Measuring Knightian Uncertainty and Risk with
Textual Analysis: An Examination of Cash Holdings
and Derivatives Use ∗
Richard Friberg†
Thomas Seiler‡
March 21, 2016
Abstract
We construct text-based indices to measure the overall uncertainty, risk and
Knightian uncertainty faced by firms. Our dataset builds on the dictionaries developed by Loughran and McDonald (2011) and covers all annual reports of U.S. firms
filed with the SEC between fiscal year 1995 and 2013. We find that high risk firms
are clustered in competitive industries, producing homogeneous goods using well established technologies. Knightian uncertainty is prominent in high-tech industries,
for example semi-conductors or pharmaceuticals, and industries that experienced
a large technological shock over our sample period. We also find intuitive and statistically robust correlations between our indices and a firm’s credit ratings and its
market beta. We extend a standard model for a firm’s liquidity needs to include
Knightian uncertainty. In line with the extended model, we find firms facing higher
Knightian uncertainty to hold more cash and firms facing higher risks to be more
likely to hedge using derivatives.
Keywords: Cash holdings; Hedging; Knighian Uncertainty; Risk; Textual Analysis
JEL: D81; G31; G32.
∗
Financial support from the Jan Wallander and Tom Hedelius Foundation is gratefully acknowledged.
Thomas Seiler thanks Martin Seiler and Ruben Andrist for helpful discussions.
†
Richard Friberg ([email protected]).
‡
Thomas Seiler ([email protected]).
1
1
Introduction
When placing bets at a roulette table we can rely on objective probabilities to guide our
actions. In many other cases we lack such objective probability measures and we may
then rely on subjective probabilities, reflecting our best guesses and the bets that we
are willing to enter. In the subjective expected utility framework, that was formalized
by Savage (1954), the distinction between the two types of probabilities is mute. Being
able to use subjective probabilities when objective ones are missing clearly simplifies
the analysis of decision problems and Savage’s framework has supplied the foundation
for mainstream economics and finance. Indeed, such a path was reluctantly suggested
already by Keynes (1937, 214): “Nevertheless, the necessity for action and for decision
compels us as practical men to do our best to overlook this awkward fact [the difference
between objective and subjective probabilities] and to behave exactly as we should if we
had behind us a good Benthamite calculation of a series of prospective advantages and
disadvantages, each multiplied by its appropriate probability, waiting to be summed.”
While the subjective expected utility framework has proven convenient to work with,
a large experimental literature inspired by the thought experiments of Ellsberg (1961)
indicates that many of us prefer decision problems with known probabilities to decision
problems with unknown probabilities. A focus on this distinction stretches back to at
least Knight (1921) and we sometimes refer to situations featuring objective probabilities
as risk and situations where objective probabilities are missing as Knightian uncertainty.
Following the experimental literature, decision-theoretic models have been developed by
for instance Gilboa and Schmeidler (1989) and Schmeidler (1989) that explain the experimental patterns by introducing the notion of ambiguity aversion.1 A handful of articles
have used such models of ambiguity averse agents to examine asset price determination and investment behavior (see for instance Epstein and Wang (1994); Nishimura and
Ozaki (2007); Miao and Wang (2011)). However, the empirical question of whether firms
differentiate between risk and Knightian uncertainty in decision making remains largely
unanswered. A key difficulty that one faces when attempting to trace the links between
behavior and Knightian uncertainty is how to empirically distinguish the two. The richness of theoretical work on Knightian uncertainty therefore contrasts with a paucity of
empirical field work.
The present article seeks to start filling the gap by presenting firm-level measures
of risk and Knightian uncertainty based on textual analysis of all 10-K statements for
1
See Etner et al. (2012) for a survey of the literature
2
U.S. firms covering the fiscal years from 1995 up to and including 2013. As we cover
later, a number of recent articles have used textual analysis as a prism to explore firm
behavior. One of the more influential articles is that by Loughran and McDonald (2011)
which emphasizes the need to base textual analysis of 10-K statements on word lists
specifically tailored to such texts. As our starting point, we take the list of “uncertainty” words compiled by Loughran and McDonald (2011) and classify terms into subcategories. Uncertainty words associated with objective probabilities, like “variance”,
“volatility” or “frequently”, we classify as risk. Those terms explicitly associated with
subjective probabilities (e.g. “believe”, “perhaps”), ambiguous outcomes (e.g. “ambiguous”,“indeterminate”) or Donald Rumsfeld’s memorable term “unknown unknowns” (e.g.
“sudden”,“unforeseen”) are classified as Knightian uncertainty. We use the occurrence
of these words in firms’ annual reports to define indices of overall uncertainty and a split
between risk and Knightian uncertainty.
With the indices in hand our first question is one of description: How have indices
developed over time and what are the cross-sectional patterns of Knightian uncertainty
and risk? The industries facing highest Knightian uncertainty are high-tech industries,
like the semiconductor industry and pharmaceuticals. Knightian uncertainty is also high
in industries that have been experiencing a drastic technological shock over our sample
period, like the advent of digital cameras for photo labs or the internet for video rental
firms. High risk firms are centred in competitive industries with relatively homogeneous
goods, like silver ores or metal cans.
Second, we relate our indices to classical risk measures. Our overall uncertainty index
is positively and robustely correlated with firms’ betas. Our subindices are also positively
associated with market risk, but the results are less robust. Our uncertainty measures
are also positively related to credit risk as measured by firm ratings. Counterfactual
simulations using our indices shows, that these associations are not just statistically
significant, but also economically relevant. We take these intuitive patterns as evidence,
that our measures are capturing distinct aspects of overall uncertainty.
Finally we link our measures to firm policy. The list of potential issues to examine
is long but we choose to focus on cash holdings which is particularly interesting from a
perspective of opportunities and threats in different states of the world. A number of
articles have documented that cash holdings by U.S. firms have increased greatly over
the last few decades (see e.g. Opler et al. (1999); Bates et al. (2009); Almeida et al.
(2014) provide a survey). For instance, Bates et al. (2009) show that cash-to-asset ratios
for U.S. industrial firms more than doubled between 1980 and 2006 and that the increase
3
was most marked for firms in industries that had higher cash-flow volatility.
Much of the work on cash holdings has taken the type of models proposed by Froot
et al. (1993) as a starting point: firms want to manage liquidity in such a way that they
are able to finance investments and ensure survival also in adverse states of the world.
This relates to the precautionary motive for holding cash that was proposed by Keynes
(1936). However, cash is of course only one way to provide liquidity and firms can attempt
to avoid costly shortfalls in liquidity by insuring against risks with derivatives, securing
credit lines or maintaining a strong enough financial position that they expect to have
ready access to credit also in unfavorable states of the world. The relative attractiveness
of these different ways of ensuring liquidity are liable to depend on beliefs about the
existence and likelihoods of different states of the world.
As we explore below, an advantage of cash is that it is valuable in many states of the
world and in this sense we expect greater Knightian uncertainty to be associated with
more cash holdings. Indeed, in our empirical analysis we establish that higher Knightian
uncertainty is associated with higher cash holdings but find no link between cash holdings
and our text based measure of risk. We are able to explore one more aspect of liquidity
management, namely the use of derivatives. For derivatives use the relation with our text
based measure of risk is positive and significant. In contrast, our measure of Knightian
uncertainty is not significantly related to the use of derivatives.
Taken together these results then are supportive of the notion that firms do distinguish
between risk and Knightian uncertainty. While there is a rapidly growing literature on the
use of text based risk measures we are only aware of one precursor that examines both
risk and (Knightian) uncertainty at the firm level using text based analysis, Avramov
et al. (2014) who use 10-K statements from 2001 until 2010. They find that risk shocks
are associated with effects on a number of variables such as leverage, investment and
cash holdings whereas they find little effects of uncertainty shocks, a result that they
interpret as supporting that uncertainty shocks induce a “wait-and-see” strategy. Thus,
similar to our results, theirs indicate that firms differentiate between risk and Knightian
uncertainty. As we discuss below, their definitions of risk differs substantially from ours
and focuses on negative shocks. Their definition of Knightian uncertainty is also much
narrower than ours.
Our paper is structured as follows. In the next section we sketch a model of cash
holdings and derivatives use under risk and Knightian uncertainty. In section 3 we then
detail the construction of the data set and elaborate on how we set out to measure risks
and Knightian uncertainty. In section 4 we describe our estimation strategy and then
4
in section 5 we examine the development of our uncertainty indices over time and study
cross-sectional patterns. As a further validation exercise we relate our measures to firm
betas and to credit ratings in section 6. We then turn to the study of cash holdings and
hedging in section 7 and then conclude in section 8.
2
Knightian Uncertainty, Risk and Cash Holdings:
Theory and Empirical Predictions
Triggered by the marked increase in cash holdings by U.S. firms in the last decades a
burgeoning literature examines the determinants of cash holdings and the interaction
with other variables affecting liquidity, such as bank credit lines (Sufi (2009)), hedging
(Disatnik et al. (2014) and debt maturity (Harford et al. (2014)). Much of the work points
to that Keynes’s 1936 precautionary savings motive is a key motivation for the increase in
cash holdings.2 The precautionary savings motive arises because the potential for shocks
combine with credit constraints. Credit constraints imply that a firm may not be able to
access credit in states of the world where responding to threats or opportunities imply
the need for additional funds. As shown in the survey of Almeida et al. (2014) previous
work indicates that more financially constrained firms and firms that face more variable
conditions hold more cash. In other words, cash holding is positively related to risk when
the latter is given a broad interpretation.
The precautionary motive for holding cash may intuitively be related to Knightian
uncertainty. A large war chest of liquid reserves may be useful in managing many situations, allowing freedom to act also in states of the world that were hard to foresee or
states whose probability might be hard to gauge. In contrast, insurance or derivatives
contracts may be useful in managing risks of specific events such as a plant burning down
or the market price of an input rising above a particular level. State contingent contracts
of this type are however less suited to manage broad uncertainties in a fluid environment.
Even if the preceding discussion is intuitive it may be useful with a modeling framework
to fix ideas. We first present the model of Almeida et al. (2014), who in turn build on
Holmstrom and Tirole (1998) and Tirole (2006) to frame our discussion of decisions under
risk. We then extend the model to examine choice under Knightian uncertainty.
2
Other motivations are a transactions motive and speculative motive which are also due to Keynes
(1936)), lowering taxes (Foley et al. (2007)) and entrenched managers wanting to give themselves greater
latitude (Jensen (1986)).
5
2.1
Cash Holding and Hedging under Risk
At time 0, a firm is equipped with assets A and can invest amount I in a business
opportunity. At time 1 there is a liquidity shock ρ with probability λ. If the shock
occurs, the firm needs to pay ρI to keep the investment alive. Should the liquidity needs
not be met, the project will be terminated which results in a return of 0. If the project
is continued, it will produce a return RI with probability pi . The firm can affect the
probability of success of the project by either being diligent or not. If it is diligent, the
probability of success is pG ,otherwise the probability of success will be lower at pB . If the
firm decides not to be diligent, it can secure a private benefit B. The firm can borrow
money at any time from a risk neutral outside investor, but it might be credit constrained
due to the moral hazard problem. We are interested in the optimal contracts offered in
these conditions. To focus the analysis on the interesting aspects of the problem, we
assume an investor wants to induce high effort in the firm and that in this case, the net
present value would be positive, even after a liquidity shock. Figure 1 presents the main
features of the model.
In terms of our distinction between risk and Knightian uncertainty, we might want
to think of the uncertainty λ around the liquidity shock as a situation of risk for which
a frequentist risk measure could potentially be devised. Since the probability of success
pi depends solely on the firm’s behaviour, it could be viewed as a rather subjective
probability, hence falling into our domain of Knightian uncertainty. The model setup,
however, does not take account of this distinction. It is thus akin to a situation of risk
or a world where agents do not distinguish between risk and Knightian uncertainty.
Given our assumptions, the optimal second best contract with an outside investor
characterises the amount of investment I and the distribution of the returns of the investment. It solves the following program:
max(Rf − λρ)I − A
pG Rf ≥ pB Rf + B
I(R − Rf ) − λρI ≥ I − A
(ICC)
(P C)&(BC)
The firm maximizes its expected return. The return offered to the firm needs to satisfy
its incentive compatibility constraint (ICC) – otherwise the high effort case would not
be implemented. Moreover, the contract also needs to satisfy the investors participation
constraint (PC) – the investor needs to be willing to lend money to the firm. The
6
RI
0
0
-I
RI
i=G,B
0
-ρI
0
0
1
2
time
Figure 1: A framework for liquidity management.
return to the investor needs to be larger than the amount invested, even after taking into
account the possibility of a liquidity shock in period 1. The participation constraint also
corresponds to a budget constraint for the firm.
Assume the (ICC) is binding we then have:
Rf =
B
pG − pB
The highest pledgable income of the firm is given by the expected return on investment
minus the minimal incentive payments necessary to implement high effort in period 2:
ρ0 ≡ pG
B
R−
pG − pB
.
The assumptions imply that the firm will invest as much as possible, which implies
that the participation or budget constraint will bind and the firm will invest:
I∗ =
A
.
1 − ρ0 + λρ
(1)
A key feature of the model is the wedge between pledgable income ρ0 and the expected
7
return of the project ρ1 = pH R. If the firm is hit by a liquidity shock we assume that the
liquidity shock ρ is sufficiently low that it is worth to continue also in this state (ρ < ρ1 )
but that the necessary follow up investment is greater than pledgeable income (ρ0 < ρ):
ρ0 < ρ < ρ1 ≡ pG R.
The firm will thus not be able to source the money needed to cushion a negative liquidity
shock in period 1. To be able to finance investment in the case where a liquidity shock
hits the firm needs to have liquidity and hence cash C ∗ equal to :
C ∗ = (ρ − ρ0 )I ∗ = (ρ − ρ0 )
A
.
1 − ρ0 + λρ
(2)
Almeida et al. (2014) use this expression to characterize the precautionary demand for
cash. For a given investment, the higher the possible liquidity needs, the more liquidity
is kept. Cash holdings allow the firm to keep profitable investments alive even when
bad shocks happen. Note that if the firm invested all its assets in period 0, but did not
borrow, it would not be able to access credit in period 1 after a liquidity shock. The
borrowing capacity is then too low in state λ. Before the state is realized it can borrow
and keep part of the funds as a reserve but after the liquidity shock is realized there is
not sufficient borrowing capacity.
Following Almeida et al. (2014) we can also explore hedging with derivatives. Consider
a state contingent contract that pays yλ in state λ and y1−λ in state 1 − λ. Assume that
this contract can be purchased at a actuarially fair price of 0. Purchasing
yλ = (ρ − ρ0 )I
ρ − ρ0
y1−λ = −λI
1−λ
(3)
(4)
would then provide sufficient liquidity in period 1 even in the absence of precautionary
cash holdings. In that case cash and hedging would be perfect substitutes. In practice
a constraint might imply that only a limited amount can be hedged with derivatives, a
limit that we denote by yλmax . Using the superscript P H to denote partial hedging the
associated cash level would then be given by
C P H = (ρ − ρ0 )I ∗ − yλmax .
