Internal Information Quality and the Sensitivity of Investments to Market Prices and

Accounting Profits

Shane Heitzman*

Marshall School of Business – University of Southern California

Mengjie Huang

Gabelli School of Business – Fordham University

January 10, 2018

Abstract:

We ask whether the quality of internal information matters for investment decisions. We predict

that investment is more sensitive to internal profit signals and less sensitive to external price signals

when managers have higher quality internal information. Consistent with recent theoretical and

empirical research, we proxy for internal information quality using observable information

properties. We find that the sensitivity of investment to profitability is increasing, while the

sensitivity of investment to market-to-book is decreasing in internal information quality. Our focus

on internal information and decision making offers new and unique insights on the importance of

information quality and complements the growing literature on the role of external reporting

quality in reducing financing frictions.

Keywords: Investment; Information Quality; Internal Information; Accounting

JEL Classification: M41; G31; D81; D83

We appreciate comments from John Gallemore, Ed Maydew, Nemit Shroff, Toni Whited, Jerry Zimmerman, two

anonymous reviewers, and workshop participants at the University at Buffalo, University of Minnesota, University

of Arizona, and the European Accounting Association Conference.

*Corresponding author. 701 Exposition Blvd. HOH 822, Los Angeles, CA 90089.

email: sheitzma@marshall.usc.edu. phone: 213-740-6531.

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Internal Information Quality and the Sensitivity of Investments to Market Prices and

Accounting Profits

1. Introduction

Expanding our understanding of financing constraints on investment behavior, a growing body

of research examines the role of external reporting quality in relaxing these constraints.1 The

evidence to date suggests that higher quality financial reporting reduces asymmetric information

between insiders and outsiders, controlling moral hazard and adverse selection problems that

preclude efficient investment.

In this paper, we pivot the focus on information quality toward a key determinant of efficient

decision making within the firm: the quality of the manager’s internal information (Kinney 1999).

Specifically, we ask how the quality of internal information affects the sensitivity of investment

decisions to signals from internal and external sources. 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 firm’s capital budget with signals obtained from internal sources

(profit forecasts) and external sources (market values). The quality of a given investment signal is

determined by the timeliness and precision of the information it provides about the relevant

investment decision. The manager that obtains timelier and more precise forecasts of investment

profitability from internal sources should place more weight on that information and less weight

on the market’s belief reflected in price.2 While both signals can be incrementally informative,

their influence on the decision should depend on their relative timeliness and precision.

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 (2016). 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 examines the impact of external information

environments on the identification of investment projects (Badertscher, Shroff and White 2013). 2 The attributes of efficient investment in internal information clearly vary across firms as a function of firm

complexity and the diffusion of specific information within the firm (Fama and Jensen 1983). A firm’s equilibrium

2

To motivate the relation between internal profitability signals and investment responses,

consider the well-known positive empirical association between investment and operating profits

(the typical empirical proxy for cash flow). While early studies attribute this result to a financing

constraints mechanism (e.g., Fazzari, Hubbard and Petersen 1988), it also arises when operating

profit is informative about economic performance and thus investment opportunities (Alti 2003;

Cooper and Ejarque 2003). Because a manager’s investment decisions depend on the expected

profitability of those decisions, this opens the door for the sensitivity of investment to profitability

to depend on the quality of the manager’s internal signals. Thus, for a given shock to expected

profitability, the manager with timelier and more precise internal information about expected

profits can reallocate capital more efficiently. Therefore, we predict that as the quality of the

manager’s internal information improves, the sensitivity of investment to internal performance

signals, such as forecasted profitability, becomes stronger.

In contrast, a manager with lower quality internal information is more likely to look to sources

outside the firm. For publicly traded firms, stock prices provide an observable signal of the

market’s information. Empirical evidence suggests that the manager’s investment decisions do

respond to these external market signals, especially when private information production by

outsiders is better reflected in stock prices (Luo 2005; Chen, Goldstein and Jiang 2007; Bakke and

Whited 2010). When the manager’s comparative information advantage improves—such as

through upgrades to the internal information systems—their response to price signals should

weaken. Thus, we predict that as the quality of the manager’s internal information improves,

investment decisions should become less sensitive to market prices.

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).

3

We use the sum of capital expenditures and research and development to proxy for investment,

market-to-book to proxy for the market’s valuation of investment opportunities, and earnings

before depreciation and R&D to proxy for profitability. This is consistent with the empirical

definitions of investment, Q and cash flow used in much of the prior literature. Our empirical tests

focus on the sensitivity of investment to market-to-book and profitability conditional on proxies

for internal information quality. We predict that as internal information quality improves, the

correlation between investment and internal signals (profits) becomes stronger, while the

correlation between investment and external signals (market values) becomes weaker.

We follow Gallemore and Labro (2015) and Goodman et al. (2014) and base our primary

proxies for internal information quality on the speed of earnings news, managerial earnings

guidance, internal control weaknesses and unintentional financial statement errors. The use of

observable instruments for unobservable internal information constructs is unavoidable.

Fortunately, the correspondence between the quality of the manager’s internal information and the

quality of what they report externally (and hence what we can observe) is arguably high. This

assertion is supported theoretically by Hemmer and Labro (2008) who show that the decision

usefulness of external financial reporting is inherently tied to the quality of information for

managerial decision making, and empirically by Dichev et al. (2013) who find that 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.”

Complementing this direct evidence, several other studies document consistent links between

the properties of internal information and external reporting. For example, firms with weaknesses

in their internal controls also appear to have lower quality GAAP accruals (Ashbaugh-Skaife et al.

2008; Doyle, Ge and McVay 2007), face higher borrowing costs (Costello and Wittenberg-

4

Moerman 2011), are slower to release their financial statements (Ettredge, Li and Sun 2006) and

provide less accurate guidance (Feng et al. 2009). 3 Brazel and Dang (2008) find that firms

implementing an ERP system release their audited financial statements with less delay, while

Dorantes et al. (2013) find that ERP firms issue external earnings guidance more often and with

higher quality. Using novel settings to identify investments in internal information, both Samuels

(2016) and Ittner and Michels (2017) provide evidence that internal information quality is

positively reflected in external reporting quality. Shroff (2017) and Cheng, Cho and Yang (2017)

find that changes in external reporting requirements (i.e. GAAP) can alter the internal information

environments managers rely for decision making.

While it is conceivable that managers could obscure high quality internal information when

reporting to shareholders, they cannot generate high quality financial reports and disclosures from

low quality internal information. We are not the first to recognize that a sort on the observable

attributes of external information is also a sort on the unobservable attributes of internal

information. Thus, we rely on prior literature to select our internal information proxies and take

steps to ensure that our interpretations are not confounded by external reporting incentives.

Our main result is consistent with our prediction: investments by managers with higher quality

internal information are more sensitive to profits and less sensitive to market prices. This finding

is robust across a diverse set of information quality proxies including the speed of earnings release

and managerial guidance and is consistent with the interpretation that managers with high quality

internal information are less likely to defer to the market’s opinion of investment opportunities.

Instead, they place more weight on timely internal information about shocks to profit opportunities.

3 See also Cheng, Dhaliwal and Zhang (2013) who argue that investments in internal controls (following disclosure

of a material weakness) reduce financing constraints by improving the quality of information reported to capital

markets.

5

To provide additional support for our conclusions, we first conduct tests that exploit time-

series shocks to internal information quality. First, we examine how firms respond to external and

internal investment signals after they remedy internal control weaknesses. We predict and find that

when internal control problems are fixed, internal information quality increases and subsequent

investment becomes more sensitive to accounting profits and less sensitive to market prices.

Second, we use the adoption of SFAS 142 as an exogenous shock to firms’ internal information

environment. SFAS 142 compliance likely requires managers to acquire additional information,

thus enriching the internal information environment (Cheng, Cho and Yang 2017) and by

extension, internal decision making and control. Our results suggest that affected (goodwill) firms

rely more on profit signals and less on price signals when investing in the post-SFAS 142 period.

Although our results suggest that internal information quality affects investment responses in

predictable ways, they are subject to a number caveats. First, we acknowledge the implications

from prior research that agency conflicts cause investment to be sensitive to internal funding and

control for cash holdings in the regression.4 The inclusion of cash follows from our assumption

that the stock of internal funds offers a cleaner proxy for the cash-based agency conflicts described

by prior research (Jensen 1986; Biddle, Hilary and Verdi 2009; Nikolov and Whited 2014) and is

consistent with research on external financing-based motives for cash holdings (Opler et al. 1999;

Bates, Kahle and Stulz 2006). Consistent with Biddle, Hilary and Verdi (2009) and others, we find

that an increase in information quality reduces the sensitivity of investment to cash holdings.

Second, we recognize that the quality of the external signal (price) is also relevant and leads to

4 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.

Additionally, moral hazard problems can lead managers to spend cash 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.

6

reverse predictions on the sensitivity of investment to external and internal signals. Employing

proxies for stock price informativeness used in prior research, our evidence suggests that more

informative price signals increase the sensitivity of investment to market prices and decrease its

sensitivity to profits. Third, the moral hazard hypothesis predicts that high quality information

reduces the sensitivity of investment to prices because managers in those firms are less likely to

respond opportunistically to mispriced equity. To address this, we include proxies for mispricing

and their interactions with market-to-book. Our results are unchanged. Finally, to address the

evidence that shows that measurement error in market valuations biases the coefficient on market-

to-book toward zero (Erickson and Whited 2000), we follow Shroff (2017) and utilize techniques

developed in Erickson and Whited (2002) and Erickson, Jiang and Whited (2014) to correct for

measurement error in the proxy for investment opportunities. Our results are robust.

