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Fair Value Measurements, Information Asymmetry, 
Liquidity, and Firm Value 

 

 

Joseph Reid 
East Carolina University 

 

 

 
This study examines whether the implementation of FASB Accounting Standards Codification on Fair 
Value Measurements (ASC 820-10) impacts information asymmetry, liquidity, and firm value. ASC 
820-10 was designed and implemented under the premise it would improve financial reporting quality 
and comparability of fair value measurements in financial reports by requiring firms to disclose activity 
within and between fair value measurement levels. Increased disclosure that reduces information 
asymmetry (risk) will increase financial statement readability and increase liquidity. If the disclosed 
information lacks precision, the value of the information is discounted and its effect on investor 
perception becomes ambiguous. This study examines 10-K and 10-Q filings of firms with level 2(3) 
fair value activity from 2007 through 2012. Initial results reveal ASC 820-10 did decrease liquidity for 
firms with material transfers furthermore some investors and analysts assign value to financial 
statement information based on relevancy and understandability. Taken together these results signal 
to standard setters the increased mandatory disclosures around the measurement of unobservable 
inputs (level 3 securities) are value relevant and economically significant. This study extends the 
literature on the relationship between fair value relevance, information asymmetry, and information 
precision and contributes to the debate on the efficacy of unobservable units in fair value 
measurements.  

 
Keywords: Fair value, Liquidity, Information asymmetry, Disclosure, Valuation, Financial reporting 
quality 
 

Introduction 
 The Financial Accounting Standards Board (FASB) increased the disclosure requirements 
related to Fair Value Measurements & Disclosures, Accounting Standards Codification 820-10 (ASC 
820-10), in 2010 to improve financial reporting transparency in financial reporting.1 The new 
disclosures required by the FASB require that firms disclose: (1) transfers in and out of Levels 1 and 
2; and (2) activity in Level 3 fair value measurements. This disclosure requirement is significant because 

 
1 The new disclosures and clarifications of existing disclosures are effective for interim and annual reporting periods 

beginning after December 15, 2009, except for the disclosures about purchases, sales, issuances, and settlements in 

the roll forward of activity in Level 3 fair value measurements. Those disclosures are effective for fiscal years 

beginning after December 15, 2010, and for interim periods within those years  (FASB, 2006, 2010). 

http://journals.sfu.ca/abr ADVANCES IN BUSINESS RESEARCH  

2021, Volume 11, pages 28-40 

http://journals.sfu.ca/abr


 

29 
 

it attempts to reduce information risk associated with fair value measurements, a contemporaneous 
and economically relevant issue.  
 Prior literature establishes a link between information risk and market liquidity (Greenstein & 
Sami, 1994; Welker, 1995; Coller & Yohn, 1997; Healy et al., 1999; Leuz & Verrecchia, 2000; Rajgopal 
& Venkatachalam, 2011). Information risk refers to an investor’s ability to accurately ascertain the 
valuation parameters for a particular asset (Riedl & Serafeim, 2011). Theoretically, the increased 
disclosure mandated by the revised standard will reduce information risk and improve the financial 
reporting transparency of financial statements (Diamond & Verrecchia, 1991; Botosan, 1997). In turn, 
the improved transparency of financial statements will increase liquidity and reduce the cost of capital, 
subsequently affecting firm value (Affleck-Graves et al., 2002). The relationship detailed above is still 
an open empirical question within the context of fair value measurements, specifically those 
measurements involving unobservable inputs.  
 This study examines the annual and quarterly filings of firms with level 2 and level 3 fair value 
activity from 2007 through 2012 and identifies a sample of 404 firms with disclosed transfer activity 
and 371 firms, serving as the control group, without material transfer activity. The overall results reveal 
that information risk increased as a result of ASC 820-10 adoption and the effect is significantly 
different for firms affected relative to firms not materially affected by ASC 820-10. Additionally, 
results further reveal that firm value increased for firms as a result of ASC 820-10 however, firm value 
decreased for firms with material transfers relative to firms not materially affected by ASC 820-10.  
 ASC 820-10 was adopted with the intent to increase financial statement transparency by 
reducing information asymmetry surrounding fair value measurements. However, additional 
disclosures do not necessarily result in additional useful information for the reader. FASB member 
and leader of the FASB’s Disclosure Framework project believes that:  

