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Market Risk Disclosure and Crash Risk: Evidence from Textual Analysis
Shingo Goto College of Business
The University of Rhode Island Ballentine Hall, 7 Lippitt Road,
Kingston, RI 02881 [email protected]
Xin Luo College of Business Administration
Marquette University 1225 W. Wisconsin Ave,
Milwaukee, WI 53233 [email protected]
Zhao Wang College of Business
The University of Rhode Island Ballentine Hall, 7 Lippitt Road,
Kingston, RI 02881 [email protected]
October 2018
Abstract
The usefulness of market risk disclosures, mandated by SEC in Item 7A of Form 10-K, has
been a subject of ongoing debate. Using Latent Dirichlet Allocation, we cluster the textual
information in Item 7A into eight latent topics. Disclosures of two topics, both related to risks
in input costs and output prices, are associated significantly with lower future stock price crash
risk. One of them conveys significant information about future crash risk among low-accrual
firms, while the other does so among high-accrual firms. Overall, this study provides a strong
case against the argument that Item 7A provides only boilerplate information.
Keywords: Disclosure; Risk; Market Risk Disclosure; Crash Risk; Latent Dirichlet
Allocation; LDA; Textual Analysis; Language Processing; Boilerplate; Item 7A.
1
Market Risk Disclosure and Crash Risk:
Evidence from Textual Analysis
Abstract
The usefulness of market risk disclosures, mandated by SEC in Item 7A of Form 10-K, has been
a subject of ongoing debate. Using Latent Dirichlet Allocation, we cluster the textual information
in Item 7A into eight latent topics. Disclosures of two topics, both related to risks in input costs
and output prices, are associated significantly with lower future stock price crash risk. One of
them conveys significant information about future crash risk among low-accrual firms, while the
other does so among high-accrual firms. Overall, this study provides a strong case against the
argument that Item 7A provides only boilerplate information.
Keywords: Disclosure; Risk; Market Risk Disclosure; Crash Risk; Latent Dirichlet Allocation;
LDA; Textual Analysis; Language Processing; Boilerplate; Item 7A.
2
Investors, financial analysts, regulators, and other market participants generally agree on the
necessity of improving the quality of disclosures that firms make to the public about their
exposures to market risk, including interest rate risk, foreign currency exchange rate risk,
commodity price risk, equity price risk, and so on. Enhancing the quality of market risk disclosures
should help investors improve the process of security valuation and analysis (CFA Institute 2016)
and reduce investors’ panics and sensitive trading behaviors in response to unfavorable changes in
market conditions (e.g., Rajgopal 1999; Linsmeir et al. 2002; Thornton and Welker 2004).
With the objective of enhancing the quality of corporate financial disclosures, the US
Securities Exchange Commission (SEC) issued Financial Reporting Release (FRR) No. 48 in
1997, which mandates firm managers to narratively disclose quantitative and qualitative
information about the risk of loss arising from market prices and rates for debt, equity, currency,
commodity, and other traded instruments as well as their derivatives in Item 7A of Form 10-K
filings.1 Managers should also describe how they manage or hedge the risk exposures associated
with market fluctuations.
While more than 20 years have passed since the SEC mandated narrative market risk
disclosures in Item 7A, people have different views on the usefulness of Item 7A. For example,
investors and financial analysts surveyed by CFA Institute (2016) generally express a low level of
satisfaction with current market risk disclosures, owing to a large amount of boilerplate
information in narrative market risk disclosures.2 There are widely-held perceptions that, while
1 These disclosure requirements are described in Item 305 of Regulation S-K introduced under the Securities
Exchange Act of 1933. In particular, mandated market risk exposures prompt the firms to provide information about any market risk sensitive instruments that affect the firms’ financial conditions, in conjunction with the disclosure of quantitative information about market risks of derivatives and hedging (SFAS No.133, No.161). Please see Kawaller (2004) for an introduction to the disclosure of derivatives under SFAS No. 133.
2 While CFA Institute’s (2016) discussion mainly surrounds the financial instrument risk disclosure under International Financial Reporting Standards (IFRS), market risk disclosure requirements are very similar between IFRS and US GAAP. Please see KPMG (2017), especially pp.496-498.
3
firms’ market risk disclosures may have the appearance of valid disclosures, in actuality many of
them only provide routine, redundant, non-specific, and boilerplate information (e.g., Abraham
and Shrieves 2014; CFA Institute 2016; SEC 2016). In a recent concept release, SEC (2016) calls
for further discussions on the usefulness of current market risk disclosures and on strategies to
improve their informativeness.3
Given the ongoing discussions of the usefulness of Item 7A, this study undertakes a question
whether Item 7A provides significant information to the public. Specifically, we ask how a firm’s
narrative disclosure in Item 7A is related to the firm’s future stock price “crash risk” (Chen et al.
2001). We focus on the relationship between risk disclosure and future crash risk in this study
because crash risk captures a dimension of downside risk that most market participants and firm
managers are concerned about. Crash risk often reflects the effects of negative jumps in stock
prices (e.g., Jin and Myers 2006; Hutton et al. 2009) that are difficult to contain once they occur.
As such, any information that helps predict or mitigate future crash risk should have significant
economic values for market participants (e.g., Xiong et al. 2016).
A significant challenge in analyzing the information content of Item 7A is the large degree of
discretion each firm manager has on the content and the lack of compatibility in narrative market
risk disclosures across firms (Hodder and McAnally 2001). The textual information in Item 7A is
highly unstructured and difficult to classify, quantify, and compare across firms. To this end, we
apply a Latent Dirichlet Allocation (LDA) model (Blei et al. 2003; Bao and Datta 2014) to tease
out latent thematic information (topics) in Item 7A. LDA is essentially a Bayesian text clustering
3 Mr. Wesley Bricker, the Chief Accountant at SEC, recently emphasized the importance of firm managers’
attention and financial reporting oversight of firms’ market risk disclosures. Please see his remarks before the 2018 Baruch College Financial Reporting Conference: “Working Together to Advance Financial Reporting” on May 3, 2018, in New York, NY. https://www.sec.gov/news/speech/speech-bricker-040318#_ftn9.
4
algorithm that simultaneously uncovers and quantifies latent topics in text documents, without
imposing a set of predefined keywords or topics. Under the assumption that a firm's narrative
disclosure is a finite mixture of topics each of which is a distribution over a fixed vocabulary of
unspecified keywords, LDA classifies narrative market risk disclosures into a small number of
latent topics. We then examine how the disclosure of each latent topic is related to future crash
risk. As we discuss below, we find that some uncovered latent topics in Item 7A convey significant
information about the firms’ future stock price crash risk.
This study makes a few important steps to improve our understanding of the role and effects
of narrative market risk disclosures in Item 7A. First of all, to our knowledge, this is the first study
that investigates the relationship between mandatory market risk disclosures and future stock price
crash risk. Second, this study contributes to the ongoing regulatory debate over the usefulness of
mandatory market risk disclosures. Specifically, evidence of this study provides a strong case
against the argument that Item 7A merely provides boilerplate information. Finally, while it is not
necessarily easy to analyze and compare narrative market risk disclosures across firms in the
presence of boilerplate information, this study implements an LDA text clustering algorithm to
uncover significant information in Item 7A that helps investors improve the process of security
analysis and valuation. The information helps reduce investors’ panics and sensitive trading
behaviors in response to unfavorable changes in market conditions, thereby curtailing crash risk.
MAIN FINDINGS AND INTERPRETATIONS
The Information Content of Narrative Market Risk Disclosures
We find that two latent topics in narrative market risk disclosures, which are related to risk
exposures in the firms’ input costs and output prices, are negatively and significantly correlated
5
with future crash risk. Corroborating earlier studies that demonstrate the informativeness of market
risk disclosures (e.g., Rajgopal 1999; Linsmeir et al. 2002; Thornton and Welker 2004), this study
provides a strong case against the commonly-cited argument that narrative market risk disclosures
in Item 7A provide only boilerplate information. While a large proportion of narrative disclosures
appear to be fairly generic or non-specific, some disclosures convey useful information to the
public.
To proceed with the analysis, we define and measure stock price “crash risk” in two ways.
Following Chen et al. (2001), one of the two measures is the “negative coefficient of skewness of
firm-specific daily returns” (NCSKEW), and the other is the “down-to-up volatility” (DUVOL)
which is the log ratio of the return volatility in the down days to the return volatility in the up days.
Higher values of NCSKEW and DUVOL indicate larger downside risk for a given level of volatility,
thereby measuring the degree of the stock price crash risk. 4
To tease out the highly unstructured textual information in narrative market disclosures, we
follow Bao and Datta’s (2014) implementation of LDA. In a sample of US firms between 2002
and 2016, we uncover eight latent topics in their narrative market risk disclosures in Item 7A. We
then classify and quantify each firm’s narrative market disclosure by (i) topic assignments (i.e.,
which topics does the firm discuss in Item 7A?) and (ii) topic proportions (i.e., what are the
proportions of topics discussed in the firm’s Item 7A?). We then examine if the assignment and
the proportion of each topic are correlated with two measures of future stock price crash risk,
NCSKEW or DUVOL.
4 As Chen et al. (2001) note, we are adopting a narrow and euphemistic definition of “crashes” by associating it
solely with two measures of negative skewness of the return distribution. The two “crash risk” measures have been widely adopted in the literature. Please see Jin and Myers (2006), Hutton et al. (2009), Kim et al. (2011), Callen and Fang (2013, 2015a, 2015b, 2017), Kim et al. (2014), Kim and Zhang (2016), among many others.
6
Only two out of the eight topics emerge as significant in predicting future crash risk. The two
informative topics are related to exposures that affect the firms’ output prices and/or input costs.
The first of the two is associated with “commodity price risk and derivatives.” The other
informative topic is associated with “risks in product prices and materials costs,” that have
significant effects on the firms’ future sales and operating costs. Disclosures of these topics,
especially the former, are negatively and significantly correlated with future crash risk. In
particular, the disclosure of “commodity price risk and derivatives” predicts future crash risk
beyond the variables that have shown to be strong predictors of crash risk (e.g., Chen et al. 2001;
Jin and Myers 2006; Hutton et al. 2009; Callen and Fang 2013; and Kim and Zhang 2016).
One consistent interpretation, along the line of the recent risk disclosure theory of Heinle and
Smith (2017) and Heinle et al. (2018), is that disclosures of these topics help reduce the perceived
uncertainty surrounding the firms’ future cash flow volatilities (not the level of volatilities), when
the firms’ cash flows are sensitive to changes in input (materials) costs and changes in output
(product) prices. Firms with large commodity price risk exposures tend to have large uncertainty
in the volatility of their cash flows with large downside risk. Some firms actively engage in
hedging activities to reduce their cash flow volatilities while others do not. Discussions of
“commodity price risk and derivatives” and “risks in product prices and materials costs” in Item
7A appear to help reduce the perceived uncertainty surrounding the risk exposures of the firms’
future cash flows, which also help curtail the firms’ future crash risk.