Our study contributes to the literature in several ways. First, we build on the descriptive theory

that links attributes of the firm’s information environment to its managers’ investment decisions,

extending prior research by focusing on the equilibrium relation between the design of the internal

information system and decision making within the firm. Second, we empirically examine the role

of internal information quality by exploiting its intrinsic link to observable information constructs.

This builds on a promising stream of research that expands the boundaries of inquiry on the internal

information environment and leverages the coordination of information demands between users

inside and outside the firm to construct empirical proxies for internal information quality. Third,

our empirical strategies focus on the identification of internal information effects while controlling

for the influence of asymmetric information problems between managers and capital providers. In

doing so, our focus on internal information and decision making complements the growing

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literature on the importance of external reporting quality in reducing financing frictions that

impede efficient investment.

2. The Investment Framework and Hypothesis Development

2.1. The empirical investment framework

We rely on structure extended from neoclassical investment theory to motivate the link

between internal information quality, investment, and internal and external investment signals.

Under the q theory developed in Hayashi (1982) and Summers (1981), in perfect markets without

financial frictions, investment is determined solely by marginal q, which should capture all the

factors relevant to the investment decision. Because theoretical q is unobservable, the 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.:

𝐼𝑖𝑡

𝐴𝑖𝑡−1= 𝛼0 + 𝛼1

𝑀𝑖𝑡−1

𝐴𝑖𝑡−1+ 𝛼2

𝐸𝐵𝐷𝑖𝑡

𝐴𝑖𝑡−1+ 𝛼3

𝐶𝑎𝑠ℎ𝑖𝑡−1

𝐴𝑖𝑡−1+ 𝛼4𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1

+ 𝛼5 ln(𝐴𝑖𝑡−1) + 𝑒𝑖𝑡

(1)

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 interpretation of market-to-book as

the market’s valuation of investment opportunities at the beginning of the period, there is growing

evidence that the coefficient on market-to-book increases when managers incorporate feedback

from external signals in market prices into their investment decisions (Chen et al. 2007; Bakke and

8

Whited 2010). There is mixed evidence that the market-to-book coefficient picks up opportunistic

responses to market mispricing (Polk and Sapienza 2009; Bakke and Whited 2010).5

EBD is operating profits or earnings before depreciation (and R&D). We employ operating

profit (EBD) because both the cash flow and accrual components should reflect a manager’s

internal information about expected profitability. However, the economics literature first

employed EBD as an empirical proxy for liquidity. Fazzari et al. (1988) show that investment is

positively correlated with EBD, concluding from that the existence of costly external finance (and

perhaps motivating its early use in the accounting and finance literature on financing constraints).

Subsequent work by Kaplan and Zingales (1997) raises important questions about the reliance on

investment-cash flow associations to identify the existence of financing constraints. The emerging

consensus in the literature is that the loading on EBD is likely not a story of financing constraints.6

Operating earnings naturally reflect information about the profitability of investment

opportunities, paving the way for alternative explanations for 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

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 induce positive bias on the coefficient on

5 Market-to-book is also correlated with the conservatism in firm’s accounting policies. However, the impact on our

interpretations is not clear unless conservatism is correlated with internal information quality such that the

conditional investment sensitivities are driven by bias in market-to-book triggered by conservatism. In a recent study

on investment and firm-level conservatism, Lara, Osma and Penalva (2016) report a negative correlation between

various conservatism measures and market-to-book, which mitigates our concerns. 6 In the accounting, Bushman, Smith and Zhang (2012) provide evidence that the loading on accrual-based proxies

for cash flows in the economics literature can be partially explained by co-movement in capital expenditures and

working capital investment suggesting that investment sensitivity to EBD is not a story about financing constraints.

9

operating earnings as current profitability must play a larger role in explaining investment when

market-to-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. In this paper, we assume that managers have rational

expectations and form unbiased forecasts about profitability. This implies that profits realized

during the period (EBD) are a viable proxy for forecasted profits at an earlier time when the

investment decision is made. Thus, we view operating profits primarily as an internal signal of

productivity shocks and opportunity costs relevant for investment, and not as a proxy for internal

funds.

That said, we recognize the important role that internal funding plays in mitigating financing

constraints when external financing is costly and structure our analysis to include the most direct

proxy for the availability of internal funding—cash and short term investments (“cash”) held at

the beginning of the period. The finance literature finds that firms build cash balances when they

anticipate financing constraints (Opler et al. 1999; Almeida, Campello, and Weisbach 2004; Bates,

Kahle and Stulz 2009). Biddle et al. (2009) incorporate cash holdings and leverage into their

measure of external financing frictions. We include leverage in the model 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.7

2.2. Empirical predictions: internal information quality and investment responses

7 In place of cash holdings, we explored other financial constraint measures as robustness checks, including the KZ

index based on Kaplan and Zingales (1997), the Whited and Wu (2006) index, the SA index from Hadlock and

Pierce (2010), and the combined cash and leverage measure as in Biddle et al. (2009). The main inferences are

unchanged under all these alternative specifications. 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 and obtain similar inferences.

10

We are interested in whether an increase in the quality of internal information will cause

investment to become more sensitive to internal investment signals and less sensitive to external

signals.8 M/A and EBD serve as proxies for a) an external signal of investment opportunities

provided by the firm’s market price, and b) an internal signal of expected profitability, respectively.

The manager’s investment responses to each of these signals depend on the signals’ relative quality.

All signals that are informative should be used. However, the decision maker should place more

weight on the higher quality signal, causing investment to become more sensitive to that signal.

An investment in high quality internal information provides the manager with timelier and

more precise internal feedback about the firm’s productivity and opportunity costs. Such

information enhances the quality of profit forecasts for both recent and proposed investments. This

improves the manger’s ability to reallocate capital to the most valuable projects in a timelier

fashion. Under the internal information quality hypothesis, the sensitivity of investment to internal

profit signals is increasing in the quality of internal information.9

In contrast, as internal information quality declines, external sources of information such as

market prices can become more important. The potential for market prices to guide these capital

allocation decisions at the firm was recognized early on by Hayek (1945) with recent work

including Dow and Gorton (1997), Dye and Sridhar (2002), Luo (2005), Gao and Liang (2013)

and Zuo (2016). This literature starts with the assumption that managers are well-informed about

8 To be clear, in this paper, internal and external information refers to the source of the investment signal, i.e.

whether the signal is derived inside the firm (from the internal management system) or outside the firm (from

market prices). This is different from the internal and external economic forces that affect investment: internal

factors are created by the firm, such as firm-specific technologies, resource and investment opportunities; external

factors are created outside the firm, such as political and competitive factors. Managers and the market incorporate

both internal and external factors in developing their investment signals, but with different comparative advantage. 9 An “investment in higher quality internal information” could potentially overlap with the “high ability manager”;

whether high ability managers are more likely to make those investments, or the high ability managers become that

way because of the greater investment in internal information quality, the implication would be that managers with

higher quality information are “better able” to synthesize information from forecasts and translate those into

actionable and profitable investments.

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inside factors such as the firm’s technological capabilities and specific investment opportunities.

The value of those investment opportunities is also affected by outside forces like competition,

product demand, political and geographical considerations and other factors about which the

manager may be relatively less informed. This opens the door 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.10

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 firm’s

stock price. Since managers that obtain higher quality internal information for decision making

should depend less on external signals for investment decisions, the internal information quality

hypothesis predicts that the sensitivity of investment to market valuations is decreasing in the

quality of internal information.11

2.3. Alternative explanation: External financial reporting quality effects

In this paper, we focus on the role of internal information quality in the capital allocation

process. Although we view this as distinct from research emphasizing the role of external financial

reporting quality in mitigating agency conflicts that affect investment, it is related conceptually

through the emphasis on the attributes of the information environment and empirically through the

10 For example, as Dye and Sridhar (2002) theorize and Luo (2005) demonstrates empirically, managers can make

an announcement of a proposed transaction, observe the market reaction to the announced transaction, and decide

whether to proceed based on the reaction. Unfortunately, such overt instances are relatively uncommon. In general,

the researcher’s ability to infer the precise context of a price movements is tricky without a clear measure of the

underlying news. Instead, we opt to rely on the more general assumption that managers have some ability (even if

noisy) to infer market information from price movements in context. That context might be the latest unemployment

figures, an announcement of tax reform in the EU, a competitors’ announcement of a successful innovation. The

manager can use price movements of its own stock and its peers’ stock in this process. 11 The market obtains at least some of its information from financial reports and disclosures made by managers. As

the quality of internal information improves, the quality of reporting and disclosure also improves, and market prices

can become more efficient and more highly correlated with actual investment decisions. In this world, higher

information quality should increase the sensitivity of investment to both the internal profit signal (𝛼1) and the

external market price signal (𝛼2). We do not find this prediction empirically.

12

basic investment model and the choice of information proxies. In this section, we briefly discuss

the prior research on external financial reporting effects and describe how our methodology

accounts for these potentially confounding effects in order to enhance our inferences about internal

information effects.

Theory. Following Bushman and Smith (2001) an important way for financial reporting attributes

to influence investment by controlling agency problems: 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 generate private benefits. In

response, shareholders control moral hazard and adverse selection costs 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 improves the

manager’s ability to implement projects that generate private benefits or increases marginal

financing costs sufficient to turn a project’s NPV negative.

Motivated by the literature documenting that investment appears sensitive to internal funds,

recent accounting studies use this agency-based framework to test predictions about the role of

external reporting quality for investment. These studies generally argue that reporting high quality

information to external users reduces financing frictions and thus increases investment efficiency.