8
(5)
2.2
Cash Holdings and Hedging under Knightian Uncertainty
Formally treating forward looking decisions when there is no probability distribution and
with a potential for unforeseen contingencies is by its very nature a challenge. One way
of formalizing Knightian uncertainty is due to Gilboa and Schmeidler (1989). In their
framework we assume that agents’ beliefs regarding the likelihood of a possible state of
the world is captured by a set of probabilities P rather than by a unique probability.
While this perhaps fails to capture the full richness of Knightian uncertainty3 it has
proven relatively convenient to work with and is used by for instance Epstein and Wang
(1994), Nishimura and Ozaki (2007) and Caballero and Simsek (2013). Extending Savage’s framework to include an axiom of ambiguity aversion, Gilboa and Schmeidler (1989)
show that an individual under their axioms would follow a maximin expected utility rule.
That is, such an individual would choose the action that yielded the highest expected
utility under the most pessimistic probability p from the set P . She would thus strictly
prefer action f over action g if and only if:
minEp (f ) > minEp (g) .
p∈P
p∈P
(6)
Let us assume that decision makers in firms correspond to this rule, but that investors
continue to be rational profit maximizers. In a first minimal extension to the setting above
assume that there is not a unique probability pG of a good state if the firm is diligent but
that instead agents believe that the success probability in the high effort case belongs to
a convex set of probabilities P . For concreteness we follow Nishimura and Ozaki (2007)
and let the set P be defined as P = [pG − , pG + ]. We can then think of a higher as higher “Knightian uncertainty”. For instance if pG = 0.8 and = 0.1 agents would
believe that the probability of a successful investment p would lie somewhere between
p = 0.7 and p = 0.9.
Applying the Gilboa and Schmeidler (1989) result to the setting above and assuming
Knightian uncertainty surrounding pG , the firm’s moral hazard problem will be increased
as the perceived chances of success in the high effort case are now lower. This increases
the minimum amount of cash flow from investment that need to be channelled to the firm
3
See Binmore (2008) for a wide-ranging investigation of different aspects of Knightian uncertainty
9
to induce high effort, which lowers the pledgeable income to:
ρKU
0
B
≡ (pG − − pB ) R −
pG − < ρ0 .
(7)
In consequence, the cash holdings under Knightian uncertainty will be higher than before:
C KU = (ρ − ρKU
0 )
A
1−
ρKU
0
+ λρ
> (ρ − ρ0 )
A
≡ C ∗.
1 − ρ0 + λρ
(8)
This allows us to formulate our first hypothesis:
Hypotheses 1 Firms faced with higher Knightian uncertainty will hold more cash.
In a somewhat looser interpretation of the framework we also note that hedging techniques will help shield the firm against the risk due to the liquidity shock but offers no
help to manage Knightian uncertainty. Such a result is close to the original intuition in
Knight (1921). In the words of Rigotti and Shannon (2005, 203) “If risk were the only relevant feature of randomness, well-organized financial institutions should be able to price
and market insurance contracts that only depend on risky phenomena. Uncertainty, however, creates frictions that these institutions may not be able to accommodate.” We thus
hypothesize that:
Hypotheses 2 Firms faced with higher risk are more likely to use derivatives for
hedging purposes.
A simple extension of a standard cash flow model thus yields intuitive predictions
of how Knightian uncertainty might affect cash holdings. However, if we introduced
Knightian uncertainty on other sources of uncertainty in our model, the predictions could
change. If we had Knightian uncertainty on the probability of a liquidity shok λ, this
would drive a wedge between investors’ and firms’ perceptions of risk. Hedging contracts,
even though actuarially fair, would look too expensive from a firm perspective, while
cash holdings would not be affected as the underlying moral hazard problem remains
untouched. Similarly, introducing uncertainty on the probability of success in the low
effort case would dampen the moral hazard problem and reduce cash holdings. In either
case, Knightian uncertainty has a distinct impact on equilibrium behavior of firms which
warrants further empirical investigation.
10
3
Data
We combine data from several sources. The indexes on Knightian uncertainty and risk
that are key to our analysis are based on Form 10-K. Since the fourth quarter of 1994,
U.S. companies had the opportunity to file their Form 10-K, which essentially corresponds
to the firms’ annual reports, digitally with the Electronic Data Gathering Analysis and
Retrieval (EDGAR) system of the SEC. From May 6, 1996 onwards all companies fulfilling
certain size requirements were asked to make their filings via EDGAR. A filing contains
multiple documents, one of which is the actual Form 10-K, which we will use for our
analysis. The structure of Form 10-K is strictly regulated and has not changed much
over the years (see Nelson and Pritchard (2007)).4
Using the quarterly master index files available on the SEC server, we identify all
10-K and 10-KSB filings starting January 1 1994 and ending December 31 2014.5 For
each filing, we select the Form 10-K document only and exclude all exhibits, PDFs, Excel
files and images attached to the filing. Next, we remove all XBRL and ASCII data from
the document. Parsing the document into a tree structure, allows us to identify specific
HTML elements. We select and delete all tables with more than 15% numeric characters,
because such tables do most likely not contain actual text. Using the Jericho Java library,
we extract all remaining text from the reduced Form 10-K. If a Form 10-K document is
not stored in HTML format, which is the case for older filings, we still run this procedure,
but won’t be able to detect and remove tables. Indices for earlier years are thus likely to
suffer from more measurement error. The raw text data is split into an array of tokens
using ‘blanks’,‘tabs’ and punctuation to identify them. For each document we go through
all tokens and construct word counts using the overall uncertainty, risk and Knightian
uncertainty word lists. As measures of document length, we count the number of tokens
in each document as well as the number of words that occur in the complete Loughran
and McDonald (2011) dictionary.
Our procedure follows the recommendations by Loughran and McDonald (2011) closely,
except that we base our text measures on the actual Form 10-K only, which excludes all
attached exhibits. We believe this is the most relevant text body when trying to measure the risk environment of a company. To test our procedure, we ran it including the
exhibits, which produced word counts that are highly correlated with their values. For
4
The appendix contains a detailed overview on the structure of Form 10-K and changes in the technical
implementation of EDGAR filings.
5
Specifically, we download all 10-K/A, 10K405, 10-K405/A, 10-KSB, 10-KSB/A, 10-KSB40 and 10KSB40/A.
11
example, constructing a comparable uncertainty word count results in a correlation factor
of 0.96 with their uncertainty word count.
For each firm and fiscal year, we just keep one document, which we select as follows.
We prefer 10-K and 10-K405 over 10-KSB and 10-KSB40. We prefer any of those to the
appendix filings. Only the most preferred filing is kept for each firm and fiscal year. Last,
we delete all filings with less than 2000 tokens. We further drop all observations prior to
fiscal year 1995, as filing via EDGAR only became mandatory in May 1996. After these
limitations we have a sample of 197,109 observations.
Using the original filing names on the EDGAR server, we match our data with the
data of Loughran and McDonald (2011). In addition to the text data we construct, we
can make use of their list of positive and negative word counts as well as the share of
litigious words.
Our next source of data is Compustat. We match our text data to annual Compustat
data using the SEC Analysis Suite linking table via WRDS. The linking table provides us
with a link between the unique SEC identifier CIK and the Compustat identifier gvkey,
as well as information on the quality of the link. We drop observations which were not
linked or flagged to be weak links (flag variable in linking table not equal 2 or 3). Where
multiple links exist, we just keep the strongest. We are able to match a total of 156,116
observations.
For most of the Compustat data we follow Bates et al. (2009). We use Compustat measures of cash (CHE) and assets (AT) to generate the cash ratio (CHE/AT).
Size is measured as ln(AT). We in addition use the book value of equity (CEQ), share
price (PRCC F) and common shares outstanding (CSHO) to generate market-to-book
value as (AT-CEQ+PRCC F×CSHO)/AT. We use operating income (OIBDP), interest expenditure (XINT), taxes (TXT) and dividends to calculate cash flows to assets
as (OIBDP-XINT-TXT-DVC)/AT. Working capital is measured by WCAP and capital
expenditure by CAPX. We use long-term debt (DLTT) and debt in current liabilities
(DLC) to calculate leverage as (DLTT+DLC)/AT. Research and development expenditures are captured by XRD. As in Bates et al. (2009) we set these expenditures to 0 if the
variable is missing in Compustat. Acquisitons are measured by AQC/AT and in addition
we create a dummy variable that takes the value 1 if the firm paid a dividend (DVC>0).
We also follow Bates et al. (2009) and calculate a measure of cash flow variability at
the industry level. For each firm and fiscal year we calculate the standard deviation of
cash flow to assets for the previous 10 years (we require at least three observations per
firm to calculate this firm specific volatility). We take the average of these firm specific
12
volatilities within each industry (two digit SIC) and fiscal year and term it “industry
sigma”.
We furthermore use Compustat data to generate a dummy variable to indicate whether
a firm used derivatives. We denote a firm as a derivatives user in a given fiscal year if either
of the Compustat variables AOCIDERGL or HEDGEGL is different from zero. The first
of these measures unrealized gains and losses from financial hedging which offset variations
in future cash flows. Thus variable is available from 2001 onwards and is supposed to
capture commodities, foreign exchange rate and interest rate hedges designated as cashflow hedges. This has previously been used to create a dummy variable for hedging in
Chang et al. (2013). The second measure captures gain or loss on ineffective hedges and
has been used by for instance MacKay (2015).
Compustat is also the source for data used to relate debt ratings to our measures
of uncertainty. We use monthly Standard&Poor’s long-term debt ratings (SPLTICRM)
and match them by fiscal year and month to the firms in our data. We allow firms to
have multiple securities. The long-term ratings, we code as follows: we assign a value 7
to a bond if it is rated AAA, 6 for AA ratings, 5 for A ratings, 4 for B ratings, 3 for
BB, 2 for BBB and 1 to everything below. For explanatory variables we are inspired by
Blume et al. (1998). In addition to the variables defined before, we calculate interest rate
coverage (XINT+ OIADP)/XINT where OIADP is operating income after depreciation,
operating income to sales (OIBDP/SALE) and debt to asset ratio (DLTT/AT).6
We drop observations with negative assets and sales. Not all variables are present for
all firms and for the regressions on cash holdings and hedging in section 7 we exclude
financial firms (SIC 6000-6999) and utilities (SIC 4900-4999). These regressions are run
on a sample of 69,566 observations. To limit the potential for measurement error to
distort findings we winsorize Compustat data at the 1% and 99% level. Following Bates
et al. (2009) we winsorize leverage to be between 0 and 1. All nominal variables are
deflated by CPI with base 2010. Descriptive statistics on the variables used in the cash
holding regressions are presented in table A.5 in the appendix.
For our CAPM regressions we obtain financial market data via CRSP at a daily
frequency. We calculate fiscal-year and filing-year matched betas using holding period
returns (RET). We match this with the Fama/French return series data provided via
WRDS: we use the one month treasury bill return as the risk free rate and the value6
Blume et al. (1998) include average short term borrowings (BAST) in their calculation of leverage
but for us this reduces sample size substantially and in the regressions reported in section 6 short term
borrowings are omitted from total debt.
13
weighted return on all NYSE, AMEX and NASDAQ stocks as market portfolio. We also
run a three-factor model using the small minus big (SMB) and high minus low (HML)
portfolio return series.
Finally, in describing the cross-sectional patterns we also use some measures from
other sources that can plausibly be linked to the degree of Knightian uncertainty. We use
the measure of contract-specifity of intermediate inputs at the six-digit NAICS level from
Nunn (2007). We use a dummy for high technology industries which builds on Hecker’s
1999 work for the Bureau of Labor Statistics.
3.1
Using 10-K Statements to Measure Knightian Uncertainty
and Risk
3.1.1
Previous Measurement in the Literature
As large amounts of text data have become easily available researchers have started to use
this for quantitative analysis. In influential work Baker et al. (2015) develop a measure
for economic policy uncertainty based on newspaper articles. Their index is available
for several countries and also on a industry level within the U.S. The core of their index
draws from search results of 10 large US newspapers. An article is related to economic
policy uncertainty if it contains certain key words the authors defined. Their paper also
provides a set of key words related to fiscal policy, monetary policy, health care, national
security, regulation, foreign sovereign debt, entitlement programs and trade policies. We
have calculated their measures for our set of 10-K statements as well but make little use
of it in the present paper.
More closely related to our study are articles that create firm level measures of risk
building on 10-K statements.7 Most of these studies calculate a text based measure of
risk and relate it to stock market valuation of the firm. Li (2006) creates a measure
of risk by counting the frequency of the permutations of “risk” and “uncertainty” in
the whole 10-K filing for the years 1994 to 2005. He finds that higher risk sentiment
in the 10-K form is associated with lower future earnings and with lower stock returns
over a 12 month period. In Kravet and Muslu (2013) the level of analysis is not words,
7
Most text based studies focus on companies’ voluntary and involuntary disclosure and generate
measures for its quantity, quality and content i.e. how much is reported, how accessible or readable
is it and what is its general tone or sentiment. The obtained measures are then connected either to
accounting data or to market returns. Combined Cole and Jones (2005), Li (2010) and Kearney and Liu
(2014) provide a comprehensive overview to this literature. Prior to the availability of online corpuses,
manual analysis is reviewed by Jones and Shoemaker (1994).
14
but sentences. A sentence is defined to disclose risks if it contains at least one word
from a risk dictionary the authors compiled. Splitting Form 10-K into its subsections,
the authors can tabulate where they find the most risk related sentences. Most risky
sentences are found under Item 1 where a company’s business is described. Since 2005
this section contains Item 1A where companies need to discuss risks potentially affecting
their business. Another hot spot of risk related sentences is found in Item 7, the MD&A
section of the annual report. Together these two items make up 75% of all risk related
sentences. Their measure of risk disclosure is statistically and economically significantly
correlated with financial market indicators and analyst forecast dispersion. Campbell
et al. (2014) examine the information content of mandatory risk disclosures under Item
1A in Form 10-K since 2005. To construct their risk indices the authors build on the risk
categorization words by Nelson and Pritchard (2007).8 They complement these lists using
Latent Dirichlet Allocation (Blei et al. (2003)). This allows them to obtain key word lists
for (1) financial risk, (2) litigation risk, (3) tax risk, (4) other systematic risk and (5)
other idiosyncratic risks. They find that firms facing more risk, disclose more risk factors.
Moreover, the type of risk a firm faces is also correlated with the amount of disclosure
towards that risk. Bao and Datta (2014) apply unsupervised learning algorithms to the
analysis of 10-K risk factor disclosure. They focus on risk disclosure under Item 1A and
use a combination of the algorithms developed in Blei et al. (2003) and Huang and Li
(2011) to simultaneously source and name risk topics.
