Internal Information and Investment Sensitivities to Market Value and Cash Flow
Shane Heitzman*
Marshall School of Business – University of Southern California
Mengjie Huang
Simon Business School – University of Rochester
October 2, 2014
Abstract:
A growing literature shows how external information quality affects investment responses to
market value and cash flow—frequently used proxies for investment opportunities and internal
funds. However, such associations are also shaped by internal information quality. We predict that
investment is more sensitive to internal cash flow signals and less sensitive to external market
value signals when managers have higher quality internal information. In line with recent
theoretical and empirical research, we proxy for internal information quality using observable
information properties. We find that the sensitivity of investment to cash flow is increasing while
the sensitivity of investment to market-to-book is decreasing in information quality. This evidence
is consistent with internal information-based predictions and inconsistent with external
information-based explanations. Our results offer a new and unique insight robust to several
alternative explanations and hold up using recently-developed techniques to correct for
measurement error in market valuations.
Keywords: Investment; Information Quality; Reporting Quality; Internal Information; Financing
Constraints
We appreciate comments from John Gallemore, Nemit Shroff, Toni Whited, Jerry Zimmerman and workshop
participants at the University of Minnesota.
*Corresponding author. 701 Exposition Blvd. HOH 822, Los Angeles, CA 90089.
email: shane.heitzman@marshall.usc.edu. phone: 213-740-6531.
Internal Information and Investment Sensitivities to Market Value and Cash Flow
1.
Introduction
Expanding our understanding of financing constraints and investment behavior, a growing
body of research examines the role external information quality plays in reducing these constraints.
The maintained assumption is that higher quality financial reporting reduces asymmetric
information between insiders and outsiders, controlling adverse selection problems that increase
external financing costs and moral hazard problems when managers deploy firm resources for their
own benefit. Investment sensitivities to market-to-book and cash flow are frequently employed to
measure these sources of investment inefficiencies. The empirical prediction that follows is that
investment is more sensitive to market values and less sensitive to the availability of internal funds
when information quality improves. The evidence to date has largely supported these predictions.1
In this paper, we shift the focus away from the external use of information and toward a key
determinant of decision-making within the firm: internal information quality. Specifically, we ask
how the quality of internal information affects the sensitivity of investment decisions to both the
external investment signals obtained from market values and the internal investment signals
obtained from cash flow forecasts.
We consider this question from the perspective of a manager who has imperfect information
about the value of the firm’s investment opportunities and updates the capital budget using signals
obtained from external sources (market values) and internal sources (cash flow forecasts). The
attributes of internal information sources vary across firms as a function of firm complexity and
1
See, for example, Biddle and Hilary (2006), Biddle, Hilary and Verdi (2009), Beatty, Liao and Weber (2010a and
b), Balakrishnan, Core and Verdi (2014), Balakrishnan, Watts and Zuo (2014). While this focus on capital allocation
is crucial for understanding the economic consequences of financial reporting, Bushman and Smith (2001) argue
that financing constraints is just one potential channel. Recent work has begun to examine the impact of external
information environments on the identification of investment projects (Badertscher, Shroff and White 2013).
1
the diffusion of specific information within the firm (Fama and Jensen 1983). A firm’s equilibrium
investment in the internal information system equates the marginal costs (such as the cost of
gathering and communicating information) with the marginal benefits (more efficient decisions).
The manager that obtains more precise internal forecasts of investment profitability should place
more weight on that information and less weight on the market’s opinion. Under the internal
information hypothesis we propose, the sensitivity of investment to market valuations and cash
flow (the usual proxies for investment opportunities and internal funds in the empirical investment
literature) will therefore depend on the quality of the manager’s internal information about
investment opportunities.
To understand the role of internal signals for investment sensitivities, consider the positive
association between investment and cash flow. Fazzari, Hubbard and Petersen (1988) and others
attribute this to a financing constraint story. However, this association can also arise when
operating profit is informative about investment opportunities (Alti 2003; Cooper and Ejarque
2003). As internal information quality improves, the manager obtains timelier and more precise
internal signals about investment profitability. For a given shock to expected cash flows, the
manager with higher quality internal information can reallocate capital to the most valuable
projects more efficiently. Thus, an internal information hypothesis predicts that as information
quality improves, investment decisions become more sensitive to internal accounting (i.e. cash
flow or profitability) signals.
In contrast, when the manager has low quality internal information, they are more likely to turn
to external opinions to supplement that information. Stock prices provide an observable signal of
external information and empirical evidence suggests that the manager’s investment decisions do
respond to (learn from) these external market signals (Luo 2005; Chen, Goldstein and Jiang 2007;
2
Bakke and Whited 2010). As the manager’s comparative information advantage improves,
however, their response to market signals should weaken. Under the internal information
hypothesis, managers with better internal information rely less on external market signals to guide
capital allocation, causing observed investment decisions to become less sensitive to market
valuations.
We use capital expenditures plus research and development to proxy for investment, marketto-book to proxy for investment opportunities, and earnings before depreciation to proxy for cash
flows (consistent with prior literature). Our empirical tests focus on the sensitivity of investment
to market-to-book and operating earnings conditional on information quality. The internal
information hypothesis predicts that as internal information quality improves, the sensitivity to
internal signals (cash flows) strengthens while the sensitivity to external signals (market values)
weakens. To provide comparability to prior literature, we consider several alternative explanations
for the impact of information quality on the sensitivity of investment to market-to-book and cash
flow. These alternatives follow from prior literature, are based on the presumption that information
quality proxies reflect information asymmetry between insiders and outsiders, and typically
generate predictions opposite those under an internal information story.2
2
For example, under an adverse selection hypothesis, improving information quality reduces financing frictions
when firms need external capital and thus reduces the sensitivity of investment to internal funding proxies such as
cash flows, while increasing the sensitivity to investment opportunities. Additionally, moral hazard problems can
lead managers to spend free cash flow or exploit overpriced equity to undertake projects that generate private
benefits. Under a moral hazard hypothesis, improving information quality enhances monitoring and reduces the
sensitivity of investment to both market valuations and internal funds. Alternatively, a growing stream of evidence
shows that measurement error in market valuations biases the coefficient on market-to-book toward zero (Erickson
and Whited 2000). As noise in market valuations increases, the relative explanatory power of accounting-based
proxies also increases. If increasing information quality reduces information asymmetry and mitigates market
mispricing of investment opportunities, the sensitivity of investment to market-to-book should increase while the
sensitivity to cash flow should decrease (opposite the predictions under the internal information hypothesis).
3
Our information quality proxies include the precision of accruals, the speed of annual report
filing, agreement among analysts and managerial guidance. While we are constrained to using
observable instruments for unobservable internal information, we note that a) nearly every
empirical study relies extensively on proxies for unobservable attributes, and b) there is a high
degree of correspondence between the quality of the manager’s own information and the quality
of what they report to shareholders (and hence what we can observe). Hemmer and Labro (2008)
show that the decision usefulness of external reporting is inherently tied to the quality of
information for internal decision making. In a recent survey conducted by Dichev et al. (2013),
over 80% of CFOs rank internal use of externally reported earnings as “very important” and
“emphasize the use of ‘one number’ for internal and external communications”, providing direct
evidence that properties of the internal and external reporting systems are highly aligned. Finally,
internal control quality is a key contributor to the quality of internal information for decisionmaking (Kinney 1999). Evidence from disclosed internal control deficiencies show that lowquality internal information translates into low quality external reporting and disclosure decisions
such as accrual estimation and communications with the market (Ashbaugh-Skaife et al. 2008,
Feng, Li and McVay 2009). Thus, although the quality of managers’ internal information is
unobservable, we follow Gallemore and Labro (2014) and Goodman et al. (2014) and allow
observable information properties to serve as instruments for internal information quality.
Our main result: investments by managers with higher quality information are more sensitive
to cash flow and less sensitive to market valuations. This finding is robust across a diverse set of
information quality proxies, contrasts with prior work on external information quality, and is most
consistent with the interpretation that managers with high quality information are less likely to
4
defer to the market’s opinion of investment opportunities. Instead, they place greater weight on
timely internal information about shocks to profit opportunities.
We conduct several tests to address competing explanations. First, to control for potential
agency costs that cause investment to be sensitive to internal funds, we control for cash holdings
in the regression. This reduces the demand on operating earnings as an instrument for internal
funds as used in Fazzari et al. (1988) and Biddle and Hilary (2006) and allows us to interpret
operating earnings as an investment signal. Consistent with Biddle, Hilary and Verdi (2009) and
others, we find that increased information quality reduces the sensitivity of investment to cash
holdings. Second, the moral hazard hypothesis predicts that high quality information reduces the
sensitivity of investment to market-to-book because managers in those firms are less likely to
respond opportunistically to mispriced equity. To distinguish this interpretation from the internal
information hypothesis, we include proxies for mispricing and their interactions with market-tobook. Our results are unchanged. Third, information quality is likely correlated with firm
complexity, which has been shown to affect investment sensitivities (Shin and Stulz 1998). We
control for proxies for complexity based on geographic and industry diversification following
Bushman et al. (2004), as well as their interactions with our variables of interest, and find similar
results. Fourth, using the Bushman, Smith and Zhang (2012) decomposition of operating earnings
(the typical cash flow proxy) into its cash flow and accrual components, we find the sensitivity of
investment to both the cash flow and accrual component is increasing in information quality. For
these firms, both current cash flows and future cash flows (proxied by accruals) appear to have
information about investment opportunities that managers respond to. Finally, utilizing techniques
developed in Erickson and Whited (2002) and Erickson, Jiang and Whited (2014) to address
5
measurement error concerns in the proxy for investment opportunities, we show our results (as
well as many of those documented in prior studies) are robust to measurement error corrections.