Under an adverse selection narrative, an increase in the quality of information reported to the

market reduces the sensitivity of investment to internal funds and increases the sensitivity of

investment to investment opportunities. The basic idea is that when external funds are costlier than

internal funds, investment will be constrained by the availability of internal funds. An

improvement in the quality of information reported to capital providers reduces the adverse

13

selection costs of external financing and thus reduces the reliance on internal sources of project

funding.12

Under a moral hazard narrative, managers respond opportunistically to market mispricing and

internal funding shocks that facilitate empire building. With a positive internal funding shock, the

manager would rather take on a negative NPV project that provides private benefits than distribute

excess funds to investors. Reporting high quality information provides directors and investors with

timely and precise information about managerial decisions ex post, reducing ex ante incentives for

opportunistic investment when the firm holds excess cash or its equity is overpriced. 13

Evidence. Biddle and Hilary (2006) find that cash flows explain investment better in countries

with less transparency.14 Biddle et al. (2009) argue that firm-level reporting quality reduces

deviations from predicted investment, the impact of incentives to over- or under-invest and the

sensitivity of investment to cash holdings. Beatty, Liao and Weber (2010a) show that the impact

of reporting quality on investment-cash flow sensitivity is weaker when firms can resolve

asymmetric information through private debt markets, while Beatty, Liao and Weber (2010b) find

12 We recognize the possibility that the impact of information quality could be priced. 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). 13 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. 14 Biddle and Hilary (2006) is not directly comparable to our study. They focus primarily on a cross-country

comparison of financial constraints and reporting quality. They derive country-level measures of financial

constraints based on within-country regressions of investment on market-to-book and operating cash flow, and

compare those to country-level measures of earnings quality (smoothness, timeliness, loss avoidance and

aggressiveness). The idea is that firms in countries with higher quality earnings should not be as sensitive to cash

flow for investment. The results, based only on 33 observations at the country level, support that prediction. In their

firm-specific tests within the US, they rely on investment-cash flow sensitivities to test for the presence of financing

constraints and reporting quality’s ability to mitigate them. In addition to the findings in Bushman et al. (2012) who

cast doubt on using investment-cash flow sensitivities as a measure of financial constraints, Biddle and Hilary

(2006) use an uncommon measure of investment-cash flow sensitivity that lacks controls for investment

opportunities embedded in market valuations or other controls such as size and leverage. To our knowledge, this

approach has not been adopted by the literature.

14

that firms with low reporting quality finance investment through leases rather than purchases.15

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, and Balakrishnan, Watts and

Zuo (2016) who argue that a negative shock to the credit market has a stronger impact on

investment for less conservative firms. Shroff, Verdi and Yu (2014) tackle the agency problem

within the firm, showing that investment decisions by subsidiaries in high quality local information

environments are more responsive to local investment opportunities.

This aforementioned research on external financial reporting quality is relevant to our study

insofar as we rely on a tight link between internal and external information to derive our empirical

proxies for internal information quality. Thus, we must account for the potential that our empirical

proxies for internal information quality could pick up attributes of external financial reporting

quality that affect adverse selection and moral hazard problems that cause managerial investment

decisions to depend on the availability of internal funds—the dominant mechanism in prior

research. While this alternative explanation generally works to bias against finding evidence

consistent with the internal information hypothesis, we further control for these effects by

interacting our information quality proxies with the level of cash holdings. Because our proxies

15 Unlike our paper, Beatty, Liao and Weber (2010a) find that higher accounting quality leads to a drop in the

investment-cash flow association. But the two papers are very different and caution should be exercised in

comparing the results. Beatty et al. are interested in the impact of accounting quality when firms are issuing debt.

Thus, their sample is restricted to firms that issued public or private debt in the year of investment. This narrows the

sample considerably. Additionally, unlike our focus on internal information, they focus on earnings-based measures

of accounting quality, including: accrual quality, earnings persistence, the explanatory power of an earnings

persistence model, and the earnings-cash flow correlation. Moreover, these proxies are measured in the year of

investment (as opposed to lagged as in our study). Given these large and fundamental differences, our results do not

have obvious implications for their stud and vice versa and we do not attempt to reconcile the difference.

15

are potentially sorts on external reporting quality, we predict the interaction will be negative: an

increase in information quality should reduce investment sensitivity to cash holdings.16

3. Empirical Methodology and Results

3.1. Research design

To test the impact of internal information quality on investment sensitivity to external price

and internal profit signals, we interact our proxies for internal information quality with market-to-

book and operating earnings. Moreover, we include an interaction between internal information

quality and cash holdings as a control for the impact of information quality on adverse selection

costs and moral hazard problems. Specifically, we run the following regression:

𝐼𝑖𝑡

𝐴𝑖𝑡−1= 𝛿𝑗 + 𝜏𝑡 + 𝛼1

𝑀𝑖𝑡−1

𝐴𝑖𝑡−1+ 𝛼2

𝐸𝐵𝐷𝑖𝑡

𝐴𝑖𝑡−1+ 𝛼3

𝐶𝑎𝑠ℎ𝑖𝑡−1

𝐴𝑖𝑡−1+ 𝛼4𝐼𝐼𝑄 + 𝛼5

𝑀𝑖𝑡−1

𝐴𝑖𝑡−1

× 𝐼𝐼𝑄 + 𝛼6

𝐸𝐵𝐷𝑖𝑡

𝐴𝑖𝑡−1× 𝐼𝐼𝑄 + 𝛼7

𝐶𝑎𝑠ℎ𝑖𝑡−1

𝐴𝑖𝑡−1× 𝐼𝐼𝑄 + 𝛼8𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1

+ 𝛼9 ln(𝐴𝑖𝑡−1) + 𝑒𝑖𝑡.

(2)

IIQ is a proxy for internal information quality. M/A serves as the proxy for the external signal

based on market prices and EBD serves as the proxy for the internal profit signal. 𝛿𝑗 and

16 Conceivably, the same managers that invest more in high quality internal information are also creating an

asymmetric information problem that increases adverse selection costs—assuming that this information is not shared

with market participants. Thus, what we label high quality internal information is potentially a proxy for low quality

external information and thus our empirical evidence is observationally equivalent to prior studies on external

reporting quality and financing constraints. We think this explanation is unlikely to be at work in our study for at

least four reasons: 1) the empirical association between internal information quality measures (filing speed,

guidance, etc.) and external reporting quality measures (analyst coverage and forecast accuracy) is positive,

supporting our assertion that a sort on high internal information quality is a sort on high external reporting quality

even if some firms make active attempts to muddy their information, 2) the association between internal information

quality proxies and proxies for adverse selection in equity markets is generally negative, supporting the view that

high quality internal information reduces asymmetric information with the market; 3) the logic implies that the

increase in internal information quality leads to a higher cost of debt and possibly equity, running contrary to a large

body of evidence, and 4) to the extent this mechanism is at work, it should actually play out on cash holdings as that

best represents (in the model) the availability of internal funding and thus the potential for adverse selection

problems to crop up. This does not appear to be the case.

16

𝜏𝑡 represent industry and year fixed effects and are included to control for differences in industry

organization and practices and time trends.17 Standard errors are clustered at the firm and year

level.

3.2. Sample and variable construction

We begin with a sample of 83,645 firm years drawn from the sample period 1988 through

2015. We require the firm to be publicly traded on NYSE, NASDAQ or AMEX and have sufficient

information 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.

We measure internal information quality using five proxies that draw on intuitive connections

to the internal information environment.18 In doing so, we acknowledge the concerns expressed by

Leuz and Wysocki (2016) who note that measures of external reporting quality based on the quality

of a firm’s accruals are problematic for examining real effects like investment. Because they are

inherently connected to the firm’s underlying economics, interactions between earnings and

accruals quality can explain variation in observed investment for reasons that have nothing to do

with the quality of the accounting-based performance signal. To mitigate the direct influence of

economic performance on our choice of proxies, we adopt a set of proxies that do not rely on

17 Although there does not appear to be a uniform consensus, a review of the related literature suggests that a typical

study of investment and information employs some combination of industry, firm or year fixed effects. In our main

tests, we employ industry and year fixed effects and cluster standard errors at the firm and year level. To facilitate

better interpretation of the results, we rank the continuous IIQ measures into deciles that lie between 0 and 1. This

procedure essentially removes much of the within-firm variation, and attempts at using firm fixed effects with our

ranking variable is highly sensitive when firms do not vary in their ranking over the sample period. In a robustness

test described in Section 4.6 we used the raw IIQ measure and estimate the model with firm and year fixed effects.

Our results are robust. 18 An alternative approach is to look at macro-level measures of the information environment, where increasing the

amount of information available about 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, the

proprietary costs of disclosure can increase the manager’s incentive to reduce the quality of externally reported

information, but in general, firms appear to benefit from operating in more transparent environments.

17

accruals and is arguably less directly afflicted by this concern. In addition, the regressions include

profitability and market-to-book as independent variables, effectively controlling for the

covariation in performance and the information quality measures.

The first proxy, FilingSpeed, is the length of time it takes the firm to release earnings 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 earnings figure following Jennings, Seo 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 second proxy, Guidance, is an indicator 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, 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. Our third proxy is GuidanceAccuracy, the average accuracy of annual

management earnings guidance issued in the three years prior to the investment year. Among

managers that provide forecasts, higher quality information systems facilitate information

gathering and enable the manager to make more accurate internal forecasts. Dorantes et al. (2013)

show that the accuracy of firms’ earnings guidance increases following improvements to

information systems.