Although it is difficult to draw an unambiguous causal conclusion about the effects that drive
our results, our evidence provides a clear case against the null hypothesis that Item 7A contains
only boilerplate information. In particular, narrative disclosures of risk exposures related to
“commodity price risk and derivatives” and “risks in product prices and materials costs” convey
7
useful information about the firms’ future crash risk, suggesting the informativeness of some
narrative market risk disclosures in Item 7A.
In contrast, disclosures of the other six topics do not convey significant or consistent
information about future crash risk. These topics are apparently related to disclosures of interest
rate risk and foreign currency exchange rate risk that are discussed in different contexts. While we
refrain from making strong claims from out textual analysis, many disclosures of these risk
exposures may not be specific enough for investors to remove their perceived uncertainty about
the volatility of the firms’ future cash flows.
Market Risk Disclosure, Earnings Management, and Crash Risk
The recent literature emphasizes the behavior of entrenched managers to delay the release of
bad news (the bad news hoarding behavior) as a prominent driver of stock price crash risk. (Please
see the next section for a review.) Consistent with this explanation, the literature has shown that
aggressive earnings management with accruals is positively associated with crash risk (Jin and
Myers 2006; Hutton et al. 2009) while accounting conservatism is negatively associated with crash
risk (Kim and Zhang 2016). Our evidence also confirms that accruals tend to be positively
associated with future crash risk as measured by NCSKEW.
If narrative market risk disclosures help mitigate the managers’ bad news hoarding behavior,
they should help reduce the firms’ future crash risk more among firms with more aggressive
earnings management. To see if/how our textual analysis supports this argument, we group our
sample into two subsamples sorted by the degree of earnings management, as measured by
working capital accruals (Frankel and Sun 2018). Our subsample analysis offers a couple of
empirical insights into the two latent topics that are correlated with future crash risk.
8
First, among firms with high accruals, the disclosure (topic assignment) of “commodity price
risk and derivatives” is not significantly correlated with future crash risk. The negative correlation
between the disclosure (topic assignment) of “commodity price risk and derivatives” is
concentrated among firms with low accruals. Within the subsample of firms with low accruals,
firms that discuss the “commodity price risk and derivatives” has significantly lower future crash
risk than those that do not. We do not see similar differences for the proportion of the same topic
in Item 7A.
Second, we find that the proportion of the discussion (but not the assignment) of “risks in
product prices and materials costs” in Item 7A is negatively correlated with future crash risk
among firms with high accruals, but not among firms with low accruals. Within the subsample of
firms with high accruals, firms that provide the relatively voluminous discussion of their “risks in
product prices and materials costs” tend to have lower future crash risk.
Although this simple subsample analysis would not allow us to draw a strong causal
conclusion about the effects of market risk disclosures on future crash risk, our findings are
consistent with the following interpretations:
• Disclosures of the “commodity price risk and derivatives” help lower future crash risk not
because they mitigate the bad news hoarding behavior (aggressive earnings management)
by entrenched managers, but because they reduce the uncertainty surrounding the firms’
cash flow volatilities, consistent with the theory put forth by Heinle and Smith (2017) and
Heinle et al. (2018). The significant correlation between the disclosure of “commodity
price risk and derivatives” and future crash risk is thus concentrated among firms that
maintain relatively transparent financial disclosures.
• Among the firms with high accruals, those providing detailed disclosures of “risks in
9
product prices and materials costs” may invest more heavily in building inventories and
other working capital to capture growth opportunities (e.g., Zhang 2007; Wu et al. 2010).
These firms exhibit high accruals without implying aggressive earnings management (bad
news hoarding). Consequently, these firms have lower crash risk than other firms with high
accruals. Put differently, the proportion of the disclosure of “risks in product prices and
materials costs” is helpful to separate between firms with aggressive earnings management
(bad news hoarding) and those with active working capital investments.
The evidence indicates that we can further refine the information content of some market risk
disclosures by conditioning on the firms’ accruals. While not all narrative market risk disclosures
are informative, some disclosures in Item 7A, especially those of the “commodity price risk and
derivatives” and “risks in product prices and materials costs,” clearly convey non-boilerplate
information to the public.
BACKGROUNDS AND RELATED LITERATURE
Some Theoretical Backgrounds on Risk Disclosures
Although the market risk disclosure in Item 7A is mandatory, a firm manager has a large
discretion over how much and what sort of information to disclose (e.g., Hodder and McAnally
2001). On the one hand, there are firms that only restate disclosure requirements or merely provide
their generic risk management policies. On the other hand, there are also firms that commit
themselves to provide specific and detailed information about their exposures to various market
risk factors. It is, therefore, worthwhile to review the recent literature on disclosure theories.
In general, investors and regulators prefer transparent disclosures. It is often argued that firms
that consistently make detailed, timely, and informative disclosures should face lower costs of
10
public equity and debt capital (e.g., Easley and O’Hrara 2004; Hughes et al. 2007). Arthur Levitt,
the former chairman of SEC, claims that high-quality accounting standards are desired because
they lower the cost of capital (Levitt 1998). Mazumdar and Sengupta (2005) report that firms with
higher financial reporting quality also pay lower loan spreads on their bank loans, too.
However, transparent disclosure can be a double-edged sword for the shareholders. Suppose
that a firm discloses that its cash flows increase faster due to a decline in the price of a certain rare
metal that is a crucial input for the firm’s production. The disclosure of increased cash flows should
have immediate positive valuation effects, but the information about the declining price of the rare
metal price may reveal proprietary information about the firm’s production. The information may
also encourage the firm’s potential competitors to enter the market, which can increase the rare
metal price and hence affect the firm’s future cash flows negatively. Thus firm managers have to
trade off the benefits of providing transparent disclosures against the proprietary costs of
disclosures that can be harmful to the shareholders’ interests (e.g., Verrecchia 1983, 1990,
2001).5 Gao (2010) also argues that the relationship between the quality of financial disclosures
and the cost of capital may not necessarily be negative when disclosures affect the disclosing firms’
investment decisions.
While traditional theoretical discussions of disclosure decisions are traditionally concerned
with disclosures of accounting items (e.g., earnings, cash flows, sales, asset values, etc.), a few
recent studies discuss benefits and costs of providing quality risk disclosures. For example,
Jorgensen and Kirschenheiter (2003) propose a model of equilibrium strategies for voluntarily
disclosing information about the firms’ risk exposures. In their model, firms with low cash flow
5 Skinner (1994, 1995) point out that expected litigation costs have material effects on managers’ voluntary
disclosure decisions.
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volatilities choose to disclose their risk exposures voluntarily and truthfully at a cost. These firms
tend to have low costs of capital. Their theory also implies that, when outside investors are more
uncertain about the volatility of the firm’s cash flows, the firm is more likely to disclose its risk
exposures voluntarily. Meanwhile, firms with high cash flow volatilities tend to refrain from
disclosing their risk exposures. These firms provide boilerplate information in mandatory risk
disclosures.
Heinle et al. (2018) recently propose a risk disclosure theory building on Heinle and Smith
(2017). In their model, investors’ perceived uncertainty about the risk exposure of a firm’s cash
flows introduce skewness and excess kurtosis (and all higher moments) in the perceived
distribution of the firm’s future cash flows. Mitigating the uncertainty through risk disclosure may
have positive or negative effects on the firm’ equity value depending on whether the uncertainty
induces positive or negative skewness in the perceived distribution. If the uncertainty induces
positive skewness, the firm would refrain from providing risk disclosures to reduce the uncertainty,
as that would increase the firm’s cost of capital. In this case, the firm should provide boilerplate
information in mandatory risk disclosures. But when the uncertainty induces negative skewness in
the perceived distribution of future cash flows, the firm would curtail crash risk (negative
skewness) by reducing the uncertainty through risk disclosures. This would also lower the firm’s
cost of capital.
To provide a practical perspective on risk disclosure theories, let’s consider an airline
company as an example. Market participants are aware that the company’s cash flows are sensitive
to oil (fuel) price fluctuations. Large increases in oil prices can have large negative effects on the
company’s future cash flows. On the other hand, decreases in oil prices do not necessarily have
large positive effects on future cash flows because the companies are under constant competitive
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pressures to lower airfares. The volatility of the company’s future cash flows, however, depends
on the extent to which the company hedges its oil price risk. Outside investors are uncertain about
the volatility of the company’s future cash flows, which induces negative skewness in their
perceived distributions. In this case, both Jorgensen and Kirschenheiter’s (2003) theory and Heinle
et al.’s (2018) theory imply that the airline company discloses information about its oil price risk
exposure as well as its hedging activities in order to reduce the investors’ uncertainty surrounding
the volatility of the company’s future cash flows. Such risk disclosure should also help curtail the
downside risk (crash risk) in the company’s equity value.
In practice, most airline companies discuss their fuel price risk exposures, along with their
interest rate risk exposures and foreign currency risk exposures, in Item 7A. However, different
airline companies disclose their fuel price risk exposures and other market risk exposures
differently in Item 7A. For example, in 10-K for the fiscal year 2017, Delta Airlines provides
narrative market risk disclosures with about 470 words in Item 7A, among which the company
discusses its fuel price risk exposures with only 64 words. In contrast, Southwest Airlines’
narrative market risk disclosures in Item 7A are much lengthier with more than 2,740 words. As a
part of the discussion, the company discusses how it uses commodity derivatives to hedge fuel
price risk in detail with more than 460 words. As this simple comparison suggests, some companies
provide more detailed information about their market risk exposures than their industry peers.
Earlier Studies on Stock Price Crash Risk
Since an influential paper by Chen et al. (2001), a growing body of the literature examines the
determinants of stock price crash risk. Recent studies emphasize the role of “bad news hoarding”
by firm managers in driving stock price crash risk. In the presence of large information asymmetry,
firm managers have incentives to withhold bad news from the public while releasing good news
13
truthfully in a timely manner (Shin 2003; Jin and Myers 2006; Kothari et al. 2009; Hutton et al.
2009). This tends to result in a stockpile of bad news. When bad news accumulates and passes a
threshold, managers are unable to withhold bad news and release it to the market suddenly at once,
leading to a sharp decline in stock price, that is, a stock price crash.