Our study contributes to the literature in several ways. First, we build on the descriptive theory
that links attributes of the information environment to the firm’s investment decisions, focusing
on the importance of internal information quality in capital budgeting. Our paper differs from
existing research by focusing on the equilibrium relation between the design of the internal
information system and decision making within the firm. This internal focus generates unique
empirical predictions that do not overlap with those under an external reporting focus. Second, we
empirically examine the role of internal information quality by exploiting its intrinsic link to
external information quality. This builds on a promising stream of research that expands the
boundaries of inquiry on the internal information environment by leveraging the coordination of
information demands by users inside and outside the firm to construct empirical proxies for
internal information. Third, we conduct our analyses using standard investment regressions to
provide comparability to prior research on the importance of financing constraints and allow for
new interpretations about internal information effects. Our empirical strategies allow us to
uniquely identify internal information effects and rule out the influence of information asymmetry
between managers and shareholders on the interpretations. Fourth, we implement recently
developed techniques to correct for measurement error in market-based proxies for investment
opportunities. Importantly, our findings as well as those in recent work on information quality and
financing constraints are robust to measurement error correction under many specifications.
6
2.
Prior Literature
To understand the economic consequences of accounting choices, a natural starting point is the
association between reporting decisions and real decisions—capital investment. Bushman and
Smith (2001) describe several potential ways that financial reporting attributes can influence
investment. One relies on predictions from agency theory: shareholders delegate investment
decisions to a manager with more precise information about the firm’s investment opportunities.
The self-interested manager has an incentive to choose projects that provide private benefits. In
response, moral hazard and adverse selection costs are controlled through incentive alignment and
monitoring. The assignment of decision rights, incentive structures and monitoring mechanisms
are therefore chosen to maximize firm value. Information plays a key role: a reduction in
information quality can affect the firm’s marginal investments if it, a) improves the manager’s
ability to implement projects that generate private benefits, or b) increases marginal financing costs
sufficient to turn a project’s NPV negative.
The majority of the empirical accounting research on the relation between financial reporting
and investment follows this agency-based path. Motivated by the literature documenting that
investment appears sensitive to cash flows (Fazzari et al. 1998), recent accounting studies use this
framework to test predictions about the role of information quality for investment. The usual
economic interpretation of the results is that high quality external information reduces financing
frictions and thus increases investment efficiency. 3 Biddle and Hilary (2006) find that the
sensitivity of investment to cash flows is stronger in countries with less transparency. Biddle et al.
3
For example, accounting information can provide contracting parties with more information to monitor managerial
performance. Financial information that is more informative about investment opportunities and managerial actions
allows superiors to write incentive contracts based on reported performance. This also reduces the ability and
incentives to mislead superiors about potential project payoffs and thus reduces the likelihood the manager will
overinvest in self-serving projects. External financing sources are also more willing to offer favorable financing terms.
Private lending resolves information asymmetries between managers and lenders, but not necessarily within the firm.
This suggests that firms are more likely to finance marginal investment with internal funds.
7
(2009) argue that firm-level information quality reduces deviations from predicted investment, the
impact of incentives to over- or under-invest and the sensitivity of investment to cash flows.
Beatty, Liao and Weber (2010a) show that the impact of reporting quality on investment-cash flow
sensitivities is weaker when firms can resolve asymmetric information through private debt
markets, while Beatty, Liao and Weber (2010b) find that firms with low reporting quality finance
capital through leases rather than debt. Other studies that rely on financing frictions as the main
channel for reporting quality include Balakrishnan, Core and Verdi (2014) who show that a
negative shock to collateral values has a stronger effect on investment for firms with lower
reporting quality. In a similar vein, Balakrishnan, Watts and Zuo (2014) argue that a negative
shock to the credit market has a stronger impact on investment for less conservative firms.
A second avenue for information quality to influence investment is driven by the notion that—
independent of agency conflicts—capital budgeting is more efficient when the decision maker has
higher quality information. One approach to this question is to look at macro-level measures of the
information environment, where increasing the amount of information available to peer firms
improves identification of investment opportunities. Shroff, Verdi and Yu (2014) and Badertscher
et al. (2013) find that firms invest more in response to their investment opportunities when they
operate in environments where other firms are providing more public disclosure. Of course,
proprietary costs of disclosure can increase the manager’s incentive to reduce external information
quality, but in general, firms appear to benefit from operating in more transparent environments.
In this paper, we shift the focus away from understanding how investment is affected by the
quality of information released to capital markets and toward understanding how investment is
affected by information quality within the firm. In doing so, we argue that the relation between
investment and determinants such as market valuations and cash flow can also be explained by
8
variation in the quality of internal information. High-quality internal information, including the
design of internal controls, is essential for efficient decision-making and control (Kinney 1999).
However, optimal investment in internal information quality will also depend on the organizational
structure, ownership structure, the nature of the assets, tax planning and compliance and demands
imposed by other external reporting requirements.
Internal information quality is generally unobservable. A variety of proxies for external
information quality based on reporting and disclosure attributes have been developed to answer
questions about the role of asymmetric information between managers and (external) capital
markets. We adopt the view that such measures can also proxy for the quality of internal
information available to the decision maker. This presumed link between the attributes of
unobservable internal information and observable external information is strongly supported by
recent research. Hemmer and Labro (2008) develop a theoretical model that links the decision
usefulness of information reported externally to the decision usefulness of information available
internally. Survey evidence in Dichev et al. (2013) finds that over 80% of responding CFOs rate
the use of earnings internally as very important and state that, “the tight link between internal and
external uses of GAAP earnings is also consistent with research that investigates the investmentrelated consequences of earnings quality.”
Empirical evidence suggests that internal information quality has a direct and positive link to
external information quality. For example, firms with disclosed internal control weaknesses appear
to have lower accrual quality (Ashbaugh-Skaife et al. 2008, Doyle, Ge and McVay 2007), face
higher borrowing costs (Costello and Wittenberg-Moerman 2011), are slower to file their financial
statements (Ettredge, Li and Sun 2006) and provide less accurate guidance (Feng et al. 2009).
Cheng, Dhaliwal and Zhang (2013) argue that investments in internal controls (following
9
disclosure of a material weakness) reduce financing constraints by improving external information
quality. Investments in internal information quality have also been identified through a firm’s
adoption of an enterprise resource planning (ERP) system. Brazel and Dang (2008) find that firms
implementing an ERP system release public financial statements with less delay. Dorantes et al.
(2013) find that these firms are also able to issue external earnings guidance more often and with
higher quality. Consistent with this, Goodman et al. (2014) argue that high quality internal
information is reflected in their external forecasts. Using management guidance behavior to
instrument for internal information quality, they find that managers that are more active and
accurate in providing guidance also make better acquisitions.
Because the quality of internal and external information is highly correlated (as discussed
above), research questions that focus on the quality of internal information for decision making
can rely on proxies constructed from observable external sources. We adopt this approach here
and take several steps to ensure our results are not explained by external information quality
predictions.
3.
Empirical Framework and Hypothesis Development
3.1.
The empirical investment framework
Under the q theory developed in Hayashi (1982) and Summers (1981), in perfect markets
without financial frictions, investment is determined solely by (unobserved) marginal q, which
should capture all the factors relevant to the investment decision. A basic empirical model is
implemented with a market-based proxy for q and is usually adapted to include other factors
predicted to affect the investment decision, i.e.:
10
𝐼𝑖𝑡
𝑀𝑖𝑡−1
𝐸𝐵𝐷𝑖𝑡
𝐶𝑎𝑠ℎ𝑖𝑡−1
= 𝛼0 + 𝛼1
+ 𝛼2
+ 𝛼3
+ 𝛼4 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1
𝐴𝑖𝑡−1
𝐴𝑖𝑡−1
𝐴𝑖𝑡−1
𝐴𝑖𝑡−1
(1)
+ 𝛼5 ln(𝐴𝑖𝑡−1 ) + 𝑒𝑖𝑡
I/A is investment scaled by beginning total assets, where investment is capital expenditures plus
research and development. M/A is the beginning market-to-book asset ratio, the most commonly
used empirical proxy for q. Evidence from the investment literature shows that the coefficient on
market-to-book is decreasing in adjustment costs and measurement error in market-based proxies
for q (Erickson and Whited 2000). However, building on the fact that market-to-book represents
the market’s valuation of investment opportunities, there is growing evidence that the coefficient
on market-to-book increases when managers incorporate feedback from market prices (Chen et al.
2007; Bakke and Whited 2010) and mixed evidence that the market-to-book coefficient picks up
opportunistic responses to market mispricing (Polk and Sapienza 2009; Bakke and Whited 2010).
EBD is operating profits, or earnings before depreciation (and R&D). The use of operating
profits as the default empirical proxy for cash flow originates in the economics literature (Fazzari
et al. 1988, Kaplan and Zingales 1997). Fazzari et al. (1988) show that investment is positively
correlated with EBD, interpreting the effect as evidence of costly external finance and inspiring its
use in the accounting literature on financing constraints (e.g., Biddle and Hilary 2006). In empirical
tests we refer to this cash flow proxy as operating earnings (or EBD) to distinguish it from its cash
flow and accrual components.