“Some of this information becomes boilerplate, or it may not be material to the reporting 
entity…that makes it harder for the reader to find information even if they know what they’re 
looking for. And it may cause them to miss information that they did not know how to look 
for. (Journal of Accountancy, 2012)”  

The fundamental research objective of this study is to determine whether the increased disclosure 
mandated by ASC 820-10 decreases information risk and its subsequent relation to firm value.  
 Overall, the results of this study conclude that the mandatory disclosure requirement of ASC 
820-10 does increase information asymmetry as the reliability and relevance of the information 
provided to investors about fair values is in question. The uncertainty about the disclosed information 
has a significant negative impact on liquidity and firm value. Investors likely view the disclosure 
required by ASC 820-10 as superfluous and its meaning ambiguous and thus is viewed negatively. 
These results have significant policy implications as the FASB and IASB currently debate effectively 
streamlining disclosures. Furthermore, this study highlights that the relationship between information 
asymmetry and disclosure is directly affected by the precision and relevance of information released.  
  

Background and Hypotheses Development 
 Prior studies have established the link between information risk and market liquidity 
(Diamond & Verrecchia, 1991; Greenstein & Sami, 1994; Welker, 1995; Coller & Yohn, 1997; Healy 
et al., 1999; Leuz &Verrecchia, 2000; Rajgopal & Venkatachalam, 2011). Information risk refers to an 
investor’s ability to accurately ascertain the valuation parameters for a particular asset (Riedl & 
Serafeim, 2011). Therefore, information risk is partially determined by the degree of information 
asymmetry, informational advantage, between informed and less-informed investors. Diamond and 
Verrecchia (1991) analytically show that disclosure policies that reduce information asymmetry will 
increase the liquidity of a firm’s securities by inducing traders to take larger current positions in 
securities. Greenstein and Sami (1994) examined the impact of the SEC’s segment disclosure 



 

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requirement and found that increased disclosure had a significant impact on the bid-ask spread, a 
proxy for information asymmetry. Welker (1995) directly examines the relation between disclosure 
policy and liquidity by departing from the traditional event study methodology approach and focusing 
on the nondisclosure period. After controlling for return volatility, trading volume and share price 
results reveal a significant negative relation between disclosure and bid-ask spread. Results indicate 
that the effects of increased disclosure occur beyond the initial information release period. Leuz and 
Verrecchia (2000) analyze the economic consequences of increased disclosure for a sample of German 
firms and finds that firms that commit to increased disclosure experienced lower bid-ask spreads and 
share turnover. Affleck-Graves et al. (2002) show that firms with more predictable earnings show 
decreases in the adverse selection component of the bid-ask spread. These results further substantiate 
the link between information risk and liquidity. All other things being equal, decreased information 
risk (e.g., more predictable earnings) increases market liquidity. A more recent study by Rajgopal and 
Venkatachalam (2011) examines returns from 1962 to 2001 and shows that higher idiosyncratic return 
volatility is significantly negatively related to information quality. An increase in idiosyncratic return 
volatility is related to information risk and is effectively priced - influencing the cost of equity capital 
and liquidity (Goyal & Santa-Clara, 2003; Easley & O’Hara, 2004). 
 Overall, the studies above support the link between liquidity and information risk however a 
more subtle, yet equally important point is present. Valuation is theoretically linked to market liquidity 
through information risk. At its core, firm valuation reflects the underlying value of the assets and 
liabilities of a firm in addition to the expected future cash flows discounted for appropriate risk. If 
investors (analysts) are unable to accurately forecast expected future cash flows because of heightened 
information risk or accurately value assets or liabilities because of illiquidity, firm value is ultimately 
affected. This study seeks to analyze the relationship between liquidity, information risk, and firm 
value in the fair value context.  
 The association between liquidity and information risk is important for Level 3 fair value 
measurements, measured using unobservable inputs to measure fair value to the extent that observable 
inputs are not available, because investors appear to demand compensation for firms that carry illiquid 
securities (Amihud & Mendelson, 1986; Brennan & Subrahmanyam, 1996). Firms with transfers 
between fair value measurement categories signal to investors an increase (decrease) in the objectivity 
and comparability of the transferred securities. Theoretically, transfers from (to) level 3 decrease 
(increase) information risk and increase (decrease) liquidity.  Information risk can be directly linked to 
market liquidity through the theories of incomplete information, estimation risk, information 
asymmetry, and impacts on future cash flows (Legoria et al., 2008; Ng, 2011). Incomplete information 
(Merton, 1987) arises when investors are unaware of all investment opportunities, which results in a 
smaller investor base and lower stock price. Estimation risk arises when investors are uncertain about 
the return distribution parameters which leads investors to demand higher required rates of return 
(Barry & Brown, 1984). Information asymmetry risk (Easley & O’Hara, 2004) arises when informed 
investors exploit their informational advantage to earn trading gains at the expense of less-informed 
investors. 
 Ng (2011) finds that higher information quality is negatively associated with liquidity risk and 
that relationship between information quality and cost of capital is economically significant. Additional 
analysis reveals that unexpected changes in market liquidity exacerbate the negative relationship 
between information quality and liquidity risk. Ng defines liquidity risk as “the sensitivity of the stock’s 
return to unexpected changes in market liquidity” while a liquid market is one with narrow bid-ask 
spreads in which large trades can be absorbed without significantly moving market prices (Black, 