Chen et al. (2001) emphasize that increases in stock trading volume predict future crash risk
as they capture differences in opinions and many bearish investors tend to get sidelined in the
presence of short-sale constraints. Chen et al. (2001) indeed find that an increase in trading volume
relative to trend over the prior six months is positively related to stock price crash risk, consistent
with an argument that differences of opinion can lead to stock price overvaluation and subsequent
crash in the presence of short-sale constraints. While emphasizing the role of investor
heterogeneity and trading volume in predicting the crash, Chen et al. (2001) suggest that managers
tend to disclose good news right away, while dribbling bad news out slowly.
Managers of opaque firms have more scope for hiding bad news from the market. Empirical
evidence indeed indicates that opaque firms are likely to have higher crash risk. Firms with high
accruals tend to have higher future crash risk than those with low accruals (e.g., Jin and Myers
2006; Hutton et al. 2009), and firms with more conservative accounting practices tend to have
lower crash risk than others (Kim and Zhang 2016). Kim et al. (2014) show that firms that release
high quality corporate social responsibility (CSR) disclosures voluntarily to the market tend to
exhibit lower stock price crash risk than others.
Callen and Fang (2015a) argue that short interests predict higher stock price crash risk because
short sellers tend to detect bad news hoarding by the managers. Chang et al. (2017) find that high
stock liquidity (as measured by a low effective spread) induces short-term pressure and increases
managers’ ex ante incentives to withhold bad news. As high liquidity also facilitates the exit of
14
transient institutions, high liquidity can magnify the ex post stock price reactions to bad news
releases, thereby increasing crash risk. Callen and Fang (2013) also find that firms in industries
with high litigation-risk tend to have low crash risk.
Another strand of the literature focuses on the asymmetric information content between
positive news and negative news in the presence of bad news hoarding behavior of firm managers.
Shin (2003) shows that, when a manager has the discretion to disclose or withhold news that is
unverifiable, full disclosure is not supported in equilibrium, but a strategy that discloses all
observed good news and withholds all bad news is, providing a rationale for bad news hoarding
behavior. In his model, risk-averse investors require higher discount rates as they observe more
negative information, leading to large reactions of stock prices to bad news than to positive news,
thereby increasing the conditional negative return skewness (crash risk).6
Prior Applications of Textual Analyses to Risk Disclosures
An increasing number of studies apply language processing algorithms to analyze textual
information in firms’ risk disclosures. 7 For example, Campbell et al. (2014) measure the
informativeness of a firm’s risk factor disclosure by counting the number of risk-related keywords
in Item 1A of Form 10-K. Kravet and Musulu (2013) similarly measure the degree of risk
exposures by counting the number of sentences with at least one predefined risk-related keywords,
including {can/cannot, could, may, might, risk*, uncertain*, likely to, subject to, potential*,
6 Goto et al. (2009) show that non-US firms can attenuate the negative relation between cash-flow news and
discount rates when they cross-list their shares in the US markets with more stringent disclosure requirements than their own local markets. By cross-listing in markets with more stringent disclosures, foreign firms can commit themselves to provide more truthful disclosures
7 An increasing number of studies have applied textual analysis to parse 10-K disclosures. Please see Hadlock and Pierce (2010), Loughran and McDonald (2011), Bodnaruk et al. (2015), among others, for studies to gauge financial constraints from textual information in 10-K filings. Textual analysis has also been widely used in analyzing the stock market’s reactions to public news since the pioneering studies by Tetlock (2007) and Tetlock et al. (2008). Please see a recent article by Heston and Sinha (2017) and references cited therein.
15
vary*/varies, depend*, expos*, fluctuat*, possibl*, susceptible, affect, influenc*, and hedg*}.
Hope et al. (2016) employ the Stanford Named Entity Recognition (NER) tool to extract
specific entity names in Item 1A of Form 10-K. These specific entity names include names of
persons, locations, organizations, quantitative numbers in percentages, money values in dollars,
times and dates. They then define the “specificity’’ of risk disclosures by counting the number of
these specific entity names scaled by the total number of words in that section. They show that the
stock market reacts more to the 10-K filings with higher “specificity.” Similarly, analysts’
reliability in assessing the firms’ fundamental risk tends to be higher for firms with more specific
risk disclosures.
Instead of analyzing texts, Loughran and McDonald (2014) propose to use the “file size” of
10-K to proxy the readability of financial report. They argue that financial report users can
effectively obtain more information from more readable financial reports than from less readable
ones. Ertugrul et al. (2017) find that the readability measured by 10-K file size is negatively related
to future stock price crash risk.
Textual information in narrative disclosures is highly unstructured. Same risk-related
keywords may be discussed in completely different contexts by different companies. We thus
employ an unsupervised topic model that does not prespecify any keywords or classifications
before parsing textual documents. In particular, we employ an LDA model (Blei et al. 2003) to
tease out common latent topics in the textual information of narrative market risk disclosures in
Item 7A. A few recent studies, including Dyer et al. (2017) and Brown et al. (2018), use LDA
though their objectives and questions are very different from ours.
The traditional LDA assumes that the order of words appeared in a document does not matter,
and it also does not consider the sentence boundaries. Bao and Datta (2014) argue that this
16
assumption is not reasonable for risk disclosures because each sentence is unlikely to cover
multiple risks. In addition, they assume words in the same sentence belong to the same topic. Bao
and Datta (2014) demonstrate the applicability of their sentence-based LDA model by extracting
30 latent topics from textual information in Item 1A.8 Item 1A discusses a wide range of risk-
related topics including environmental risk, litigation risk, regulation risk, supply chain disruption
risk, and so on. Due to the high dimensionality, many of the extracted latent topics do not yield
straightforward interpretations, or they allow for multiple interpretations. This study employs the
sentence-based LDA model of Bao and Datta (2014) by focusing on eight latent topics in Item 7A.
By focusing on eight topics, we are able to provide intuitive interpretations of the latent topics, all
of which are related to changes in market prices or rates and derivatives.
RESEARCH DESIGN
Data and Sample
We source our data from Form 10-Ks from the SEC EDGAR database, CRSP, and Compustat.
We focus on the CRSP-Compustat-EDGAR merged sample of firm-years but exclude firms
operating in the financial industry (SIC codes 6000-6999) and the public utility industry (SIC
codes 4000-4999). We delete observations if any dependent variables or control variables used in
our regressions are missing. Thus, our final sample of market risk disclosures includes 45,417
firm-year observations for the period from 2002 to 2016.9
8 As a clustering algorithm, LDA is similar to K-means clustering algorithm in that we need to specify the number
of topics in advance. This study chooses eight to start with but it would be interesting to examine how the number of topics affect the analysis. We leave this analysis for future follow-ups.
9 Although Item 7A initiated in 1997, Item 7A contained very limited information before 2002.
17
Extracting Latent Topics in Item 7A
In this paper, we characterize market risk exposures as the topics disclosed in Item 7A and
utilize Bao and Datta’s (2014) sentence-based LDA model to extract these topics. The traditional
LDA model is a three-level hierarchical Bayesian model, which considers text documents as
mixtures of topics composed of individual words with certain probabilities (Blei et al. 2003). In
other words, each topic is a distribution of words, and each document is a mixture of corpus-wide
topics. This methodology has recently been used in several accounting studies (e.g., Brown et al.
2018; Dyer et al. 2017). This statistical model allows users to cluster keywords with their
probability and capture corresponding market risk exposures expressed in a descriptive format.
Thus, the key inferential problem for the sentence-based LDA model is “computing the
posterior distribution of the hidden variables θ (topic proportions) and z (topic assignments) given
the model parameters and the observed documents w”:
( ) ( )( )
, , | ,, | , ,
p z wp z w
p wθ α β
θ α β = (1)
In our study, we first program in Python to read all Item 7A disclosures in our sample and
analyze word distribution from the pool of all Item 7A disclosures. Second, we use Bao and Datta’s
(2014) LDA model to analyze the words from the pooled text to identify eight topics that the words
fit in. Our estimation of parameters and latent variables follow the Bayesian estimation method
described in Heinrich (2008) and Bao and Datta (2014). Third, we assign each sentence in every
Item 7A to a topic and summarize most frequently used words in each topic. We then predict
whether each topic appears and, if so, its proportion, in a specific disclosure based on the words
mixture of this disclosure. Since the LDA model considers each disclosure is a mixture of topics,
we also calculate the proportion of each topic in an Item 7A. Panel A of Table 1 exhibits keywords
18
for each topic extracted by the LDA model.
As Panel A of Table 1 shows, different topics may share some keywords. To summarize the
risk exposures associated with each topic, we read multiple documents that have very high
proportion for one specific topic (greater than 95%) and very low proportion for other topics (less
than 5%). Employing these documents allow us to largely avoid the noise from topics that share
some keywords so that we can tease out the meaning behind each list of keywords. We report our
interpretation of topics in Panel B of Table 1.
For regression analysis, we create dummy variables for each topic (topic assignment, z). The
dummy variable is set to equal to one if the firm’s Item 7A covers a particular topic, zero otherwise.
Thus, we use A_TOPIC1 through A_TOPIC8 to refer to these dummy variables. For regression
analysis, we also employ the proportion of each topic (θ) in Item 7A (P_TOPIC1 through
P_TOPIC8). Each P_TOPIC ranges between 0 and 1.
<Insert Table 1 Here>
Measurement of Stock Price Crash Risk
We estimate annual stock price crash risk for each firm using two widely-used firm-specific
measures and employ one-year-ahead (fiscal year T+1) crash risk as dependent variables in our
regressions. Our first measure is the negative coefficient of skewness of firm-specific daily returns
(NCSKEW). Following prior literature (Chen et al. 2001; Jin and Myers 2006; Hutton et al. 2009;
Callen and Fang 2013), we first estimate the following expanded market and industry index model
to compute firm-specific daily returns:
, 1, , 1 2, , 1 3, , 4, , 5, , 1 6, , 1 ,i t i i m t i j t i m t i j t i m t i j t i tr r r r r r rα β β β β β β− − + += + + + + + + + ε (2)
where ri,t is the stock return on firm i for day t, rm,t is the market return for day t that is measured
19
by the return on the CRSP value-weighted market index, and rj,t is the industry return for day t.
We computed value-weighted returns for each industry based on the first two digits of SIC codes.
Next, the residual return from Eq. (1) is used to estimate the firm-specific daily return, Ri,t,
which is defined as the natural logarithm of one plus the residual return. We compute NCSKEW
for firm i over fiscal year T by taking the negative of the third moment of firm-specific daily returns
and adjusting it by the standard deviation of firm-specific daily returns raised to the third power:
( ) ( )( )( )33
3 2 22, , ,1 / 1 2i t i t i tNCSKEW n n R n n R
= − − − − ∑ (3)
where n is the number of daily returns in fiscal year T. An increase in NCSKEW represents a more
left-skewed distribution of stock returns, which indicates a higher probability of a price crash.
