0.0027 0.0058[0.09] [0.39] [0.08] [2.82] [2.15] [3.28] [1.67] [4.74]

NI/TA -0.0003 0.0016 0.0000 0.003 0.0032 0.0041 0.003 0.0021[-0.20] [1.26] [-0.01] [2.33] [2.82] [3.45] [2.49] [1.95]

CFAFAT/MVE -0.0010 -0.0013 -0.0008 0.0016 0.0078 0.0054 0.0054 0.0066[-0.59] [-0.88] [-0.73] [1.49] [5.84] [3.67] [3.83] [3.77]

CFAF/MVE -0.0033 -0.0015 -0.0015 0.0042 0.0062 0.0067 0.0051 0.0089[-1.91] [-1.02] [-1.41] [3.20] [4.43] [4.41] [3.29] [5.19]

CFO/MVE -0.0034 -0.003 0.0017 0.0069 0.0051 0.0049 0.0067 0.0069[-2.07] [-2.15] [1.48] [5.26] [3.53] [3.61] [4.46] [4.30]

CFIM/MVE -0.0012 -0.0027 0.0002 0.0009 0.0048 0.0068 0.0092 0.0059[-0.69] [-2.17] [0.13] [0.70] [3.83] [4.56] [5.13] [4.11]

OP/MVE 0.0031 0.0024 0.0037 0.0032 0.0033 0.0053 0.0046 0.0085[2.29] [1.80] [2.81] [2.24] [2.11] [3.23] [2.77] [4.07]

GP/MVE 0.0006 0.0029 0.0042 0.0049 0.0047 0.0059 0.0068 0.0064[0.50] [2.42] [3.38] [3.79] [3.19] [3.96] [3.87] [3.89]

NI/MVE -0.0011 0.0009 0.0023 0.0032 0.0036 0.0042 0.0048 0.0073[-0.64] [0.67] [1.59] [1.92] [2.34] [3.30] [2.90] [4.20]

43

Panel C: P10 (value-weighted returns)

Dependent AverageVariable Return α MKTRF SMB HML UMD LIQ R2

CFAFAT/TA 0.927 0.0070 0.997 -0.203 -0.391 0.030 -0.051 0.827[2.68] [4.57] [25.69] [-3.29] [-6.69] [1.00] [-1.51]

CFAF/TA 1.000 0.0078 0.963 -0.236 -0.414 0.055 -0.022 0.828[2.98] [5.36] [25.42] [-4.02] [-7.62] [1.98] [-0.65]

CFO/TA 0.989 0.0075 0.968 -0.235 -0.383 0.062 -0.016 0.822[2.95] [5.05] [25.29] [-3.91] [-6.99] [2.34] [-0.47]

CFIM/TA 0.929 0.0071 0.982 -0.233 -0.472 0.078 -0.046 0.859[2.73] [5.28] [28.95] [-4.53] [-9.61] [2.23] [-1.40]

OP/TA 0.886 0.0066 1.021 -0.240 -0.543 0.110 -0.073 0.836[2.44] [4.35] [27.08] [-4.45] [-9.47] [4.22] [-2.07]

GP/TA 1.044 0.008 0.924 -0.085 -0.191 0.086 -0.108 0.773[3.30] [4.96] [22.65] [-1.70] [-3.21] [2.26] [-2.74]

NI/TA 0.878 0.0068 0.946 -0.257 -0.483 0.060 -0.018 0.854[2.65] [5.05] [29.30] [-5.65] [-10.16] [1.78] [-0.53]

CFAFAT/MVE 1.244 0.0081 0.977 0.214 0.546 -0.401 0.117 0.852[3.07] [4.93] [22.38] [3.78] [6.88] [-7.75] [2.50]

CFAF/MVE 1.262 0.0076 1.006 0.166 0.545 -0.356 0.18 0.845[3.12] [4.65] [22.79] [3.05] [6.95] [-8.40] [3.89]

CFO/MVE 1.062 0.0051 1.082 0.174 0.680 -0.325 0.098 0.861[2.54] [2.90] [21.32] [3.27] [8.59] [-7.31] [2.03]

CFIM/MVE 1.258 0.007 1.036 0.134 0.681 -0.286 0.133 0.859[3.18] [4.46] [26.44] [3.02] [10.74] [-6.82] [3.25]