 

31 
 

1971).2 When investors buy shares of a stock, the broker quotes an asking price (the “ask”). 
Conversely, if the investor attempts to sell the same shares, the broker quotes a lower bid price (the 
“bid”) with the difference between the two prices being the “bid-ask spread” (Callahan et al., 1997). 
The spread between the bid and ask prices is the market maker’s source of gross profit and must be 
wide enough to cover the market maker’s operating costs including adverse selection costs (Stoll, 
1978). Prior research (Amihud & Mendelson 1986; Copeland & Galai 1983; Glosten & Harris 1988; 
Stoll 1989) suggests that the spread is comprised of three types of costs facing the dealer: order 
processing costs, inventory holding costs, and adverse selection costs. The order-processing costs are 
the dealer's costs of arranging trades and clearing transactions and include such items as the exchange 
seat, floor space rent, informational service costs, labor costs, and the opportunity cost of the market 
maker’s time. 3 The inventory holding costs are the dealer's costs of carrying the necessary inventory 
of stock to be able to trade on demand. Lastly, the adverse selection costs are the losses the market 
maker sustains when trading with investors who are privy to more precise private information about 
the true worth of the security4  and is commonly referred to in the literature as “information 
asymmetry.” In response to information asymmetry and related adverse-selection costs, a market 
maker will reduce market depth (Kyle, 1985) and widen spreads (Glosten & Milgrom, 1985).5 
Specifically, Amihud and Mendelson (1986) find a positive association between bid-ask spreads and 
stock returns and Brennan and Subrahmanyam (1996) show a positive relation between stock returns 
and inverse market depth. Overall, these studies indicate that investors demand a premium for holding 
illiquid stocks, and lower market liquidity impacts firm value. In summary, the aforementioned 
theories predict that compliance with ASC 820-10 should (1) reduce incomplete information, (2) 
reduce estimation risk, (3) reduce information asymmetry, and/or (4) impact expected future cash 
flows. Based on the above discussion, this study hypothesizes: 

 
H1:  Fair value disclosure requirement ASC 820-10 has a positive effect on firm liquidity. 
 
H2:  Fair value disclosure requirement ASC 820-10 has a positive effect on firm value.   

 
Sample Selection and Description 

 The sample was compiled using a combination of hand-gathered procedures and available data 
from Compustat and CRSP databases. First, all firms with any activity in level 2 and level 3 were 
identified in Compustat resulting in 816 firms. Next, the number of firms in the initial sample was 
reduced by 41 as corresponding data to compute liquidity and firm value proxies were incomplete for 
these firms. The above procedures result in a final sample of 775 firms and 6,853 firm-quarter 
observations.  

 
2In this study the distinction between liquidity risk and liquidity is made strictly for definitional purposes with respect 

to different streams within the finance and accounting academic literature. The empirical findings in Pastor and 

Stambaugh (2003) and Ng (2011) imply that the effect of financial reporting quality on the cost of capital and market 

liquidity is significant.  
3 Bollen et al. (2004) finds that in the short run, order processing costs are largely fixed and their contribution to the 

size of the bid-ask spread should be minimal. Also, in a highly competitive market, bid-ask spreads should equal the 

expected marginal cost of supplying liquidity, in which case order-processing costs may be irreverent.  
4 “The adverse selection component of the spread is closely related to information flows in capital markets and is, 

therefore, of the most potential interest to accountants.” (Callahan et al, 1997)  
5 Accounting theory of disclosure states that value relevant disclosure mitigates resource misallocation on the capital 

market by reducing information asymmetries between insiders and investors and the associated cost of capital. 