Because operating earnings also reflect information about the profitability of investment
opportunities, several alternative explanations arise to explain the positive correlation between
operating earnings and investment (Hennessy, Levy and Whited 2007). First, when firms have
market power, the proxy for average q (market-to-book) will diverge from true investment
opportunities (marginal q), allowing current earnings to explain more of the variation in current
11
period capital allocation (Cooper and Ejarque 2003; Moyen 2004). Second, the significance of
operating earnings in the investment regression depends on measurement error in the market’s
valuation of current and future investments. This can bias the coefficient on operating earnings up
as current profitability must play a bigger role in explaining investment decisions when marketto-book is a poor proxy for investment opportunities (Erickson and Whited 2000). Third, following
Jensen (1986), operating earnings can also load if managers view excess cash flows as a source of
capital for empire building. Finally, Bushman et al. (2012) provide evidence that the loading on
earnings-based proxies for cash flows in the economic literature can be explained by simple comovement in capital expenditures and working capital investment. In this paper, we rely on the
role of operating earnings in providing an internal signal about productivity shocks and opportunity
costs relevant for investment. To rule out these alternative explanations, we include cash holdings
to proxy for internal funds (discussed next) and conduct a variety of robustness tests.
Because the likelihood the firm will face a binding financing constraint is more appropriately
associated with the stock of cash than the flow, and to be consistent with the subset of studies in
accounting that use cash holdings as the proxy for internal liquidity (e.g. Biddle et al. 2009), we
include cash holdings at the beginning of the period. By not forcing the coefficient on EBD to take
on a financing constraint explanation, EBD serves as a cleaner proxy for the internal cash flow
signal we are interested in.4 We include leverage following Hennessy’s (2004) finding that debt
overhang distorts investment and results in under-investment and because investment and leverage
could be endogenously correlated if a positive shock to investment opportunities leads to both an
increase in investment and an increase in debt issuance.
4
To address the concern that multicollinearity arising from correlation between cash holding and cash flow might
bias our results, we exclude cash holding from the regressions in robustness tests. The main inferences are
unchanged.
12
3.2.
The role of internal information quality in shaping investment responses
These common investment determinants—market-to-book and EBD—also serve to proxy for
external signals provided by the market and internal signals based on internal forecasts of
profitability. The sensitivity of the investment decision to each of these signals depends on the
relative precision of these signals in the internal decision making process. Because the manager
should place more weight on a signal that is more precise, investment decisions will become
relatively more sensitive to the more precise signal. In our context, higher quality internal
information should be associated with more precise internal signals for decision making. Thus,
under the internal information hypothesis we propose, an increase in internal information quality
will cause investment to be more sensitive to the internal accounting signal (earnings) and less
sensitive to the external market signal (market-to-book).
Investments in high quality internal information provide the manager with timelier and more
precise feedback about the firm’s productivity and opportunity costs. Such information enhances
the quality of profit forecasts on both recent and proposed investments. This improves the
manger’s ability to reallocate capital to exploit profit opportunities in a timelier fashion. We argue
that realized earnings and cash flow for the period proxy for expected profitability at the time
investment decisions are made. Given rational expectations, however, managers will form
unbiased forecasts about current period profitability implying that realized profits serve as a viable
proxy for expected profits. Under the internal information hypothesis, the sensitivity of investment
to internal profit signals is increasing in internal information quality.
As internal information quality falls, however, outside sources of information such as the
market can become more important. The potential for market prices to guide capital allocation
decisions in the firm was recognized early on by Hayek (1945) and more recent work includes
13
Dow and Gorton (1997), Dye and Sridhar (2002), Luo (2005), Zuo (2013), and Gao and Liang
(2013). Managers are well-informed about inside factors such as the firm’s technological
capabilities and specific investment opportunities. But the value of those opportunities to
shareholders is also affected by outside forces like competition, product demand, political and
geographical considerations and other factors about which the manager may be relatively
uninformed. This creates an opportunity for outside investors to have a comparative information
advantage and an economic incentive to reveal their information through trading. Their trades
make prices more efficient and communicate more information to managers. Consistent with this,
Chen et al. (2007) and Bakke and Whited (2010) find that investment is more sensitive to market
values when more private information production is reflected in the stock price. Managers that
obtain higher quality internal information for decision-making, however, are less likely to depend
on these outside signals. Under the internal information hypothesis, the sensitivity of investment
to market valuations is decreasing in internal information quality.
3.3.
Competing hypotheses
Because we presume a tight link between internal and external information quality in order to
test the internal information hypothesis, we provide an alternative view to recent attempts to
understand the effect of information quality in reducing investment-related adverse selection and
moral hazard costs that arise with asymmetric information between insiders and outsiders. Under
the adverse selection hypothesis, an increase in information quality reduces the sensitivity of
investment to internal funds and increases the sensitivity of investment to investment opportunities.
The basic idea is the following: when external funds are costlier than internal funds, investment
will be constrained by internal funds. An improvement in external information quality reduces
adverse selection costs (such as the cost of debt), and reduces the reliance on internal funds for
14
project funding. Because firms can implement investment opportunities with fewer constraints, the
sensitivity to investment opportunities increases. 5 Predictions under the adverse selection
hypothesis run in the opposite direction of the internal information hypothesis.
Under the moral hazard hypothesis, managers respond opportunistically to market mispricing
and internal funding shocks that facilitate empire building. With internal funding shocks, the
manager would rather take on a negative NPV project that provides private benefits than distribute
excess funds to investors. High quality information provides the board and investors with timely
and precise information about these managerial decisions, reducing incentives for opportunistic
investment. Improved monitoring also reduces the manager’s ability to exploit mispricing in
capital markets. Under a moral hazard argument, improving information quality reduces the
sensitivity of investment to both internal funds and market valuations.6
Finally, our predictions are also related to econometric issues caused by measurement error in
market valuations. As the market’s information about the firm degrades, mispricing in the firm’s
securities potentially increases. In these situations, investment will have a weak correlation with
market valuations if managers believe that market valuations are incorrect. Therefore, as
measurement error in market valuations attenuates the coefficient on M/A, it can also bias
coefficients on other variables in the model and in any direction. Because operating earnings has
information about profit opportunities, prior evidence suggests that the coefficient on operating
earnings is biased upward when market valuations measure investment opportunities with error. If
high quality information reduces market mispricing, the coefficient on market-to-book should
5
We recognize the possibility that the impact of information quality could be priced in market valuations. However,
the evidence is mixed on whether financial reporting quality is a priced risk factor (Francis et al. 2005, Core et.al
2008). Prior studies have relied on the adverse selection channel to justify investment-q sensitivity as a proxy for
investment efficiency (e.g., Shroff et al. 2014).
6
Both the internal information hypothesis and the moral hazard hypothesis predict that increasing information
quality reduces the sensitivity of investment to market valuations. While recent evidence does not support a strong
role for mispricing-based investment, we include specific mispricing controls in a later section.
15
increase while the coefficient on operating earnings should decrease, opposite the predictions
under the internal information hypothesis.7 To maintain simplicity and flexibility in our primary
regression analyses we do not correct for this and so the effects we document are net of any related
measurement error. However, we return to an explicit consideration of measurement error—and
address its impact on inferences in prior research—in a later section.
3.4.
Research design
To test the impact of information quality on investment sensitivity to market and accounting
signals, we interact proxies for information quality with market-to-book and operating earnings.
Moreover, we include an interaction between information quality and cash holding as a control for
the impact of reporting quality on adverse selection costs and moral hazard problems that affect
investment sensitivity to internal funds. Specifically, we run the following regression:
𝐼𝑖𝑡
𝑀𝑖𝑡−1
𝐸𝐵𝐷𝑖𝑡
𝐶𝑎𝑠ℎ𝑖𝑡−1
𝑀𝑖𝑡−1
= 𝛿𝑗 + 𝜏𝑡 + 𝛼1
+ 𝛼2
+ 𝛼3
+ 𝛼4 𝐼𝑄 + 𝛼5
× 𝐼𝑄
𝐴𝑖𝑡−1
𝐴𝑖𝑡−1
𝐴𝑖𝑡−1
𝐴𝑖𝑡−1
𝐴𝑖𝑡−1
+ 𝛼6
𝐸𝐵𝐷𝑖𝑡
𝐶𝑎𝑠ℎ𝑖𝑡−1
× 𝐼𝑄 + 𝛼7
× 𝐼𝑄 + 𝛼8 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1 + 𝛼9 ln(𝐴𝑖𝑡−1 )
𝐴𝑖𝑡−1
𝐴𝑖𝑡−1
(2)
+ 𝑒𝑖𝑡
where IQ is a proxy for information quality. M/A serves as the proxy for the external market signal
and EBD serves as the proxy for the internal cash flow signal. 𝛿𝑗 are industry fixed effects and 𝜏𝑡
are year fixed effects. Fixed effects are included to control for information differences attributable
to differences in industry organization and practices and time trends. Standard errors are clustered
at the firm level. Empirical predictions under the alternative hypotheses are summarized below:
7
If improvements in transparency also reduce measurement error in EBD, this prediction is more difficult to sign.
16
Hypothesis
Internal information
Adverse selection
Moral hazard
Measurement error
3.5.
𝜶𝟓
–
+
–
+
Predicted sign on:
𝜶𝟔
+
–
–
–
𝜶𝟕
?
–
–
?
Sample and variable construction
We begin with a sample of 75,491 firm years drawn from the sample period 1988 through
2012. We require the firm to be publicly traded on NYSE, NASDAQ or AMEX and have sufficient
information on cash flows and accruals to calculate at least one of our information quality
measures. We exclude firms with SIC codes between 6000-6999 and 4900-4999. Firm-years with
asset growth exceeding 100% are deleted to avoid the effects of large M&A transactions and
seasoned equity offerings.