A key reference for text analysis within the field of finance is Loughran and McDonald
(2011). Their focus is on creating high quality word lists well suited for use in the analysis
of financial text and their indices are also available on WRDS. Loughran and McDonald
(2011) count the amount of “uncertain” text in IPO prospectuses (Form S1) in the U.S.
and link it to first day returns, offer price revisions and stock volatility for 1887 firms
in the time period 1997 to 2010. They assume that the amount of uncertainty words in
Form S1 is a direct measure for the ex-ante uncertainty of the IPO valuation. Thus more
uncertain tone in Form S1 should impact IPO valuation. They find that a combination of
uncertain, weak modal and negative words in S-1 are powerful variables to explain IPO
underpricing, absolute offer revisions and subsequent volatility. Uncertainty words alone
8
Nelson and Pritchard (2007) analyze a small selected sample of Form 10-K documents between 1996
and 2003. As risk measures they define the number of words in specific subsections of Form 10-K and the
number of discrete risk factor disclosed, which are counted manually. On top risk factors are categorized
into different risk categories like product and service risks, demand side risks, foreign operation risks,
legal and regulation risks. Their results show that companies’ risk disclosures are informative, but could
contain an unexplained upward trend since removing a risk factor from the annual report does not provide
a lot of benefits, even after the risk subsided.
15
did not seem to provide the expected results.
Summing up: previous work using textual analysis supports the view that text regarding risk in 10-Ks is related in meaningful ways to stock market developments and analysts
forecasts. As noted in the introduction the distinction between risk and Knightian uncertainty has not been pursued in text analysis of 10-K forms before with one exception:
Avramov et al. (2014) create measures for risk and uncertainty based on Form 10-K.
Their measures of risk and uncertainty are based on a dictionary approach, where they
designed a list of 84 words related to risk and 12 words related to uncertainty. One
common definition of risk is as the possibility of negative outcomes and Avramov et al.
(2014) base their text-based measure of risk on this definition and thus words like “loss”
and “competition” are counted as risk words in their framework. This kind of definition
of risk is commonly used in dealing with operational risks (such as fire in a production
unit) whereas our definition aims to stay close to risk as it is commonly defined in finance. They find that risk shocks lead companies reduce their leverage, investment and
employment. However their inclusion of negative words in the risk index means that the
interpretation of result will be quite different from ours, which we turn to now.
3.1.2
The Measures Used in this Article
As our starting point we define the set O that corresponds to the words that Loughran
and McDonald (2011) have identified with overall uncertain tone. There are 298 such
words . We further define two subsets of O that contain the words corresponding to
risk R and Knightian uncertainty K. The word lists that we create are reproduced in
table A.3 in the Appendix.
We classify an ‘overall uncertainty’ word as a risk word if it: captures frequency
(often, frequently,. . . ), refers to statistical terms, (probability, volatility,. . . ) or captures
minor imprecision (approximate,. . . ). This aims to capture that risk concerns objective
probabilities and moments of a probability distribution. We hypothesize that terms such
as these are used to describe situations amenable to statistical analysis and which we can
describe as risk, somewhat broadly defined.9 There are 74 risk words in our word list.
We categorize an overall uncertainty word as a Knightian uncertainty word if it clearly
expresses subjective probability (believe, perhaps,. . . ).10 We also categorize words as
9
See Friberg (2015) for a book length treatment
Note that the theoretical literature on Knightian uncertainty focuses on individual choice whereas
corporate settings typically involve several decision makers. Crès et al. (2011) argue that several decision
makers who differ in their subjective probabilities imply that corporate decision problems might have to
10
16
Knightian uncertainty words if they clearly describe ambiguity or hard to interpret observations (ambiguous, indeterminate,. . . ). This is clearly close to the interpretation of
Knightian uncertainty as capturing “ambiguity” in the spirit of Ellsberg (1961). Finally,
one sense of the notion of Knightian uncertainty is that probabilities are hard to define
because it is hard to determine the possible states-of-the-world ex ante. In theoretical
work this has been analyzed as unforeseen contingencies (Dekel et al. (1998)). This flavor of Knightian uncertainty is also related to what Donald Rumsfeld termed “unknown
unknowns”: things we don’t know that we don’t know. We therefore also define words
capturing surprises (sudden, unforeseen,. . . ) as Knightian uncertainty. There are 127
words on our Knightian uncertainty word list.
Some words from the Loughran and McDonald (2011) list of uncertainty words we
were not able to unambiguously classify into either category. For example, the word “risk”
is unclassified because firms don’t use it having our distinction of risk and Knightian uncertainty in mind. Perusing a few 10-K forms is sufficient to see that firms discussion of
“risk factors” includes many aspects that are clearly Knightian uncertainty. On the other
hand when firms discuss “market risks” in Form 10-K they typically pertain to risk more
narrowly defined. We also left a number of words that correspond to updating of probabilities unclassified (“reassess”, “recalculate”,. . . ). While we may associate Bayesian
updating with subjective probabilities it is clear that also an objective probability should
be updated as new information arrives.
For each document d we thus calculate three main indices: an overall uncertainty
index based on the original Loughran and McDonald (2011) “uncertainty” word list and
two subindices measuring risk and Knightian uncertainty. The index values vary by
document d and since we keep only one 10-K filing per firm and fiscal year the indices
equivalently vary by firm i and fiscal year t. Let wj be an indicator variable that takes
the value 1 for word j and 0 otherwise. We use the master word list of Loughran and
McDonald (2011), containing a total of 85,131 words to the define the set of possible
words and denote this set by M DL. Let nijt denote the number of occurences of word j
for firm i in fiscal year t. Given these primitives we define
P
ni,j,t wj
) × 100 where O={Overall Uncertainty Words}
n
w
j∈M
PDL i,j,t j
ni,j,t wj
Knightian U.i,t = ( P j∈KU ni,j,t wj ) × 100 where KU ={Knightian Uncertainty Words}
j∈M
DL
P
ni,j,t wj
Riski,t = ( P j∈R ni,j,t wj ) × 100 where R={Risk Words}
j∈M DL
Overall U.i,t = ( P
j∈O
be characterized using sets of probability distributions as in Gilboa and Schmeidler (1989), even though
each individual might have a well defined subjective probability for each state of the world.
17
We thus scale our index value with the length of a document as measured by word
count using the Loughran and McDonald (2011) word list. We also tested for robustness
with alternative length measures as well such as the number of tokens and our results are
robust to such changes as well as using the log of indices rather than their levels.
4
Taking Predictions to the Data
In the empirical analysis we explore a number of dimensions regarding the measures of
Knightian uncertainty and risk that we create. First, however, we believe, it is important
to conceptually clarify what it is we are measuring. A priori, we measure the textual
tone in a large set of corporate disclosure documents. We do this in a way we believe
relates to a firm’s perception of risk and Knightian uncertainty. We hypothesize that the
environment of the firm is characterized by a certain degree of Knightian uncertainty or
risk and that this exhibits a stable correlation with the tone measures we constructed.
Previous research on the informational content of Form 10-K and the surrounding regulatory environment lend support to this idea. However, basing our analysis on word
counts is associated with several caveats, that we share with other articles following this
methodology.
If we want to interpret our findings as the effects of uncertainty on firm behavior we
need to assume a stable link between our indices, the tone, and the underlying concepts
we try to measure, risk and uncertainty. At the same time the indices may be affected
by both idiosyncratic and systematic noise. Idiosyncratic noise occurs, for example, if
the persons writing the 10-K filings in a specific firm changes. Due to different writing
styles or vocabularies, our index could then show a change in uncertainty levels, where
none occurred.
Systematic errors occur, for example, if reporting standards are applied more or less
strict over time (either globally or in a specific industry). Underreporting could occur
in order not to negatively affect a company’s share value. Overreporting could occur
in order to mitigate litigation risks. By that logic, firms are incentivized to include as
many contingencies as they can in their reports, irrespective of the underlying uncertainties. Note however that the accounting literature generally finds annual reports to be
informative on risks. Also the SEC has made it repeatedly clear that the reported risk
disclosures can only hold up in court if they are tailored to the companies actual risk
situation. Simply adding boiler plate risk factors to a report does not decrease litigation
risk.
18
Furthermore, our indices are not sensitive to word context. Most trivially, we do
not account for negations. If a firm mentions in its business outlook, that surprising or
unexpected changes in its markets are not to be expected, we would still count the words
‘surprising’ and ‘unexpected’ as indicative for Knightian uncertainty, even though, the
context would suggest otherwise. Loughran and McDonald (2011) analyse the language
of Form 10-K statements in detail and find negations not to be a common feature of such
reports. A related problem we face are classification issues. Words might sometimes
be indicative for Knightian uncertainty and sometimes for risk. Also, words might not
be used by authors of 10-K filings having the concepts we defined here in mind i.e. it
could be used wrongly. By providing principles on which we make our classification into
different uncertainty categories, we strive to make our classification transparent and if in
doubt we move uncertainty words into the unclassified category.
A final issue is that our indices might be affected by compositional changes of the
underlying text body. Beginning in 2005 the SEC mandated, each company filing a Form
10-K needs to report “the most significant factors that make the company speculative or
risky” (Regulation S–K, Item 305(c), SEC 2005). Prior to 2005, risk reporting was primarily encouraged through the 1995 Private Securities Litigation Reform Act (PSLRA).
A key robustness check for our results is thus to split samples before and after 2005.
Based on the discussion above we therefore expect our text based measures of uncertainty to reflect characteristics of the environment but that the link is noisy. To highlight
some of the issues regarding estimation and interpretation we exemplify with our regression analysis of cash holding. We estimate the following equation where Cashit are
cash holdings of firm i in fiscal year t and β, κ, θ are coefficients to be estimated. The
statistical error term is denoted by εit .
Cashit = γt + βXit + κKUit + θRit + αi + εit
(9)
Let us discuss the variables in turn. As we document below there is an upward trend
in Knightian uncertainty and it is well established that there is an upward trend in cash
holdings (Almeida et al. (2014)). The coincident trends could simply reflect a spurious
relation and to control for a time trend we include fixed effects for each fiscal year, γt .
We also include a set of covariates Xit which include all the variables used in Bates et al.
(2009). KUit is our measure of Knightian uncertainty and Rit is our measure or risk. If
there is a positive relation between the extent of uncertainty that the firm faces and the
language that it uses, as captured by our indices, we can use equation (9) to estimate the
19
relation between Knightian uncertainty and risk on the one hand, and cash holdings on
the other.
Some of the issues faced when relating text to actions are reminiscent of those faced in
the expanding literature that examines self reported measures of well-being. As argued by
Bertrand and Mullainathan (2001) measurement errors are likely to be less of a concern
when we use self reported measures as a control rather than as a dependent variable. Year
to year noise in a firm’s 10-K would appear as attenuation bias, biasing the estimated
coefficients toward zero. Different firms may persistently use different tones and wording
even if they face the same degree of Knightian uncertainty or risk. To capture such effects
we include firm fixed effects, αi , in our most stringent specifications. This practice is
equivalent to estimating the relation in first differences, using only firm specific variation
in Knightian uncertainty and risk to identify the respective coefficients.
To conclude: Our indices are measures of uncertainty, as they try to quantify different
forms of uncertainty. Our theory presented above suggests a positive relation between
Knightian uncertainty and cash holdings. The question that we can pose and attempt
to answer is thus whether firms use language that is consistent with the joint hypotheses
of them making a distinction between risk and Knightian uncertainty and acting in line
with our model. We examine whether language use correlates with observed behavior
and firm characteristics in meaningful ways.
5
Knightian Uncertainty and Risk in 10-K Forms –
A First View
We now turn to a first examination of the uncertainty indices that we create. In figure 2
we graph the shares of different uncertainty indices over time. We observe that the share
of overall uncertainty words has shown a steady increase over the period of study: from
comprising slightly less than 1% of words to comprising close to 1.6% of words. The
share of risk words has been rather stable at slightly more than 0.3% with a slight decline
after 2004. In contrast, the share of Knightian uncertainty words have seen a sharp and
sustained increase: rising from less than 0.2% in 1995 to more than double this level in
2014.
The sustained increase in Knightian uncertainty words raises the concern that there
may be a ratchet effect, that text simply gets added over the years and that this increase
does not reflect real changes in the perceived level of Knightian uncertainty. It is therefore
20
.35
.4
1.6
.3
1.4
.25
1.2
.15
.2
1
.8
Share of uncertainty words
1995
2000
2005
Fiscal Year
Share of overall uncertainty
words (left-hand axis)
Share of risk words
2010
2015
Share of Knightian
uncertainty words
Figure 2: Development of overall uncertainty, Knightian uncertainty and risk in Form
10-K, fiscal year 1995 to 2014 between 1996 and 2014.
of interest to examine changes in the Knightian uncertainty indices at the firm level as
we do in table 1. The increases in the overall mean is reflected in that in most years both
the mean and the median firm feature increases in their share of Knightian uncertainty
words. We also see substantial dispersion however with both increases and decreases at
the firm level, something which lends hope to our identification strategy of estimating
the impact of Knightian uncertainty while controlling for firm level fixed effects.
The highest average change table 1 is in 2005, which coincides with the addition of
the risk section 1A to the 10-K. To avoid that such regulations or trends drive regression
results we include fixed effects at the level of fiscal years and in robustness checks we also
split the sample at 2005. If one measures uncertainty with financial market data or with
wording in newspapers (as in Baker et al. (2015)) years with dramatic news like 2001 and
2008 stand out. While these years show relatively high mean increases in our measure of
Knightian uncertainty, the distribution of changes across the years is quite stable.
21
fyear
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Total
mean
.
.0150282
.0104843
.020517
-.0030837
-.0034289
.0145193
.020089
-.0042373
.0070171
.0209254
.0117347
.0060438
.0107678
.0017453
.0075994
.0059022
.0086496
.0031296
.000184
.0084497
sd
.
.0580067
.0579159
.0620458
.0648598
.0626354
.0653891
.0668805
.0628988
.0593125
.0742164
.0661183
.0561709
.0603381
.0548871
.0526532
.0487898
.0466713
.0469097
.0571255
.0609885
p5
p50
p95
.
.
.
-.0628241 .0064441 .1156333
-.0760001 .0071078 .1031423
-.0651275 .0152193 .1208823
-.0927885 -.0051285 .0970551
-.0975619 -.0038381 .098516
-.0770737 .0096018 .120146
-.0736951 .0169397 .1268762
-.0939047 -.0069337 .0967921
-.0773164 .0025972 .1089507
-.0710739
.009944 .1474055
-.0714289 .0050515 .1179589
-.0720708 .0045818 .0859541
-.0761984 .0102862 .0995827
-.0744266 .0004327 .0840947
-.0645557 .0058652 .0825067
-.0572723 .0041372 .0732675
-.0547435 .0082235 .0700371
-.0605241 .0012671 .0703789
-.0738202 .0019075 .0704791
-.0763952 .0046142 .1049673
N
0
4338
7937
8300
8332
8557
8583
8162
7699
7351
7314
7274
7050
6658
6404
6115
5837
5550
5273
1169
127903
Source: v9.dta
Table 1: Descriptive statistics for firm level changes in Knightian Uncertainty index.