OP/MVE 1.078 0.0053 1.116 0.172 0.515 -0.479 0.252 0.814[2.26] [2.16] [22.55] [1.89] [4.79] [-4.32] [4.34]

GP/MVE 1.141 0.0065 1.054 0.378 0.574 -0.511 0.158 0.816[2.40] [2.83] [18.57] [4.36] [5.35] [-4.89] [2.35]

NI/MVE 1.109 0.0061 1.009 0.029 0.400 -0.217 0.200 0.792[2.91] [3.32] [20.34] [0.52] [5.95] [-4.11] [4.48]

44

Panel D: P10-P1 (value-weighted returns)

Dependent AverageVariable Return α MKTRF SMB HML UMD LIQ R2

CFAFAT/TA 0.642 0.0071 -0.195 -0.339 -0.184 0.149 -0.179 0.206[2.14] [2.57] [-3.00] [-3.82] [-1.63] [2.84] [-2.65]

CFAF/TA 0.819 0.0093 -0.203 -0.265 -0.17 0.123 -0.189 0.161[2.73] [3.24] [-2.90] [-2.75] [-1.26] [2.10] [-2.59]

CFO/TA 0.786 0.011 -0.211 -0.311 -0.167 0.15 -0.153 0.185[2.61] [3.94] [-3.08] [-3.19] [-1.26] [2.41] [-2.17]

CFIM/TA 0.489 0.0104 -0.220 -0.294 -0.102 0.167 -0.147 0.193[1.56] [3.64] [-3.19] [-3.06] [-0.81] [2.89] [-2.07]

OP/TA 0.488 0.0094 -0.303 -0.399 -0.339 0.126 -0.225 0.277[1.56] [3.32] [-4.30] [-4.40] [-3.45] [1.99] [-3.31]

GP/TA 0.491 0.0069 -0.250 -0.235 -0.661 0.273 0.079 0.393[2.10] [2.81] [-4.08] [-2.99] [-6.73] [5.36] [1.25]

NI/TA 0.056 0.0059 -0.043 0.094 -0.305 0.073 -0.067 0.13[0.17] [2.61] [-0.71] [1.49] [-3.42] [1.52] [-1.08]

CFAFAT/MVE 0.716 0.0082 -0.090 0.198 0.356 -0.279 -0.092 0.263[2.77] [3.46] [-1.47] [2.15] [4.02] [-4.42] [-1.56]

CFAF/MVE 0.766 0.0084 -0.073 0.136 0.387 -0.247 -0.046 0.251[3.03] [3.66] [-1.19] [1.67] [4.06] [-4.97] [-0.76]

CFO/MVE 0.554 0.0054 -0.029 0.154 0.668 -0.22 -0.096 0.35[1.91] [2.16] [-0.43] [1.68] [5.69] [-3.75] [-1.65]

CFIM/MVE 0.729 0.0083 -0.153 0.074 0.449 -0.122 -0.120 0.226[2.71] [3.38] [-2.32] [0.86] [5.60] [-2.25] [-1.96]

OP/MVE 0.699 0.0057 -0.089 0.198 1.133 -0.568 0.260 0.62[1.67] [2.07] [-1.22] [2.14] [7.07] [-5.89] [3.34]

GP/MVE 0.581 0.0048 -0.097 0.504 1.155 -0.635 0.152 0.657[1.37] [1.85] [-1.41] [5.81] [7.59] [-6.78] [1.96]

NI/MVE 0.425 0.0037 -0.256 -0.276 0.589 0.120 0.190 0.321[1.19] [1.13] [-2.95] [-2.71] [4.50] [1.09] [2.24]

45

Panel E: P10-P1 (equal-weighted returns)

Dependent AverageVariable Return α MKTRF SMB HML UMD LIQ R2

CFAFAT/TA 0.687 0.0085 -0.140 -0.279 0.034 0.047 -0.059 0.247[3.65] [5.05] [-3.15] [-4.30] [0.52] [1.48] [-1.48]

CFAF/TA 0.618 0.0081 -0.175 -0.343 -0.023 0.111 -0.06 0.310[2.97] [4.48] [-3.62] [-4.72] [-0.33] [2.70] [-1.26]

CFO/TA 0.579 0.0075 -0.183 -0.344 0.032 0.124 -0.053 0.327[2.69] [4.09] [-3.75] [-4.57] [0.46] [2.99] [-1.14]