Our second measure is down-to-up volatility, DUVOL, which is defined as the natural
logarithm of the ratio of the standard deviation in the down days to the standard deviation in the
up days. More specifically, we calculate DUVOL as follows:
( ) ( )2 2, , ,log 1 / 1i t u i t d i t
Down UpDUVOL n R n R
= − −
∑ ∑ (4)
where nu is the number of up days over the fiscal year T and nd is the number of down days over
the fiscal year T. Similar to NCSKEW, a higher DUVOL corresponds to higher stock price crash
risk because it indicates a more left-skewed distribution of stock returns. Callen and Fang (2013)
suggest that this measure is less likely to be affected by a small number of extreme returns.
Control Variables
Following prior literature on stock price crash risk (e.g., Chen et al. 2001; Jin and Myers 2006;
Hutton et al. 2009; Callen and Fang 2013), we employ the following control variables in our
20
regression analyses: stock return kurtosis (KUR), stock return volatility (SIGMA), cumulative
returns(CUM_RET), book-to-market ratio (BM), leverage ratio (LEV), return on equity (ROE),
firm size (LNSIZE), stock turnover (DTURNOVER), and working-capital accruals (ACCRUALS).
KUR is defined as the kurtosis of firm-specific daily returns over the fiscal year. SIGMA is
defined as the standard deviation of firm-specific daily returns over the fiscal year. CUM_RET is
the cumulative firm-specific daily returns over the fiscal year. We follow Fama and French (2008)
to calculate BM as the natural logarithm of the ratio of the book value of equity to the market value
of equity. LEV is the book value of all liabilities divided by total assets at the end of the fiscal year.
ROE is the income before extraordinary items divided by the book value of equity at the end of
the fiscal year. LNSIZE is defined as the natural logarithm of the market value of equity at the end
of the fiscal year. DTUROVER is the difference between the average monthly share turnover over
the fiscal year and that over the previous fiscal year. ACCRUALS is defined as the change in
accounts receivable plus the change in inventory minus the change in accounts payable minus the
change in taxes payable plus the change in other assets (Frankel and Sun 2018).
Since stock return characteristics such as crash risk exhibit persistence over time, we include
the lagged dependent variable (one of the two crash risk proxies) as a control variable. This and
other control variables are measured one year (fiscal year T) before the dependent variables. We
control for Fama-French 48-industry membership dummies and year fixed effects. We winsorize
each continuous variable at 1% and 99% of its distribution. Standard errors are clustered at the
firm level.
21
EMPIRICAL RESULTS
Descriptive Statistics
Table 2 presents descriptive statistics for the variables used in our study. The mean (median)
values NCSKEW and DUVOL are -0.021 (-0.125) and 0.011 (-0.007), respectively. These values
are close to numbers reported in previous studies. Figure 1 reports time variations of our two crash
risk measures, NCSKEW and DUVOL, between 2002 to 2016. The cross-sectional mean of
NCSKEW spiked in 2002 and 2008 and fell sharply thereafter. Callen and Fang (2017) suggest that
the first spike and fall in 2002-2003 correspond to the effects of the Sarbanes-Oxley Act that
attenuated the withholding of bad news. The second spike and fall in 2008-2009 correspond to the
global financial crisis. The time variation of DUVOL has been much less volatile than that of
NCSKEW.
< Insert Figure 1 Here >
Using Bao and Datta’s (2014) implementation of LDA in a sample of US firms between 2002
and 2016, we uncover eight latent topics in Item 7A. A firm’s disclosure of each topic is
characterized by two latent variables: (i) topic assignment and (ii) topic proportion. If a firm-year
discloses a particular topic, the topic assignment of the firm-year takes a value of 1 for the topic
and 0 otherwise. The topic proportion is the estimated proportion which each firm-year devotes to
disclose a particular topic in Item 7A. For example, if a firm-year devotes 50% of total sentences
in discussing a particular topic, the firm-year takes a value of 0.5 for that topic.
To run LDA, we require each text document (Item 7A) to contain more than 100 words.
However, the majority of firms have very short narrative market risk disclosures under Item 7A
with only 100 words or less. While we are able to apply LDA to obtain topic allocations and topic
22
proportions for 22,116 firm-years (48.7% of the sample), the other 23,301 firm-years (51.3% of
the sample) have no topic allocations or topic proportions. There are a few reasons for very short
narrative disclosures in Item 7A. Some of the 23,301 firm-years may not have any material market
risk exposures to report in Item 7A. Others may choose to provide as little information as possible
in mandatory market risk exposures in Item 7A. There are also firms that discuss their market risk
disclosures in other sections of 10-K filings and keep the Item 7A minimal. Distinguishing among
these possibilities is interesting but difficult, and we leave it for future work. However, since most
of the firm-years with very short Item 7A provide examples of uninformative or unimportant
market risk disclosures, they should provide useful information to our analysis. We, therefore,
retain these 23,301 firm-years without topic assignments or topic proportions by replacing their
missing values with zero. We also include a dummy variable to indicate these firms.10 As it turns
out, excluding 23,301 firm-years from the analysis does not change our conclusions we discuss
below.
Among the eight latent topics extracted in our LDA procedure, Topic 4 and Topic 1 are the
market risk exposures that are most commonly discussed in Item 7A. Around 43.9% and 43.0%
of our firm-years discuss Topic 4 and Topic 1, respectively, in Item 7A of their 10-K filings (Table
2, Panel A). These correspond to about 90% of the 22,116 firm-years that provide Item 7As with
more than 100 words in their narrative market risk disclosures. Disclosures of these two topics
also have larger proportions than those of other topics in Item 7A. Furthermore, proportions of
10 The situation here is somewhat similar to a case in which we examine the information content of R&D
expenses. As Koh and Reeb (2015) report, some firms leave R&D expenses blank in their financial statements even when they engage in patent activities actively. Koh and Reeb (2015) suggest that, when true R&D in blank firms tends to be small, replacing missing R&Ds with zero and adding a dummy variable to indicate blank R&D firms would be more appropriate than excluding the blank R&D firms altogether. In a different context, Pontiff and Woodgate (2008), McLean, Pontiff, and Watanabe (2009) replace negative book values with zero and includes a dummy variable to indicate firms with negative book equity values, rather than excluding them from the sample.
23
Topic 1 have high correlations with those of other topics, except Topics 2 and 7 (Table 2, Panel
B). Proportions of Topic 4 also have high correlations with those of other topics, except Topic 2.
Thus Topics 1 and 4 appear to capture commonly discussed topics in Item 7A.
In contrast, Topics 2 and 7 are the least commonly discussed topics of market risk exposures.
Around 28.3% and 29.2% of our sample-years discuss these topics (Table 2, Panel A). These
correspond to about 60% of the firms-years that provide Item 7As with more than 100 words.
Proportions of these topics have low correlations with those of other topics. As we described
above, these topics are associated with exposures that affect the firms’ output prices and/or input
costs. Topic 2 is related to “commodity price risk and derivatives,” and Topic 7 is associated with
“risks in product prices and materials costs” that have significant effects on the firms’ future sales
and operating costs. Disclosures of these topics convey information about the volatility of future
cash flows than disclosures of other topics, and their proportions may exhibit larger variations
across firms.
Not surprisingly, the two measures of crash risk, NCSKEW and DUVOL, are highly correlated
with each other with a correlation coefficient of 0.61 in Table 2, Panel B. In Panel B, we can also
see that the proportion of each topic tends to increase with market capitalization (LNSIZE) and
decrease with the book-to-market ratio (BM). That is, large growth firms tend to spend more than
100 words in Item 7A. These characteristics are also associated with future NCSKEW (one of the
crash risk measures), though the correlation coefficients are not very large. Correlations between
our crash risk measures (NCSKEW, DUVOL) and our control variables are consistent with the prior
literature.
< Insert Table 2 Here >
Table 3 reports relations between the disclosure of each topic and our control variables that
24
are known to predict future crash risk. Panel A reports Probit regressions of topic assignments and
Panel B reports OLS regressions of topic proportions. After controlling for industry effects and
year effects, the disclosure (assignment) of Topic 2, “commodity price risk and derivatives,” is
positively associated with return on equity (ROE) and firm size (LNSIZE), and negatively
associated with the stock return volatility (SIGMA), cumulative returns (CUM_RET), book-to-
market ratio (BM), financial leverage (LEV), and accruals (ACCRUALS). These control variables,
except accruals (ACCRUALS), also predict the disclosure of Topic 7, "risks in product prices and
materials costs." Firms with low financial leverage (LEV) tend to disclose Topic 5, while firms
with high financial leverage tend to disclose Topic 6 in Item 7A.
< Insert Table 3 Here >
Market Risk Disclosures and Future Crash Risk
We examine the impact of market risk exposures on stock price crash risk by estimating the
following baseline regression model:
, 0 , , ,
j Ki t j i T K i T i t
KCRASH TOPIC CONTROLS YearDummies IndustryDummiesβ β ε= + + ϒ + + +∑ (5)
for each topic j=1,…,8, where CRASHi,T+1 represents our two crash risk measures, NCSKEW and
DUVOL. TOPICj refers to each of our eight topics of market risk exposures (either assignments or
proportions) extracted from Item 7A of 10-K forms. We focus on j because it represents the effect
of each topical disclosure (assignment or proportion) on firm-specific crash risk. CONTROLSK
summarizes the set of control variables.
Table 4 reports the regression results of Eq. (4). We first examine the predictive relation
between disclosures of eight latent topics and future crash risk without controlling for other
predictors of future crash risk. In Table 4, we only include lagged dependent variable and dummies
25
in the indicator of missing latent topics (MISS) as the control variable in addition to year- and
industry-fixed effects. In all regressions, coefficients on MISS are negative and significant,
consistent with the notion that firms with very short narrative market risk disclosures (in Item 7A)
do not have material risk exposures to report.
In Panel A, we use NCSKEW as the dependent variable and examine the coefficient on each
topic assignment. We find that the coefficients on Topic 2 assignment (-0.048; t=-2.16) and Topic
7 assignment (-0.069; t=-3.20) are negative and statistically significant at the 5% and 1% levels,
respectively. Coefficients on Topic 3 and Topic 5 assignments are positive and moderately
significant at the 10% level. In Panel B, we repeat the analysis with DUVOL as the dependent
variable instead of NCSKEW. Coefficients on Topic 2 and Topic 7 assignments are negative and
significant at the 5% and 10% level, respectively. The coefficient on Topic 6 is positive and
moderately significant at the 10% level. Putting together, Topic 2 and Topic 7 assignments are
negatively correlated with the two measures of future crash risk (NCSKEW and DUVOL).