However, the existence and magnitude of this effect depends on the perceived credibility of the disclosure (Gu and 

Li, 2007).  



 

32 
 

 To gather disclosure data on selected firms, the 10-K Wizard search engine was employed to 
search quarterly, and annual reports filed beginning in Q3 2009. Transfers between fair value 
hierarchical levels were identified with a keyword search for all occurrences of “transfers to/from level 
3 (III)” and “Level 2 (3) reclassification” in quarterly and annual financial statements. This process 
produced a subsample of 404 firms with transfers between level 2 and level 3 and 371 firms that 
reported “no material activity between level s “and/or “adoption of ASC 820-10 does not materially 
affect the financial statements.” 
 
Table 1 – Description of Sample Firms and Transfer Activity Type 

 

Panel A: Sample Reconciliation # of Firms 
All Firms in Compustat database with Level 2/Level 3 fair value activity 816 

Firms with missing financial data 41 

Final Sample 775 

  
Firms reporting no material activity between levels 371 

Firms reporting material activity between levels 404 

Panel B: Type of Transfer Activity 

 Assets Liabilities Total 

Level 3 Activity n % n % n % 
Transfer In 2,508 90% 1,476 53% 3,984 71% 

Transfer Out 294 10% 1,326 47% 1,620 29% 

       

Totals 2,802 100% 2,802 100% 5,604 100% 

 
 Panel A of Table 1 describes the final sample of 775 firms and subsample of firms with (404) 
and without (371) transfer activity while Panel B of Table 1 reports level 3 transfer activity. Panel B 
shows that a considerable majority (71%) of the transfer activity was reclassifications of 
assets/liabilities into level 3 fair value category.  
 

Research Methodology and Empirical Proxies 
 
Proxies for Liquidity 
 Liquidity is captured via three measures- the bid-ask spread, share turnover, and price impact.  
The bid-ask spread (BA) represents the market maker’s profit including inventory, processing, and 
adverse selection costs. A wider spread indicates a higher degree of information asymmetry and 
information risk which results in decreased liquidity. Following Fu et al. (2012) the bid-ask spread is 
calculated daily as (Ask-Bid)/ ((Ask+Bid)/2). The second proxy, share turnover (ST), is a common 
measure of liquidity and is calculated as the daily total of shares traded divided by the average shares 
outstanding. A higher share turnover indicates a more liquid security. Following Daske et al.(2008) 
and Fu et al.(2012) the third and final proxy, price impact (PI), captures the ability to trade a security 
without an impact on price. Price Impact is calculated as the daily absolute return divided by trading 
volume in dollars ($). 
 Following prior literature (Fu et al., 2012), the following control variables are used to test the 
relation between liquidity and ASC 820-10: 
Size = Total assets at the end of the previous period, log-transformed 
Turnover = median daily turnover ratio in a quarter, log-transformed 
Volatility = standard deviation of daily return in a quarter, log-transformed 



 

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Nasdaq = 1 if traded on the NASDAQ exchange; 0 otherwise 
 
Proxies for Firm Value 
 Firm value is measured via two proxies-Tobin’s Q and Enterprise Value ratios. Tobin’s Q 
(TQ) is a well-established proxy for firm value in the accounting and finance academic literature and 
is calculated as the sum of the equity market value of assets and book value of liabilities divided by the 
sum of equity book value and the book value of liabilities. Enterprise Value (EV) represents the value 
of a firm inclusive of the company’s debt and therefore represents a more accurate measure of 
valuation when compared to other measures of firm value (i.e., market capitalization). Enterprise value 
is calculated as the sum of quarterly market capitalization, long-term debt, minority interests, and 
preferred stock reduced by total cash and cash equivalents.  
 Following prior literature (Dang et al. 2019), the following control variables are used to test 
the relation between firm value and ASC 820-10: 
Growth = (Sales Revenuet – Sales Revenuet-1)/Sales Revenuet 
Size = Total assets at the end of the previous period, log-transformed 
Leverage = Total Debt/Total Assets 
ROA = Net Profit/Total Assets 
ROE = Net Income/Total Equity 
 