To explore the development over time further let us also examine the share of negative
words over the years in table 2. Here we see that years like 2001 and 2008 are indeed
marked by high mean changes. This indicates that text in the 10-K statements responds
to events but also stresses a value of separating first from second moments. A possible
implication is that Knightian uncertainty varies over time for individual firms but that
there has been a general upward trend over the last two decades. Large negative shocks are
reflected in the wording of 10-K statements but have limited effect on reported uncertainty
levels.
Now we turn to an exploration of cross-sectional patterns regarding uncertainty words.
In Table 3 we present a ranking at the 4-digit SIC level of industry. The highly uncertain
industries lie in commodity trading and the high tech industry, mostly pharmaceutics and
information technology. Various insurance products also rank high in our overall uncertainty index. Looking at the industries with the highest average Knightian uncertainty
22
fyear
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
Total
mean
.
.0034878
.0285813
.06261
.0208575
.0258639
.0832856
.1023908
.0337652
.004727
.0241459
.0098145
.012518
.0772285
.0420401
.0252619
.0204126
.009701
.0182089
.0251055
.0357653
sd
.
.4476998
.4412214
.4209227
.4090811
.4111565
.3947616
.3588071
.3399111
.3312751
.322765
.2806519
.2666336
.2639846
.2551447
.2431001
.2417076
.2266456
.2363295
.2341668
.3421948
p5
p50
p95
N
.
.
.
0
-.6966192 -.0120813 .7570938
4338
-.6950539 .0125281 .7823936
7937
-.6432931 .0571617 .7791672
8300
-.6633104 .0079127 .7163876
8332
-.6563323 .0092186 .7221191
8557
-.5691761
.069697 .738206
8583
-.4747252 .0927472 .6841725
8162
-.5020456 .0207438 .5961291
7699
-.5416507 -.0029266 .5718455
7351
-.5128888 .0207949 .545155
7314
-.4336737 .0037694 .4751588
7274
-.4163116 .0064606 .4452152
7050
-.3374114 .0741675 .5075642
6658
-.358114 .0316514 .4610705
6404
-.3575368 .0166856 .4072177
6115
-.3364747 .0140134 .386673
5837
-.3352382 .0045158 .3633066
5550
-.3063474 .0085652 .3612424
5273
-.3203707 .0187039 .3822762
1169
-.5099271 .0240369 .6041715 127903
Source: v9.dta
Table 2: Descriptive statistics for firm level changes in negative words index.
values, we can identify two set of industries. First, we find a strong representation of high
tech industries again, namely IT and pharmaceutics. Industries such as these are indeed
common examples of markets where drastic innovations are important (see e.g. Arrow
(1962)), where competition is for the market rather than in the market (Geroski (2003))
and where disruptive innovation exerts a strong influence (Christensen (1997)). These
industries can then arguably be characterized by a tendency for new innovations to crowd
out existing products and by its very nature it may be hard to have objective probability distributions regarding the nature of new innovations. Knightian uncertainty is thus
expected to be relevant to describe the randomness. Second, we find industries, like the
photofinishing industry, which are not in general characterized by such innovations, but
which did experience a large shift in their business model over our sample period. In the
specific example, the advent of digital photography and the availability of high quality
printing devices has fundamentally affected the industries’ business model.
23
Overall uncertainty
Rank Industry
1
2
3
4
5
Commodity Contracts
Brokers & Dealers
Semiconductors &
Related Devices
Rubber & Plastics
Footwear
Miscellaneous Products
Of Petroleum & Coal
Surety Insurance
Fire, Marine & Casualty
6
Insurance
Accident & Health
Insurance
Pharmaceutical
8
Preparations
9 Electrical Work
7
10
Hospital & Medical
Service Plans
Knightian Uncertainty
SIC
Average Industry
code
Services-Photofinishing
Laboratories
Semiconductors & Related
Devices
Medicinal Chemicals &
Botanical Products
Computer Communications
Equipment
Computer Storage Devices
Risk
SIC
Average Industry
code
SIC
Average
code
7384
0.40 Silver Ores
1044
0.75
3674
0.40
6513
0.59
2833
0.39 Pulp Mills
2611
0.50
3576
0.39 Truck Trailers
3715
0.45
6221
1.54
3674
1.54
3021
1.51
2990
1.51
6351
1.50
3572
0.39 Metal Cans
3411
0.44
6331
Retail-Apparel & Accessory
1.49
Stores
5600
Secondary Smelting &
0.39
3341
Refining Of Nonferrous Metals
0.44
6321
1.48 Services-Services, Nec
8900
0.38 Paperboard Mills
0.43
2834
1.46
7371
0.38
1731
6324
Services-Computer
Programming Services
1.46 Services-Video Tape Rental
Auto Controls For Regulating
1.45 Residential & Comml
Environments
Operators Of Apartment
Buildings
2631
7841
Millwood, Veneer, Plywood, &
2430
Structural Wood Members
0.37 Electric Housewares & Fans
3634
3822
0.37
Gas & Other Services
Combined
4932
0.43
0.43
0.43
Table 3: Top 10 industries in terms of our overall uncertainty, Knightian uncertainty and
risk indices.
High risk industries are centred around products that are relatively homogeneous
and rely on established technologies such as silver ore and pulp. In that sense, the role
of innovation in these industries is less pronounced and randomness is instead to an
important degree linked to fluctuations in market prices.
Similarly we can look at the bottom ten industries in terms of our risk indices. We
present these in the appendix in Table A.6. The intuition for the low ranking industries
is less clear than for the high ranking industries, but all listed industries can plausibly be
understood as low risk and uncertainty sectors.11
While reporting rankings of our indices across the different industries is intuitively
appealing we can also provide a more systematic examination of the cross-sectional patterns and in table 4 we examine the pairwise correlation between the various measures of
11
A noteworthy exception is the industry of asset backed securities, which ranks last in all our uncertainty dimensions. This is at odds with (today’s) conventional wisdom about the large risks that
were present in these markets. Having a closer look at these filings reveals that most of the reports are
relatively short. Ninety percent of the reports in the asset backed security industry are shorter than 6790
tokens, which is about ten A4 pages. To compare, 90% of all other reports in our sample are longer than
8177 tokens. Looking at the time series pattern in this industry a little bit closer, we find a sharp drop in
our uncertainty indices after 2001. Prior to this, the ABS industry had an uncertainty level close to the
top 10 industries in our ranking. The drop in uncertainty levels was accompanied by a sharp reduction
in document length and followed between 2003 and 2007 by a sharp increase of the number of companies
operating in the industry. We take this as evidence, that sometimes our measure can significantly diverge
from the actual level of risk or Knightian uncertainty in a industry.
24
risk and uncertainty and some firm and industry characteristics. The correlation between
our uncertainty measures is positive but only .09 for the correlation between Knightian
uncertainty and risk. The cash ratio is positively correlated with our measure of Knightian uncertainty but negatively with our measure of risk. In contrast, derivatives use is
negatively related to Knightian uncertainty but positively to risk. These results are in
line with our theoretical predictions and we will revisit them in our regression analysis.
The ranking of industries above suggested that high tech industries might be more
prone to experience Knightian uncertainty and indeed the correlation is positive between
our measure of Knightian uncertainty and a dummy for high tech industries. Finally, for
a limited set of industries we have a measure of contract specificity from Nunn (2007)
which shows a positive correlation with Knightian uncertainty but negative with risk.
Such a pattern is to be expected if transacting in a market implies more volatile prices
whereas closer cooperation with suppliers implies that strategic interaction, and hence
Knightian uncertainty, takes on a larger role. Many other correlations are possible to
examine and by and large they paint a picture that accords well with intuition and our
discussion above.
Summarizing this descriptive section we conclude that the cross-sectional patterns of
Knightian uncertainty and risk broadly coincide with what theory leads us to expect.
Furthermore there is substantial year to year variation in the Knigthian uncertainty
index at the firm level. The average levels of the uncertainty indices across the years
see relatively little direct response to major shocks such as the terrorist attacks of 2001
or the financial crisis of 2008. If idiosyncratic risk/uncertainty is important relative to
aggregate risk/uncertainty the two observations are not contradictory. Indeed previous
work comparing firm level volatility in sales and stock returns support such a view; firm
level volatilty is much higher than aggregate volatility and the firm level volatility has
shown a sustained increase over the last few decades (see e.g. Campbell et al. (2001),
Comin and Philippon (2006),Castro et al. (2015)). Even so, the relative stability in the
face of major news events surprised us. One possible interpretation is that firms take a
well reasoned view of uncertainty. A simple example with respect to risk might clarify the
intuition. While somewhat silly we believe that the example may be helpful. Suppose
that I wrote in my annual report that there was a possibility of me having a traffic
accident. If an accident then actually occurs this would be a major news item for me
and if I were listed on the stock market my stock would tumble. But if I had a well
reasoned view of possible risks ex ante I note that my risk of having a traffic accident in
the future is not increased by me just having had one. Thus, the time series dimension
25
Variables
Overall U.
Knightian U.
Risk
Cash Ratio
Deriv
High Tech
Contract S.
Overall
Uncertainty
1.000***
0.774***
0.437***
0.238***
0.044***
0.130***
0.085***
Knightian
Uncertainty
1.000***
0.088***
0.238***
-0.055***
0.130***
0.156***
Risk
Cash
Ratio
1.000***
-0.087*** 1.000***
0.166*** -0.228***
-0.062*** 0.327***
-0.084*** 0.275***
Deriv
High
Tech
1.000***
-0.105*** 1.000***
-0.154*** 0.467***
Contract
Specificity
1.000***
Table 4: Pairwise correlations. *** p<0.01, ** p<0.05, * p<0.1
of risk and uncertainty measured this way may be rather stable, indeed unless unforeseen
contingencies are crucial we should expect it to be rather stable. In contrast the cross
sectional dimension may vary greatly. My risk of having a traffic accident is much higher
if I love to bike on densely trafficked streets rather than rarely venture outside my block
and then only on foot.
6
Relation to Classic Uncertainty Measures
In this section we relate our measures of uncertainty to firm betas and corporate debt
ratings. Relating to our discussion on identification, these regressions pose an additional
identification problem. We assumed that our tone measures are noisy measures of a
firms risks and uncertainties. For decisions made by the firm itself (cash holdings and
derivatives use) there is little reason to expect a causal effect from the word count itself
on actions. In contrast, when we relate our indices to other risk measures outside the
firm, like beta or credit ratings, there may in principle be a causal effect of the tone on
the outside measure. To illustrate this, consider analysts at a large rating agency reading
a company’s annual report before assigning a rating. The way the report was written
in itself could influence their judgement. Similarly, investors might be influenced by the
tone of an annual report when deciding whether to sell or hold on to an asset in turbulent
times, thus affecting beta. Our coefficient estimates will thus confound the effects of risk
and uncertainty with the effects of the tone itself.
26
6.1
Relation to Estimates of Beta
We analyse the relation of our indices to betas from a simple CAPM regression and a
three factor model as introduced by Fama and French (1993). To obtain a measure for
beta we use daily stock market data from CRSP. As risk free rate we use the one month
treasury bill rate and the market portfolio is calculated as the value-weighted return
on all NYSE, AMEX and NASDAQ stocks. We match betas by fiscal year and delete
observations with less than 245 observations in a fiscal year. We winsorize betas at the
1% level.
In Table 5 we report the correlation of the fiscal year beta with our overall uncertainty
index. We find a strongly positive relation between our index and the betas which survives
the inclusion of additional controls, time and firm fixed effects. In column 1 and 2 we show
the raw regression coefficients between our index and betas. The coefficient is particularly
high for the standard CAPM regression, but we also find a considerable correlation with
the three-factor beta. In column 3 and 5 we add additional risk measures inspired by
the set of controls in Blume et al. (1998) and Bates et al. (2009) as well as time and
firm fixed effects. A bit odd, we find the debt to capital ratio to be negatively related to
betas – higher leverage is thus related to less systematic risk. Also against expectations,
the share of positive words is associated with higher systematic risk. The sign of the
other regressors is as expected and our uncertainty measure is still significantly positively
related to our beta measures. The correlation is also economically significant. A one
percentage point increase in our index, increases beta by roughly 0.1. To get a further
idea of the economic impact, we show standardized regression coefficients in column 4
and 6. A one standard deviation shock to our uncertainty index, increases betas between
0.5 and 0.7 standard deviations. Given a standard deviation of roughly 0.6 for either beta
measure, we get an effect on beta of roughly 0.03 to 0.04 from a one standard deviation
shock to our uncertainty measure. We also note that after initially being positively related
to beta as well, the risk measure suggested by Bates et al. (2009) turns insignificant in
the more stringent regressions.
Next we check to what extent the correlation differs in our risk and Knightian uncertainty indices. The results are provided in Table 6. We now just show the coefficient
estimates of direct interest to us. The sign of the coefficients on the traditional risk
measures included as controls remain unchanged or the coefficients become insignificant.
There is a strong positive correlation between both our sub-indices and market beta.
In the case of the standard CAPM betas, the risk coefficient become insignificant in the
27
more stringent specification. The correlation between our indices and the three-factor
model betas remains statistically significant in the more stringent specification too. We
should also highlight that a zero coefficient on risk does not imply that there is no relation
between a firm’s riskiness and its market beta. There is simply no additional explanatory
power in our risk index after controlling for other risk factors and fixed effects. Our
result is in line with the gist of results in for instance Caballero and Simsek (2013)
where investors are more prone to shun Knightian uncertainty in turbulent times. There
might be not only a flight to certainty but also a flight to risk and away from Knightian
uncertainty.
28
VARIABLES
Overall U.
(1)
Beta
(2)
F/F Beta
(3)
Beta
(4)
Std. c.
(5)
Beta
(6)
Std. c.
0.531***
(0.0109)
0.172***
(0.00999)
0.126***
(0.0185)
0.0828***
(0.0244)
0.0296***
(0.00874)
0.114***
(0.00796)
1.37e-05
(2.28e-05)
-0.00255**
(0.00107)
0.00801
(0.0354)
-0.0667**
(0.0313)
-0.0116
(0.0605)
0.0721
0.0815***
(0.0172)
0.0469**
(0.0233)
0.0365***
(0.00933)
0.112***
(0.00707)
-1.45e-05
(2.25e-05)
-0.00137
(0.00116)
-0.00941
(0.0386)
-0.0367
(0.0350)
-0.0247
(0.0574)
0.0473
Positive
Negative
ln(Assets)
Interest Coverage
Op Income to Sales
Avg Lt Debt to Asset
Debt to Capital
Industry Sigma
Beta
Constant
Observations
R-squared
Controls
Year FE
Firm FE
0.0968***
(0.0143)
0.612***
(0.0140)
88,642
0.088
88,642
0.010
0.0243
0.0235
0.410
0.00353
-0.0184
0.00288
-0.0280
-0.00209
0.486***
(0.0336)
60,565
60,565
0.594
0.594
YES
YES
YES
YES
YES
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
0.0139
0.0294
0.410
-0.00377
-0.0100
-0.00343
-0.0156
-0.00453
0.698***
(0.0357)
60,565
0.467
YES
YES
YES
60,565
0.467
YES
YES
YES
Table 5: Regressions using fiscal year matched market betas.