In Panel C and Panel D, we repeat the same exercise using Topic proportions rather than Topic
assignments. In regressions of NCSKEW (Panel C), only the coefficient on Topic 7 proportion is
reliably different from zero. It is negative (-0.239; t=-3.42) and significant at the 1% level. In
regressions of DUVOL (Panel D), coefficients of Topic 2 and Topic 7 proportions are negative and
significant at the 10% and 5% level.
These regression results suggest that disclosures of Topic 2 (commodity price risk and
derivatives) and Topic 7 (risks in product prices and materials costs) are negatively and
significantly correlated with future crash risk. In contrast, disclosures of the other topics do not
convey significant or consistent information about future crash risk. These topics are apparently
related to disclosures of interest rate and foreign currency exchange rate exposures, though they
26
are discussed in different contexts.11
< Insert Table 4 Here >
To take a deeper look at the information content of these two topical risk disclosures (Topics
2 and 7), we control for other variables that are known to predict future crash risk (please see
Section 3.4) and tabulate regression results in Table 5. In general, higher excess kurtosis (KUR)
and recent specific stock returns (CUM_RET) are positively associated with future crash risk,
measured by either NCSKEW or DUVOL. Recent volatility of specific stock returns (SIGMA), and
book-to-market ratio (BM) are negatively associated with future crash risk. Firms with large size
(LNSIZE), large increases in trading volume (DTURNOVER), and large accruals (ACCRUALS) are
positively and significantly correlated with future NCSKEW, but not with future DUVOL. These
results are consistent with the findings of Chen et al. (2001), Hutton et al. (2009), Callen and Fang
(2013), and Zhang (2013), among others.
After controlling for these predictors of future crash risk, disclosures of Topic 2 (commodity
price risk and derivatives) remain significant in predicting future crash risk negatively (Table 5),
no matter whether we use Topic 2 assignment or Topic 2 proportion. Coefficients disclosures of
Topic 7 (risks in product prices and materials costs) are negative but only moderately significant
(at the 10% level) in the regressions of future NCSKEW but not in those of future DUVOL. This
exercise shows that disclosures of Topic 2 (commodity price risk and derivatives) provide
significant and incremental information about the firms’ future crash risk. The information
revealed by disclosures of Topic 7 (risks in product prices and materials costs), on the other hand,
appears to be largely reflected in recent characteristics of stock returns (KUR, SIGMA, CUM_RET)
11 For example, Topics 3, 4, and 8 share the following keywords in their vocabularies, {foreign, currency,
exchange}, suggesting that they are related to foreign currency exposures. While these three topics are related to foreign currency exposures, they capture different aspects of foreign currency exposures.
27
and the book-to-market ratio (BM).12
< Insert Table 5 Here >
Grouping by Accruals
As we discussed above, the recent literature emphasizes the role of the bad news hoarding
behavior of entrenched as a major source stock price crash risk. Consistent with the effects of bad
news hoarding behavior, the literature has shown that opaque financial reporting is positively
associated with crash risk (Jin and Myers 2006; Hutton et al. 2009) while accounting conservatism
is negatively associated with crash risk (Kim and Zhang 2016).
If the bad-news-hoarding behavior of entrenched managers drives crash risk and if disclosure
of the “commodity price risk and derivatives” mitigates the managers’ bad news hoarding
behavior, the risk disclosure should help mitigate the crash risk more among firms with aggressive
earnings management (large accruals). To see if/how the degree of earnings opacity affects the
information content of narrative market risk disclosures. We group the sample into two subsamples
sorted by the degree of earnings management, as measured by working capital accruals (Frankel
and Sun 2018). Specifically, the top-half subsample of accruals consists of firm-years with accruals
above the industry median in each year, and the bottom-half subsample consists of the rest.
We summarize our subsample analyses for Topic 2 and Topic 7 in Table 6 and Table 7,
respectively. Interestingly, ROE tends to be positively associated with future crash risk among the
subsample of firms with high accruals, consistent with a view that these firms’ earnings tend to be
12 One of the topics (Topic 6), concerned with the “interest rate risk of debts and swaps,” is positively associated
with future crash risk when we control for known predictors of future crash risk. However, the topic does not show a significant correlation with future crash risk when we do not include the control variables. We leave it for future research to investigate further if Topic 6 provides real incremental information about future crash risk.
28
inflated.
Our evidence indicates that disclosures of “commodity price risk and derivatives” are more
negatively correlated with future stock price crash risk when they are made by firm managers who
are less aggressive in earnings management than others. The significantly negative relation
between the discussion of Topic 2 (topic assignment), “commodity price risk and derivatives,” is
more pronounced among firms with lower accruals. For example, the coefficients on Topic 2
assignment are -0.098 (t=-3.15) for NCSKEW and -0.03 (t=-3.21) for DUVOL after controlling for
the known predictors of crash risk, among the bottom half subsample of accruals, i.e., among the
firms that manage earnings less aggressively than others. These coefficients are not reliably
different from zero among the top half subsample of accruals, indicating that the disclosures of
Topic 2 do not provide incremental information about future crash risk among the firms with high
accruals (aggressive earnings management). However, we do not find a discernible difference in
the coefficients of Topic 7 (assignment) between the top-half and bottom-half subsamples of
accruals.
< Insert Table 6 Here >
Disclosures of the Topic 2, “commodity price risk and derivatives,” appear to help reduce the
firms’ future crash risk when they are made by firm managers who are not engaged in aggressive
earnings management. The evidence is consistent with an interpretation that disclosures of the
“commodity price risk and derivatives” can help lower future crash risk not because they mitigate
the bad news hoarding behavior of entrenched managers, but because they reduce the uncertainty
surrounding the firms’ cash flow volatilities, as argued by Heinle and Smith (2017) and Heinle et
al. (2018).
< Insert Table 7 Here >
29
Put differently, our LDA textual analysis suggests the uncertainty surrounding future cash
flow volatilities as an important determinant of future crash risk that is different from the effects
of the bad news hoarding behavior of entrenched managers on which many recent studies have
focused. When investors are uncertain about the risk exposures of a firm’s future cash flows,
providing information about the risk exposures may help lower the firm’s stock price crash risk.
This theory, put forth by Heinle and Smith (2017) and Heinle et al. (2018), appear to provide a
consistent explanation for the useful information provided by some topics discussed in market risk
disclosures (Item 7A), such as the commodity price risk and derivatives and risks in product
prices and materials costs.
Discussions
Taking on a question if the textual information in a firm's narrative market risk disclosure
provides useful information about the firm’s future crash risk, we are able to uncover two latent
topics that exhibit significant predictability of future crash risk. The two topics, “commodity price
risk and derivatives” and “risks in product prices and materials costs” are related to risk
exposures in the firms’ input costs and output prices. In particular, disclosures of the former convey
significant incremental information about the firms’ future crash risk even after we control for
other known predictors of crash risk. The evidence provides a strong case against the widely-held
perceptions that the mandatory market risk disclosure provides only boilerplate information.
However, this study is also subject to a few limitations and shortcomings.
First, one of the major limitations of this study is the difficulty of identifications. While LDA
is useful to tease out a small number of latent topics in narrative market risk disclosures, it is not
clear a priori how many topics we should cluster the textual information into. Furthermore, while
30
the latent topics uncovered by LDA are quite intuitive, they still allow for multiple interpretations.
They only provide proxies for certain risk exposures.
Second, our empirical implementation of LDA focuses on the market risk disclosure in Item
7A. This reflects our objective of addressing the ongoing discussion about the informativeness of
Item 7A. Meanwhile, we are aware that a few recent studies focus on Item 1A that discusses much
broader spectrum of risk exposures each firm has (Sribunnak and Wong 2006; Kravet and Muslu
2013; Campbell et al. 2014; Hope et al. 2016), since the SEC made Item 1A mandatory in 2005.
Bao and Datta (2014) demonstrate the applicability of the sentence-based LDA model (which we
rely on) by teasing out 30 latent topics from Item 1A, though many of these topics do not yield
straightforward interpretations. It would be interesting to extend our analysis to include additional
risk types (e.g., environmental risk, litigation risk, regulation risk, supply chain disruptions, etc.)
that may help predict the firms’ crash risk. We leave this topic for future research.
Third, we conduct our LDA analysis in a pooled sample of firm-years between 2002 and 2016.
The analysis is inherently an in-sample analysis as is the case with most panel data analysis.
Although we discuss correlations between disclosures in year t and crash risk in year t+1, these
correlations do not necessarily imply out-of-sample predictability. Our analysis is confined to in-
sample predictability. This is because the topic in year t is extracted from the entire pooled sample
of firm-years. We do not view this as a serious concern as most panel regressions used in many
academic studies share the same limitation. However, investors who are interested in
implementing our analysis in real-time should consider running LDA recursively with rolling or
expanding windows.
31
Conclusion
This study provides a new angle to the regulatory debate on the informativeness of narrative
market risk disclosures in Item 7A of Form 10-K filings. While many market participants would
agree on the importance of improving the quality of market risk disclosures, there are concerns
about their compatibility (e.g., Hodder and McAnally 2001). There are also widely-held
perceptions that many market risk disclosures provide only boilerplate information (e.g., Abraham
and Shrieves 2014; CFA Institute 2016; SEC 2016).
To shed light on the ongoing discussion, we employ a language processing algorithm based
on LDA (Blei et al. 2003; Bao and Datta 2014), to cluster the textual information in Item 7A into
eight latent topics. We then examine how the disclosure of each topic (topic assignment or topic
proportion) is related to the firm’s future stock price crash risk.
The majority of the latent topics extracted from Item 7A via LDA do not provide significant
information about future crash risk. However, this study still uncovers two latent topics that are
negatively and significantly correlated with future stock price crash risk. These two latent topics
are associated with exposures of the firms’ cash flows to risks in their input costs or/and output
prices. The first topic is about the “commodity price risk and derivatives” and the second topic is
about the “risks in product prices and materials costs.” In particular, the former topic is correlated
with future crash risk even when we control for other predictors of crash risk. This suggests that
the disclosure of “commodity price risk and derivatives,” conveys significant and incremental
information about the firms’ future crash risk.
A further analysis suggests that the negative correlation between the disclosure (topic
assignment) of the “commodity price risk and derivatives” and future crash risk is concentrated
32
among low-accrual firms than among high-accrual firms. We also find that, among high-accrual
firms, firms that spend a large proportion of Item 7A in disclosing “risks in product prices and
materials costs” have a significantly lower crash risk. By conditioning on accruals, we can
enhance the information content of the disclosures of “commodity price risk and derivatives” and
“risks in product prices and materials costs” substantially.