CFIM/TA 0.594 0.0088 -0.310 -0.385 -0.104 0.184 -0.089 0.469[2.47] [4.80] [-6.47] [-4.65] [-1.59] [4.29] [-1.81]

OP/TA 0.437 0.0071 -0.220 -0.383 -0.798 0.288 0.031 0.570[1.52] [3.47] [-4.31] [-5.24] [-9.43] [4.64] [0.62]

GP/TA 0.257 0.0033 0.0000 0.024 -0.129 -0.031 -0.019 0.02[1.17] [1.44] [0.00] [0.27] [-1.55] [-0.58] [-0.30]

NI/TA -0.073 0.0024 -0.349 -0.655 -0.412 0.307 0.031 0.564[-0.23] [1.11] [-6.59] [-7.66] [-4.57] [5.07] [0.68]

CFAFAT/MVE 0.876 0.0079 0.036 0.140 0.481 -0.08 -0.062 0.285[4.33] [4.26] [0.77] [1.88] [7.29] [-1.23] [-1.41]

CFAF/MVE 0.903 0.0086 0.007 0.094 0.52 -0.124 -0.072 0.328[4.15] [4.49] [0.13] [1.24] [7.17] [-1.81] [-1.44]

CFO/MVE 0.842 0.0079 -0.011 0.035 0.669 -0.154 -0.054 0.429[3.38] [3.86] [-0.20] [0.41] [9.01] [-2.10] [-1.09]

CFIM/MVE 0.885 0.0092 -0.119 -0.036 0.470 0.058 -0.158 0.332[3.86] [4.51] [-2.16] [-0.44] [6.87] [0.73] [-2.95]

OP/MVE 0.816 0.0077 0.028 -0.052 0.92 -0.566 0.166 0.594[2.05] [2.90] [0.45] [-0.43] [6.90] [-5.29] [2.59]

GP/MVE 0.827 0.0071 0.041 0.286 1.211 -0.632 0.099 0.641[1.86] [2.53] [0.68] [2.00] [9.04] [-5.45] [1.43]

NI/MVE 0.198 0.0036 -0.253 -0.430 0.219 -0.011 0.089 0.320[0.69] [1.42] [-4.27] [-4.07] [2.41] [-0.13] [1.66]

46

Table 6: Returns and Yield Measures and Various Return Horizons

This table measures the average one-month-ahead (Panel A), six-month-ahead (Panel B), andtwelve-month ahead (Panel C) return (%) on value-weighted portfolios of non-financial firms sortedon the basis of various measures are defined in Table 3 with P1 the lowest decile S&P 1500 stocksand P10 the highest decile stocks, and P10-P1 is the return difference. Std Dev is the standarddeviation, T-stat is the t-statistic of the significance of P10-P1, Min is the minimum return, Maxis the maximum return, and IR is the information ratio measured as annualized P10-P1 returndivided by the annualized standard deviation of the return difference.

Panel A: Three-Month Returns

Measure P1 P10 P10-P1 Std Dev T-stat Min Max IR

CFAFAT/TA 1.811 3.598 1.786 7.903 [3.44] -18.180 29.872 0.452CFAF/TA 1.584 3.691 2.107 8.066 [3.97] -19.829 30.423 0.522CFO/TA 1.684 3.717 2.033 8.057 [3.83] -20.919 30.188 0.505CFIM/TA 2.191 3.602 1.411 8.856 [2.42] -23.145 27.233 0.319OP/TA 2.198 3.462 1.265 8.708 [2.21] -27.720 21.504 0.291GP/TA 2.471 3.973 1.502 5.626 [4.06] -12.348 25.809 0.534NI/TA 3.186 3.429 0.244 8.299 [0.45] -24.221 20.945 0.059

CFAFAT/MVE 2.604 4.341 1.736 7.320 [3.61] -20.142 35.201 0.474CFAF/MVE 2.565 4.645 2.079 7.759 [4.07] -18.316 38.174 0.536CFO/MVE 2.456 4.018 1.563 8.171 [2.91] -20.556 29.747 0.382CFIM/MVE 2.423 4.512 2.088 7.460 [4.25] -17.380 26.080 0.560OP/MVE 2.253 4.188 1.935 11.426 [2.57] -33.748 64.878 0.339GP/MVE 2.253 4.188 1.935 11.426 [2.57] -33.748 64.878 0.339NI/MVE 2.875 4.027 1.152 9.771 [1.79] -31.252 35.465 0.236