We run several robustness checks on these results which we report in table A.9 and
A.10 in the appendix. We check whether results differ before and after 2005 when the
SEC changed risk reporting standards. The regression coefficients from a raw regression
of beta on our indices remain statistically significant and positive. In the more stringent
model, the results hold for the overall uncertainty index and the standard betas. For the
Fama/French betas we can identify a positive effect only after 2005, but not before, albeit
29
VARIABLES
Knightian U.
Risk
(1)
Beta
(2)
F/F Beta
(3)
Beta
(4)
Std. c.
(5)
F/F Beta
(6)
Std. c.
1.456***
(0.0310)
0.223***
(0.0303)
0.550***
(0.0282)
0.302***
(0.0305)
0.291***
(0.0503)
0.0665*
(0.0378)
0.0739***
(0.0246)
0.0286***
(0.00879)
0.530***
(0.0329)
0.0591
0.141***
(0.0467)
0.101***
(0.0362)
0.0457*
(0.0234)
0.0379***
(0.00942)
0.713***
(0.0347)
0.0289
Positive
Negative
Constant
Observations
R-squared
Controls
Year FE
Firm FE
0.286***
(0.0132)
0.571***
(0.0135)
88,642
0.095
88,642
0.020
0.0146
0.0216
0.0228
60,565
60,565
0.593
0.593
YES
YES
YES
YES
YES
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
60,565
0.467
YES
YES
YES
0.0225
0.0136
0.0305
60,565
0.467
YES
YES
YES
Table 6: Regressions using fiscal year matched market betas.
the coefficient estimate is positive too. Looking at the split between risk and Knightian
uncertainty, we can identify a differential impact only for the standard betas before 2005,
but not after. In the case of the Fama/French betas we can’t identify any differential
effects anymore. A worry with our methodology was that risk reporting in annual reports
might be dominated by motives to mitigate litigation risks for firms. To control for this
possibility, we ran our regressions using the share of litigious words as constructed by
Loughran and McDonald (2011) as an additional control. Including this variable, does
not affect our coefficient estimates at all.
As mentioned before, we also checked whether our results are sensitive to calculating
beta on a filing-year or a fiscal-year basis. Using the fiscal year as a basis for stock
market calculations is more appropriate, if the text in annual reports primarily relates
to the events during that fiscal year. It has the advantage, that it covers the same timeperiod as the other controls in the regression. A filing-date based beta could be more
appropriate if the textual data in the annual report contains information that has become
available only after the end of the fiscal year, but that has already been picked up by
30
investors. Tables A.7 and A.8 in the appendix report the corresponding regression results
using filing-year. We get slightly more observations than in our original regressions, as
we know for each observation when it was filed, but we do not always know the fiscal-year
end for each filing. Our results are robust to the alternative matching procedures.
We conclude from this analysis that firms using more uncertain tone in their annual
reports are also firms that react stronger to stock market turbulences. There is some
evidence of a differential impact of risk and Knightian uncertainty, namely the latter is
associated with slightly higher betas, but in general the evidence is not very robust.
6.2
Relation to Credit Ratings
We next consider the relationship between our uncertainty measures and credit ratings,
a measure for credit risk.12 For this, we use Standard&Poor’s long-term debt ratings
that are provided via Compustat. We roughly follow the empirical approach of Blume
et al. (1998). We use an ordered probit model to estimate the effects of uncertain tone on
credit ratings. In assigning a credit rating, rating agencies usually start with a set of initial financial indicators which cover the company’s interest coverage, its profitability and
its leverage to form an initial opinion. We thus include interest rate coverage (XINT+
OIADP)/XINT where XINT is interest rate expenditure and OIADP is operating income after depreciation, operating income to sales (OIBDP/SALE), debt to asset ratio
(DLTT/AT) as controls. As additional measures of a firm’s riskiness we include the average cash flow volatility as suggested by Bates et al. (2009) and reuse the results from our
simple CAPM regressions. We include fiscal year matched measures of systematic (beta)
and unsystematic market risk (squared residuals). Because of its empirical importance,
we also include the log of a firms assets as a proxy of firm size as an additional control.
Further, we control for the share of positive and negative words in a firm’s annual report.
To pick up sector-invariant time variation we include time dummies. We cant include
firm dummies or industry dummies in our probit specification, because that would have
led to over-fitting the model.
12
We classify ratings as risk measures, even though downgrading can cause cost of funds of companies
to go up and thereby cause it to be riskier.
31
VARIABLES
Overall U.
(1)
Rating
(2)
Rating
-0.214***
(0.0704)
-0.365***
(0.109)
Risk
Knightian U.
Positive
Negative
Unsys risk
ln(Assets)
Interest Coverage
Beta
Op Income to Sales
Avg Lt Debt to Asset
Debt to Capital
Industry Sigma
Observations
Year Dummies
Inv Grade Only
0.830***
(0.110)
-0.316***
(0.0436)
-1.433***
(0.0993)
0.496***
(0.0221)
0.00289***
(0.000695)
-0.489***
(0.0348)
0.0411***
(0.0147)
-1.916***
(0.206)
-0.218
(0.199)
-0.370*
(0.201)
0.512***
(0.169)
-0.213***
(0.0788)
-0.661
(0.491)
0.342***
(0.0359)
0.00284***
(0.000759)
-0.496***
(0.0881)
-0.00202
(0.0416)
-4.212***
(0.529)
1.773***
(0.457)
0.357
(0.388)
(3)
Rating
(4)
Rating
-0.482***
(0.136)
-0.708***
(0.206)
0.845***
(0.110)
-0.311***
(0.0439)
-1.429***
(0.0988)
0.489***
(0.0226)
0.00296***
(0.000693)
-0.486***
(0.0348)
0.0410***
(0.0145)
-1.877***
(0.206)
-0.245
(0.199)
-0.362*
(0.201)
-0.809***
(0.231)
-0.732**
(0.332)
0.513***
(0.167)
-0.230***
(0.0800)
-0.667
(0.489)
0.338***
(0.0362)
0.00288***
(0.000760)
-0.502***
(0.0873)
-0.00356
(0.0401)
-4.119***
(0.530)
1.716***
(0.459)
0.358
(0.389)
19,270
9,847
19,270
YES
YES
YES
YES
NO
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
9,847
YES
YES
Table 7: Probit models including overall uncertainty, risk and Knightian uncertainty.
32
The results of our probit estimations are provided in table 7. We estimate four different
models. In column (1) and (2) we estimate a model using our overall uncertainty measure.
In the first estimation we include all firms, while in the second estimation we only include
firms with an investment grade rating. We repeat the same sample split in column (3)
and (4) of the table, but include our risk and Knightian uncertainty split as regressors
instead. Note that sample size is considerably smaller than in the previous regressions,
which is mainly due to the scarcity of rating information in Compustat.
Since we have coded ratings in ascending order, a higher rating implies less credit risk.
The sign of our coefficient estimates can thus be interpreted as indicating an opposite shift
in credit risk i.e. a negative estimate implies a change in the regressor is associated with
higher credit risk. We find that a higher share of positive words, higher interest coverage
and higher operational income to sales are associated with less credit risk. Equally
intuitively, we find a larger share of negative words, more systematic or unsystematic risk
and higher debt to be positively related to credit risk. This result holds across all model
specifications.
We find our overall uncertainty measure to increase with credit risk. Turning to the
split between risk and Knigthian uncertainty, we find both measures to increase with
credit risk as well.
A better understanding of the impact of a change in our indices can be obtained
by using counterfactual simulations. We construct four counterfactuals. In the first
three scenarios, we shock each firm with a one standard deviation shock to its value of
the overall uncertainty, risk and Knightian uncertainty index. In the fourth scenario,
we simultaneously shock the risk and Knightian uncertainty index with a one standard
deviation increase for each firm. We use our models to calculate predicted probabilities for
a firm to have a specific rating in each of the scenarios.The average of these probabilities
corresponds to the simulated distribution of ratings in each scenario. Table 8 shows the
results of these simulations.
Our model predicts a one standard deviation increase in overall uncertainty would
reduce the share of triple A rated firms between 13% and 22%. A risk shock would
reduce this share between 11% and 19%, while a Knightian uncertainty shock would
decrease it between 15% and 16%. In the model with below investment grade firms,
our model shifts the probability mass away from investment grade firms towards noninvestment grade firms as a reaction to uncertainty shocks. In the model without below
investment grade ratings, the probability mass accumulates in the lowest possible rating
category.
33
Base
CCC-D
B
BB
BBB
A
AA
AAA
2.4%
18.5%
27.9%
31.1%
16.5%
2.7%
0.9%
(1)
Overall
shock
Δ/Base
8%
6%
2%
-2%
-6%
-10%
-13%
All ratings
(2)
(3)
Risk
K.U.
shock
shock
Δ/Base Δ/Base
7%
9%
5%
7%
2%
3%
-1%
-2%
-5%
-7%
-8%
-11%
-11%
-15%
(4)
Dual
shock
Δ/Base
17%
12%
4%
-4%
-12%
-19%
-24%
Base
59.1%
33.7%
5.4%
1.7%
Investment grade ratings only
(1)
(2)
(3)
(4)
Overall
Risk
K.U.
Dual
shock
shock
shock
shock
Δ/Base Δ/Base Δ/Base Δ/Base
7%
-8%
-16%
-22%
6%
-7%
-14%
-19%
5%
-6%
-12%
-16%
10%
-13%
-25%
-32%
Table 8: Relative change in counterfactual distributions of SP long-term credit ratings
after one standard deviation shocks to overall uncertainty, risk and Knigthian uncertainty.
In the combined scenario we simultaneously shock risk and Knightian uncertainty by one
standard deviation.
We conclude that our uncertainty indices are related to credit risk in a statistically
and economically meaningful way, even after controlling for other risk measures.
7
Relation Between Cash Holding, Derivatives use,
Knightian Uncertainty and Risk
Having established that our uncertainty measures are related in an intuitive way with
industry characteristics and classical risk measures, we now turn to an examination of
the relation between cash holdings, Knightian uncertainty and risk.
Bates et al. (2009) explain the increase in cash holdings over the last few decades by
observing that firms’ cash flows have become more volatile and holding more cash serves
as an insurance against this risk. We take Bates et al. (2009) as a starting point and
construct all the determinants of cash holding that they identified in their paper.
In column (1) of Table 9 we run a basic regression, corresponding to column (9) in
Table III of (Bates et al., 2009). As seen the coefficient on industry sigma is positive at
around 0.4 and significant. It is similar to the corresponding coefficient in Bates et al.
(2009) when they don’t control for year effects (0.37) but higher than the specification
where they do control for year effects (0.09). Thus, also for the period that we examine
higher variability of cash flows at the level of the industry correlate with more cash holdings by firms in that industry. In column (2) we add our measure of overall uncertainty
and in column (3) we in addition add measures of positive and negative words. As seen
34
the text based measure, which is at the firm level, is positive and significant in addition
to the industry sigma. Positive words are associated with more cash holding and negative
with less cash holding. Roughly speaking column (3) thus separates first moments from
text analysis (positive or negative) from second moments (uncertainty). Note that this
is conceptually important as many definitions of risk are available.
In column (4) we report the results of a specification which splits between Knightian
uncertainty and risk. We find Knightian uncertainty to be positively related to cash holdings, while risk is negatively related. The effect of cash flow volatility is still significantly
positive. In column (5) and (6), we repeat the exercises in (3) and (4), but now include
firm fixed effects. The firm fixed effects soak up much of the variation and industry sigma
is small in magnitude and not significant in these specifications. However, the degree of
Knightian uncertainty in a firm’s annual report still has a statistically significant effect
on cash holdings. The effect is also economically important. The mean cash ratio in our
sample is 0.197 and the mean value of the Knightian uncertainty words is 0.32. Increasing
the share of Knightian uncertainty words by one standard deviation (0.12) would then
imply that the cash ratio increased by 2.2 percentage points to 0.219.
One way to illustrate the quantitative importance of different variables is to report the
coefficients from a standardized regression where all variables have been standardized by
demeaning and dividing by the standard deviation (thus computing z-scores). The last
column reports such coefficients using the same specification as in column (5). Expressed
this way a one standard deviation increase in Knightian uncertainty increases the cash
ratio by 0.038 standard deviations. The coefficients on the z-scores can be used to gauge
the relative importance of different explanatory variables and we conclude that Knightian
uncertainty has a statistically and economically important relation to cash holdings. In
contrast, the relation between our risk measure and cash holdings is neither economically
nor statistically significant in our preferred specification.
35
VARIABLES
(1)
Cash
(2)
Cash
Knightian U.
Risk
Positive
Negative
Industry Sigma
0.371***
(0.0210)
Market to Book 0.00559***
(0.000395)
ln(Assets)
-0.00421***
(0.000840)
Cash Flow
-0.00888***
(0.00267)
Working Capital
-0.00222
(0.00254)
Capex
-0.395***
(0.0163)
Leverage
-0.317***
(0.00710)
RnD
5.57e-05*
(3.37e-05)
Dividend
-0.0759***
(0.00362)
Acquisition
-0.255***
(0.0183)
Overall U.
Constant
Observations
R-squared
Controls
Year FE
Firm FE
0.242***
(0.0122)
69,566
0.269
YES
YES
NO
0.202***
(0.00990)
0.0517***
(0.00320)
0.297***
(0.0182)
0.00506***
(0.000387)
-0.0104***
(0.000806)
-0.00286
(0.00261)
-0.00207
(0.00249)
-0.327***
(0.0156)
-0.274***
(0.00689)
4.88e-05
(3.03e-05)
-0.0500***
(0.00356)
-0.232***
(0.0173)
0.101***
(0.00544)
-0.157***
(0.0170)
(3)
Cash
0.173***
(0.0156)
-0.0514***
(0.0103)
0.203***
(0.0105)
0.0585***
(0.00337)
0.312***
(0.0187)
0.00505***
(0.000388)
-0.00805***
(0.000817)
-0.00370
(0.00260)
-0.00131
(0.00249)
-0.319***
(0.0155)
-0.279***
(0.00688)
5.00e-05
(3.19e-05)
-0.0535***
(0.00361)
-0.222***
(0.0169)
-0.0717***
(0.0161)
(4)
Cash
0.0375***
(0.00626)
0.00503**
(0.00227)
0.00298
(0.0145)
0.00411***
(0.000381)
-0.00709***
(0.00216)
0.0135***
(0.00305)
-0.00249
(0.00257)
-0.253***
(0.0162)
-0.167***
(0.00674)
1.42e-05***
(4.93e-06)
0.0109***
(0.00317)
-0.141***
(0.0130)
0.0198***
(0.00482)
0.175***
(0.0136)
69,566
69,566
69,566
0.327
0.319
0.773
YES
YES
YES
YES
YES
YES
NO
NO
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(5)
Cash
(6)
Standardized c.