To measure internal information quality, we use four proxies originally inspired by research
on the role of financial reporting and disclosure in capital markets that also have a high degree of
correspondence to the properties of internal information. The first, AccrualPrecision is the
mapping between accruals and cash flows described in Dechow and Dichev (2002) and modified
by McNichols (2002). Because accrual choices reflect the manager’s estimates of cash flows,
accrual precision naturally captures the quality of the manager’s own information about future
cash flows (though still reflecting discretion in how they report that information).8 We construct
the measure following prior research, using a minimum of seven years of data in which the final
measure of accruals is in the year before the investment year. Specifically, we estimate the
following cross-sectional model with at least 20 observations in each industry-year using the
Fama-French 48-industry classification:
8
It can also capture the effect of business model shocks, reducing its power as a proxy for transparency (Owens, Wu
and Zimmerman 2013).
17
𝐶𝐴𝐶𝐶𝑡 = 𝛾0 + 𝛾1 𝐶𝐹𝑂𝑡−1 + 𝛾2 𝐶𝐹𝑂𝑡 + 𝛾3 𝐶𝐹𝑂𝑡+1 + 𝛾4 𝛥𝑅𝐸𝑉𝑡 + 𝛾5 𝑃𝑃𝐸𝑡 + 𝜈𝑡 ,
(3)
where 𝐶𝐴𝐶𝐶𝑡 is total current accruals in year t, 𝐶𝐹𝑂𝑡 is cash flow from operations in year t, 𝛥𝑅𝐸𝑉𝑡
is change in revenue from year t – 1 to year t, and 𝑃𝑃𝐸𝑡 is net property, plant and equipment in
year t. AccrualPrecision is the standard deviation of the firm’s annual residual over the five year
period multiplied by -1.
The second proxy, FilingSpeed, is the length of time it takes the firm to release its financial
statements once the fiscal period closes. It is estimated as the number of days between the end of
the year and the release of the annual report following Jennings, Stoumbos and Tanlu (2014) and
multiplied by -1. Because of the tight connection between information used internally and that
reported externally, managers with low quality internal information will need longer to prepare
external financial statements (Dorantes et al. 2013), delaying the auditor’s ability to provide an
opinion (Ashton, Willingham and Elliott 1987).
The third proxy, Agreement, is the dispersion in analyst forecasts measured after the release of
annual report in of the previous year, multiplied by -1. Managers can communicate with the market
in a variety of ways not reflected in our other proxies (such as conference calls). Managers with
higher quality information are more likely to use those venues to reduce disagreement among
analysts (Lang and Lundholm 1996). To reduce measurement error caused by outliers and to
improve comparability across the marginal effects, we rank AccrualPrecision, FilingSpeed and
Agreement into deciles and scale them to range between 0 and 1 prior to running the regressions,
with high valuations capturing high quality information.
The last proxy, Guidance, is a dummy variable equal to 1 if managers make at least one
quarterly or annual earnings forecast during the previous year. If providing forward-looking
information to the market increases managers’ exposure to litigation risk (Cutler, Davis and
18
Peterson 2013), managers with the highest quality information will be most likely to provide
guidance. Thus, as suggested by Goodman et al. (2014), earning guidance behavior is a plausible
instrument for internal information quality.
In Panel A of Table 1, we report descriptive statistics for the main variables in our model. In
Panel B, we report correlations. Our information quality measures are positively correlated with
each other, but no correlation is greater than 0.28, consistent with our intent to identify independent
constructs for information quality. Notably, our measures are correlated with firm size. Larger
firms have higher accrual precision, file annual reports faster, have more agreement among
analysts, and are more likely to provide guidance. To mitigate size-driven interpretations, we
control for size in the investment regressions.
4.
Results
Our main analysis of the relation between information quality and investment sensitivity is
reported in Table 2. The benchmark regression is reported in column (1) and the coefficients on
M/A, EBD and Cash are all positive and significant consistent with prior research. In columns (2)
through (5) we report the results from interacting each of our four information quality proxies with
our proxies for market valuations, operating earnings and cash holdings. Across all four
information quality measures, the interaction term between M/A and IQ is significantly negative.
The coefficient on the interaction between M/A and AccrualPrecision of -0.026 (t-stat = -8.38) in
column (2) implies that increasing AccrualPrecision by one decile reduces the sensitivity of
investment to market valuations by 0.003 (0.026 / 9). These effects implies that investmentmarket-to-book sensitivities fall from 0.029 for firms with the lowest accrual precision to near zero
(0.003) for firms with the highest accrual precision. Estimates based on FilingSpeed and
Agreement in columns (3) and (4) yield similar inferences. At firms that issue guidance, the
19
sensitivity of investment to market-to-book is 68% lower than at firms that do not issue guidance
(-0.015 / 0.022, t-stat = -9.76). Across all measures, investment becomes significantly less sensitive
to market valuations as information quality increases. These results support the internal
information hypothesis which predicts that as internal information quality improves, managers
place less weight on external market signals.
Turning to the impact of information quality on investment sensitivity to internal profit signals,
the interactions between EBD and the information quality proxies are consistently positive and
both statistically and economically significant. In column (3), for example, the coefficient on the
interaction between EBD and FilingSpeed of 0.222 (t-stat = 10.37) implies that a one-decile
decrease in the time to file the financial reports (roughly one week), leads to a 0.025 (0.222 / 9)
stronger sensitivity of investment to internal accounting signals. The effects of improvements in
AccrualPrecision and Agreement are similar at 0.025 (0.228 / 9) and 0.030 (0.270 / 9). When
benchmarked to managers with the lowest information quality, managers with the highest
information quality are three to six times more responsive to internal accounting signals when
making investment decisions. For managerial guidance, firms that issue guidance are more than
twice as sensitive to earnings as firms that do not ((0.123 + 0.100) /0.100). Similar to the results
on market valuations, these results support the hypothesis that managers with higher quality
internal information are significantly more sensitive to information about expected profits.
Prior research provides an alternative set of predictions based on adverse selection and moral
hazard when information quality reflects asymmetric information between insiders and outsiders.
The results we provide on the interaction between IQ and EBD are inconsistent with these
alternative hypotheses. However, the negative coefficient on the interaction between IQ and M/A
is consistent with better information quality reducing incentives to exploit mispricing. We deal
20
with that result in the next section. For now, we turn to the interaction between IQ and Cash which
is our test for the asymmetric information explanations documented by Biddle et al. (2009) and
others. Consistent with those studies, we find robust evidence that investment becomes less
sensitive to cash holdings—the proxy for internal funds—as information quality improves. This
finding is consistent with the interpretation that firms with higher information quality face fewer
financing constraints and are less likely to have managers overinvest excess cash holdings.
To illustrate how investment responses vary with information quality across the various
proxies, we sort firm-years into the top and bottom 40% of AccrualPrecision, FilingSpeed and
Agreement. For Guidance we compare the groups that provide guidance to those that do not. We
then estimate equation (2) for each group and depict the coefficients on M/A, EBD and Cash.
Figure 1 shows how investment sensitivities shift as information quality increases. Across all four
measures, the coefficient on M/A decreases with information quality and the coefficient on EBD
increases with transparency, supporting the internal information hypothesis. Consistent with prior
findings that better financial reporting quality decreases the sensitivity of investment to internal
funds, the coefficient on cash holding generally decreases in information quality.
5.
Additional Analysis
5.1.
Mispricing
The response to market-to-book we document can arguably be driven by managerial reactions
to stock valuations if managers have incentives to exploit mispricing. Keynes (1936) suggests that
there is irrationality in stock prices which affects the pattern of equity financing and firms’
investment behavior. 9 However, evidence on this point is mixed. Gilchrist, Himmelberg and
Huberman (2005) and Polk and Sapienza (2009) find that investment is correlated with mispricing
The “irrational investors approach” in behavioral finance assumes that stock market inefficiencies encourage rational
managers to respond to mispricing. See Baker, Ruback and Wurgler (2007) for a complete discussion.
9
21
proxies. However, Bakke and Whited (2010) find that firms mostly likely to face overvaluation
ignore the mispricing when making investment decisions.
Despite this, we admit the mispricing explanation given findings in recent research that
misreporting firms and their peers invest significantly more during the misreporting period. Kedia
and Philippon (2009) argue that these firms have incentives to invest and hire to support market
expectations of strong investment opportunities. McNichols and Stubben (2008) predict that the
managers in those same firms invest more because they have optimistic expectations of cash flows
and discount rates based on the reported information. Beatty, Liao and Yu (2013) find that the
peers of fraud firms also invest heavily. Polk and Sapienza (2009) propose a catering story where
investment responds to both over- and under-pricing.
To control for the potential impact of mispricing on the investment responses to market and
accounting signals conditional on information quality, we include measures of mispricing and the
interactions between mispricing and the variables of interest in the regressions.
Our two
mispricing proxies are constructed based on information revealed after market-to-book is
measured, but presumably known by the manager when market-to-book is observed. The first
proxy is CAR, the buy-and-hold abnormal return over fiscal year t using the Fama and French
three-factor model. Large negative (positive) abnormal returns during the year investment is
measured imply that market valuations at the start of the year were too high (low). The second,
Surprise, is realized earnings per share for year t – 1 less the analyst consensus forecast for year t
– 1 measured during the last month of year t – 1, scaled by price at the beginning of year t. For
both measures, we take the absolute values so that a higher value indicates more mispricing in
either direction. We also rank these two measures into deciles when including them in the
regressions.