The fourth proxy, NoICW, equals 1 if the firm does not disclose any internal control weakness

in the year prior to investment. According to Feng et al. (2009), internal control problems could

18

lead to erroneous internal management reports, reducing the quality of the manager’s internal

information set. Our final proxy, NoError, is an indicator variable for the absence of restatements

due to unintentional errors. Managers rely on accurate internal records and well-designed

information systems for decision making (Gallemore and Labro 2015). Unintentional errors

potentially spill over to investment decisions by distorting internal management reports. To reduce

measurement error caused by outliers and to improve comparability across the marginal effects,

we rank FilingSpeed and GuidanceAccuracy into deciles and scale them to range between 0 and 1

prior to running the regressions, with high values corresponding to higher 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 0.27, consistent with our intent to identify independent

constructs for information quality. Notably, our measures are correlated with firm size. Larger

firms file annual reports faster, are more likely to provide guidance, have higher guidance accuracy,

are less likely to disclose internal control weaknesses and are more likely to have restatements

resulting from unintentional errors. To mitigate size-driven interpretations, we control for size in

the investment regressions.

3.3. Main results

The main results are reported in Table 2. The benchmark investment 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 (6) we report the results from interacting each of our

five information quality proxies with our proxies for market valuations, operating earnings and

cash holdings. Across all five information quality measures, the coefficient on the interaction

between M/A and IIQ is significantly negative. The coefficient on the interaction between M/A and

19

FilingSpeed of -0.019 (t-stat = -11.88) in column (2) implies that increasing FilingSpeed by one

decile reduces the sensitivity of investment to market valuations by 0.002 (0.019 / 9). These effects

imply that investment-market-to-book sensitivities fall from 0.029 for firms with the slowest filing

speed to 0.010 for firms with the fastest filing speed. Estimates based on management guidance

yield similar inferences. At firms that issue guidance (column 3) the sensitivity of investment to

market-to-book is 45% lower than at firms that do not issue guidance (-0.010 / 0.022, t-stat = -

6.08). We find similar effects when we focus on the accuracy of the guidance in column (4). In

columns (5) and (6), we find that investment in firms with higher internal control quality or no

restatements is also significantly less sensitive to market values. 19 Thus, across all measures,

investment becomes significantly less sensitive to market valuations as internal information quality

increases. These results support the internal information quality hypothesis which predicts that as

internal information quality improves, managers place less weight on external market signals.

Turning to the impact of internal information quality on investment sensitivity to internal profit

signals, the interactions between EBD and the information quality proxies (IIQ) are positive and

generally statistically and economically significant. In column (2), for example, the coefficient on

the interaction between EBD and FilingSpeed of 0.147 (t-stat = 9.44) implies that a one-decile

decrease in the time to report earnings (roughly one week), leads to a 0.016 (0.147 / 9) stronger

sensitivity of investment to internal accounting signals. In column (3) we find that investment by

firms that issue guidance is more than twice as sensitive to earnings ((0.123 + 0.100) / 0.100) as

investment by firms that do not (t =8.64). Based on this smaller sample of guidance firms with

sufficient data to calculate GuidanceAccuracy, we find some evidence that improvements in

19 For NoICW and NoError, we also conducted propensity-score matching analyses to control for differences in

multiple dimensions between ICW and No ICW firms, as well as firms with and without unintentional financial

statement errors. Similar inferences can be made from this analysis.

20

guidance accuracy increase the sensitivity of investment to earnings, although the effect is only

marginally significant (t = 1.72). Compared to firms with internal control weaknesses, firms

without weaknesses (NoICW) are about two-thirds more responsive to internal accounting signals

((0.052 + 0.037) / 0.052). However, regressions using the restatement-based proxy NoError (col.

6) show no evidence that firms without unintentional errors are significantly more responsive to

internal signals. Similar to the results on market valuations, these results generally support the

internal information quality hypothesis: investments are more sensitive to internal profit signals

when managers have higher quality internal information.20

Prior research suggests that adverse selection and moral hazard problems matter when

information quality reflects asymmetric information between insiders and outsiders. To address

this, we return to the interaction between IIQ and Cash that serves as our control for the agency-

based asymmetric information explanations explored in Biddle et al. (2009) among others.

Consistent with those studies, we find that investment becomes less sensitive to cash holdings

when FilingSpeed, Guidance and GuidanceAccuracy increase, suggesting that higher information

quality mitigates the propensity of managers to overinvest or reduces adverse selection costs of

external financing.21

20 One possibility is that information quality has a differential impact on investment sensitivities to the cash flow and

accrual portions of earnings. For example, managers might discount the signal in forecasted accruals more than

forecasted cash flows when information quality is low. Following Bushman et al. (2012), we decompose EBD into

its cash flows and accruals components and find results consistent with internal information quality having a

differential impact on the strength of the relation between investment and cash flows relative to accruals. The

effective is positive for both, consistent with our main results, but stronger for interactions with cash flows. Because

we lack strong ex ante expectations about the impact of information quality on the components of EBD, and because

the results are not consistent across these components, we are hesitant to draw strong inferences from these tests and

leave further analysis to future research. 21 The negative coefficient on the interaction between IIQ and M/A is also consistent with the moral hazard

argument. To the extent low information quality provides managers with incentives to exploit mispricing by

overinvesting when prices are high, or underinvesting when prices are low, the variation in investment sensitivity to

market prices explained by information quality could be attributable to moral hazard problems and not to variation

in the scope for learning from prices. We examine this explicitly in Section 4.4. and find that our results are not

driven by this mispricing explanation.

21

To illustrate how investment sensitivities vary with information quality across the various

proxies, we sort firm-years into two groups based on the top and bottom 40% of FilingSpeed and

GuidanceAccuracy. Guidance, NoICW, and NoError are indicator variables that already partition

the firms into two groups. We then estimate equation (2) for each group and depict the coefficients

on M/A, EBD and Cash. Figures 1a through 1c shows how investment sensitivities shift as

information quality increases. Across all five measures, the coefficient on M/A decreases in

information quality (fig. 1a) while the coefficient on EBD increases in it (fig. 1b), supporting the

internal information quality 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 (fig. 1c).

3.4. Evidence from the remediation of internal control weaknesses

In our main analysis in we find that firms with internal control weaknesses are more responsive

to market signals and less responsive to internal profits when making investment decisions (Table

2, column 5). In this section, we narrow the investigation to firms with internal control weakness

and ask whether investment responses at those firms change after the control weakness is

remediated. Specifically, we estimate the following model:

𝐼𝑖𝑡

𝐴𝑖𝑡−1= 𝛿𝑖 + 𝛼1

𝑀𝑖𝑡−1

𝐴𝑖𝑡−1+ 𝛼2

𝐸𝐵𝐷𝑖𝑡

𝐴𝑖𝑡−1+𝛼3

𝐶𝑎𝑠ℎ𝑖𝑡−1

𝐴𝑖𝑡−1+ 𝛼4𝑃𝑜𝑠𝑡_𝐼𝐶𝑊 + 𝛼5

𝑀𝑖𝑡−1

𝐴𝑖𝑡−1

× 𝑃𝑜𝑠𝑡_𝐼𝐶𝑊 + 𝛼6

𝐸𝐵𝐷𝑖𝑡

𝐴𝑖𝑡−1× 𝑃𝑜𝑠𝑡_𝐼𝐶𝑊 + 𝛼7

𝐶𝑎𝑠ℎ𝑖𝑡−1

𝐴𝑖𝑡−1× 𝑃𝑜𝑠𝑡_𝐼𝐶𝑊

+ 𝛼8𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1 + 𝛼9 ln(𝐴𝑖𝑡−1) + 𝑒𝑖𝑡 .

(3)

The sample includes only firms disclosing an internal control weakness and is estimated using the

years of the disclosed weakness and two years following disclosure and remediation. Post_ICW is

an indicator variable for the two years following the disclosure of internal control problems. Under

22

the internal information hypothesis, we predict that investment becomes more responsive to

earnings (α6 > 0) and less responsive to market valuations (α5 < 0) after the internal control

problems are fixed. The results, reported in Table 3, are consistent with this: the coefficient on the

interaction term between EBD and Post_ICW is significantly positive (coeff. = 0.014; t-stat = 2.16)

suggesting greater investment sensitivity to profits after the weakness is fixed. In contrast,

investment becomes less sensitive to market valuations after the remediation, with a coefficient on

the interaction between M/A and Post_ICW of -0.01 (t-stat = -2.08). Combined with the results in

Table 2 (col. 5), these results suggest that internal control weaknesses drive down the quality of

internal information quality for investment purposes, causing managers to be less responsive to

internal information when internal controls are compromised. After the weaknesses are remedied,

however, the increase in internal information quality appears to drive managers of internal control

weakness firms to respond more to operating profits and less to market prices. In untabulated

analyses, we find no difference in the investment responses between non-ICW firms and ICW

firms after the weakness is remedied.

3.5. Evidence from a change in accounting methods

Shroff (2017) argues that changes in reporting standards can influence internal information

demands. In the absence of an external mandate, some firms may choose not to collect certain

information when the marginal costs exceed the marginal benefits. But when the information is

mandated, these marginal costs become irrelevant to the decision and internal information can

improve. In this section, we analyze a specific change centered on the implementation of SFAS

142. SFAS 142 discontinues the systematic and mechanical amortization of acquired goodwill and

replaces it with periodic impairment tests that require managers estimate the fair value of the firm’s

reporting units. These provisions can induce investment in internal information systems to provide

23

reasonable judgments and estimates of the values or reporting units’ assets as required to comply

with GAAP.