0.0716***
(0.0135)
-0.0109
(0.0102)
0.0333***
(0.00626)
0.00378*
(0.00229)
0.00442
(0.0145)
0.00407***
(0.000380)
-0.00653***
(0.00216)
0.0133***
(0.00304)
-0.00271
(0.00258)
-0.254***
(0.0162)
-0.167***
(0.00674)
1.42e-05***
(4.90e-06)
0.0108***
(0.00317)
-0.140***
(0.0129)
0.0380
-0.00607
0.0262
0.00758
0.00226
0.105
-0.0712
0.0554
-0.0122
-0.0762
-0.194
0.00747
0.0197
-0.0492
0.184***
(0.0128)
69,566
0.774
YES
YES
YES
69,566
0.774
YES
YES
YES
Table 9: Cash holding regressions following Bates et al. (2009). Standard errors clustered
36
at firm level.
We now turn to derivatives use with the aid of our proxy variables for whether a
firm engages in hedging with derivatives. We estimate regressions using data from 2001
onwards only as the dependent variable is unavailable prior to this. In Table 10 we relate
the dummy on derivatives use to our measures of uncertainty in addition to the same
covariates as in the cash flow regressions. In column (1) we include our measure of overall
uncertainty and find that it is not significant, neither statistically nor economically. At
first surprisingly industry sigma is negative, implying that firms in industries with more
volatile cash flows are less likely to use derivatives. Positive words are associated with
more hedging and negative with less hedging. A finding that is also surprising at first but
consistent with the evidence in Rampini et al. (2014), where a need to post collateral for
derivatives implies that the most financially constrained firms do not use derivatives at
all. In column 2 we examine risk and uncertainty and find that risk is positively related
to financial hedging whereas Knightian uncertainty exhibits a negative relation.
Regressions where we include firm fixed effects are more appealing as there is less
scope for unobserved heterogeneity to influence results. In column (3) our measure of
overall uncertainty is positively related to derivatives use. Most interesting are the results
in column (4) where we see that risk shows a statically significant, positive relation to
derivatives use. Moreover the effect is quantitatively non-trivial. The mean value of our
risk index is 0.32 and increasing this with one standard deviation (0.13) is thus associated
with an increase in the probability of using derivatives by 2.3 percentage points, from
0.214 to 0.237. In contrast, the measure of Knightian uncertainty is negatively related
to derivatives use but only borderline significant. As in the case of cash holdings we
can also use the coefficients from a regression on standardized variables to compare the
quantitative importance of different variables, a way of presenting results which reinforces
the view that there is a statistically and economically significant link between our measure
of risk and derivatives use.
We have examined a long list of robustness issues and the overall pattern where
Knightian uncertainty is positively related to cash holdings and risk positively related to
derivatives use. These results hold also when we estimate relations in log-log, log-levels or
using logit rather than linear probability to estimate the probability of using derivatives.
Of particular importance may be if relations change with the requirement introduced in
2005 that “risk factors” should be discussed in section 1A of the 10-K. In table A.14
in the appendix we show that the results are stable across these two periods. In that
table we also consider a more narrow definition of derivatives use and also shows little
difference with our main specifications.
37
VARIABLES
(1)
Deriv
Knightian U.
Risk
Positive
0.0445**
(0.0187)
Negative
-0.0475***
(0.00818)
Industry Sigma
-0.0850***
(0.0301)
Market to Book
0.00364***
(0.000347)
ln(Assets)
0.0742***
(0.00208)
Cash Flow
-0.0186***
(0.00218)
Working Capital
0.00206
(0.00222)
Capex
0.0278
(0.0478)
Leverage
0.164***
(0.0130)
RnD
-1.90e-05***
(3.34e-06)
Dividend
0.116***
(0.0118)
Acquisition
0.0414*
(0.0247)
Overall U.
-0.000354
(0.0125)
Constant
0.142***
(0.0308)
Observations
R-squared
Controls
Year FE
Firm FE
(2)
Deriv
(3)
Deriv
-0.180***
(0.0353)
0.229***
(0.0294)
0.0708***
0.0184
(0.0187)
(0.0184)
-0.0315***
-0.00596
(0.00837)
(0.00714)
-0.0775***
-0.0383
(0.0298)
(0.0295)
0.00393*** 0.00127***
(0.000350)
(0.000260)
0.0710***
0.0382***
(0.00207)
(0.00462)
-0.0175*** -0.00809***
(0.00218)
(0.00197)
0.00244
0.00237
(0.00224)
(0.00173)
0.0292
0.0196
(0.0478)
(0.0353)
0.159***
0.0944***
(0.0130)
(0.0131)
-1.91e-05***
-7.53e-07
(4.02e-06)
(2.52e-06)
0.114***
0.00782
(0.0117)
(0.0120)
0.0485*
0.0371
(0.0249)
(0.0229)
0.0410***
(0.0144)
0.0986***
0.123***
(0.0304)
(0.0333)
(4)
Deriv
(5)
Standardized c.
-0.0637*
(0.0384)
0.179***
(0.0324)
0.0344*
(0.0185)
0.00296
(0.00726)
-0.0384
(0.0294)
0.00137***
(0.000262)
0.0377***
(0.00459)
-0.00791***
(0.00198)
0.00279
(0.00175)
0.0173
(0.0353)
0.0940***
(0.0132)
-6.27e-07
(2.45e-06)
0.00872
(0.0119)
0.0384*
(0.0230)
-0.0176
0.0539
0.0143
0.00300
-0.0113
0.0214
0.241
-0.0205
0.00783
0.00277
0.0624
-0.000221
0.00901
0.00732
0.130***
(0.0317)
46,035
46,035
46,035
46,035
0.262
0.267
0.699
0.700
YES
YES
YES
YES
YES
YES
YES
YES
NO
NO
YES
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
46,035
0.700
YES
YES
YES
Table 10: The determinants of hedging. Linear probability estimation for fiscal years
38
2001 to 2013. Standard errors clustered at firm level.
We conclude that our results are consistent with the idea that cash is useful to hedge
many different uncertainties, in particular those for which probability is hard to assess.
At the same time, financial hedging seems to be more often used by firms that are exposed
to risk in a classical sense.
8
Conclusions
We create indices of Knightian uncertainty and risk using textual information for all 10-K
statements from fiscal year 1995 until fiscal year 2013 with some coverage of fiscal year
2014. We first validate our indices and find them to be related to industry characteristics,
ratings and stock market beta in ways that are broadly consistent with predictions. A
closer examination of cash holdings and derivatives yield results that are consistent with
firms acting in line with a standard model of cash holding extended to include Knightian
uncertainty. Our findings thus are consistent with the idea that firms make a distinction
between Knightian uncertainty and risk and that the distinction is not only of interest to
the decision theoretic literature spurred by Ellsberg (1961) but also seems to be capturing
elements of decision making in the field.
We hope that the index of Knightian uncertainty that we create can be of use for
future work that wants to study the links between Knightian uncertainty and decisions
in other dimensions. Aggregated to industry level it might for instance be related to
issues relating to the boundary of the firm. One aspect that has been the focus of some
interest is the relation between risk, uncertainty and investment. Whether investment is
increasing or decreasing in risk has been the focus of much interest and some theoretical
results suggest that there may be different effects from increased risk and Knightian
uncertainty (Nishimura and Ozaki (2007)). One may expect that not only the level of
investment, but also the type of investment (for instance the level of flexibility) may be
affected by the extent of risk and Knightian uncertainty. We leave this for future research.
39
A
A.1
A.1.1
Appendix
Details Regarding Form 10-K.
The structure of filings
We distinguish between 10-K filings and documents. The term filing refers to a file that
is downloaded via the EDGAR server. Each filing starts with a header that contains
information about the filer, such as address, phone number, but also in which industry
it is operating according to Standard Industry Classification (SIC) codes. The header is
followed by one or more documents, separated by special identification tags.13 Documents
can be of different types, identified again by a special tag. Not all documents in a 10K filing will be relevant for textual analysis. Some documents contain copies of other
parts of the filing in different data formats. It is therefore important to understand what
the different document types are and what information they contain. We can sort the
document types according to the way data is stored:
1. Text and Hypertext Markup Language (HTML): 10-K and exhibits
2. American Standard Code for Information Interchange (ASCII): graphics, Excel tables, PDF and ZIP
3. eXtensible Business Reporting Language (XBRL): accounting data
The set of documents stored in plain text or HTML is, luckily, also the set of documents we are interested in from a text analysis point of view. The actual 10-K document
and attached exhibits are either stored as plain text documents, or, after June 1999, as
HTML documents. The SEC has adopted a variant of the HTML 3.2/4.0 standard and
the Commissions filer manual indicates which code is actually accepted in filings. In
particular, it excludes the use of tags that indicate executable code such as Javascript.
It is also forbidden to use nested table structures, something that used to be quite common in the early days of the internet. Nested table structures make it hard to extract
financial data from tables, because the structure is not easily machine readable. For our
purposes, we will have to eliminate the HTML markup from the document and take a
reasonable stance on how to deal with the information contained in tables, before we can
meaningfully count words.
13
Documents are demarcated by ‘DOCUMENT’-tags and the header is identified by a ‘SEC-HEADER’tags.
40
Another set of documents in a 10-K filing are stored in ASCII-format. Such information is not directly amenable to textual analysis, as it is not in plain text. However, in our
case, we do not have to access these documents anyway, since the text we are interested
in is contained in the 10-K documents and the exhibits, both are stored in plain text or
HTML. Documents stored in ASCII are either grahpics, tables or PDF. The first two are
anyway not amenable to textual analysis. As for the PDFs, they are just unofficial copies
of either the exhibits or the 10-K form. The Commission allowed such copies since 1999,
but mandated that they essentially contain the same information as the documents they
are a copy of (except for the graphics and layout).
The third group of documents contains accounting data in XBRL, which is a markup
language that was designed to be machine-readable. The Commission started to allow
XBRL filings in 2005, but it is not mandatory to provide accounting information in
this form. However, for future research, this might be an interesting data source. The
standardized, machine-readable format should allow to extract accounting data from a
larger set of companies automatically. The SEC already provides lists of filings containing
such data, to allow scripted downloading from their servers. From a text-analysis point
of view this information is not interesting, and we neglect it.
For our analysis, we will construct three different data sets. The first, we call the
fulldocs dataset. It is based on all text that is contained in documents not of the type
excel, graphic or PDF. Thus we start with the complete filing and exclude documents that
we find not to be of interest. The second dataset we construct looks only at documents
of the type 10-K, all other documents are neglected. Theoretically, a filing could contain
more than one document of the type 10-K, but we have not verified this possibility. The
third dataset we build is only based on the text data contained in documents of the
type exhibit. Conceptually, the word count of the fulldocs dataset should equal the word
count of exhibits plus 10-K, though minor deviations might occur. Having this data split
available allows us to run robustness checks in an empirical analysis. If the fullDocs data
is used, are the results driven by the information contained in the exhibits or by the
information in the actual 10-K documents. Also, the fulldocs dataset is compatible with
the data constructed in previous research. It thus gives us an easy robustness checks for
our algorithm.
41
A.1.2
Electronic filing timeline
A precise understanding of the history of the SECs electronic filing system is essential
to understand the possibilities of text analysis based on 10-K filings. The SEC began to
mandate electronic filings through its Electronic Data Gathering, Analysis, and Retrieval
system (EDGAR) on February 1993. The process to move files onto the EDGAR system
began on April 26, 1993. On December 19, 1994, the Commission made the EDGAR rules
final and applicable to all concerned parties. On January 30, 1995 phase-in commenced.
Ending May 6, 1996, all public domestic companies were required to make their filings
on EDGAR, except for filings made in paper because of a hardship exemption. We can
thus speak of a reasonably complete dataset from at least 1997 onwards.
The key facts of the phase-in of EDGAR are presented below. Table A.2 provides a
complete overview of filing relevant amendments and changes to the EDGAR rules.
• On June 28, 1999 the Commission started accepting HTML documents as well as
supplemental PDF documents in EDGAR filings. As a consequence, filings became
increasingly bigger after 1999.
• Since May 30, 2000, the SEC accepts HTML documents with image and graphic files
included as ASCII code. Unlike the appended PDF documents in the filings, it is
not straightforward to exclude these sections from textual analysis, since they occur
within a given document (and are not separate documents). The SEC also allowed
the extended use of hyperlinks – new hyperlinks can link between documents in one
filing.
• Since November 4, 2002 foreign issuers have to make their filings via EDGAR.
This could potentially introduce a shift in our sample, however, non-U.S. public
companies usually file their annual reports with the SEC on different forms than
10-K, so the problem should be minor.
• Since March 16, 2005 the SEC allows voluntary submissions in XBRL (eXtensible
Business Reporting Language) format as exhibits to specified EDGAR filings under
the Exchange Act and the Investment Company Act. We exclude XBRL information from our text analysis; for future research it might interesting to note, that
XBRL is a machine readable data structure and that the SEC provides us with lists
of companies that use XBRL in their reporting. Potentially one could make use of
the accounting data provided in XBRL format.
42
The SEC’s acceptance of HTML, images, XBRL and PDF documents as filings has
significantly affected the file size of filings. The full year of 10K data for 1997 takes up
3.82 GB of disk space. Ten years later, the full 10K data for 2007 takes up 20.1GB of
disk space. The year 2014 take up 117GB of disk space.
A.1.3
The EDGAR FTP server
All EDGAR filings are accessible via an anonymous FTP server ftp://ftp.sec.gov. To
facilitate accessing filings via a scripted downloading process, the SEC provides daily
and quarterly lists of filings containg the companies name, the submitted form type, the
company’s Central Index Key (CIK), the date of filing and the complete path of the file
on the FTP server. These lists are available sorted by company name, form type or CIK
number. In addition, the SEC also provides a list of all filings including XBRL data.
Sometimes the filer might request to remove or correct a previous filing. For example,
because the document was a duplicate of a previously filed document or it contained
sensitive information. Such corrections are only reflected in the daily indices if they
occurred on the same business day. Corrections that occur on subsequent business days
are not reflected in the daily indices. This might lead to missing file errors when one uses
daily indices for scripted bulk downloads of EDGAR filings. The quarterly indices do not
suffer from this issue and are thus preferable for scripted downloading.