22
Measured by |CAR| (Table 3, Panel A), mispricing has a positive but inconsistently significant
impact on the sensitivity of investment to market valuations: the interaction between M/A and
mispricing is 0.003 with a t-stat of 1.14 (column 1). In Panel B of Table 3, the interaction is positive
and significant under |Surprise|. There is also some limited evidence that mispricing reduces the
sensitivity of investment to accounting signals: the interaction between EBD and mispricing is
significantly negative under |Surprise|, while negative but sometimes insignificant under |CAR|.
The interaction between Cash and mispricing is consistently positive and significant, suggesting
the availability of internal funds limits incentives to exploit mispricing. After controlling for
mispricing-based investment, all of our variables of interest still maintain the predicted signs.
5.2
Firm complexity
We consider the robustness of our results to a potentially important determinant of internal
information quality: organizational complexity. As Fama and Jensen (1983) argue, complex
organizations are characterized by information that is diffused among many agents within a firm.
Agents with specific knowledge about investment opportunities have incentives to distort internal
information in the competition for resources. Shareholders benefit from investments in internal
information systems used to support efficient decision making within the firm and reduce agency
conflicts among agents. For example, suppose complexity is measured by the dispersion in
business lines or geographic locations. In these firms, frictions in the information gathering and
aggregation process produce information that is of lower quality relative to a single industry or
single country firm (Bushman et al. 2004). But such firms also benefit from greater investments in
internal information to control agency problems and exploit synergies. For example, firms with
transfer pricing opportunities that arise from diversity in business line and geographic operations
23
are better able to identify and support tax avoidance plans when internal information is higher
quality (Gallemore and Labro 2014).
While we make no directional prediction on the net relation between information quality and
complexity, a correlation between information quality and complexity will affect inferences if
complexity also has implications for the operation of internal capital markets and thus investment
sensitivities. Resource reallocation in diversified firms could cause investment to behave
differently from standalone firms. For example, internal capital markets allow firms to allocate
firm resources to divisions with the best investment opportunities, causing investment for these
firms to become less sensitive to internal funding considerations versus a single industry firm (Shin
and Stulz 1998).
We use the industry and geographic diversification measures from Bushman et al. (2004) as
our proxies for complexity and rank them in deciles to lie between 0 (low diversity) and 1 (high
diversity). In Panel A of Table 4, we provide the correlation between information quality and
complexity conditional on firm size. The results are mixed across different information quality
measures. For example, AccrualPrecision is negatively correlated with geographic diversification
(ComplexGeo) but positively correlated with industry diversification (ComplexInd), while we
observe the opposite for FilingSpeed.
In Panel B of Table 4, we examine the robustness of our findings to controlling for industry
and geographic diversity and their interactions with market values, operating profits and cash
holdings. Investment at more complex firms tends to be less responsive to market valuation and
more responsive to operating profits. In Column 1, the interactions between M/A and the
complexity measures are significantly negative (-0.014 for ComplexGeo and -0.006 for
ComplexInd), and the interactions between EBD and the complexity measures are significantly
24
positive (0.099 for ComplexGeo and 0.059 for ComplexInd). For more complex firms, market
valuation reflects the market opinion on the overall firm but is not a good indicator for true
investment opportunities for individual segments. These segments are more likely to rely on their
own profit forecasts when making investment decisions. We find some limited evidence that
investment in more diversified firms is less sensitive to cash holdings.
Importantly, our primary findings are intact. Controlling for variation in complexity and its
association with investment sensitivities, firms with higher quality information are more sensitive
to accounting signals and less sensitive to market signals, consistent with the internal information
hypothesis. Interestingly, the interaction between Cash and IQ becomes insignificant when IQ is
measures as FilingSpeed or Guidance, suggesting that firm complexity may explain some of the
prior results for the adverse selection and moral hazard hypotheses.
5.3.
Leasing
Prior work has shown that reporting quality is associated with the lease versus buy decision.
Beatty et al. (2010b) find that firms with lower financial reporting quality are more likely to lease
their assets, suggesting a substitution effect between acquired assets and leased assets. To address
the potential confounding impact of lease on the relation between information quality and
investment responses to M/A and EBD, we add operating leases to our measure of investment.10
We define lease investments in year t as the capitalized change in future lease obligations between
t – 1 and t (specific definition provided in the Appendix). The results, summarized in Table 5, are
very close to our main regression results.
10
We also add rental expense back to our measure of cash flow.
25
5.4.
Co-movement between fixed and working capital investment
Bushman et al. (2012) show that investment-cash flow sensitivity can reflect the co-movement
between capital and working capital investment. The intuition is that an expansion in fixed assets
is naturally accompanied by a build-up in working capital. However, the co-movement hypothesis
does not invalidate our argument about accounting signals in investment opportunities since
accruals also contain information about profitability. To investigate this possibility, we decompose
cash flow into cash flow from operations and working capital accruals. In column (1) of Table 6
we report the results of a benchmark regression. Similar to Bushman et al. (2012) we also find a
positive association between capital investment and working capital accruals.11 In columns (2)
through (5) we interact both cash flows and accruals with information quality proxies and find that
the coefficients on the interaction between information quality and both working capital accruals
and cash flows is positive and significant. In other words, managers respond to expected profits
regardless of whether such forecasts result in cash flows before the end of the period.
6.
Measurement Error Correction
Erickson and Whited (2000, 2012) show that significant measurement error exists in market-
based proxies for marginal q. Our primary concern with measurement error in market values is its
potential correlation with the construct of interest: information quality. For example, if there are
proprietary costs to disclosing information about the profitability of investments, the manager has
incentives to protect the rents generated by current and future investments and thus obscure the
information communicated to the market. If the information in the financial reports is relevant for
valuation, firms with the least informative financial reports (highest proprietary cost of disclosure)
11
Unlike Bushman et al., we also find that investment is sensitive to cash flows from operations. The differences
between our results and Bushman et al. (2012) can be explained by sample selection and investment definition.
Bushman et al. focus on manufacturing firms and define investment as capital expenditures divided by beginning
PPE.
26
will have the most measurement error in market valuations and hence the greatest attenuation bias,
resulting in a lower estimated sensitivity of investment to market-to-book. Thus, proprietary costs
of disclosure (or any other driver of discretion in reducing external information quality) could
induce a positive correlation between information quality and the sensitivity of investment to
market-to-book that does not exist. Measurement error in the proxy for investment opportunities
could also bias the coefficient on cash flow and other regression variables in any direction.
To overcome this important issue, we employ a technique developed in Erickson et al. (2014)
that uses higher-order cumulants from the distribution of market-to-book to correct for the effect
of variation in market values that managers ignore. This approach allows us to focus on the
variation in market-to-book that matters for investment. Since this technique does not facilitate the
use of interaction terms between M/A and other regression variables, we sort firms into the top and
bottom 40% of each measure of information quality. First, we provide a direct estimate of the
amount of measurement error in M/A in Panel A of Table 7 across these groups. 𝜏 2 is an index that
is increasing in measurement quality and measures the amount of variation in market-to-book
managers deem relevant for investment. We find that the information in market prices (relevant
for investment) does not increase when information quality is high. Instead, 𝜏 2 is significantly
higher in the low information quality group measured by AccrualPrecision and Agreement.
We then estimate equation (2) within each group. The results are reported in Panel B of Table
7. Coefficients are reported with standard errors in brackets. Relative to standard OLS, the
coefficient estimates on M/A are substantially larger once we correct for the bias induced by
measurement error in the market’s valuation of the firm, while the coefficients on EBD are similar
and the coefficients on Cash much smaller. Despite these differences in magnitudes, the directional
impact of improvements in information quality is the same as under OLS. The significance levels
27
reported next to the coefficients in the high information quality partition are a test of the difference
across the two groups. They nearly uniformly indicate that higher information quality significantly
reduces the sensitivity of investment to market valuations, increases the sensitivity to earnings and
decreases the sensitivity to cash holdings.
7.
Conclusion
The question of whether and how information quality affects firms’ investment decisions is a
topic of growing interest and importance in accounting and financial economics. The bulk of the
literature to date focuses on the role of financial reporting quality in mitigating asymmetric
information problems or on the impact of industry or economy level transparency. In doing so,
these studies generate predictions for how external information quality affects the relation between
investment and its determinants such as investment opportunities and internal funds.
In this paper, we propose an alternative view. We turn the focus from external to internal
information and ask how variation in the quality of internal information for decision making relates
to the firm’s investment responses to external market and internal accounting signals. Across four
unique measures of information quality, we find consistent evidence that investment by firms with
higher quality internal information is less sensitive to the market’s valuation of investment
opportunities and more sensitive to the internal profit signal captured by earnings.
While we assume a tight link between internal and external accounting quality to support our
use of reporting-based proxies to capture internal information, our empirical design either rules
out or tests separately several alternative explanations based on information asymmetry between
insiders and outsiders. Our main findings about the conditional effect of information quality on
investment sensitivities to market-to-book and operating earnings are inconsistent with predictions
derived under adverse selection and moral hazard hypotheses. However, our findings relating to
28
cash holdings do support these hypotheses. In supplemental analysis, we account for the influential
role of complexity on both information quality and internal capital markets and find that our results
are robust.