Cheng, Cho and Yang (2017) show that SFAS 142 results in firms making improvements to

the quality of internal information. Thus, we expect investment to become more sensitive to

accounting profits and less sensitive to market values following SFAS 142 for those firms most

affected by the standard. 22 To test this, we take the six years around the adoption of SFAS 142

and perform a difference-in-difference design:

𝐼𝑖𝑡

𝐴𝑖𝑡−1= 𝛿𝑗 + 𝛼1

𝑀𝑖𝑡−1

𝐴𝑖𝑡−1+ 𝛼2

𝐸𝐵𝐷𝑖𝑡

𝐴𝑖𝑡−1+ 𝛼3

𝐶𝑎𝑠ℎ𝑖𝑡−1

𝐴𝑖𝑡−1+ 𝛼4𝑃𝑜𝑠𝑡_𝑆𝐹𝐴𝑆142 + 𝛼5

𝑀𝑖𝑡−1

𝐴𝑖𝑡−1

× 𝑃𝑜𝑠𝑡_𝑆𝐹𝐴𝑆142 + 𝛼6

𝐸𝐵𝐷𝑖𝑡

𝐴𝑖𝑡−1× 𝑃𝑜𝑠𝑡_𝑆𝐹𝐴𝑆142 + 𝛼7

𝐶𝑎𝑠ℎ𝑖𝑡−1

𝐴𝑖𝑡−1

× 𝑃𝑜𝑠𝑡_𝑆𝐹𝐴𝑆142 + 𝛼8𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1 + 𝛼9 ln(𝐴𝑖𝑡−1) + 𝑒𝑖𝑡.

(4)

𝑃𝑜𝑠𝑡_𝑆𝐹𝐴𝑆142 is an indicator variable set to 1 for the post-SFAS 142 period. Control firms do

not report any goodwill in the six-year period. Treatment firms report goodwill in all six years.

The results are reported in Table 4 and show that for treatment firms the interaction between M/A

and 𝑃𝑜𝑠𝑡_𝑆𝐹𝐴𝑆142 is significantly negative (-0.009, t-stat = -9.48), while the interaction between

EBD and 𝑃𝑜𝑠𝑡_𝑆𝐹𝐴𝑆142 is significantly positive (0.040, t-stat = 2.44). These interactions are

insignificant at control firms while the difference in coefficients between treatment and control

22 Intuition would suggest that a test of an acquisition-related standard’s effect across investment types should

manifest in a stronger relation between measures of investment opportunities and future acquisitions. However, on

close inspection, this prediction is less straightforward. On its face, the standard is about valuing the reporting unit

and the individual assets. But in five years, it may not matter whether the unit was acquired or created as reporting

units, even when acquired and bringing along goodwill, are also making investments in capital equipment and R&D

after the acquisition. The basic framework of SFAS 142 has a direct impact on the methods used for valuation

which should apply to all investment as long there is some goodwill to value initially. To the extent managers can

leverage the innovations in the information system – even if triggered by an outside standard – to its internal

decisions (as conjectured by Hemmer and Labro 2008), such a positive externality has room to exist. While SFAS

142 is closely related to investment decisions, other accounting rule changes that improve the informativeness of the

internal information system, such as SFAS 106 in Shroff (2017), potentially serve to test our hypotheses as well. We

use SFAS 142 rather than SFAS 106 given the relative ease of identifying when and which firms are affected and

the more direct link to investment.

24

firms is significant (col. 3). Consistent with the internal information quality hypothesis, we find

that firms likely to experience an increase in internal information quality because of an external

shock to reporting requirements appear to rely more on internal profit signals and less on external

market signals after the change.

4. Additional Analysis

4.1. Does the informativeness of the external price signal matter?

To this point, we have focused on the variation in internal information quality and its impact

on affects investment responses to profit and price signals. But one can also ask what would happen

if we shift our focus to the quality of the external signal, that is, the quality of the firm’s stock price

as a measure of information about investment opportunities. The predictions are similar, but in the

opposite direction: when market prices are more informative because of private information

production, investment should be relatively more sensitive to these external price signals (as in

Chen et al. 2007 and Bakke and Whited 2010) and less sensitive to internal profit signals. To

implement this, we replace our IIQ proxies with proxies for stock price informativeness (PI) drawn

from the finance literature.

Our first two proxies are firm-specific direct measures of price informativeness. Stock return

variation comes from public news and private information production by investors. Following

Chen et al. (2007), price nonsynchronicity (𝜓) is the component of stock return not associated with

industry or market movements. It represents the informativeness of stock prices as it is indicative

of greater information production by investors in the firm’s stock (Durnev, Morck and Yeung

2004). All else equal, the higher the nonsynchronicity in prices, the more information price

contains. Our second measure is the probability of informed trade adjusted for liquidity (APIN)

25

and is estimated from a market microstructure model and measures the level of information

asymmetry between informed and uninformed investors (Easley, Hvidkjaer and O’Hara 2002;

Duarte and Young 2009). Because the estimation of APIN relies on trading by privately informed

investors, it also proxies for the degree of private information production reflected in market prices.

Our third proxy is an indirect measure of price informativeness. Following Badertscher et al.

(2013), PublicProp is measured as the number of publicly-traded firms in the firm’s industry

divided by the total number of firms in the industry as reported by the Census Bureau. Badertscher

et al. (2013) argue that public firm presence enriches the external information environment as

publicly traded firms must disclose large amounts of information. Conceivably, this could enhance

the informativeness of the firm’s own stock price as investors assemble more macro and industry

news and communicate that information through trading.

The results are reported in Table 5. We find similar results for all proxies. Consistent with prior

research, investment is significantly more sensitive to market-to-book when the external signal

(price) is more informative. New to the literature, we find that the investment sensitivity to

profitability is weaker when prices are more informative, significantly so when informativeness is

measured as price nonsynchronicity (t = -5.21) or the proportion of public firms in the industry (t

= -5.40). Taken together with our earlier results from Table 2, the results in Table 5 paint a

consistent picture that investment responses to external price signals and internal accounting

signals behave in ways predicted by their relative informativeness.

4.2. Alternative measures of investment

We examine the robustness of our results under alternative measures of investment. We first

use two additional measures: capital expenditures alone consistent with early investment research,

and capital expenditures plus both R&D and acquisitions. The results, not tabulated, are

26

qualitatively similar to the results reported in Table 2. Second, we examine investment in R&D

separately. R&D represent firm-specific technological factors for which managers are not only

comparatively well-informed but also more likely to hide from outsiders because of the proprietary

costs of disclosure. Thus, variation in internal information quality may have less power to explain

the sensitivity of R&D to market prices. In untabulated results, we find that the sign of the

coefficient on the interactions between IIQ and M/A is uniformly negative whether investment is

defined as capital expenditures or R&D. 23 Third, we consider leasing which is ignored by

traditional investment definitions. Prior work shows that reporting quality is associated with the

lease versus buy decision, and 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 leases on the relation between

information quality and investment responses to M/A and EBD, we also add operating leases to

our measure of investment.24 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, not tabulated, are not qualitatively different from our baseline results.

4.3. Alternatives to the firm-specific price signal and internal profit signal

The market value of the firm, relative to book value, serves as our primary proxy for the

external price signal. One concern is that internal information quality and market valuations are

not independent. On one hand, more accurate internal information leads to better disclosure quality,

which in turn affects market value (Feng et al. 2009). On the other hand, the informativeness of

market prices can change the quality of internal information, as managers glean information useful

23 In terms of magnitude, the coefficient estimates are larger when investment is defined as R&D, however, they are

only significantly different form zero in two out of the five measures (FilingSpeed and Guidance). The coefficient

on the interaction between IIQ and M/A is negative and statistically significant across all five proxies. 24 When doing this, we also add rental expense back to our measure of cash flow.

27

for decision making from market prices (Chen et al. 2007). Similarly, Zuo (2016) finds that

managerial learning from market prices affects the quality of voluntary disclosure. To partially

mitigate these and other concerns that arise because of our inability to cleanly separate the

information channels, we replace the firm’s market-to-book ratio with one based on industry

valuations. Industry Q remains a valid proxy for the external price signal; for firms that are not

publicly traded, it is the only comparable price signal available. We also replace operating profits

(EBD) with sales growth as an alternative internal signal. Sales growth is usually used in lieu of

market-to-book as a proxy for growth opportunities when market prices are not available

(Badertscher et al. 2013, Shin and Stulz 1998), or to mitigate the concern on measurement error in

q (Whited and Wu 2006). In our setting, sales growth is calculated using accounting numbers,

therefore it is another candidate for the internal signal of investment opportunities. Unlike EBD,

however, it ignores information on costs. Compared to our baseline results from Table 2, the results

in Table 6 suggest qualitatively similar, thought slightly weaker results using industry Q and sales

growth.

4.4. Mispricing

In Table 2, we find that lower quality internal information is associated with investment that

is more sensitive to market prices. However, this sensitivity can arguably be driven by managerial

reactions to stock valuations when managers have incentives to opportunistically exploit

mispricing of the firm’s shares. Keynes (1936) suggests that there is irrationality in stock prices

which affects the pattern of equity financing and firms’ investment behavior.25 However, evidence

on this point is mixed. Gilchrist, Himmelberg and Huberman (2005) and Polk and Sapienza (2009)

25 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.

28

find that investment is correlated with mispricing, while Bakke and Whited (2010) conclude that

firms largely ignore 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 sensitivity of investment to market and

accounting signals, we add to the regression proxies for mispricing from prior research and the

interactions between mispricing and the variables of interest. Our two mispricing proxies are

constructed based on information revealed after market-to-book is measured, but presumably

known by the manager when the market valuation 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 investment year 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. We assume that the response to mispricing is

symmetric and use absolute values such that a higher value indicates more mispricing, regardless

of either direction. We also rank these two measures into deciles when including them in the

regressions.