Panel B: Six-Month Returns

Measure P1 P10 P10-P1 Std Dev T-stat Min Max IR

CFAFAT/TA 4.275 7.217 2.942 12.240 [3.65] -24.207 41.896 0.340CFAF/TA 3.866 7.265 3.400 12.061 [4.28] -22.853 41.202 0.399CFO/TA 4.091 7.337 3.246 12.277 [4.02] -24.132 39.851 0.374CFIM/TA 4.212 7.284 3.072 13.125 [3.56] -28.812 42.071 0.331OP/TA 4.833 6.994 2.161 12.762 [2.57] -35.584 31.882 0.240GP/TA 5.169 8.348 3.179 7.888 [6.13] -15.504 31.783 0.570NI/TA 6.578 7.008 0.430 12.107 [0.54] -34.101 28.370 0.050

CFAFAT/MVE 5.763 8.556 2.793 11.275 [3.77] -25.817 45.529 0.350CFAF/MVE 5.458 8.990 3.532 11.917 [4.50] -23.932 54.126 0.419CFO/MVE 5.112 8.121 3.009 12.669 [3.61] -31.090 41.945 0.336CFIM/MVE 5.207 8.983 3.776 12.079 [4.75] -37.005 47.482 0.442OP/MVE 5.074 8.695 3.621 17.105 [3.22] -48.616 90.347 0.299GP/MVE 5.097 8.879 3.782 19.824 [2.90] -49.186 90.897 0.270NI/MVE 5.750 7.890 2.139 15.036 [2.16] -45.316 59.371 0.201

47

Panel C: Twelve-Month Returns

Measure P1 P10 P10-P1 Std Dev T-stat Min Max IR

CFAFAT/TA 9.301 14.379 5.078 19.893 [3.88] -49.363 83.059 0.255CFAF/TA 8.722 14.242 5.520 19.313 [4.34] -44.934 70.266 0.286CFO/TA 9.234 14.344 5.110 20.000 [3.88] -48.936 76.418 0.256CFIM/TA 7.771 13.870 6.099 21.566 [4.30] -61.148 61.966 0.283OP/TA 10.454 13.604 3.150 18.530 [2.58] -54.246 62.154 0.170GP/TA 11.145 16.333 5.188 11.279 [6.99] -22.022 40.303 0.460NI/TA 11.459 13.653 2.194 19.942 [1.67] -75.912 45.506 0.110

CFAFAT/MVE 11.126 16.734 5.608 18.093 [4.71] -47.331 59.867 0.310CFAF/MVE 9.940 17.430 7.490 18.279 [6.23] -44.268 62.081 0.410CFO/MVE 9.775 16.176 6.401 20.650 [4.71] -90.129 64.469 0.310CFIM/MVE 11.125 16.945 5.820 18.539 [4.77] -87.021 63.190 0.314OP/MVE 10.568 16.660 6.092 22.725 [4.07] -38.039 109.377 0.268GP/MVE 10.428 16.124 5.696 30.276 [2.86] -56.879 136.297 0.188NI/MVE 11.214 15.311 4.097 22.912 [2.72] -54.225 76.405 0.179

48

Table 7: Returns and Yield Measures, Sector Neutral Results

Panel A measures the average one-month-ahead return (%) on value-weighted portfolios ofnon-financial firms sorted on the basis of various measures are defined in Table 3 with P1 thelowest decile S&P 1500 stocks and P10 the highest decile stocks, and P10-P1 is the returndifference. Portfolios are created by sorting within GICS sectors to ensure sector neutrality. StdDev is the standard deviation, T-stat is the t-statistic of the significance of P10-P1, Min is theminimum return, Max is the maximum return, and IR is the information ratio measured asannualized P10-P1 return divided by the annualized standard deviation of the return difference.Panel B presents regression results for monthly data from October 1994 to December 2013.Average returns are in excess of the one-month Treasury bill rate (from Ken French’s website).The dependent variables are the P10 (highest) less P1 (lowest) portfolio value-weighted returnsfor non-financial firms sorted on the basis of various measures described in Table 3. Portfolios arecreated by sorting within GICS sectors to ensure sector neutrality. The independent variables arebased on Carhart’s (1997) extension of the Fama-French (1993) three-factor model where MKTRFis the market risk premium (market return in excess of the risk-free rate), SMB is the small minusbig (size) factor, HML is the high minus low (market-to-book) factor, and UMD is the up minusdown (momentum) factor; as well as LIQ, Pastor and Stambaugh’s (2003) liquidity factor—seeequation (1). R2 is the adjusted R-square. T-statistics, reported in parentheses, are based onWhite’s (1980) heteroscedasticity-consistent standard errors are reported below estimates. Theintercept, α, is interpreted as the alpha or excess return.