Results supporting the internal information hypothesis can also be regarded as an outcome of
efficient investment in internal information, where the incentive to invest in high quality
information depends on the potential value of that information. Firms that benefit most from
generating high quality decision-relevant internal forecasts of profitability should be the same
firms where investment decisions are inherently more sensitive to that information. Thus, finding
that high internal information quality firms have higher investment sensitivity to operating profit
is entirely consistent with optimal investment in information quality. The endogenous nature of
internal information quality implies that our framework cannot be used to argue that firms can
simply improve value by investing in internal information. Rather, it provides a novel view on the
role of information quality in shaping the relation between investment and its determinants.
Moreover, it suggests that exogenous shocks to external reporting demands can shape decision
making within the firm insofar as internal information systems must adapt to meet those demands.
Finally, we are also the first in accounting to explicitly address measurement error problems
in the proxy for investment opportunities. Measurement error has been shown to cause spurious
inferences about financing constraints in earlier investment research, yet the implications for
accounting research have not yet been documented. Though not obvious ex ante, our results (as
well as earlier results using cash holdings to proxy for internal funds as in Biddle et al. 2009) are
robust to this correction. Taken together, our findings document a logical association between
internal information quality and investment decisions that shape how the manager relies on market
and accounting signals.
29
Appendix: Variable definitions
Variable name
Investment
M/A
EBD
Cash
WCACC
CFO
Leverage
Size
Construction
Capital investment (COMPUSTAT item CAPX) plus research and
development expense (XRD) divided by beginning total assets (AT)
Market to book ratio, calculated as (AT+CSHO*PRCC_F-CEQ-TXDB)/AT
Cash flow from operations plus research and development expense divided by
beginning total assets: (IB+DP+XRD)/AT
Cash holdings, including cash and cash equivalents, divided by total assets.
Calculated as CHE/AT.
Working capital accruals, the difference between change in non-cash current
assets and change in current liabilities, calculated as (ΔACT-ΔCHE)-( ΔLCTΔDLC-ΔTXP) divided by beginning total assets
Cash flow from operations, calculated as EBD-WCACC
The ratio of long-term debt to the sum of long-term debt and the market value
of equity: DLTT/(DLTT+CSHO*PRCC_F).
Log of total assets (AT)
AccrualPrecision The ranked inverse of the Dechow and Dichev accrual quality measure
including the McNichols adjustment. Specifically, it is the standard deviation
of the residuals from year t-5 to year t-1 from equation (3).
FilingSpeed
The ranked inverse of the number of days between fiscal year end and filing
date, averaged over the past three.
Agreement
The ranked inverse of analyst forecast dispersion, measured right after the
release of financial statements in year t-1.
Guidance
A dummy variable taking value 1 if the firm makes at least one quarterly or
annual management forecast of EPS in year t-1.
CAR
Cumulative abnormal returns from the Fama-French three-factor model over
fiscal year of investment. Factor loadings are obtained by regressing daily
returns on market, SMB, and HML in the prior fiscal year. Require at least
100 observations to run the regression.
Surprise
Analyst forecast error. Actual EPS minus analyst forecast scaled by beginning
price, where analyst forecast is the last consensus forecast before year end.
Lease
New operating lease incurred, calculated as the change in capitalized operating
lease in year t. We assume a 10% discount rate, and discount back the lease
obligations in year t+1 though t+5 (MRC1 through MRC5). For lease
obligations after that (MRCTA), we assume that lease payment in the year t+5
(MRC5) continues at that amount and discount it back to year t.
ComplexGeo
Revenue-based Hirfindahl-Hirschman index for geographic segments,
calculated as the sum of the squares of each segment’s sales as a percentage
of the total firm sales (multiplied by -1).
ComplexInd
Revenue-based Hirfindahl-Hirschman index for industry segments, calculated
as the sum of the squares of each segment’s sales as a percentage of the total
firm sales (multiplied by -1).
30
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35
Figure 1a
OLS Investment sensitivity to market-to-book as a function of information quality
Investment sensitivity to market-to-book
0.040
0.035
0.030
0.025
0.020
0.015
0.010
0.005
0.000
Low IQ
AccrualPrecision
High IQ
FilingSpeed
Agreement
Guidance
Figure 1b
OLS Investment sensitivity to operating profits as a function of information quality
Investment sensitivity to operating profits
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
Low IQ
AccrualPrecision
High IQ
FilingSpeed
Agreement
Guidance
36
Figure 1c
OLS Investment sensitivity to cash holdings as a function of information quality
Investment sensitivity to cash holding
0.18
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
Low IQ
AccrualPrecision
High IQ
FilingSpeed
Agreement
Guidance
Figures 1a-1c depict the investment responses to market valuations (market-to-book), operating earnings (earnings
before depreciation, the usual proxy for cash flow), and cash holdings. We sort firms into low and high information
quality (IQ) groups using the top and bottom two quintiles of each of the four information quality measures (except
for Guidance). We then estimate equation (2) within each group and plot the coefficient estimates 𝛼1 in Fig. 1a, 𝛼2
in Fig. 1b, and 𝛼3 in Fig. 1c.:
𝐼𝑖𝑡
𝑀𝑖𝑡−1
𝐸𝐵𝐷𝑖𝑡
𝐶𝑎𝑠ℎ𝑖𝑡−1
= 𝛼0 + 𝛼1
+ 𝛼2
+ 𝛼3
+ 𝛼4 𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1 + 𝛼5 ln(𝐴𝑖𝑡−1 ) + 𝑒𝑖𝑡
𝐴𝑖𝑡−1
𝐴𝑖𝑡−1
𝐴𝑖𝑡−1
𝐴𝑖𝑡−1
37
Table 1
Descriptive statistics
Panel A: Distribution statistics
N
Mean
Std. Dev.
25%
50%
75%
Investment
75,491
0.123
0.123
0.041
0.086
0.160
EBD
75,491
0.111
0.158
0.052
0.114
0.186
M/A
75,491
2.002
1.614
1.099
1.482
2.261
Cash
75,491
0.192
0.220
0.028
0.101
0.280
Leverage
75,491
0.174
0.212
0.002
0.091
0.278
Size
75,491
5.707
2.083
4.194
5.588
7.114
AccrualPrecision
41,595
-0.047
0.036
-0.060
-0.037
-0.023
FilingSpeed
68,657
-60.611
60.139
-65.000
-47.000
-34.000
Agreement
45,239
-0.012
0.039
-0.009
-0.003
-0.001
Guidance
51,058
0.302
0.459
0.000
0.000
1.000
CAR
65,957
0.099
0.542
-0.204
0.063
0.350
Surprise
46,289
-0.012
0.108
-0.003
0.000
0.002
Panel B: Pearson (Spearman) correlations
(1)
(2)
(3)
(4)
(1)Investment
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
1
0.15
0.36
0.37
-0.25
-0.20
-0.16
0.01
-0.08
-0.10
0.02
0.04
(2)EBD
0.37
1
0.10
-0.05
-0.17
0.14
0.11
0.15
0.21
0.16
0.10
(3)M/A
0.38
0.36
1
0.39
-0.35
-0.13
-0.15
0.02
0.08
0.08
0.01
-0.14
0.06
(4)Cash
0.30
0.09
0.36
1
-0.42
-0.30
-0.22
0.01
-0.10
-0.05
-0.02
0.03
(5)Leverage
-0.29
-0.25
-0.48
-0.57
1
0.25
0.10
-0.05
-0.17
-0.03
0.07
-0.11
(6)Size
-0.15
0.09
-0.05
-0.26
0.34
1
0.39
0.25
0.09
0.24
-0.15
0.07
(7)AccrualPrecision -0.05
(8)FilingSpeed
0.13
0.08
-0.05
0.15
0.42
1
0.14
0.10
0.11
-0.07
0.06
0.26
0.16
-0.19
0.00
-0.04
0.42
0.24
1
0.07
0.19
-0.09
0.07
(9)Agreement
-0.02
0.35
0.37
-0.06
0.10
0.14
0.21
1
0.09
-0.09
0.29
(10)Guidance
-0.06
0.00
0.08
0.06
0.26
0.10
0.28
0.17
1
-0.05
0.04
0.18
-0.16
-0.03
0.00
-0.19
0.00
0.03
-0.13
-0.06
-0.09
0.03
-0.04
1
-0.11
(11)CAR
(12)Surprise
0.05 0.16 0.10 0.09 -0.07 0.09 0.04 0.12 0.10 0.06 0.04
1
This table reports the summary statistics for the main sample of firm-years from 1988-2012. Investment is capital
investment plus R&D expense divided by beginning total assets. EBD is earnings before depreciation plus
depreciation expense plus R&D expense divided by beginning total assets. M/A is the ratio of market value of
assets to book value of assets. Cash is cash and cash equivalents divided by total assets. Leverage is long-term
debt divided by the sum of long-term debt and market value of equity. Size is the log of book value of total assets.
AccrualPrecison is the Dechow and Dichev accrual quality measure with the McNichols adjustment. FilingSpeed
is the time it takes the firm to file its financial statements, multiplied by -1. Agreement is analyst forecast
dispersion multiplied by -1. Guidance is a dummy variable equal to 1 if the firm issues at least one quarterly or
annual management EPS forecast. Surprise is the annual earnings surprise. CAR is the annual cumulative
abnormal return. In the correlation table, numbers in bold are significant at the 1% level.
38
Table 2
The Impact of Information Quality on Investment Responses to Market and Accounting Signals
Dependent variable = Investment
Accrual
Precision
FilingSpeed
Agreement
IQ =
(1)
M/A
(2)
0.018***
(20.39)
EBD
0.120
(16.27)
***
(14.82)
Cash
(3)
0.029***
0.139***
(22.08)
IQ
0.071
M/A*IQ
(15.79)
***
(4.95)
(15.00)
(8.43)
-0.026***
-0.023***
(-9.09)
***
(7.99)
Size
Obs.