Despite a few limitations and shortcomings, evidence in the paper provides a strong case
against the commonly-held perceptions that Item 7A provides only boilerplate information. In
particular, this study shows that some firms’ narrative market risk disclosures, especially those
related to the “commodity price risk and derivatives” and “risks in product prices and materials
costs,” convey relevant information that is negatively and significantly correlated with future stock
price crash risk. Clearly, they convey useful, non-redundant, and non-boilerplate information to
the public, which should help investors and analysists improve the process of security analysis and
valuation, stock selection, and portfolio risk management.
33
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Figure 1. Cross-sectional Means of Crash Risk Measures over the Sample Period 2002-2016
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Table 1. Latent Topics Extracted from Narrative Market Risk Disclosures (Item 7A)
Panel A. Keywords Extraction
TOPICS Keywords
1 Interest, rates, market, value, fair, changes, increase, debt, risk, change, cash, approximately, million, decrease
2 Value, price(s), fair, market, risk, gas, derivative, natural, financial, contracts, oil, commodity, instruments, credit
3 Foreign, contracts, currency, instruments, derivative, financial, forward, exchange, use, hedge, rate, interest, enter, exposure, trading
4 Foreign, currency, exchange, changes, rate(s), interest, risk, market, financial, results, exposed, fluctuations, exposure, operations
5 Investment(s), cash, securities, market, financial, equivalents, credit, portfolio, rate, money, risk, marketable, debt, million
6 Rate(s), interest, million, debt, credit, outstanding, variable, fixed, swap, amount, borrowings, based, facility, revolving
7 Price(s), products, cost(s), market, raw, sales, certain, future, operating, significant, material(s), subject
8 Foreign, currency, million, exchange, net, denominated, dollar, income, value, currencies, losses, approximately, gains, sales, ended
Panel B. Interpretation of Topics
TOPICS Description
1 Topic 1 discusses whether and how changes in interest rates affect the fair value of firms’ debt instruments and cash flows and tools that firms utilize to manage risks associated with fluctuations in interest rates.
2 Topic 2 discusses firms’ exposure to commodity risk derived from price volatility for production in the oil markets. It also addresses what derivatives are used to hedge these commodity risks and whether the use of derivatives will limit firms’ access to credit facilities.
3 Topic 3 discusses whether firms are exposed to foreign currency exchange and interest risks, what financial instruments are used to hedge these risks, and whether firms enter transactions for trading or speculative purpose.
4 Topic 4 discusses whether adverse changes in financial market prices or rates such as interest and foreign currency exchange risks will cause fluctuations in results of operations.
5
Topic 5 discusses firms’ holding of cash, cash equivalents, and marketable securities. It also addresses the extent to which firms invest excess cash and cash equivalent in money market account that invests in debt securities or short-term financial instruments so that firms can minimize the exposure due to adverse changes in interest rates.
6 Topic 6 discusses how firms’ fixed- and variable- rate debt obligations and interest rate swap are impacted by interest risks and the detailed information regarding interest on borrowings under various credit facilities.
7 Topic 7 discusses firms’ exposure to market risks derived from raw materials and how the fluctuations in the exchange rates of dominant currencies affect the cost of materials, sales, and future operating results.
8 Topic 8 discusses how the foreign currency exchange rates relative to the dominated currencies affect the sales and whether the comprehensive income/losses exposure to realized and unrealized foreign currency gains and losses is significant.
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Table 2. Descriptive Statistics
Panel A. Summary statistics VARIABLES N MEAN MEDIAN STD MIN P25 P75 MAX
A_TOPIC1 45,417 0.430 0 0.495 0 0 1 1 A_TOPIC2 45,417 0.283 0 0.450 0 0 1 1 A_TOPIC3 45,417 0.415 0 0.493 0 0 1 1 A_TOPIC4 45,417 0.439 0 0.496 0 0 1 1 A_TOPIC5 45,417 0.313 0 0.464 0 0 1 1 A_TOPIC6 45,417 0.367 0 0.482 0 0 1 1 A_TOPIC7 45,417 0.292 0 0.455 0 0 1 1 A_TOPIC8 45,417 0.394 0 0.489 0 0 1 1 P_TOPIC1 45,417 0.083 0 0.143 0 0 0.120 0.960 P_TOPIC2 45,417 0.040 0 0.121 0 0 0.010 0.980 P_TOPIC3 45,417 0.055 0 0.097 0 0 0.080 0.880 P_TOPIC4 45,417 0.084 0 0.137 0 0 0.140 0.940 P_TOPIC5 45,417 0.054 0 0.128 0 0 0.010 0.950 P_TOPIC6 45,417 0.067 0 0.139 0 0 0.060 0.990 P_TOPIC7 45,417 0.036 0 0.105 0 0 0.010 0.990 P_TOPIC8 45,417 0.066 0 0.126 0 0 0.080 0.970 NCSKEW 45,417 -0.021 -0.125 1.413 -3.912 -0.658 0.437 4.513 DUVOL 45,417 0.011 -0.007 0.411 -6.861 -0.229 0.226 3.441 ROE 45,417 -0.119 0.064 0.852 -5.966 -0.102 0.146 5.615 LEV 45,417 0.180 0.140 0.182 0 0.003 0.295 0.950 ACCRUALS 45,417 0.006 0.008 0.088 -0.959 -0.018 0.039 0.477 BM 45,417 -0.578 -0.629 1.096 -3.253 -1.207 -0.071 3.492 DTURNOVER 45,417 0.019 0.004 1.020 -3.224 -0.322 0.350 3.133 LNSIZE 45,417 5.997 6.014 2.077 0.339 4.501 7.455 10.295 KUR 45,417 7.761 3.818 11.237 -0.149 1.890 8.339 65.049 SIGMA 45,417 0.034 0.029 0.019 0.006 0.021 0.042 0.122 CUM_RET 45,417 0.140 0.052 0.620 -0.840 -0.239 0.366 2.714
Panel B. Pearson correlations
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (1) P_TOPIC1 1.00 (2) P_TOPIC2 0.03 1.00 (3) P_TOPIC3 0.23 0.13 1.00 (4) P_TOPIC4 0.22 0.05 0.37 1.00 (5 P_TOPIC5 0.34 -0.01 0.09 0.15 1.00 (6) P_TOPIC6 0.23 0.07 0.23 0.17 -0.02 1.00 (7) P_TOPIC7 0.04 0.13 0.11 0.12 0.00 0.08 1.00 (8) P_TOPIC8 0.13 0.03 0.34 0.37 0.12 0.11 0.08 1.00 (9) LEAD_NCSKEW 0.04 0.01 0.03 0.04 0.03 0.03 0.00 0.03 1.00 (10) LEAD_DUVOL 0.02 0.00 0.02 0.02 0.01 0.02 0.00 0.02 0.61 1.00 (11) KUR 0.02 -0.06 -0.03 -0.01 0.04 -0.02 -0.02 -0.01 -0.01 -0.01 (12) SIGMA -0.05 -0.10 -0.14 -0.09 0.02 -0.10 -0.05 -0.11 -0.12 -0.07 (13) CUM_RET 0.03 0.01 0.03 0.03 0.01 0.03 0.02 0.02 0.05 0.02 (14) BM -0.14 -0.04 -0.12 -0.14 -0.18 -0.04 -0.05 -0.12 -0.08 -0.02 (15) LEV -0.03 0.08 0.01 -0.08 -0.19 0.17 0.01 -0.07 0.00 0.00 (16) ROE 0.02 0.06 0.07 0.04 -0.05 0.07 0.04 0.06 0.06 0.04 (17) LNSIZE 0.06 0.19 0.19 0.10 0.02 0.09 0.04 0.15 0.15 0.05 (18) DTURNOVER 0.01 0.02 0.00 0.00 -0.01 0.02 0.00 -0.01 0.03 0.00 (19) ACCRUALS 0.03 0.00 0.02 0.02 0.02 0.02 0.01 0.03 0.04 0.00
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Table 3. Topic Assignments/Proportions and Firm Characteristics
Panel A. Topic assignments VARIABLES A_TOPIC1 A_TOPIC2 A_TOPIC3 A_TOPIC4 A_TOPIC5 A_TOPIC6 A_TOPIC7 A_TOPIC8 KUR -0.000 -0.001 0.000 -0.001 0.000 0.000 -0.000 0.000 (-0.10) (-1.64) (0.19) (-0.89) (0.37) (0.30) (-0.48) (0.08) SIGMA -2.357*** -1.919*** -2.465*** -2.431*** -1.380** -4.025*** -1.616*** -1.944*** (-4.48) (-3.41) (-4.64) (-4.62) (-2.46) (-7.46) (-2.94) (-3.64) CUM_RET -0.084*** -0.065*** -0.093*** -0.078*** -0.100*** -0.050*** -0.049*** -0.089*** (-6.99) (-5.20) (-7.79) (-6.58) (-7.93) (-4.16) (-4.01) (-7.35) BM -0.204*** -0.157*** -0.190*** -0.209*** -0.225*** -0.163*** -0.192*** -0.192*** (-28.17) (-20.74) (-26.25) (-28.88) (-28.19) (-22.52) (-25.21) (-26.14) LEV -0.176*** -0.127*** -0.216*** -0.365*** -1.159*** 0.440*** -0.401*** -0.463*** (-4.82) (-3.32) (-5.88) (-9.95) (-28.99) (11.98) (-10.47) (-12.40) ROE 0.043*** 0.027*** 0.036*** 0.040*** 0.025*** 0.055*** 0.038*** 0.036*** (5.05) (3.06) (4.17) (4.75) (2.82) (6.30) (4.30) (4.15) LNSIZE 0.085*** 0.074*** 0.102*** 0.093*** 0.066*** 0.055*** 0.027*** 0.102*** (19.94) (16.37) (23.74) (21.78) (14.47) (12.64) (6.14) (23.49) DTURNOVER 0.004 0.011 0.005 0.006 -0.010 0.020*** 0.008 0.000 (0.64) (1.64) (0.81) (0.92) (-1.49) (3.05) (1.22) (0.01) ACCRUALS -0.020 -0.189** -0.094 -0.078 0.101 -0.070 0.036 -0.063 (-0.26) (-2.41) (-1.27) (-1.06) (1.31) (-0.92) (0.47) (-0.85)