43
Part IV
Part III
Part II
Part 1
Parts
14
15
13
12
11
10
9A
9B
9
8
7A
7
6
5
2
3
4
1B
1A
Item
1
Security Ownership of Certain Beneficial Owners
and Management and Related Stockholder
Matters
Certain Relationships and Related Transactions,
and Director Independence
Principal Accountant Fees and Services
Exhibits, Financial Statement Schedules
Controls and Procedures
Other information
Directors, Executive Officers and Corporate
Governance
Executive Compensation
Changes in and Disagreements with Accountants
on Accounting and Financial Disclosure
Quantitative and Qualitative Disclosures about
Market Risk
Financial Statements and Supplementary Data
Properties
Legal Proceedings
empty
Market for Registrant’s Common Equity, Related
Stockholder Matters and Issuer Purchases of
Equity Securities
Selected Financial Data
Management's Discussion and Analysis of Financial
Condition and Results of Operation
Unresolved Staff Comments
Name
Business
Risk Factors
Information about relationships and transactions between the company and its directors, officers and their family
members.
Disclose the fees they paid to their accounting firm for various types of services during the year.
List of the financial statements and exhibits included as part of the Form 10-K.
Detailed disclosure about the company’s compensation policies and programs including compensation paid to top
executive officers in past year.
Information about the shares owned by the company’s directors, officers and certain large shareholders, and about
shares covered by equity compensation plans.
Information on disclosure controls and procedures and its internal control over financial reporting.
Discuss any disagreements with accountants. Many investors view this disclosure as a red flag.
Exposure to market risk, such as interest rate risk, foreign currency exchange risk, commodity price risk or equity
price risk. The company may discuss how it manages its market risk exposures.
Covers selected financial information of the past five years.
Company's perspective on business results of past financial year; risks can be discussed i.e. assessed and how
managed
Company’s significant properties (main plants, mines, other physical properties)
Information about significant pending lawsuits or other legal proceedings, other than ordinary litigation.
No information here; reserved for future regulation.
Information on equity securities: number of holders, shares, dividends etc.
Description
Description of the company’s business.
List of most sigificant risks to company or its securities. Risks might apply to company or economoy as a whole.
Usually it is not addressed how company manages risks.
Explanation of comments received by SEC staff on previously filed reports that have not been resolved.to see
whether the SEC has raised any questions about the company’s statements that have not been resolved.
Table A.1: Overview of contents in 10-K document. Information obtained via:
http://www.sec.gov/answers/reada10k.htm, accessed June 15, 2015.
Table A.2: Overview of relevant EDGAR dates
Date
Event
Release No.
In force
Fall 1984
1983 Project start
Pilot system opened
15.7.1992
23.2.1993
Release issued
33-6977
23.2.1993
Release issued
IC-19284
23.2.1993
Release issued
35-25746
23.2.1993
Release issued
33-6980
26.4.1993
31.12.1993
1.1.1994
30.6.1994
Phase in starts for test group
Phase in ends for test group
Performance evaluation start
Perfromance evaluation stops
19.12.1994
Interim rules finalised
30.1.1995
Phase in recommenced
6.5.1996
Phase in ended
1.7.1997
Minor and technical amendments
33-7427
24.10.1997
Adoption of Rule 14 of Regulation S-T of release 33-7427
33-7427
1.1.1998
12.1.1999
Rule 14 in force: no paper filing
Form 13F to be filed electronically
34-40934
15.4.1999
More electronic filings required
IC-23786
17.5.1999
Release issued
33-7684
28.6.1999
HTML and PDF accepted
33-7684
24.4.2000
Release issued
33-7855
30.5.2000
Internet filing
33-7855
10.7.2000
1.1.2001
14.5.2002
Diskettes die
Elimination of Financial Data Schedule requirement
Release issued
33-7855
33-7855
33-8099
4.11.2002
24.5.2002
Foreign issuers have to file on EDGAR
No paper filing for first time EDGAR users anymore
33-8099
33-8099
7.5.2003
Release issued
33-8230
30.6.2003
30.6.2003
21.4.2004
Release in force
Release issued
33-8230
33-8410
26.4.2004
26.4.2004
3.2.2005
Release in force
Release issued
33-8230
33-8529
16.3.2005
16.3.2005
18.7.2005
XBRL filing starts
Release issued
33-8529
33-8590
6.2.2006
Release partially in force
33-8590
12.6.2006
Release partially in force
33-8590
19.9.2005
Release partially in force
33-8590
Description
Comment
Development of EDGAR begins
Volunteers filing with both the Division of Corporation Financen and the
Division of Investment Management
EDGAR made available to voluntary filers from pilot system
explaining the EDGAR system generally and setting forth rules and procedures
that apply to electronic submissions processed by the Division of Corporation
Finance and in some cases, to those processed by the Division of Investment
Management
adopting rules specific to electronic submissions made by investment
companies under the Investment Company Act of 1940 and institutional
investment managers under Section 13(f) of the Exchange Act
adopting rules specific to electronic submissions made by public utility holding
companies and their subsidiaries under the Public Utility Holding Company Act
of 1935 which was repealed as of early 2006
relating to the payment of filing fees, by both paper and electronic filers, to the
Commission's lockbox depository at Mellon Bank in Pittsburgh, Pennsylvania,
under Rule 3a of the Rules Relating to Informal and Other Procedures
This is related to 33-6977, IC-19284, 35-25746, 33-6980.
Staff evaluates EDGAR's preformance during 6-month test period
33-7122
Phase-in proceeded according to revised schedule; SEC adopted minor
amendments according to staff experience since filing began in 1993.
All public domestic companies required to file via EDGAR
1.1.1998
28.6.1999
For a test group, electronic filing starts on interim basis.
no further phase-ins done after
No further phase-ins in this period.
The evaluation resulted in a positive assessment of the EDGAR system,
based on data gathered from within the Commission as well as from the
filers and other members of the public. Consequently, the staff
recommended that the Commission proceed with full implementation of
mandated electronic filing.
made the EDGAR interim rules final and applicable to all domestic
registrants and third parties filing with respect to those registrants.
Haven't seen the revised phase-in schedule.
From 1996 on, data set should be relatively complete (foreign
companies not required yet to file via EDGAR)
Minor and technical amendments to its rules governing electronic filing,
including the elimination of the transition rules applicable to the phase-in
period
Absent hardship extensions, paper filings not accepted anymore (if electronic
filing was required)
Filers must submit Forms 13F electronically, unless a hardship exemption is
available.
Form N-8F and applications for deregistration under Investment Company Act
Rule 0-2 to be filed in electronic format.
Adopting new rules and amendments to existing rules and forms in connection
with the first stage of EDGAR modernization
Began accepting live filings submitted to EDGAR in HTML; official filings can
now be accompanied with unofficial copies in PDF.
Adopting amendments to existing rules and forms to reflect changes in filing
requirements resulting from the implementation of the next stage of EDGAR
modernization.
Internet filing starts, HTML documents with graphic and image files accepted;
expanded use of hyperlinks.
Previously all filings in ASCII; file size not a good proxy for informational
content anymore
With the acceptance of images embedded in HTML, the EDGAR filings get
messier and larger; previously we could cut out PDF documents by
focusing on the 10-K document in the filing; now the 10-K document can
also contain ASCII images; distorting file size even more.
No filings via diskettes anymore
Elimination of Financial Data Schedule requirement
Requiring foreign issuers to file via EDGAR; no paper filing requirement for first
time EDGAR filer anymore
Foreign issuers mandatorily file via EDGAR
Sample changes?
Previously first time EDGAR useres were required to hand in a paper copy of
their filing as well
Adopting rules requiring the electronic filing of beneficial ownership reports on
Forms 3, 4 and 5 filed by officers, directors and principal security holders
(insiders) under Section 16(a) of the Exchange Act, and requiring issuers with
corporate websites to post these reports. The release also removed magnetic
cartridges as an available means of transmitting filings to the EDGAR system.
no filing via magnetic cartridges anymore
Adopting rule and form amendments requiring the electronic filing of
applications on Form ID for access codes to file on EDGAR
Adopting rule amendments to enable registrants to participate in its interactive
data initiative by submitting voluntarily supplemental tagged financial
information using the XBRL format as exhibits to specified EDGAR filings under
the Exchange Act and the Investment Company Act. Registrants choosing to
participate in the voluntary program also continue to file their financial
information in HTML or ASCII format, as currently required.
filings can now also contain XBRL data
Adopting rule amendments that require certain open-end management
investment companies and insurance company separate accounts to identify in
their EDGAR submissions information relating to their series and classes (or
contracts, in the case of separate accounts).
Adopting rule amendments that require certain open-end management
investment companies and insurance company separate accounts to identify in
their EDGAR submissions information relating to their series and classes (or
contracts, in the case of separate accounts).
The amendments also made filings under Section 17(g) of the Investment
Company Act and sales literature filed by non-NASD members with the
Commission under Section 24(b) mandatory electronic filings
The amendments also made several minor and technical amendments, that
became effective September 19, 2005.
A.2
Additional results
VARIABLES
Overall U.
Knightian U.
Risk
Negative
Positive
Cash Ratio
Deriv.
Market to Book
ln(Assets)
Cash Flow
Working Capital
Captial Exp.
Leverage
RnD
Dividend
Acquisitions
mean
1.307039
.3215403
.3381178
1.580969
.696042
.1967882
.214489
3.133267
.375409
-.1941616
-.1262943
.0563803
.2584224
2.737008
.2251531
.0242645
sd
p5
median
p95
N
.3414726 .7619606 1.305077 1.858774 69566
.1213138 .1355565 .3168317 .5270892 69566
.1277493 .1634135 .3225646 .5640919 69566
.4578125 .8277217 1.577799 2.322294 69566
.1796235 .4505101 .6742342 1.012908 69566
.2284094 .0030082 .1012097 .7250325 69566
.4104718
0
0
1
46035
5.911632 .7684357 1.580289 9.09271 69566
2.491429 -3.806299 .4594257 4.305436 69566
.9516209 -1.179079 .0520677 .1906485 69566
1.025445 -.7597701 .0349657 .3918728 69566
.0686139 .0021456 .0337241 .1966775 69566
.2656591
0
.1949257 .9092031 69566
120.3394
0
0
1.275911 69566
.4176861
0
0
1
69566
.0804475
0
0
.1601291 69566
Table A.5: Descriptive statistics for variables used in cash holding and hedging regressions
Overall uncertainty
Rank Industry (SIC 4-digit)
Prefabricated Metal
Buildings & Components
Bolts, Nuts, Screws,
431
Rivets & Washers
Concrete, Gypsum &
432
Plaster Products
Hotels, Rooming Houses,
433 Camps & Other Lodging
Places
430
Knightian Uncertainty
Risk
SIC
Average Industry (SIC 4-digit)
code
3448
3452
3270
Operators Of Apartment
Buildings
Natural Gas Transmisison &
0.96
Distribution
Savings Institution, Federally
0.96
Chartered
0.96
7000
0.94 Natural Gas Distribution
434 Flat Glass
3211
0.94
435 Knitting Mills
436 Mineral Royalty Traders
Costume Jewelry &
437
Novelties
Prepared Fresh Or Frozen
438
Fish & Seafoods
439 Asset-Backed Securities
2250
6795
3960
Savings Institutions, Not
Federally Chartered
0.92 Knitting Mills
0.91 Screw Machine Products
Prepared Fresh Or Frozen Fish
0.87
& Seafoods
SIC
Average Industry (SIC 4-digit)
code
SIC
Average
code
6513
0.20 Blank Checks
6770
0.24
4923
0.20 Wholesale-Durable Goods, Nec 5099
0.24
6035
0.20 Costume Jewelry & Novelties
3960
0.24
4924
0.20
7829
0.24
6036
0.20
3824
0.23
2250
3451
0.19
0.17
3281
6035
0.23
0.22
2092
0.17
6036
0.22
2092
0.16
6189
0.05
2092
0.77 Silver Ores
1044
0.14
6189
0.76 Asset-Backed Securities
6189
0.10
Services-Allied To Motion
Picture Distribution
Totalizing Fluid Meters &
Counting Devices
Cut Stone & Stone Products
Savings Institution, Federally
Savings Institutions, Not
Federally Chartered
Prepared Fresh Or Frozen Fish
& Seafoods
Asset-Backed Securities
Table A.6: Bottom 10 industries in terms of our overall uncertainty, Knightian uncertainty
and risk indices.
46
Overall Uncertainty Words
ALMOST
CONTINGENCY
INDEFINITELY
RECALCULATE
SOMEWHERE
ALTERATION
CONTINGENT
INSTABILITIES
RECALCULATED
SUGGEST
ALTERATIONS
CONTINGENTLY
INSTABILITY
RECALCULATES
SUGGESTED
APPARENT
CONTINGENTS
INTANGIBLE
RECALCULATING
SUGGESTING
APPARENTLY
CROSSROAD
INTANGIBLES
RECALCULATION
SUGGESTS
APPEAR
CROSSROADS
MAY
RECALCULATIONS
SUSCEPTIBILITY
APPEARED
DEPEND
NEARLY
RECONSIDER
TENDING
APPEARING
DEPENDED
PENDING
RECONSIDERED
TENTATIVE
APPEARS
DEPENDENCE
POSSIBILITIES
RECONSIDERING
TENTATIVELY
ASSUME
DEPENDENCIES
POSSIBILITY
RECONSIDERS
TURBULENCE
ASSUMED
DEPENDENCY
POSSIBLE
REEXAMINATION
UNHEDGED
ASSUMES
DEPENDENT
POSSIBLY
REEXAMINE
UNSETTLED
ASSUMING
DEPENDING
PRELIMINARILY
REEXAMINING
UNWRITTEN
ASSUMPTION
DEPENDS
PRELIMINARY
RISK
VARIANT
ASSUMPTIONS
DESTABILIZING
REASSESS
RISKED
VARIANTS
CLARIFICATION
DIFFER
REASSESSED
RISKING
VARYING
CLARIFICATIONS
DIFFERED
REASSESSES
RISKS
CONDITIONAL
DIFFERING
REASSESSING
ROUGHLY
CONDITIONALLY
DIFFERS
REASSESSMENT
SEEMS
CONTINGENCIES
HINGES
REASSESSMENTS
SOMEWHAT
Knightian Uncertainty Words
ABEYANCE
DOUBTED
PRESUMPTIONS
UNCERTAINTY
UNPROVED
ABEYANCES
DOUBTFUL
REINTERPRET
UNCLEAR
UNPROVEN
AMBIGUITIES
DOUBTS
REINTERPRETATION
UNCONFIRMED
UNQUANTIFIABLE
AMBIGUITY
HIDDEN
REINTERPRETATIONS
UNDECIDED
UNQUANTIFIED
AMBIGUOUS
IMPRECISE
REINTERPRETED
UNDEFINED
UNRECONCILED
ANOMALIES
IMPRECISION
REINTERPRETING
UNDESIGNATED
UNSEASONABLE
ANOMALOUS
IMPRECISIONS
REINTERPRETS
UNDETECTABLE
UNSEASONABLY
ANOMALOUSLY
INCOMPLETENESS
REVISE
UNDETERMINABLE
UNSPECIFIC
ANOMALY
INDEFINITE
REVISED
UNDETERMINED
UNSPECIFIED
ARBITRARILY
INDEFINITENESS
RUMORS
UNDOCUMENTED
UNTESTED
ARBITRARINESS
INDETERMINABLE
SELDOM
UNEXPECTED
UNUSUAL
ARBITRARY
INDETERMINATE
SELDOMLY
UNEXPECTEDLY
UNUSUALLY
BELIEVE
INEXACT
SPECULATE
UNFAMILIAR
VAGARIES
BELIEVED
INEXACTNESS
SPECULATED
UNFAMILIARITY
VAGUE
BELIEVES
MAYBE
SPECULATES
UNFORECASTED
VAGUELY
BELIEVING
MIGHT
SPECULATING
UNFORSEEN
VAGUENESS
CAUTIOUS
NONASSESSABLE
SPECULATION
UNGUARANTEED
VAGUENESSES
CAUTIOUSLY
PERHAPS
SPECULATIONS
UNIDENTIFIABLE
VAGUER
CAUTIOUSNESS
PRECAUTION
SPECULATIVE
UNIDENTIFIED
VAGUEST
CONCEIVABLE
PRECAUTIONARY
SPECULATIVELY
UNKNOWN
Table A.3: Word List Used in This Article:
47 Consisting of a Split of Uncertainty Word
List from Loughran and McDonald (2011), Continued on Next Page.