29

For both |CAR| and |Surprise|, the results in Table 7 suggest 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 generally insignificant

under |CAR|. Importantly, the coefficients on the interactions between IIQ proxies and M/A, EBD

and Cash are qualitatively unchanged. While there is some evidence that mispricing matters, it

does not affect our inferences about the impact of internal information quality on investment

sensitivity to market values and profitability.

An open question here is whether the sign of the mispricing matters. While studies such as

Bakke and Whited (2010) employ unsigned measures, one can argue that overpricing might affect

investment responses more than a similar magnitude of underpricing. To investigate this, we

estimate the regressions separately for over- and under-priced firm years, where over-pricing is

indicated by a negative Surprise or negative CAR. The untabulated results, we find that controlling

for the magnitude of mispricing, the impact of internal information quality on the sensitivity of

investment to prices and profit does not depend on the direction of the mispricing.

4.5 Organizational Complexity

We consider the robustness of the 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. 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).

30

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 allocation 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 at 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 examine the robustness of our main findings after controlling for

industry and geographic diversity and their interactions with market values, operating profits and

cash holdings. We do not tabulate the results here, however, our primary findings are intact.

4.6. Econometric considerations: fixed effects and clustering

In the empirical investment literature, there is wide variation in the specification of the

investment model, the type of fixed effects, and the approach to correcting standard errors. Our

primary results are estimated using industry and year fixed effects which account for unobserved

impact of time- and industry-based shocks to investment opportunities. In addition, we cluster

standard errors at the firm and year level to account for cross-sectional and time-series correlation

in the error terms.

To examine the robustness of our inferences to this design choice, we report the results from

estimating our model using this approach. In this model, we allow IIQ proxies to take on their

unranked values to ensure sufficient time-series variation within firm. We include firm and year

fixed effects and cluster standard errors by industry and year. The results are reported in Table 8.

Our inferences are qualitatively similar across all five measures of internal information quality.

31

An increase in IIQ is associated with an increase in the sensitivity of investment to accounting

earnings, and a decrease in the sensitivity of investment to market valuations.

4.7. Econometric considerations: measurement error

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)

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 the quality of external reporting) 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 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 is similar to the approach used by Shroff

(2017) and 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 the 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 9

32

across these groups. 𝜏2 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 necessarily increase when

information quality is high.

We then estimate equation (2) within each group. The results are reported in Panel B of Table

9. Coefficients are reported with standard errors in brackets. Relative to 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 level reported

next to the coefficients in the high information quality partition is a test of the difference across

the two groups. They uniformly indicate that higher information quality significantly increases the

sensitivity of investment to operating profits and decreases the sensitivity to market values and

cash holdings.

5. Conclusion

The effect of information quality on 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 external 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 reporting and disclosure quality affects the relation between investment and its

determinants like investment opportunities and the availability of internal funds.

33

In this paper, we propose an alternative view. Shifting to a decision-making focus on internal

information, we ask how variation in the quality of internal information changes the investment

responses to external market price and internal accounting profit signals. Across five measures of

internal 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 operating earnings. While we assume

a tight link between internal and external reporting quality to support our choice of proxies for

unobservable internal information, we take several steps to isolate competing explanations based

on asymmetric information between insiders and outsiders.

The evidence supporting the internal information quality hypothesis can be interpreted as a

natural result of efficient investment in internal information, where the incentive to invest in high

quality information depends on its potential value. 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 profits is entirely

consistent with optimal investment in internal information. The endogenous nature of internal

information quality implies that our framework cannot be used to argue that firms can

unconditionally increase 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.

In terms of standard setting, an interesting implication of our findings is that changes in

external reporting requirements can shape decision making within the firm insofar as internal

information systems must adapt to meet those demands. Based on our results, it seems premature

34

to assert that higher quality external reporting can only improve investment efficiency by reducing

asymmetric information problems between managers and owners. Rather, the full complement of

information effects, external and internal, must be acknowledged.

35

Appendix: Variable definitions

Variable name Construction

APIN Probability of informed trade, adjusted for liquidity following Duarte and

Young (2009).

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.

Cash Cash holdings, including cash and cash equivalents, divided by total assets.

Calculated as CHE/AT.

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).

EBD Earnings before extraordinary items plus research and development expense

divided by beginning total assets: (IB+DP+XRD)/AT

FilingSpeed The ranked inverse of the number of days between fiscal year end and the

earnings release (per Compustat), averaged over the past three years.

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.

GuidanceAccuracy The average accuracy of annual management earnings forecasts issued in the

three years prior to the investment year. Management forecast accuracy is

measured as the absolute value of the difference between management EPS

forecast and the actual EPS, scaled by stock price three days prior to the

management forecast and multiplied by -1.

Ind_Q Industry Q, calculated as the sum of market value of equity and book value of

debt of all firms in an industry divided by aggregate total assets in that

industry.

Investment Capital investment (COMPUSTAT item CAPX) plus research and

development expense (XRD) divided by beginning total assets (AT).

IIQ Information quality proxies.

Leverage The ratio of long-term debt to the sum of long-term debt and the market value

of equity: DLTT/(DLTT+AT).

M/A Market to book ratio, calculated as (AT+CSHO*PRCC_F-CEQ-TXDB)/AT.

NoError An indicator variable taking value 1 if the firm did not have financial

restatements because of unintentional errors.

36

NoICW An indicator variable taking value 1 if the firm did not disclose any internal

control weaknesses (either 302 or 404) in the year prior to investment.

Post_ICW An indicator variable taking value 1 for the two years after ICW disclosure.

Post_SFAS142 An indicator variable taking value 1 for the post SFAS 142 period.

PublicProp The number of public firms divided by the total number of firms in the 4-digit

NAICS industry, obtained from the Census Bureau.

Sales_gr Percentage sales growth.

Size Log of total assets (AT).

Surprise Annual earnings surprise. Actual EPS minus analyst forecast scaled by

beginning price, where analyst forecast is the last consensus forecast before

year end.

ψ Idiosyncratic volatility, measured over the fiscal year of investment.

Calculated as 𝜓 = ln (1−𝑅𝑖

2

𝑅𝑖2 ), where 𝑅𝑖

2 is the coefficient of determination

from regressing daily firm returns on the market return and industry return.

37

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Figure 1a

OLS Investment sensitivity to market-to-book as a function of information quality

Figure 1b

OLS Investment sensitivity to operating profits as a function of information quality

0.000

0.005

0.010

0.015

0.020

0.025

0.030

Low IIQ High IIQ

Investment sensitivity to market-to-book

FilingSpeed Guidance GuidanceAccuracy

NoICW NoError

0.000

0.050

0.100

0.150

0.200

0.250

0.300

Low IIQ High IIQ

Investment sensitivity to operating profits

FilingSpeed Guidance GuidanceAccuracy

NoICW NoError

43

Figure 1c

OLS Investment sensitivity to cash holdings as a function of information quality

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 (IIQ) groups using the top and bottom two quintiles of each of the four information quality measures (except

for Guidance, NoICW and NoError). We then estimate equation (1) within each group and plot the coefficient

estimates 𝛼1 in Fig. 1a, 𝛼2 in Fig. 1b, and 𝛼3 in Fig. 1c.:

𝐼𝑖𝑡

𝐴𝑖𝑡−1

= 𝛼0 + 𝛼1

𝑀𝑖𝑡−1

𝐴𝑖𝑡−1

+ 𝛼2

𝐸𝐵𝐷𝑖𝑡

𝐴𝑖𝑡−1

+ 𝛼3

𝐶𝑎𝑠ℎ𝑖𝑡−1

𝐴𝑖𝑡−1

+ 𝛼4𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1 + 𝛼5 ln(𝐴𝑖𝑡−1) + 𝑒𝑖𝑡

0.050

0.075

0.100

0.125

0.150

0.175

0.200

Low IIQ High IIQ

Investment sensitivity to cash holding

FilingSpeed Guidance GuidanceAccuracy

NoICW NoError

44

Table 1

Descriptive Statistics

Panel A: Distribution statistics

N Mean Std. Dev. 25% 50% 75%

Investment 83,645 0.137 0.156 0.043 0.089 0.170

EBD 83,645 0.105 0.194 0.051 0.115 0.188

M/A 83,645 2.175 2.058 1.120 1.532 2.295

Cash 83,645 0.203 0.229 0.031 0.110 0.298

Leverage 83,645 0.208 0.209 0.016 0.165 0.326

Size 83,645 5.608 2.112 4.073 5.482 7.031

FilingSpeed 80,843 -51.373 24.662 -63.000 -47.000 -34.000

Guidance 55,917 0.290 0.454 0.000 0.000 1.000

GuidanceAccuracy 11,050 -0.020 0.031 -0.026 -0.008 -0.002

NoICW 33,683 0.882 0.323 1.000 1.000 1.000

NoError 47,335 0.901 0.298 1.000 1.000 1.000

CAR 78,852 0.098 0.540 -0.203 0.063 0.351

Surprise 52,289 -0.011 0.108 -0.003 0.000 0.002

Panel B: Pearson (Spearman) correlations

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)