INDUSTRY FE YES YES YES YES YES YES YES YES YEAR FE YES YES YES YES YES YES YES YES OBSERVATIONS 45,400 45,400 45,400 45,400 45,400 45,400 45,400 45,400 PSEUDO R2 0.078 0.052 0.077 0.082 0.101 0.068 0.048 0.080
Panel B. Topic proportions VARIABLES P_TOPIC1 P_TOPIC2 P_TOPIC3 P_TOPIC4 P_TOPIC5 P_TOPIC6 P_TOPIC7 P_TOPIC8 KUR 0.000 -0.000*** 0.000 -0.000 0.000 0.000 -0.000 0.000 (1.18) (-3.79) (1.26) (-1.34) (1.04) (0.86) (-1.17) (1.12) SIGMA 0.095 0.102* -0.005 -0.061 0.153** -0.340*** 0.087* -0.031 (1.53) (1.90) (-0.11) (-1.03) (2.44) (-5.58) (1.71) (-0.59) CUM_RET 0.002** -0.002** -0.002*** 0.001 -0.002** 0.004*** 0.001 -0.002** (2.00) (-2.09) (-3.07) (0.82) (-2.48) (4.37) (1.33) (-2.28) BM -0.001 0.002** 0.001** -0.002** -0.005*** 0.005*** -0.002** 0.002* (-0.96) (2.21) (2.33) (-2.44) (-5.61) (4.86) (-2.26) (1.87) LEV 0.004 0.011* 0.004 -0.033*** -0.090*** 0.134*** -0.012** -0.019*** (0.62) (1.74) (0.88) (-5.66) (-14.53) (16.19) (-1.99) (-3.38) ROE 0.000 -0.001 -0.001** -0.000 -0.003*** 0.004*** 0.001 0.000 (0.51) (-0.86) (-2.03) (-0.24) (-3.71) (4.47) (1.27) (0.27) LNSIZE -0.003*** 0.006*** 0.004*** -0.002*** -0.001* -0.004*** -0.003*** 0.004*** (-4.85) (6.01) (7.96) (-3.44) (-1.72) (-5.71) (-4.68) (5.84) DTURNOVER -0.000 0.001 -0.000 0.000 -0.001*** 0.002*** -0.001 -0.001 (-0.50) (1.31) (-0.40) (0.77) (-2.87) (4.40) (-1.37) (-1.25) ACCRUALS 0.016*** -0.019*** -0.014*** 0.003 0.013** -0.006 0.002 0.004 (2.65) (-4.27) (-3.77) (0.58) (2.36) (-1.00) (0.44) (0.76) MISS -0.170*** -0.085*** -0.110*** -0.171*** -0.111*** -0.139*** -0.078*** -0.134*** (-64.02) (-29.62) (-59.62) (-69.70) (-40.39) (-49.86) (-32.45) (-52.67) INDUSTRY FE YES YES YES YES YES YES YES YES YEAR FE YES YES YES YES YES YES YES YES OBSERVATIONS 45,417 45,417 45,417 45,417 45,417 45,417 45,417 45,417 ADJUSTED R2 0.383 0.205 0.368 0.413 0.283 0.318 0.182 0.341
Notes: This table reports relations between the disclosure of each topic and our control variables that are known to predict future crash risk. A_TOPICs are topic assignments, and P_TOPICs are topic proportions. Panel A reports the results of Probit regressions of topic assignments and Panel B reports results of OLS regressions of topic proportions. All regressions include Fama-French 48 industry dummies and year fixed effects. MISS is the indicator variable that takes the value of one for firms-years without topics assignments or topic proportions due to very short market risk disclosures in Item 7A (with 100 words or less). t-statistics are reported in parentheses with robust standard errors clustered at the firm level. Continuous variables are winsorized at 1% and 99% level. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
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Table 4. Latent Topics and Future Crash Risk
Panel A. Lead NCSKEW on each topic assignment without control variables VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) A_TOPIC1 0.003 (0.11) A_TOPIC2 -0.048** (-2.16) A_TOPIC3 0.055* (1.86) A_TOPIC4 -0.018 (-0.53) A_TOPIC5 0.037* (1.66) A_TOPIC6 0.036 (1.41) A_TOPIC7 -0.069*** (-3.20) A_TOPIC8 0.017 (0.62) NCSKEW 0.039*** 0.039*** 0.039*** 0.039*** 0.039*** 0.039*** 0.039*** 0.039*** (6.36) (6.33) (6.36) (6.35) (6.36) (6.35) (6.31) (6.36) MISS -0.151*** -0.183*** -0.107*** -0.170*** -0.131*** -0.127*** -0.196*** -0.140*** (-4.71) (-8.87) (-3.66) (-5.07) (-6.42) (-5.16) (-9.69) (-5.25) INDUSTRY FE YES YES YES YES YES YES YES YES YEAR FE YES YES YES YES YES YES YES YES OBSERVATIONS 40,184 40,184 40,184 40,184 40,184 40,184 40,184 40,184 ADJUSTED R2 0.012 0.012 0.012 0.012 0.012 0.012 0.013 0.012
Panel B. Lead DUVOL on each topic assignment without control variables VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) A_TOPIC1 -0.003 (-0.36) A_TOPIC2 -0.016** (-2.46) A_TOPIC3 0.012 (1.42) A_TOPIC4 -0.006 (-0.58) A_TOPIC5 0.003 (0.46) A_TOPIC6 0.014* (1.91) A_TOPIC7 -0.011* (-1.75) A_TOPIC8 0.002 (0.20) DUVOL 0.073*** 0.073*** 0.073*** 0.073*** 0.073*** 0.073*** 0.073*** 0.073*** (11.22) (11.20) (11.22) (11.22) (11.22) (11.21) (11.21) (11.22) MISS -0.031*** -0.037*** -0.018* -0.033*** -0.026*** -0.017* -0.035*** -0.027*** (-3.06) (-4.72) (-1.88) (-3.06) (-3.80) (-1.89) (-4.34) (-3.02) INDUSTRY FE YES YES YES YES YES YES YES YES YEAR FE YES YES YES YES YES YES YES YES OBSERVATIONS 40,177 40,177 40,177 40,177 40,177 40,177 40,177 40,177 ADJUSTED R2 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007
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Panel C. Lead NCSKEW on each topic proportion without control variables VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) P_TOPIC1 0.070 (1.07) P_TOPIC2 -0.085 (-1.47) P_TOPIC3 0.070 (0.80) P_TOPIC4 0.071 (1.00) P_TOPIC5 0.065 (0.95) P_TOPIC6 -0.014 (-0.22) P_TOPIC7 -0.239*** (-3.42) P_TOPIC8 0.047 (0.64) NCSKEW 0.039*** 0.039*** 0.039*** 0.039*** 0.039*** 0.039*** 0.039*** 0.039*** (6.36) (6.35) (6.35) (6.35) (6.36) (6.36) (6.32) (6.35) MISS -0.142*** -0.162*** -0.146*** -0.142*** -0.147*** -0.156*** -0.172*** -0.148*** (-7.84) (-9.79) (-7.98) (-7.45) (-8.96) (-8.95) (-10.67) (-8.24) INDUSTRY FE YES YES YES YES YES YES YES YES YEAR FE YES YES YES YES YES YES YES YES OBSERVATIONS 40,184 40,184 40,184 40,184 40,184 40,184 40,184 40,184 ADJUSTED R2 0.012 0.012 0.012 0.012 0.012 0.012 0.013 0.012
Panel D. Lead DUVOL on each topic proportion without control variables VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) P_TOPIC1 0.012 (0.64) P_TOPIC2 -0.034* (-1.81) P_TOPIC3 0.016 (0.62) P_TOPIC4 0.022 (1.09) P_TOPIC5 -0.001 (-0.06) P_TOPIC6 0.017 (0.94) P_TOPIC7 -0.050** (-2.38) P_TOPIC8 0.012 (0.59) DUVOL 0.073*** 0.073*** 0.073*** 0.073*** 0.073*** 0.073*** 0.072*** 0.073*** (11.22) (11.20) (11.22) (11.22) (11.22) (11.21) (11.21) (11.22) MISS -0.026*** -0.031*** -0.026*** -0.024*** -0.028*** -0.026*** -0.032*** -0.026*** (-3.75) (-4.34) (-3.57) (-3.43) (-4.33) (-3.53) (-4.52) (-3.78) INDUSTRY FE YES YES YES YES YES YES YES YES YEAR FE YES YES YES YES YES YES YES YES OBSERVATIONS 40,177 40,177 40,177 40,177 40,177 40,177 40,177 40,177 ADJUSTED R2 0.007 0.007 0.007 0.007 0.007 0.007 0.007 0.007
Notes: This table reports OLS regressions of future crash risk, Eq. (4). A_TOPICs are topic assignments, and P_TOPICs are topic proportions. Dependent variables in Panels A and C are NCSKEW in year t+1, those in Panels B and C are DUVOL in year t+1. Panels A and B show the relationship between topic assignments and future stock price crash risk. Panels C and D show the relationship between topic proportions and future stock price crash risk. All regressions include Fama-French 48 industry dummies and year fixed effects. MISS is the indicator variable that takes the value of one for firms-years without topics assignments or topic proportions due to very short market risk disclosures in Item 7A (with 100 words or less). t-statistics are reported in parentheses with robust standard errors clustered at the firm level. Continuous variables are winsorized at 1% and 99% level. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
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Table 5. Latent Topics and Future Crash Risk (with Control Variables)