Panel A: One-Month

Measure P10-P1 Std Dev T-stat Min Max IR

CFAFAT/TA 0.606 3.985 [2.31] -16.943 14.375 0.527CFAF/TA 0.661 4.233 [2.37] -17.556 14.866 0.541CFO/TA 0.693 4.104 [2.57] -17.547 13.238 0.585CFIM/TA 0.525 4.210 [1.90] -18.022 10.631 0.432OP/TA 0.340 4.272 [1.21] -15.177 18.118 0.276GP/TA 0.203 3.436 [0.90] -12.193 11.459 0.204NI/TA 0.644 4.033 [2.43] -10.512 12.615 0.553

CFAFAT/MVE 0.523 3.411 [2.33] -10.482 13.318 0.532CFAF/MVE 0.619 3.490 [2.70] -11.953 13.887 0.615CFO/MVE 0.774 3.618 [3.25] -10.206 11.792 0.741CFIM/MVE 0.504 3.461 [2.21] -9.476 13.487 0.504OP/MVE 0.868 5.448 [2.42] -15.708 30.744 0.552GP/MVE 0.592 5.025 [1.79] -15.730 17.600 0.408NI/MVE 0.567 3.768 [2.29] -9.457 15.645 0.521

49

Panel B: Regression Analysis

Dependent AverageVariable Return α MKTRF SMB HML UMD LIQ R2

CFAFAT/TA 0.606 0.0087 -0.150 -0.227 -0.475 0.102 -0.047 0.201[2.31] [3.56] [-2.23] [-1.92] [-4.44] [1.61] [-0.82]

CFAF/TA 0.661 0.0085 -0.119 -0.237 -0.452 0.185 -0.013 0.213[2.37] [3.37] [-1.69] [-2.03] [-3.96] [2.72] [-0.21]

CFO/TA 0.693 0.0091 -0.123 -0.247 -0.445 0.173 -0.041 0.217[2.57] [3.69] [-1.74] [-2.22] [-4.16] [2.63] [-0.65]

CFIM/TA 0.525 0.0091 -0.113 -0.432 -0.671 0.101 -0.132 0.328[1.90] [3.81] [-1.75] [-5.42] [-6.46] [1.59] [-1.99]

OP/TA 0.340 0.0039 -0.260 0.092 -0.432 0.195 0.190 0.323[1.21] [1.61] [-4.18] [0.95] [-4.18] [3.48] [3.06]

GP/TA 0.203 0.0028 -0.161 0.046 -0.273 0.162 0.002 0.212[0.90] [1.36] [-3.25] [0.77] [-3.30] [3.24] [0.04]

NI/TA 0.644 0.0088 -0.155 -0.470 -0.257 0.234 -0.103 0.340[2.43] [3.81] [-2.57] [-6.20] [-2.81] [4.05] [-1.72]

CFAFAT/MVE 0.523 0.0068 -0.168 -0.221 0.05 -0.029 0.026 0.140[2.33] [2.92] [-2.58] [-2.58] [0.57] [-0.46] [0.46]

CFAF/MVE 0.619 0.0078 -0.178 -0.203 0.156 -0.038 -0.004 0.180[2.70] [3.39] [-2.87] [-2.84] [2.02] [-0.69] [-0.06]

CFO/MVE 0.774 0.0087 -0.094 -0.115 0.23 -0.075 -0.038 0.126[3.25] [3.53] [-1.34] [-1.56] [2.38] [-1.37] [-0.55]

CFIM/MVE 0.504 0.0077 -0.195 -0.373 -0.006 -0.009 -0.076 0.270[2.21] [3.69] [-3.11] [-5.28] [-0.08] [-0.20] [-1.20]

OP/MVE 0.868 0.0065 0.070 0.370 0.838 -0.443 0.175 0.486[2.42] [2.40] [1.07] [4.16] [6.13] [-6.02] [2.02]