(1)Investment 1 0.12 0.42 0.37 -0.13 -0.27 -0.02 -0.09 0.00 -0.03 0.01 0.02 0.04

(2)EBD 0.33 1 -0.05 -0.10 -0.13 0.15 0.28 0.10 0.06 0.13 0.03 0.16 0.10

(3)M/A 0.39 0.32 1 0.38 -0.15 -0.20 0.03 0.03 0.06 -0.03 0.02 -0.14 0.06

(4)Cash 0.32 0.07 0.38 1 -0.41 -0.30 -0.02 -0.05 0.00 0.00 0.04 -0.02 0.03

(5)Leverage -0.23 -0.18 -0.27 -0.56 1 0.22 -0.08 -0.05 -0.04 -0.02 -0.04 0.07 -0.11

(6)Size -0.20 0.06 -0.11 -0.27 0.30 1 0.36 0.24 0.07 0.13 -0.01 -0.15 0.07

(7)FilingSpeed 0.11 0.31 0.14 -0.02 -0.04 0.41 1 0.24 0.06 0.27 0.08 -0.07 0.06

(8) Guidance -0.03 0.11 0.10 -0.03 -0.02 0.26 0.27 1 0.09 0.04 0.00 -0.11 0.13

(9)

GuidanceAccura

cy 0.00 0.11 0.20 0.06 -0.04 0.15 0.07 0.10 1 0.03 0.01 -0.05 0.04

(10)NoICW -0.01 0.13 0.01 -0.01 0.00 0.13 0.23 0.04 0.12 1 0.26 -0.12 0.24

(11)NoError 0.02 0.05 0.03 0.03 -0.04 -0.01 0.06 0.00 0.04 0.26 1 0.06 0.01

(12)CAR 0.00 0.17 -0.15 0.00 0.04 -0.17 -0.06 -0.08 -0.04 -0.15 -0.07 1 -0.12

(13)Surprise 0.07 0.15 0.10 0.08 -0.07 0.08 0.06 0.13 0.05 0.03 -0.07 0.05 1

This table reports the summary statistics for the main sample of firm-years from 1988-2015. 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.

FilingSpeed is the time it takes the firm to file its financial statements, multiplied by -1. Guidance is a dummy

variable equal to 1 if the firm issues at least one quarterly or annual management EPS forecast. GuidanceAccuracy

is annual management forecast accuracy averaged over three years prior to the investment year. NoICW is a dummy

variable equal to 1 if the firm does not disclose internal control weaknesses. NoError is a dummy variable equal to 1

if the firm does not have restatements resulting from unintentional errors. CAR is the annual cumulative abnormal

return. Surprise is the annual earnings surprise. In the correlation table, numbers in bold are significant at the 1%

level.

45

Table 2

Internal Information Quality and Investment Responses to Market and Accounting Signals

Dependent variable = Investment

IIQ = Filing Speed Guidance

Guidance

Accuracy NoICW No Error

(1) (2) (3) (4) (5) (6)

M/A 0.022*** 0.029*** 0.022*** 0.013*** 0.038*** 0.024***

(5.92) (7.83) (5.80) (3.55) (10.14) (6.54)

EBD 0.130** 0.043* 0.010 0.158*** 0.052** 0.017

(2.13) (1.69) (0.37) (5.97) (1.99) (0.65)

Cash 0.109*** 0.129*** 0.125*** 0.115*** 0.090*** 0.106***

(8.46) (10.03) (9.72) (8.93) (7.00) (8.24)

IIQ 0.047*** 0.015*** 0.013** 0.022*** 0.004

(9.14) (2.86) (2.46) (4.17) (0.78)

M/A*IIQ -0.019*** -0.010*** -0.009*** -0.013*** -0.004**

(-11.88) (-6.08) (-5.70) (-7.86) (-2.24)

EBD*IIQ 0.147*** 0.134*** 0.045* 0.037* 0.005

(9.44) (5.12) (1.72) (1.93) (1.18)

Cash*IIQ -0.041*** -0.041*** -0.047*** 0.022 0.006

(-3.44) (-3.46) (-3.96) (1.04) (0.51)

Leverage 0.018*** 0.023*** 0.014 -0.013 0.040*** 0.032***

(3.41) (2.61) (1.63) (-1.46) (4.56) (3.65)

Size -0.011*** -0.013*** -0.012*** -0.007*** -0.011*** -0.013***

(-6.54) (-12.92) (-11.73) (-6.78) (-11.52) (-12.99)

Industry & Year

Dummies Yes Yes Yes Yes Yes Yes

Obs. 83,645 80,843 56,382 11,007 33,683 47,334

𝑅2 35.78% 38.36% 37.23% 38.15% 42.78% 40.20%

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 corrected for cross-sectional and time-

series correlations using a two-way cluster at the firm and year level. t-statistics are presented in parentheses

below the coefficients. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

46

Table 3

Investment Responses to Market and Accounting Signals after Remediation of Internal

Control Weaknesses

Dependent variable = Investment

M/A 0.045***

(5.41)

EBD 0.049***

(4.03)

Post-ICW 0.010

(1.15)

Cash 0.162***

(7.61)

M/A*Post-ICW -0.010**

(-2.08)

EBD*Post-ICW 0.014**

(2.16)

Cash*Post-ICW -0.009

(-0.36)

Leverage 0.065***

(3.94)

Size -0.003***

(-2.98)

Firm Fixed Effect Yes

Industry and Year Cluster Yes

Obs. 7,369

𝑅2 59.93%

This table presents analyses of the effect of internal control weakness remediation on investment responsiveness

to market and accounting signals. All variables are defined in the Appendix. Firm fixed effects are included.

Standard errors are corrected for cross-sectional and time-series correlations using a two-way cluster at the

industry and year level. t-statistics are presented in parentheses below the coefficients. ***, **, and * denote

significance at the 1%, 5%, and 10% levels, respectively.

47

Table 4

The Impact of an External Shock to Internal Information Quality

Dependent variable = Investment

Treatment group

(1)

Control group

(2)

Difference in

coefficient estimates

(3)

M/A 0.012*** 0.025***

(7.54) (8.00)

EBD 0.193*** 0.078**

(4.25) (2.03)

Cash 0.085*** 0.189***

(8.36) (4.65)

Post_SFAS142 -0.007* -0.013*

(-1.93) (-1.78)

M/A*Post_SFAS142 -0.009*** 0.004 -0.013***

(-9.48) (1.07)

EBD*Post_SFAS142 0.040** 0.034 0.005*

(2.44) (0.97)

Cash*Post_SFAS142 0.010 -0.042 0.051

(0.69) (-0.84)

Leverage -0.016** 0.096***

(-2.04) (5.30)

Size -0.005*** -0.015***

(-8.17) (-5.10)

Industry & Year

Dummies Yes Yes

Obs. 4,996 3,573

𝑅2 34.81% 40.16%

This table presents analyses on the impact of GAAP changes on firms’ investment responses to external and

internal investment signals. We use the SFAS 142 setting as a shock to firms’ internal information systems. All

variables are defined in the Appendix. All regressions include industry fixed effects Standard errors are corrected

for cross-sectional and time-series correlations using a two-way cluster at the firm and year level. t-statistics are

presented in parentheses below the coefficients. ***, **, and * denote significance at the 1%, 5%, and 10% levels,

respectively.

48

Table 5

Price Informativeness and Investment Responses to Market and Accounting Signals

Dependent variable = Investment

Price informativeness (PI) = ψ APIN PublicProp

(1) (2) (3)

M/A 0.007*** 0.015*** 0.013***

(3.93) (4.18) (7.02)

EBD 0.200*** 0.141*** 0.167***

(7.57) (2.74) (5.96)

Cash 0.097*** 0.080*** 0.024

(6.90) (2.87) (1.47)

PI -0.072*** -0.022** 0.032***

(-8.60) (-2.39) (2.66)

M/A*PI 0.025*** 0.012** 0.010**

(8.31) (2.23) (2.50)

EBD*PI -0.182*** 0.025 -0.244***

(-5.21) (0.40) (-5.40)

Cash*PI 0.116*** -0.054 0.228*

(4.95) (-1.42) (1.78)

Leverage 0.040*** 0.006 0.031***

(4.96) (0.58) (3.45)

Size -0.013*** -0.005*** -0.011***

(-10.82) (-3.29) (-9.95)

Industry & Year Dummies Yes Yes Yes

Obs. 78,997 19,888 41,968

𝑅2 26.09% 24.49% 30.92%

This table presents estimates from panel regressions of firm-year investment on market-to-book, operating earnings,

cash holdings, price informativeness measures and the interactions, leverage and size. All variables are defined in the

Appendix. To facilitate interpretation, all external information quality proxies are scaled between 0 and 1. All

regressions include industry and year fixed effects. Standard errors are corrected for cross-sectional and time-series

correlations using a two-way cluster at the firm and year level. t-statistics are presented in parentheses below the

coefficients. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

49

Table 6

Alternative Measures of Market and Accounting Signals

Dependent variable = Investment

IIQ = Filing Speed Guidance

Guidance

Accuracy NoICW NoError

(1) (2) (3) (4) (5) (6)

Ind_Q 0.010*** 0.022*** 0.023*** 0.018*** 0.006 0.017***

(3.21) (4.48) (6.68) (3.61) (0.67) (2.97)

Sales_gr 0.057*** 0.054*** 0.064*** 0.060*** 0.074*** 0.054***

(8.66) (6.26) (7.80) (8.12) (7.31) (10.37)

Cash 0.130*** 0.239*** 0.186*** 0.143*** 0.248*** 0.194***

(10.08) (11.16) (10.88) (6.67) (8.34) (10.55)

IIQ 0.070*** 0.009 0.007 0.019 0.005

(6.54) (1.38) (0.67) (1.52) (0.79)