(-3.44)
**
-0.006
0.003
(-1.24)
(1.32)
-0.032***
0.270
*
-0.191***
(8.64)
-0.047***
(-4.79)
0.008
-0.002
(-0.54)
(1.99)
(1.79)
(1.53)
-0.006***
-0.007***
-0.007***
(-14.40)
0.123***
(-12.85)
(-1.24)
(-10.73)
-0.015***
(-9.76)
***
0.009
-0.007***
0.007
(18.55)
(11.07)
-0.061***
0.154***
(18.79)
-0.005
(-14.83)
Industry & Year FE
0.222
0.100***
(10.20)
0.190***
(-13.70)
***
(10.37)
-0.127***
(-6.26)
Leverage
***
(7.53)
0.228
Cash*IQ
0.040
0.138
0.022***
(18.46)
***
(10.70)
0.171***
(13.28)
***
(-8.38)
EBD*IQ
0.048
(5)
0.035***
(18.95)
***
(3.78)
0.181***
0.039
(4)
0.028***
Guidance
(-13.69)
-0.006***
(-11.54)
Yes
Yes
Yes
Yes
Yes
75,491
41,595
68,657
45,239
51,058
22.44%
26.05%
24.94%
32.11%
25.42%
𝑅2
This table presents estimates from panel regressions of firm-year investment on market-to-book, operating
earnings, cash holdings, information quality measures and the interactions, leverage and size. All variables are
defined in the Appendix. To facilitate interpretation, all information quality proxies are scaled between 0 and
1. All regressions include industry and year fixed effects. Standard errors are clustered at the firm level. Tstatistics are presented in parentheses below the coefficients. ***, **, and * denote significance at the 1%, 5%,
and 10% levels, respectively.
39
Table 3
Is the Information Quality Effect Driven by Mispricing?
Panel A: Mispricing = |CAR| in year of Investment
Dependent variable = Investment
IQ =
M/A
AccrualPrecision
FilingSpeed
Agreement
Guidance
(1)
(2)
(3)
(4)
0.027
***
(11.19)
EBD
(12.98)
0.109***
(4.95)
Cash
0.131
M/A*IQ
0.013
Cash*IQ
EBD*|CAR|
(4.09)
(4.40)
-0.025***
-0.033***
-0.016***
0.216
(-9.59)
***
Industry & Year FE
Obs.
𝑅
2
0.244
(-10.07)
***
0.123***
(9.96)
(9.65)
(8.46)
-0.105***
-0.066***
-0.193***
-0.045***
(-3.69)
(-12.47)
0.003
0.001
0.004
(1.14)
(0.60)
(2.08)
-0.03
-0.03*
-0.04*
(-1.94)
(-1.70)
0.07
***
-0.005
0.09
***
(8.62)
0.007*
0.006
(1.25)
Size
0.230
(-13.89)
***
(7.17)
(5.52)
Leverage
0.015***
(4.04)
(-1.35)
Cash*|CAR|
0.015
(1.98)
***
-0.026***
(-5.08)
M/A*|CAR|
0.005**
(3.65)
(-8.19)
EBD*IQ
0.012
0.105***
(10.01)
0.002
(0.47)
***
0.136***
(7.88)
***
(14.19)
0.049***
(9.68)
***
0.174
0.023***
(12.56)
0.183***
(9.30)
***
(8.36)
0.042***
(8.01)
|CAR|
0.122
0.034
***
(14.32)
0.074***
(3.92)
***
(9.04)
IQ
0.029
***
(1.86)
***
-0.007
0.03
(-4.65)
**
(-0.29)
-0.04**
(-2.34)
***
0.09***
(2.96)
(7.85)
0.006
-0.001
(1.10)
***
-0.001
-0.007
(-0.15)
***
-0.006***
(-8.68)
-(12.76)
(-10.83)
(-9.53)
Yes
Yes
Yes
Yes
60,608
40,125
46,985
26.43%
33.36%
26.94%
36,507
26.81%
40
Table 3 (cont’d)
Panel B: Mispricing = |Surprise| in year t – 1 earnings
Dependent variable = Investment
IQ =
M/A
AccrualPrecision
FilingSpeed
Agreement
Guidance
(1)
(2)
(3)
(4)
0.008
***
(3.38)
EBD
0.302
(3.94)
***
(10.17)
Cash
|Surprise|
0.029
(3.47)
-0.026***
-0.035***
-0.015
(-4.05)
0.125***
(3.77)
Cash*IQ
-0.088
-0.20
(-5.79)
Cash*|Surprise|
(-5.53)
0.14***
(7.69)
Leverage
Size
0.024
Obs.
0.023
-0.11
0.013
-0.18***
(-7.12)
0.10***
(6.28)
***
0.033***
(13.01)
***
(-3.84)
0.14***
(10.37)
***
0.14***
(9.33)
**
0.017***
(4.39)
(4.96)
(2.45)
(3.43)
-0.006***
-0.007***
-0.007***
-0.007***
(-8.71)
Industry & Year FE
-0.14
-0.040***
(-4.30)
0.012***
(3.84)
***
0.057***
(3.81)
***
(-9.23)
0.031***
(11.72)
***
-0.156
-0.007***
(-4.58)
0.253***
(8.65)
***
(-3.09)
0.031***
(9.23)
EBD*|Surprise|
-0.058
-0.029
-0.039***
(-8.57)
***
(-9.31)
0.190***
(6.65)
***
(-4.11)
M/A*|Surprise|
-0.011
-0.008***
(-3.04)
-0.027***
(-5.40)
***
0.070***
(5.98)
***
(-3.44)
(-7.98)
***
-0.019
0.298***
(14.45)
0.117***
(7.69)
***
(5.11)
(-4.49)
EBD*IQ
0.019
0.225
0.005***
(3.25)
***
(8.83)
0.084***
(5.01)
***
(-5.06)
M/A*IQ
0.203
0.030
***
(9.38)
***
(7.28)
0.088***
(5.28)
IQ
0.010
***
Yes
28,858
(-13.15)
(-12.36)
(-11.53)
Yes
Yes
Yes
46,289
40,845
34,818
29.79%
29.84%
33.73%
31.27%
𝑅2
This table presents estimates from panel regressions of firm-year investment on market-to-book, operating
earnings, cash holdings, information quality measures and the interactions, mispricing measures and the
interactions, leverage and size. All variables are defined in the Appendix. To facilitate interpretation, all
information quality proxies and mispricing proxies are scaled between 0 and 1. All regressions include
industry and year fixed effects. Standard errors are clustered at the firm level. T-statistics are presented in
parentheses below the coefficients. ***, **, and * denote significance at the 1%, 5%, and 10% levels,
respectively.
41
Table 4
Is the Information Quality Effect Driven by Organizational Complexity?
Panel A: Conditional correlation between IQ and complexity
Dependent variable = IQ
IQ =
Intercept
AccrualPrecision
FilingSpeed
Agreement
Guidance
(1)
(2)
(3)
(4)
-0.091
***
(-30.88)
ComplexGeo
-0.009
(-13.15)
***
(-6.13)
ComplexInd
0.003
3.918
-12.013
(34.16)
0.020
0.017
(-7.69)
0.213***
(6.11)
***
(3.31)
7.063***
-1.365***
*
(1.81)
***
(-7.34)
0.007***
-0.036
*
(-1.95)
*
(1.67)
***
(4.29)
Size
-107.002
***
-0.107
(-1.56)
0.006*
(7.92)
(1.81)
0.138***
(27.93)
Obs.
33,533
56,583
37,587
40,674
𝑅2
14.47%
7.46%
0.51%
4.69%
Panel B: Investment responses to market and accounting signals, controlling for complexity
Dependent variable = Investment
IQ =
M/A
AccrualPrecision
FilingSpeed
Agreement
Guidance
(1)
(2)
(3)
(4)
0.031
***
(9.98)
EBD
0.043
(13.31)
*
(1.74)
Cash
0.137***
(6.93)
IQ
ComplexGeo
-0.022
(-3.74)
-0.017***
0.229
0.021
(1.32)
***
0.099***
-0.007
-0.024
(2.84)
***
0.226
(-2.39)
0.055**
-0.011***
(-6.78)
***
0.063***
(9.64)
(4.28)
-0.140***
-0.015
(-9.65)
**
-0.024***
(-4.87)
-0.030***
(-14.20)
***
(10.64)
(-3.65)
EBD* ComplexGeo
0.239
0.017***
(-4.30)
-0.018***
(-7.77)
***
-0.080***
-0.014
-0.020
0.001
(0.35)
-0.008
(-1.34)
***
0.152***
(10.10)
***
(-11.80)
0.018***
0.060***
(3.12)
0.191***
-0.003
0.027***
(11.37)
***
(12.12)
**
(3.39)
***
(-4.12)
M/A* ComplexGeo
(5.53)
0.154***
(2.16)
(7.07)
Cash*IQ
(-0.16)
(0.35)
(-5.45)
EBD*IQ
0.123
0.000
(-3.99)
M/A*IQ
-0.003
0.016
0.019***
0.034
***
(13.62)
(8.63)
(2.91)
ComplexInd
0.033
***
-0.000
(-0.26)
0.101***
(-1.63)
-0.010***
(-3.18)
0.110***
42
Cash* ComplexGeo
(3.02)
(2.08)
(3.37)
(4.04)
0.009
-0.031*
-0.041**
-0.028
(0.40)
M/A* ComplexInd
-0.006
(-1.72)
*
(-1.92)
EBD* ComplexInd
(-3.74)
0.059**
(2.00)
Cash* ComplexInd
-0.069
Industry & Year FE
Obs.