GP/MVE 0.592 0.003 0.037 0.446 0.964 -0.298 0.125 0.506[1.79] [1.16] [0.55] [5.44] [9.64] [-4.07] [1.50]

NI/MVE 0.567 0.0079 -0.100 -0.302 0.099 -0.024 -0.153 0.180[2.29] [3.30] [-1.42] [-3.91] [1.06] [-0.34] [-2.61]

50

Table 8: Returns and Yield Measures Related to Book/Price, Size, Momentum, and GrossProfit

This table compares various two-way sorts for cash flow returns, CFAFAT/TA, in Panel A, andcash flow yields, CFAFAT/MVE, in Panel B (see Table 3 for definitions). The measures are averagevalue-weighted one-month-ahead returns (%) of non-financial S&P 1500 firms sorted on the basisof each variable. 25 equal-sized portfolios are formed, first across five quintiles sorted on the basisof each variable, and then into quintiles again within each group, on the basis of book-to-price(B/P), market capitalization (size), price momentum (momentum), and gross profit-to-total assets(GP/TA). High - Low is the monthly return (%) difference between the highest and lowest cashflow returns or cash flow yield quintiles within a particular B/P, size, or momentum quintile. Thet-stat tests the significance of the high-low difference.

Panel A: Cash Flow Returns (CFAFAT/TA)

High-Low 2 3 4 High Low t-stat

Low B/P 0.316 0.518 0.962 1.034 1.196 0.880 [3.07]2 B/P 0.294 0.712 1.025 1.236 1.381 1.087 [3.59]3 B/P 0.916 1.111 0.707 1.000 1.201 0.285 [1.01]4 B/P 0.895 0.946 1.052 1.411 1.407 0.511 [1.54]High B/P 0.789 0.907 1.079 1.135 1.667 0.878 [2.59]

Small size 1.376 1.397 1.299 1.566 1.816 0.440 [1.53]2 size 0.883 1.257 1.130 1.283 1.533 0.650 [3.13]3 size 0.722 0.991 1.265 1.153 1.381 0.659 [3.26]4 size 0.762 0.995 1.123 1.226 1.438 0.676 [3.58]Large size 0.470 0.814 0.819 0.969 1.188 0.718 [3.28]

Low momentum 0.114 0.518 0.973 0.923 1.341 1.227 [3.16]2 momentum 0.672 0.821 0.986 0.932 1.165 0.493 [1.79]3 momentum 0.453 0.613 0.794 1.124 1.012 0.559 [2.07]4 momentum 0.844 0.881 1.000 1.087 0.995 0.151 [0.59]High momentum 0.873 1.046 1.081 1.221 1.467 0.593 [1.71]

Low GP/TA 0.771 0.648 0.827 1.059 0.825 0.054 [0.19]2 GP/TA 0.235 0.819 0.747 0.880 1.021 0.785 [2.31]3 GP/TA 0.619 0.743 1.101 1.153 1.077 0.458 [1.28]4 GP/TA 1.035 0.763 1.227 0.788 1.048 0.013 [0.04]High GP/TA 0.941 0.819 1.426 1.347 1.282 0.341 [1.04]

51

Panel B: Cash Flow Returns (CFAFAT/MVE)

High-Low 2 3 4 High Low t-stat

Low B/P 0.300 0.470 0.836 1.021 1.260 0.960 [2.93]2 B/P 0.378 0.560 0.985 1.381 1.216 0.838 [2.65]3 B/P 0.837 1.105 0.965 0.834 1.169 0.332 [1.29]4 B/P 1.109 0.803 1.064 1.347 1.418 0.309 [1.02]High B/P 0.818 0.741 1.278 1.414 1.468 0.650 [1.80]

Small size 1.123 1.361 1.403 1.495 2.018 0.896 [3.33]2 size 0.919 0.964 1.191 1.387 1.573 0.654 [3.47]3 size 0.806 0.749 1.090 1.284 1.531 0.725 [3.57]4 size 0.814 0.907 1.117 1.295 1.399 0.585 [2.65]Large size 0.396 0.676 0.878 1.249 1.222 0.826 [3.55]