Ind_Q*IIQ -0.007* -0.005* -0.003 -0.002* -0.002*

(-1.92) (-1.88) (-1.47) (-1.90) (-1.98)

Sales_gr*IIQ 0.023*** 0.006 0.023*** 0.020* 0.005

(3.53) (0.64) (3.41) (1.74) (0.77)

Cash*IIQ -0.118*** -0.052*** -0.037 -0.033 0.010

(-4.52) (-4.36) (-1.41) (-1.38) (0.68)

Leverage 0.003 0.034*** 0.015 -0.019* 0.071*** 0.056***

(0.23) (3.16) (1.43) (-1.73) (6.17) (4.88)

Size -0.010*** -0.011*** -0.009*** -0.004*** -0.011*** -0.010***

(-11.30) (-13.97) (-10.18) (-5.12) (-9.71) (-10.94)

Industry &

Year Dummies Yes Yes Yes Yes Yes Yes

Obs. 76,733 74,408 51,053 10,879 31,488 44,152

𝑅2 29.87% 31.81% 31.93% 32.52% 31.01% 32.07%

This table presents analyses using industry Q as an alternative proxy for external investment signal, and sales growth

as an alternative proxy for internal investment signal. We replace M/A with industry Q, replace EBD with sales

growth, and replicate the tests in Table 2. All variables are defined in the Appendix. All regressions include industry

and year fixed effects. Standard errors are corrected for cross-sectional and time-series correlations using a two-way

cluster at the firm and year level. t-statistics are presented in parentheses below the coefficients. ***, **, and * denote

significance at the 1%, 5%, and 10% levels, respectively.

50

Table 7

Internal Information Quality and the Impact of Mispricing

Dependent variable = Investment

IIQ = Filing Speed Guidance

Guidance

Accuracy NoICW NoError

(1) (2) (3) (4) (5)

M/A 0.022*** 0.014*** 0.006 0.009* 0.018***

(5.60) (6.10) (1.12) (1.76) (4.93)

EBD 0.107** 0.267*** 0.457*** 0.245*** 0.180***

(2.37) (5.63) (5.62) (4.17) (3.75)

Cash 0.081*** 0.067*** 0.014 0.017 0.012

(4.56) (5.76) (0.53) (0.60) (0.74)

IIQ 0.034*** -0.006 0.013* 0.010 0.006

(4.98) (-1.47) (1.68) (1.52) (1.57)

|CAR| 0.011*** 0.018*** 0.013 0.013** 0.016***

(3.15) (4.10) (1.64) (2.40) (4.40)

|Surprise| -0.001 -0.005 0.012 -0.027*** -0.008

(-0.06) (-0.63) (1.29) (-2.59) (-0.79)

M/A*IIQ -0.019*** -0.008*** -0.010* -0.001** -0.003*

(-7.61) (-3.19) (-1.80) (-1.96) (-1.88)

EBD*IIQ 0.299*** 0.102*** 0.005 0.092** 0.025*

(6.37) (3.91) (1.05) (2.18) (1.72)

Cash*IIQ -0.095*** -0.061*** -0.040 -0.054*** 0.032**

(-4.79) (-5.65) (-1.13) (-2.64) (2.26)

M/A*|CAR| 0.002* 0.002 0.003 0.003 0.004**

(1.82) (1.02) (0.53) (1.48) (2.19)

EBD*|CAR| -0.022 -0.014 -0.101 -0.076** -0.026

(-0.89) (-0.50) (-1.21) (-2.33) (-0.89)

Cash*|CAR| 0.071 0.062 0.075 0.083* 0.084*

(1.63) (1.06) (1.40) (1.89) (1.75)

M/A*|Surprise| 0.009 0.015** 0.015*** 0.028*** 0.010

(1.41) (2.32) (2.82) (5.34) (1.28)

EBD*|Surprise| -0.098*** -0.193*** -0.269*** -0.108** -0.100***

(-2.69) (-4.58) (-3.48) (-2.13) (-2.67)

Cash*|Surprise| 0.137 0.121 0.119 0.122 0.154*

(1.62) (1.64) (1.01) (1.44) (1.88)

Leverage 0.047*** 0.041*** 0.001 0.053*** 0.057***

(5.89) (4.35) (0.12) (5.97) (6.29)

Size -0.012*** -0.011*** -0.005*** -0.008*** -0.011***

(-10.24) (-7.04) (-4.92) (-7.47) (-6.14)

Industry & Year

Dummies Yes Yes Yes Yes Yes

51

Obs. 52,295 35,218 10,089 23,109 31,638

𝑅2 31.30% 30.53% 32.42% 30.92% 30.45%

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 corrected for cross-sectional and time-series correlations using a two-way cluster at the firm and year

level. t-statistics are presented in parentheses below the coefficients. ***, **, and * denote significance at the 1%, 5%,

and 10% levels, respectively.

52

Table 8

Firm Fixed Effects and Industry Clustering

Dependent variable = Investment

IIQ (unscaled)

= Filing Speed Guidance

Guidance

Accuracy No ICW No Error

(1) (2) (3) (4) (5) (6)

M/A 0.017*** 0.011*** 0.018*** 0.007*** 0.021*** 0.021

(5.70) (6.52) (5.65) (4.09) (7.46) (8.10)

EBD 0.032** 0.109*** 0.023*** 0.123*** 0.018** 0.011

(2.23) (3.16) (2.89) (5.46) (2.53) (2.33)

Cash 0.011** 0.034*** 0.007 0.013 0.006 0.009

(2.28) (3.12) (0.58) (0.68) (0.31) (0.32)

IIQ 0.000*** 0.016*** 0.267*** 0.003 -0.003

(3.73) (3.81) (2.74) (0.38) (-0.50)

M/A*IIQ -0.000*** -0.006*** -0.184* -0.003* -0.003*

(-3.25) (-2.76) (-1.82) (-1.92) (-1.83)

EBD*IIQ 0.001*** 0.033*** 0.642** 0.028* 0.017*

(3.98) (2.79) (2.21) (1.91) (1.83)

Cash*IIQ 0.000* -0.005 0.307 -0.008 -0.005

(-1.81) (-0.33) (0.58) (-0.57) (-0.21)

Leverage -0.046*** -0.046*** -0.058*** -0.060*** -0.031 -0.032

(-3.82) (-3.39) (-4.29) (-3.30) (-1.41) (-1.41)

Size -0.040*** -0.041*** -0.050*** -0.031*** -0.058*** -0.059***

(-4.92) (-5.01) (-5.21) (-5.58) (-4.40) (-4.44)

Firm and Year

Fixed Effects Yes Yes Yes Yes Yes Yes

Obs. 83,645 80,843 56,382 11,007 33,683 47,334

𝑅2 67.49% 68.59% 67.84% 80.27% 76.63% 76.62%

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. IIQ proxies are not scaled. All regressions include firm and year fixed effects. Standard

errors are corrected for cross-sectional and time-series correlations using a two-way cluster at the industry and

year level. t-statistics are presented in parentheses below the coefficients. ***, **, and * denote significance at the

1%, 5%, and 10% levels, respectively.

53

Table 9

Measurement Error Consistent Estimation

Panel A Estimates of measurement quality ( 𝜏2 )

IIQ = FilingSpeed Guidance GuidanceAccuracy No ICW No Error

Slow Fast No Yes Low High ICW No ICW Error No Error

M/A 0.080 0.044*** 0.079 0.039*** 0.070 0.052***

0.070 0.042*** 0.071 0.038***

[0.001] [0.002] [0.001] [0.001] [0.001] [0.001] [0.001] [0.001] [0.001] [0.001]

𝜏2 0.495 0.469** 0.451 0.470** 0.497 0.472

0.459 0.478 0.442 0.448

[0.012] [0.013] [0.011] [0.017] [0.012] [0.013] [0.012] [0.015] [0.012] [0.014]

Panel B Differential investment responses across low and high information quality groups

IIQ = FilingSpeed Guidance GuidanceAccuracy No ICW No Error

Slow Fast No Yes Low High ICW No ICW Error No Error

M/A 0.079 0.045*** 0.074 0.016*** 0.075 0.037***

0.077 0.028*** 0.071 0.025***

[0.002] [0.004] [0.002] [0.003] [0.004] [0.004] [0.004] [0.005] [0.004] [0.004]

EBD 0.105 0.116*** 0.078 0.223*** 0.072 0.189***

0.076 0.386*** 0.069 0.317***

[0.009] [0.021] [0.008] [0.016] [0.008] [0.019] [0.009] [0.029] [0.008] [0.016]

Cash 0.062 0.054*** 0.061 0.035** 0.047 0.038***

0.037 0.033** 0.026 0.038*

[0.007] [0.008] [0.007] [0.008] [0.009] [0.008] [0.009] [0.007] [0.007] [0.008]

Leverage 0.053 0.046*** 0.072 -0.015** 0.062 -0.025***

0.069 -0.046*** 0.054 -0.027***

[0.004] [0.006] [0.004] [0.007] [0.008] [0.007] [0.011] [0.008] [0.010] [0.009]

Size -0.002 -0.006*** -0.004 -0.006*** -0.004 -0.005***

-0.006 -0.006** -0.007 -0.005**

[0.000] [0.000] [0.000] [0.001] [0.002] [0.001] [0.006] [0.007] [0.008] [0.006]

𝜏2 0.513 0.419*** 0.502 0.419*** 0.492 0.411***

0.476 0.436*** 0.439 0.435***

[0.013] [0.010] [0.011] [0.014] [0.013] [0.017] [0.013] [0.018] [0.014] [0.019]

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 𝜏2), 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.