Knightian Uncertainty Words (continued)
CONCEIVABLY
PRECAUTIONS
SPORADIC
UNKNOWNS
CONFUSES
PRESUMABLY
SPORADICALLY
UNOBSERVABLE
CONFUSING
PRESUME
SUDDEN
UNPLANNED
CONFUSINGLY
PRESUMED
SUDDENLY
UNPREDICTABILITY
CONFUSION
PRESUMES
UNCERTAIN
UNPREDICTABLE
COULD
PRESUMING
UNCERTAINLY
UNPREDICTABLY
DOUBT
PRESUMPTION
UNCERTAINTIES
UNPREDICTED
Risk Words
ANTICIPATE
DEVIATING
PREDICT
RANDOMIZE
VARIANCE
ANTICIPATED
DEVIATION
PREDICTABILITY
RANDOMIZED
VARIANCES
ANTICIPATES
DEVIATIONS
PREDICTED
RANDOMIZES
VARIATION
ANTICIPATING
EXPOSURE
PREDICTING
RANDOMIZING
VARIATIONS
ANTICIPATION
EXPOSURES
PREDICTION
RANDOMLY
VARIED
ANTICIPATIONS
FLUCTUATE
PREDICTIONS
RANDOMNESS
VARIES
APPROXIMATE
FLUCTUATED
PREDICTIVE
RISKIER
VARY
APPROXIMATED
FLUCTUATES
PREDICTOR
RISKIEST
VOLATILE
APPROXIMATELY
FLUCTUATING
PREDICTORS
RISKINESS
VOLATILITIES
APPROXIMATES
FLUCTUATION
PREDICTS
RISKY
VOLATILITY
APPROXIMATING
FLUCTUATIONS
PROBABILISTIC
SOMETIME
APPROXIMATION
IMPROBABILITY
PROBABILITIES
SOMETIMES
APPROXIMATIONS
IMPROBABLE
PROBABILITY
VARIABILITY
DEVIATE
LIKELIHOOD
PROBABLE
VARIABLE
DEVIATED
OCCASIONALLY
PROBABLY
VARIABLES
DEVIATES
ORDINARILY
RANDOM
VARIABLY
Table A.4: Word list used in this article: 48
consisting of a split of uncertainty word list
from Loughran and McDonald (2011), continued from previous page.
VARIABLES
Overall U.
(1)
Beta
(2)
F/F Beta
0.522***
(0.0104)
0.174***
(0.00998)
Positive
Negative
ln(Assets)
Interest Coverage
Op Income to Sales
Avg Lt Debt to Asset
Debt to Capital
Industry Sigma
Filing F/F Beta
Constant
Observations
R-squared
Controls
Year FE
Firm FE
0.112***
(0.0136)
0.604***
(0.0140)
92,906
0.089
92,906
0.009
(3)
Beta
(4)
Std. c.
(5)
Beta
(6)
Std. c.
0.136***
0.0779
(0.0186)
0.0893*** 0.0262
(0.0246)
0.0306*** 0.0243
(0.00877)
0.114***
0.409
(0.00789)
1.94e-05
0.00499
(2.29e-05)
-0.00200* -0.0145
(0.00102)
0.0457
0.0163
(0.0388)
-0.106*** -0.0436
(0.0354)
-0.0282
-0.00508
(0.0596)
0.0971***
(0.0181)
0.0598**
(0.0246)
0.0465***
(0.0102)
0.109***
(0.00759)
-1.54e-05
(2.39e-05)
-0.00125
(0.00137)
0.0362
(0.0463)
-0.0946**
(0.0413)
-0.0132
(0.0605)
0.0528
0.490***
(0.0416)
0.411***
(0.0433)
60,911
60,911
0.594
0.594
YES
YES
YES
YES
YES
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Table A.7: Regressions using filing-year matched beta.
49
60,911
0.461
YES
YES
YES
0.0167
0.0351
0.372
-0.00377
-0.00857
0.0123
-0.0370
-0.00226
60,911
0.461
YES
YES
YES
VARIABLES
Knightian U.
Risk
(1)
Beta
(2)
F/F Beta
(3)
Beta
(4)
Std. c.
(5)
F/F Beta
(6)
Std. c.
1.455***
(0.0299)
0.211***
(0.0295)
0.565***
(0.0281)
0.284***
(0.0305)
0.346***
(0.0503)
0.0600
(0.0381)
0.0770***
(0.0248)
0.0283***
(0.00883)
0.549***
(0.0385)
0.0702
0.184***
(0.0495)
0.117***
(0.0388)
0.0569**
(0.0247)
0.0474***
(0.0103)
0.447***
(0.0399)
0.0355
Positive
Negative
Constant
Observations
R-squared
Controls
Year FE
Firm FE
0.292***
(0.0126)
0.568***
(0.0133)
92,906
0.097
92,906
0.018
0.0131
0.0226
0.0225
60,911
60,911
0.594
0.594
YES
YES
YES
YES
YES
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
60,911
0.461
YES
YES
YES
Table A.8: Regressions using filing-year matched beta.
50
0.0242
0.0159
0.0358
60,911
0.461
YES
YES
YES
(4)
Beta
(5)
Beta
Constant
0.685***
(0.0695)
0.0842
(0.0761)
0.0630
(0.0660)
0.724***
(0.0630)
0.148***
(0.0566)
-0.000557
(0.0378)
0.567***
(0.0343)
Observations
R-squared
Sample
Controls
Year FE
Firm FE
25,777
34,788
60,565
25,777
0.662
0.657
0.594
0.662
AFTER BEFORE
ALL
AFTER
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
34,788
0.657
BEFORE
YES
YES
YES
VARIABLES
Overall U.
(1)
Beta
(2)
Beta
(3)
Beta
0.0639**
(0.0306)
0.0744***
(0.0194)
0.121***
(0.0186)
-0.00149***
(0.000367)
Litigous words
Knightian U.
Risk
0.526***
(0.0356)
0.505***
(0.0339)
Table A.9: Robustness tests for fiscal-year matched beta regressions.
51
(4)
Fiscal Beta
(5)
Fiscal Beta
Constant
0.573***
(0.0630)
0.0953
(0.0685)
0.0444
(0.0601)
0.632***
(0.0573)
-0.0479
(0.0695)
0.0951*
(0.0497)
0.743***
(0.0433)
Observations
R-squared
Sample
Controls
Year FE
Firm FE
25,777
34,788
60,565
25,777
0.613
0.496
0.467
0.613
AFTER
BEFORE
ALL
AFTER
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
34,788
0.496
BEFORE
YES
YES
YES
VARIABLES
Overall U.
(1)
Fiscal Beta
(2)
Fiscal Beta
(3)
Fiscal Beta
0.0763***
(0.0277)
0.0316
(0.0245)
0.0778***
(0.0173)
-0.00106***
(0.000388)
Litigous words
Knightian U.
Risk
0.740***
(0.0450)
0.712***
(0.0362)
Table A.10: Robustness tests for fiscal-year matched Fama/French beta regressions.
52
(4)
Filing Beta
(5)
Filing Beta
Constant
0.678***
(0.0693)
0.142*
(0.0743)
0.0810
(0.0659)
0.718***
(0.0629)
0.194***
(0.0568)
-0.00727
(0.0383)
0.732**
(0.351)
Observations
R-squared
Sample
Controls
Year FE
Ind FE
Firm FE
25,941
34,970
60,911
25,941
0.666
0.657
0.594
0.666
AFTER
BEFORE
ALL
AFTER
YES
YES
YES
YES
YES
YES
YES
YES
NO
NO
NO
NO
YES
YES
YES
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
34,970
0.657
BEFORE
YES
YES
NO
YES
VARIABLES
Overall U.
(1)
Filing Beta
(2)
Filing Beta
(3)
Filing Beta
0.0826***
(0.0303)
0.0834***
(0.0196)
0.132***
(0.0186)
-0.00116***
(0.000377)
Litigous words
Knightian U.
Risk
0.687**
(0.350)
0.498***
(0.0416)
Table A.11: Robustness tests for filing-year matched beta regressions.
53
VARIABLES
Overall U.
(1)
Filing F/F Beta
(2)
Filing F/F Beta
(3)
Filing F/F Beta
0.0948***
(0.0291)
0.0546**
(0.0267)
0.0941***
(0.0182)
-0.000829**
(0.000422)
Litigous words
(4)
Filing F/F Beta
Knightian U.
Risk
Constant
0.537***
(0.0676)
Observations
R-squared
Sample
Controls
Year FE
Ind FE
Firm FE
25,941
0.607
AFTER
YES
YES
NO
YES
0.327
(0.306)
0.417***
(0.0433)
34,970
60,911
0.490
0.461
BEFORE
ALL
YES
YES
YES
YES
NO
NO
YES
YES
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
0.156**
(0.0692)
0.0857
(0.0626)
0.588***
(0.0610)
-0.0077
(0.0761
0.120*
(0.0552
0.346
(0.303
25,941
0.607
AFTER
YES
YES
NO
YES
34,970
0.490
BEFOR
YES
YES
NO
YES
Table A.12: Robustness tests for filing-year matched beta regressions.
All ratings
CCC-D
B
BB
BBB
A
AA
AAA
Baseline
2.4%
18.5%
27.9%
31.1%
16.5%
2.7%
0.9%
Investment grade ratings only
Combined
Overall
Knightian
shock to risk
Overall
Knightian
uncertainty
Uncertainty & Knightian
uncertainty
Uncertainty
shock
Risk shock shock
uncertainty Baseline
shock
Risk shock shock
2.5%
2.5%
2.6%
2.8%
19.6%
19.4%
19.8%
20.8%
28.5%
28.5%
28.6%
29.1%
30.5%
30.6%
30.4%
29.9%
59.1%
63.0%
62.5%
62.0%
15.5%
15.7%
15.3%
14.5%
33.7%
31.1%
31.4%
31.8%
2.5%
2.5%
2.4%
2.2%
5.4%
4.6%
4.7%
4.8%
0.8%
0.8%
0.8%
0.7%
1.7%
1.3%
1.4%
1.4%
Combined
shock to risk
& Knightian
uncertainty
65.3%
29.5%
4.1%
1.1%
Table A.13: Counterfactual distributions of SP long-term credit ratings after two standard deviation shocks to overall uncertainty, risk and Knigthian uncertainty. In the combined scenario we simultaneously shock risk and Knightian uncertainty by one standard
deviation.
54
(5)
Filing F/F
VARIABLES
Knightian U.
(1)
Cash
0.0626***
(0.0177)
Risk
-0.0124
(0.0116)
Positive
0.0174**
(0.00717)
Negative
0.00297
(0.00256)
Industry Sigma
-0.00879
(0.0211)
Market to Book 0.00420***
(0.000505)
ln(Assets)
0.000584
(0.00297)
Cash Flow
0.0157***
(0.00428)
Working Capital -0.00676*
(0.00393)
Capex
-0.256***
(0.0208)
Leverage
-0.172***
(0.00862)
RnD
2.68e-05
(2.05e-05)
Dividend
0.00649*
(0.00393)
Acquisition
-0.118***
(0.0138)
Constant
0.215***
(0.0108)
Observations
R-squared
Controls
Year FE
Firm FE
Fiscal years
(2)
Cash
0.0526**
(0.0211)
-0.0264
(0.0188)
0.0166
(0.0113)
0.00140
(0.00436)
-0.00688
(0.0191)
0.00280***
(0.000676)
-0.00228
(0.00418)
0.0200***
(0.00488)
-0.00463
(0.00394)
-0.243***
(0.0273)
-0.125***
(0.0116)
1.08e-05***
(2.80e-06)
0.0107**
(0.00424)
-0.160***
(0.0290)
0.211***
(0.0181)
(3)
Deriv
(4)
Deriv
-0.0659
-0.0886*
(0.0500)
(0.0510)
0.139***
0.195***
(0.0409)
(0.0463)
-0.0255
0.0371
(0.0225)
(0.0249)
0.00106
0.00979
(0.00877)
(0.0102)
-0.0134
-0.0466
(0.0359)
(0.0375)
0.000370
0.00130***
(0.000271) (0.000311)
0.0239***
0.0395***
(0.00545)
(0.00577)
-0.00357 -0.00730***
(0.00225)
(0.00255)
-0.00371
0.00230
(0.00260)
(0.00216)
-0.0447
0.0317
(0.0532)
(0.0420)
0.0346**
0.0871***
(0.0153)
(0.0168)
4.01e-06
3.75e-07
(4.29e-06) (1.17e-06)
0.0294
0.0189
(0.0184)
(0.0138)
-0.0137
0.0403
(0.0384)
(0.0266)
0.136***
0.117***
(0.0292)
(0.0386)
39,835
29,731
16,304
29,731
0.811
0.837
0.842
0.759
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
1995-2004
2005-2014 2001-2004 2005-2014
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(5)
Deriv narrow def
-0.0623
(0.0384)
0.187***
(0.0327)
0.0359*
(0.0184)
0.00335
(0.00713)
-0.0355
(0.0300)
0.00137***
(0.000261)
0.0377***
(0.00460)
-0.00779***
(0.00197)
0.00233
(0.00174)
0.00105
(0.0360)
0.0905***
(0.0131)
-5.49e-07
(2.37e-06)
0.0116
(0.0120)
0.0483**
(0.0233)
0.123***
(0.0315)
46,035
0.698
YES
YES
YES
2001-2014
Table A.14: Robustness Results. Columns (1)-(4) pre and post 2005 split. Column (5)
uses a more narrow definition of derivatives use (only relyuing on Compustat variable
55
AOCIDERGL). Standard errors clustered at firm level.
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