Dependent Variables: Lead_NCSKEW Lead_DUVOL Lead_NCSKEW Lead_DUVOL VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) A_TOPIC2 -0.052** -0.016** (-2.35) (-2.55) P_TOPIC2 -0.213*** -0.050** (-3.58) (-2.56) A_TOPIC7 -0.039* -0.007 (-1.78) (-1.08) P_TOPIC7 -0.133* -0.032 (-1.90) (-1.58) NCSKEW 0.045*** 0.045*** 0.045*** 0.045*** (7.02) (7.00) (7.05) (7.05) DUVOL 0.071*** 0.071*** 0.071*** 0.071*** (10.39) (10.36) (10.40) (10.40) MISS -0.092*** -0.079*** -0.022*** -0.017** -0.084*** -0.071*** -0.017** -0.015** (-4.48) (-4.83) (-3.04) (-2.54) (-4.17) (-4.41) (-2.35) (-2.41) KUR 0.002** 0.002** 0.001* 0.001* 0.002** 0.002** 0.001* 0.001* (2.11) (2.08) (1.88) (1.88) (2.13) (2.12) (1.89) (1.89) SIGMA -5.053*** -5.032*** -2.785*** -2.781*** -5.053*** -5.040*** -2.785*** -2.782*** (-8.66) (-8.62) (-6.72) (-6.72) (-8.65) (-8.63) (-6.72) (-6.72) CUM_RET 0.131*** 0.130*** 0.042*** 0.042*** 0.131*** 0.131*** 0.042*** 0.042*** (9.34) (9.31) (9.29) (9.28) (9.37) (9.37) (9.30) (9.30) BM -0.036*** -0.035*** -0.009** -0.009** -0.036*** -0.036*** -0.009** -0.009** (-4.98) (-4.91) (-2.39) (-2.35) (-5.03) (-5.01) (-2.40) (-2.40) LEV -0.077* -0.076* -0.007 -0.007 -0.081* -0.080* -0.008 -0.008 (-1.73) (-1.72) (-0.37) (-0.37) (-1.82) (-1.80) (-0.42) (-0.41) ROE 0.014 0.014 0.008 0.008 0.015 0.015 0.008 0.008 (1.38) (1.37) (0.66) (0.66) (1.39) (1.39) (0.67) (0.67) LNSIZE 0.063*** 0.064*** -0.001 -0.001 0.062*** 0.062*** -0.001 -0.001 (13.67) (13.89) (-0.63) (-0.49) (13.49) (13.54) (-0.70) (-0.69) DTURNOVER 0.033*** 0.033*** 0.003 0.003 0.033*** 0.033*** 0.003 0.003 (4.44) (4.46) (1.16) (1.16) (4.44) (4.42) (1.15) (1.14) ACCRUALS 0.258*** 0.256*** -0.064 -0.064 0.261*** 0.260*** -0.063 -0.064 (3.02) (3.01) (-0.68) (-0.68) (3.06) (3.05) (-0.67) (-0.67) INDUSTRY FE YES YES YES YES YES YES YES YES YEAR FE YES YES YES YES YES YES YES YES OBSERVATIONS 40,184 40,184 40,177 40,177 40,184 40,184 40,177 40,177 ADJUSTED R2 0.036 0.036 0.014 0.014 0.036 0.036 0.014 0.014
Notes: This table reports OLS regressions of future crash risk, Eq. (4). A_TOPICs are topic assignments, and P_TOPICs are topic proportions. Dependent variables in Panels A and C are NCSKEW in year t+1, those in Panels B and C are DUVOL in year t+1. Regressions in this table include additional control variables that are known to predict future crash risk. This table focuses on the effects of Topic 2 (commodity price risk and derivatives) and Topic 7 (risks in product prices and materials costs). All regressions include Fama-French 48 industry dummies and year fixed effects. MISS is the indicator variable that takes the value of one for firms-years without topics assignments or topic proportions due to very short market risk disclosures in Item 7A (with 100 words or less). t-statistics are reported in parentheses with robust standard errors clustered at the firm level. Continuous variables are winsorized at 1% and 99% level. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
45
Table 6. Grouping by Accruals: The Informativeness of Topic 2 (Commodity Price Risk and Derivatives)
Dependent Variables: Lead_NCSKEW Lead_DUVOL High Accruals Low Accruals High Accruals Low Accruals VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) A_TOPIC2 -0.007 -0.098*** -0.003 -0.030*** (-0.23) (-3.15) (-0.31) (-3.21) P_TOPIC2 -0.241*** -0.177** -0.049* -0.050* (-2.96) (-2.15) (-1.89) (-1.70) NCSKEW 0.045*** 0.045*** 0.045*** 0.045*** (4.82) (4.79) (5.03) (5.05) DUVOL 0.073*** 0.073*** 0.069*** 0.069*** (8.41) (8.39) (6.51) (6.51) KUR 0.002 0.002 0.002 0.002 0.001*** 0.001*** 0.001 0.001 (1.46) (1.44) (1.56) (1.56) (2.60) (2.59) (1.06) (1.07) SIGMA -5.255*** -5.229*** -5.072*** -5.050*** -3.082*** -3.078*** -2.692*** -2.686*** (-6.14) (-6.11) (-6.43) (-6.40) (-10.97) (-10.96) (-3.98) (-3.98) CUM_RET 0.152*** 0.151*** 0.106*** 0.106*** 0.046*** 0.046*** 0.036*** 0.036*** (7.76) (7.73) (5.43) (5.41) (8.06) (8.04) (5.28) (5.28) BM -0.033*** -0.033*** -0.034*** -0.034*** -0.007** -0.007** -0.009 -0.009 (-3.29) (-3.23) (-3.55) (-3.49) (-2.40) (-2.35) (-1.48) (-1.45) LEV -0.086 -0.082 -0.061 -0.063 -0.005 -0.004 -0.009 -0.010 (-1.33) (-1.27) (-1.01) (-1.05) (-0.25) (-0.21) (-0.28) (-0.31) ROE 0.038** 0.037** 0.001 0.002 0.024*** 0.024*** -0.002 -0.002 (2.32) (2.31) (0.11) (0.12) (5.02) (5.01) (-0.09) (-0.09) LNSIZE 0.063*** 0.064*** 0.064*** 0.065*** -0.002 -0.002 0.001 0.001 (9.56) (9.75) (10.47) (10.54) (-1.06) (-0.91) (0.21) (0.27) DTURNOVER 0.040*** 0.040*** 0.022** 0.021** 0.005 0.005 0.001 0.001 (3.91) (3.92) (2.06) (2.04) (1.54) (1.55) (0.19) (0.17) ACCRUALS 0.312* 0.308* 0.034 0.036 0.051 0.051 -0.200 -0.199 (1.69) (1.67) (0.25) (0.26) (0.93) (0.92) (-1.15) (-1.14) MISS -0.070** -0.086*** -0.115*** -0.072*** -0.017** -0.019*** -0.028** -0.015 (-2.36) (-3.68) (-4.00) (-3.14) (-2.03) (-2.87) (-2.36) (-1.31) INDUSTRY FE YES YES YES YES YES YES YES YES YEAR FE YES YES YES YES YES YES YES YES OBSERVATIONS 20,164 20,164 20,020 20,020 20,160 20,160 20,017 20,017 ADJUSTED R2 0.034 0.034 0.036 0.036 0.032 0.032 0.008 0.007
Notes: This table repeats the analysis in the previous table by splitting the sample into two subsamples groups by working capital accruals of Frankel and Sun (2018). This table focuses on the information content of Topic 2 (Commodity Price Risk and Derivatives). High Accruals (top-half) subsample consists of firm-year observations with accruals that are above the corresponding industry medians. Low Accruals (bottom-half) subsample includes the rest. All regressions include Fama-French 48 industry dummies and year fixed effects. MISS is the indicator variable that takes the value of one for firms-years without topics assignments or topic proportions due to very short market risk disclosures in Item 7A (with 100 words or less). t-statistics are reported in parentheses with robust standard errors clustered at the firm level. Continuous variables are winsorized at 1% and 99% level. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
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Table 7: Grouping by Accruals: The Informativeness of Topic 7 (Risks in Product Prices and Materials Costs)
Dependent Variables: Lead_NCSKEW Lead_DUVOL High Accruals Low Accruals High Accruals Low Accruals VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) A_TOPIC7 -0.039 -0.044 -0.006 -0.008 (-1.23) (-1.40) (-0.72) (-0.83) P_TOPIC7 -0.251** -0.022 -0.054* -0.011 (-2.36) (-0.22) (-1.87) (-0.38) NCSKEW 0.045*** 0.045*** 0.045*** 0.045*** (4.81) (4.81) (4.81) (5.09) DUVOL 0.073*** 0.073*** 0.069*** 0.069*** (8.41) (8.41) (6.55) (6.55) KUR 0.002 0.002 0.002 0.002 0.001*** 0.001*** 0.001 0.001 (1.46) (1.46) (1.46) (1.60) (2.60) (2.60) (1.08) (1.08) SIGMA -5.255*** -5.239*** -5.255*** -5.071*** -3.083*** -3.079*** -2.692*** -2.691*** (-6.14) (-6.12) (-6.14) (-6.42) (-10.97) (-10.96) (-3.98) (-3.98) CUM_RET 0.152*** 0.152*** 0.152*** 0.106*** 0.046*** 0.046*** 0.036*** 0.036*** (7.77) (7.78) (7.77) (5.44) (8.06) (8.07) (5.29) (5.28) BM -0.034*** -0.034*** -0.034*** -0.034*** -0.007** -0.007** -0.009 -0.009 (-3.34) (-3.33) (-3.34) (-3.53) (-2.42) (-2.42) (-1.48) (-1.47) LEV -0.089 -0.090 -0.089 -0.064 -0.005 -0.006 -0.011 -0.010 (-1.38) (-1.39) (-1.38) (-1.06) (-0.28) (-0.30) (-0.32) (-0.31) ROE 0.038** 0.038** 0.038** 0.002 0.024*** 0.024*** -0.002 -0.002 (2.34) (2.34) (2.34) (0.12) (5.03) (5.03) (-0.09) (-0.09) LNSIZE 0.062*** 0.062*** 0.062*** 0.064*** -0.002 -0.002 0.001 0.001 (9.46) (9.47) (9.46) (10.37) (-1.10) (-1.12) (0.16) (0.18) DTURNOVER 0.040*** 0.040*** 0.040*** 0.021** 0.005 0.005 0.001 0.001 (3.90) (3.87) (3.90) (2.02) (1.54) (1.51) (0.16) (0.16) ACCRUALS 0.315* 0.318* 0.315* 0.036 0.052 0.052 -0.199 -0.199 (1.71) (1.72) (1.71) (0.26) (0.94) (0.95) (-1.14) (-1.14) MISS -0.089*** -0.085*** -0.089*** -0.058** -0.019** -0.019*** -0.015 -0.011 (-3.05) (-3.67) (-3.05) (-2.57) (-2.30) (-2.93) (-1.32) (-1.10) INDUSTRY FE YES YES YES YES YES YES YES YES YEAR FE YES YES YES YES YES YES YES YES OBSERVATIONS 20,164 20,164 20,020 20,020 20,160 20,160 20,017 20,017 ADJUSTED R2 0.034 0.034 0.036 0.036 0.032 0.032 0.007 0.007
Notes: This table repeats the analysis in the previous table by splitting the sample into two subsamples groups by working capital accruals of Frankel and Sun (2018). This table focuses on the information content of Topic 7 (risks in product prices and materials costs). High Accruals (top-half) subsample consists of firm-year observations with accruals that are above the corresponding industry medians. Low Accruals (bottom-half) subsample includes the rest. All regressions include Fama-French 48 industry dummies and year fixed effects. MISS is the indicator variable that takes the value of one for firms-years without topics assignments or topic proportions due to very short market risk disclosures in Item 7A (with 100 words or less). t-statistics are reported in parentheses with robust standard errors clustered at the firm level. Continuous variables are winsorized at 1% and 99% level. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