Low momentum 0.326 0.437 0.886 1.189 1.734 1.408 [3.60]2 momentum 0.847 0.746 0.955 1.122 1.306 0.460 [1.64]3 momentum 0.477 0.353 0.999 1.172 1.384 0.907 [3.81]4 momentum 0.707 0.677 0.870 1.222 1.235 0.528 [2.09]High momentum 1.085 0.938 1.178 1.186 1.176 0.091 [0.32]

Low GP/TA 0.880 0.570 0.665 0.944 1.067 0.187 [0.65]2 GP/TA 0.566 0.583 0.609 0.970 1.409 0.843 [2.29]3 GP/TA 0.706 0.469 1.288 0.727 1.400 0.693 [1.84]4 GP/TA 0.873 0.380 0.801 1.313 1.709 0.836 [2.38]High GP/TA 0.876 0.843 1.394 1.530 1.494 0.618 [1.84]

52

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54

Table 10: Fama-MacBeth Regressions for Cash Flow Components

This table presents average Fama and MacBeth (1973) slope coefficients and their t-valuesfrom cross-sectional regressions. The dependent variables are the one-month ahead returnsfor each stock. The independent variables are net cash flow from operations measure (A inTable 1), Net CF Ops; net cash flow from operations measured by the indirect cash flowmethod, CFOIM; free cash flows measured as net income plus depreciation less change inworking capital less capital expenditures, FCF; Fin Act, financing activities as measured byinterest expenses +/- other financing income/expenses; Tax Act, tax activities as measuredby taxes on the income statement +/- changes in accounts payable taxes +/- changes indeferred taxes; other non-operating activities, Non-Op Act, non-operating activities asmeasured as discontinued operations/special charges +/- foreign exchange gains/losses+/- pension gains/losses/contributions; and Capex, capital expenditures. We deflate allof these variables by the book value of total assets. Other control variables include: log(BVE/MVE), the natural logarithm of the ratio of the book value of equity to market valueof equity; log(ME), the natural logarithm of the market value of equity; r1,1 is the stock’sone-month-prior return; and r12,2 is the stock’s prior year’s return skipping a month. Wewinsorize all independent variables based on the first and 99th percentiles. Adjusted R2 isthe adjusted R-square.

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Net CF Ops 0.0153 0.0141 0.0171 0.0133 0.0181 0.0167 0.0151 0.0194

[3.22] [2.83] [3.64] [2.58] [3.69] [3.53] [2.87] [3.60]CFOIM 0.0257

[3.71]FCF 0.0077

[0.93]Fin Act -0.0367 -0.0578 -0.0602 -0.0577 -0.0403 -0.0512

[-0.74] [-1.07] [-1.13] [-1.08] [-0.72] [-0.92]Tax Act -0.0133 -0.0248 -0.0243 -0.0286 -0.0223 -0.0052

[-0.93] [-1.51] [-1.60] [-1.91] [-1.49] [-0.36]Non-Op Act -0.0088 -0.0046 0.0025 -0.0157 -0.0233

[-0.82] [-0.45] [0.25] [-1.69] [-2.17]Capex -0.0186 -0.02 -0.0215 -0.0065

[-1.89] [-2.03] [-2.18] [-0.55]log(BE/ME) 0.002 0.0019 0.0019 0.0018 0.0021 0.0015 0.0013 0.0013 0.0013 0.0013

[1.71] [1.70] [1.52] [1.55] [1.73] [1.31] [1.15] [1.19] [1.10] [1.13]log(ME) 0.0008 -0.0008 -0.0008 -0.0008 -0.0008 -0.0008 -0.0008 -0.0008 -0.0009 -0.0007

[-1.36] [-1.32] [-1.38] [-1.37] [-1.38] [-1.35] [-1.37] [-1.44] [-1.52] [-1.21]r1,1 -0.024 -0.023 -0.024 -0.025 -0.024 -0.024 -0.024 -0.025 -0.025 -0.025

[-3.09] [-2.96] [-3.18] [-3.19] [-3.17] [-3.09] [-3.17] [-3.28] [-3.30] [-3.24]r12,2 -0.0009 -0.0007 -0.0011 -0.0009 -0.001 -0.001 -0.001 -0.0014 -0.0014 -0.001

[-0.24] [-0.19] [-0.30] [-0.25] [-0.29] [-0.28] [-0.29] [-0.38] [-0.41] [-0.28]Adjusted R2 5.64% 6.17% 5.91% 5.98% 6.19% 6.49% 6.73% 7.29% 7.31% 7.31%

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