-0.003
(-1.71)
-0.005
(-2.49)
0.095***
0.020
-0.089
(3.73)
***
(-4.80)
-0.006*
***
-0.007**
-0.004
(0.65)
***
(-6.39)
0.002
(0.37)
Size
-0.127
(-1.52)
(-1.42)
0.115***
(4.01)
***
(-3.45)
Leverage
-0.012
(-2.28)
***
(-5.39)
-0.010**
-0.001
(-0.15)
***
-0.005
-0.095***
(-2.25)
***
-0.005***
(-5.45)
(-8.43)
(-7.29)
(-7.85)
Yes
Yes
Yes
Yes
56,583
37,587
40,674
33,533
2
25.78%
25.35%
31.49%
26.07%
𝑅
Panel A presents Fama-Macbeth regressions of information quality measures on our two proxies for
organizational complexity. ComplexGeo and ComplexInd are the geographic and industry diversification
measures from Bushman et al. (2004), ranked in deciles to lie between 0 (low diversity) and 1 (high
diversity). For AccrualPrecision, FilingSpeed , and Agreement, we do yearly OLS regressions and report the
average coefficient estimates. For Guidance, we do yearly probit regressions and report the average
coefficient estimates. Panel B presents estimates from panel regressions of firm-year investment on marketto-book, operating earnings, cash holdings, information quality measures and the interactions, complexity
measures and the interactions, leverage and size. All remaining variables are defined in the Appendix. To
facilitate interpretation, all information quality proxies and complexity proxies are scaled between 0 and 1.
All regressions include industry and year fixed effects. Standard errors are clustered at the firm level. Tstatistics are presented in parentheses below the coefficients. ***, **, and * denote significance at the 1%, 5%,
and 10% levels, respectively.
43
Table 5
Does Investment Sensitivity Change when Operating Leases are Included?
Dependent variable = Investment + Lease
Accrual
Precision
FilingSpeed
Agreement
IQ =
(1)
M/A
0.018
(2)
***
(20.41)
EBD
0.114
(16.16)
***
(15.08)
Cash
0.140
0.028
(3)
***
0.077
(15.86)
***
(5.62)
***
(22.16)
IQ
0.182
M/A*IQ
(7.39)
-0.024***
-0.023***
-0.129
Obs.
(-9.50)
***
(10.69)
***
(-3.40)
-0.180
-0.014***
0.103***
(7.81)
***
(-12.07)
-0.045***
(-4.63)
-0.005
0.005
0.006
0.005
-0.003
(-1.46)
(1.14)
(1.46)
(0.95)
(-0.75)
-0.006***
-0.007***
-0.007***
-0.007***
(-14.47)
Industry & Year FE
-0.061
-0.032***
0.241
0.000
(0.16)
(-13.81)
***
(10.31)
***
(-6.33)
Size
0.210
-0.011
0.155***
(18.61)
**
(-2.07)
(-9.05)
***
(6.15)
Leverage
0.036
0.188
0.096***
(10.42)
***
(18.39)
***
(7.41)
0.162
Cash*IQ
0.171
0.124
0.022***
(18.49)
***
(9.97)
***
(13.27)
***
(-7.91)
EBD*IQ
0.044
0.036
(5)
***
(19.19)
***
(3.66)
***
(15.03)
0.040
0.028
(4)
***
Guidance
(-9.90)
(-13.84)
(-11.81)
-0.006***
(-10.97)
Yes
Yes
Yes
Yes
Yes
75,491
41,595
68,657
45,239
51,058
2
22.56%
25.58%
24.98%
31.59%
25.47%
𝑅
This table presents estimates from panel regressions of firm-year investment (plus capitalized operating lease)
on market-to-book, operating earnings, cash holdings, information quality measures and the interactions,
leverage and size. All variables are defined in the Appendix. To facilitate interpretation, all information quality
proxies are scaled between 0 and 1. All regressions include industry and year fixed effects. Standard errors are
clustered at the firm level. T-statistics are presented in parentheses below the coefficients. ***, **, and * denote
significance at the 1%, 5%, and 10% levels, respectively.
44
Table 6
Decomposing EBD into Cash Flow and Accrual Components Following Bushman et al. (2012)
Dependent variable = Investment
Accrual
Precision
FilingSpeed
Agreement
IQ =
(1)
M/A
0.018
(2)
***
(20.46)
CFO
0.124
(16.32)
***
(15.10)
WCACC
0.051
0.140
0.078
0.033
(22.17)
IQ
0.181
-0.026
0.230
0.150
-0.128
Size
Obs.
-0.069
0.004
-0.032
(1.40)
***
0.252
0.373
-0.191
(-9.91)
***
0.125***
(8.59)
***
0.148***
(7.94)
***
(-12.87)
*
-0.015***
-0.048***
(-4.97)
-0.005
0.008
0.006
-0.003
(1.89)
(1.93)
(1.23)
(-0.73)
-0.006***
-0.007***
-0.008***
-0.007***
(-11.01)
0.007
-0.005
(11.47)
***
(-3.80)
*
(18.60)
(-1.48)
(-14.78)
Industry & Year FE
0.224
0.155***
(18.72)
(9.88)
***
(8.39)
***
(-6.30)
Leverage
0.217
0.189
(1.72)
***
(-13.53)
***
(9.79)
***
(4.30)
Cash*IQ
-0.023
0.021*
-0.005
(-0.99)
***
(-9.04)
***
(7.90)
WCACC*IQ
0.043
***
(9.09)
***
(-8.43)
CFO*IQ
0.175
0.106***
(10.62)
(-0.27)
***
(13.50)
***
(7.61)
M/A*IQ
-0.025
0.151
0.022***
(18.57)
***
(11.37)
*
(-1.67)
***
(14.95)
0.040
0.055
0.036
(5)
***
(19.03)
***
(4.26)
*
(1.94)
***
0.028
(4)
***
(15.84)
***
(5.30)
***
(5.59)
Cash
0.029
(3)
***
Guidance
(-14.49)
(-14.11)
-0.006***
(-11.42)
Yes
Yes
Yes
Yes
Yes
73,695
41,554
67,028
44,148
49,946
2
22.76%
26.29%
25.29%
32.56%
25.83%
𝑅
This table presents estimates from panel regressions of firm-year investment on market-to-book, cash flow
from operations, working capital accruals, cash holdings, information quality measures and the interactions,
leverage and size. All variables are defined in the Appendix. To facilitate interpretation, all information quality
proxies and mispricing proxies are scaled between 0 and 1. All regressions include industry and year fixed
effects. Standard errors are clustered at the firm level. T-statistics are presented in parentheses below the
coefficients. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.
45
Table 7
Measurement Error Consistent Estimation
Panel A Estimates of measurement quality ( 𝛕𝟐 )
IQ =
AccrualPrecision
FilingSpeed
Low
M/A
𝜏
2
High
***
0.073
0.047
[0.001]
[0.002]
***
0.470
0.401
[0.013]
[0.020]
Slow
Agreement
Fast
Low
***
0.084
0.039
[0.001]
[0.001]
**
0.496
0.471
[0.012]
[0.013]
0.077
[0.002]
0.568
[0.013]
Guidance
High
0.032
***
[0.001]
0.547
***
[0.014]
No
Yes
0.078
0.038***
[0.001]
[0.001]
0.469
0.467
[0.010]
[0.018]
Panel B Differential investment responses across low and high information quality groups
IQ =
AccrualPrecision
FilingSpeed
Agreement
Guidance
Low
M/A
EBD
Leverage
Size
𝜏
2
Slow
Fast
Low
***
No
Yes
0.074
0.016***
0.079
0.041
[0.002]
[0.004]
[0.002]
[0.003]
[0.002]
[0.003]
[0.002]
[0.003]
0.107
0.113*
0.170
0.196***
0.076
0.257***
[0.008]
[0.014]
[0.009]
[0.019]
[0.007]
[0.017]
0.057
0.031***
[0.008]
0.015***
0.064***
[0.021]
***
0.042
0.024
[0.008]
[0.011]
0.073
0.086***
**
0.063
0.055
[0.007]
[0.008]
0.052
0.045**
0.104
0.026
***
0.073
0.051
0.058
High
0.074
[0.009]
Cash
High
0.012
***
[0.007]
[0.036]
[0.006]
0.017
0.008***
0.072
[0.006]
[0.008]
[0.004]
[0.006]
[0.004]
[0.011]
[0.004]
-0.008
-0.004***
-0.001
-0.006***
-0.003
-0.004
-0.004
[0.001]
[0.001]
[0.000]
[0.000]
[0.001]
[0.001]
[0.000]
[0.001]
0.598
0.504***
0.462
0.421***
0.483
0.366***
0.511
0.411***
[0.007]
0.006***
[0.013]
[0.011]
[0.013
[0.009]
[0.017]
[0.009]
[0.010] [0.015]
This table utilizes the measurement error consistent estimation procedure developed by Erickson et al. (2014), and
estimate up to the 4thorder cumulants. Panel A provides a simple regression of investment on M/A and reports the
coefficient estimates and measurement quality (denoted by 𝜏 2 ), Panel B examines how investment sensitivity to
market values, earnings, and cash holdings vary across information quality. Standards errors are reported in
brackets under coefficient estimates. ***, **, and * denote significance difference between the subgroups at the 1%,
5%, and 10% levels, respectively.
46