Table 2 – Descriptive Statistics 
 

Panel A: Descriptive Statistics by Time Period 

Pre-ASC Adoption (n = 3,997) 

Variable Mean Std. Dev Q1 Median Q3 n 
BA 1.593 4.047 0.119 0.280 1.027    3,997  

ST 547.34 708.90 114.92 356.87 718.14    3,997  

PI -0.088 0.983 -0.024 -0.001 0.006    3,997  

TQ 1.385 0.996 0.979 1.040 1.343    3,997  

EV 8.495 6.680 0.000 12.160 13.253    3,997  

Size 12.905 1.853 11.665 12.872 13.891    3,997  

Turnover 3.103 1.283 2.236 3.099 3.908    3,997  

Volatility 0.208 0.127 0.118 0.182 0.270    3,997  

Nasdaq 0.626 0.484 0.000 1.000 1.000    3,997  

Growth 12.822 3.661 11.670 12.752 13.113    3,997  

Leverage 0.162 0.065 0.000 0.607 1.125    3,997  

ROA 2.059 5.082 0.000 0.600 2.400    3,997  

ROE  1.283 0.881 0.224 1.323 1.417    3,997  

Post-ASC Adoption (n = 2856) 

BA 1.028 2.683 0.074 0.182 0.649    2,856  

ST 483.44 1847.98 100.38 278.03 572.94    2,856  

PI 0.008 0.642 -0.010 0.000 0.013    2,856  

TQ 1.395 1.039 0.978 1.031 1.337    2,856  

EV 9.026 6.850 0.000 12.297 13.425    2,856  

Size 12.873 1.923 11.654 12.803 13.869    2,856  

Turnover 3.084 1.232 2.223 2.963 3.792    2,856  

Volatility 0.156 0.083 0.097 0.140 0.195    2,856  

Nasdaq 0.628 0.483 0.000 1.000 1.000    2,856  

Growth 11.98 2.754 11.24 12.545 13.003    2,856  



 

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 Panel A of Table 2 displays the descriptive statistics for sample firms by time while Panel B of 
Table 2 provides descriptive statistics by category of transfer activity. Firms with material level 3 
transfer activity generally had larger bid-ask spreads and higher share turnover than firms without 
material transfer activity. The difference in the firm value proxies (i.e.tq and ev) for the two categories 
are significant and show that firms with material activity have significantly higher enterprise value but 
significantly less value as measured by Tobin's q. Further analysis reveals that the differences noted in 
the firm value proxies are likely driven by total debt as evidenced by the significantly higher leverage 
for firms with material transfer activity. Overall, Panel B of Table 2 indicates that firms with material 
transfers have less turnover, more volatility, and higher ROA when compared to firms without 
material transfer activity.  
 
Models for Tests of Liquidity (H1) and Firm Value (H2) 
 To test H1, the following model is used: 

Xit = β0 + β1ascit + β2fv3_trnsit + β3asc x fv3_trnsit + β4sizeit + β5turnoverit + β6volatilityit + 
β7nasdaqit +eit            (1) 

Leverage 0.266 0.118 0.000 0.484 0.980    2,856  

ROA 1.929 4.794 0.000 0.600 2.700    2,856  

ROE  1.017 1.281 0.000 1.303 1.439    2,856  

Panel B: Descriptive Statistics by Sub-Sample 

Firms Without Level 3 Activity 

(n = 4,051) 

Firms With Level 3 

Activity (n = 2,802) Diff. 

Variable Mean Median Mean Median 
BA 1.326 0.238 1.403 0.241 *** 

ST 501.869 318.453 547.946 317.723 *** 

PI -0.049 0.000 -0.047 -0.001 ** 

TQ 1.402 1.036 1.370 1.037 * 

EV 8.630 12.160 8.841 12.257 ** 

Size 12.881 12.823 12.907 12.875 * 

Turnover 3.098 3.050 3.089 3.022 * 

Volatility 0.185 0.159 0.188 0.162 * 

Nasdaq 0.633 1.000 0.618 1.000 ns 

Growth 12.000 12.550 13.300 13.010 *** 

Leverage 2.002 0.567 2.121 0.513 *** 

ROA 1.971 0.600 2.054 0.600 ** 

ROE  1.120 1.190 1.290    1.210  *** 

      

*, **, *** indicate significance at the 10 percent, 5 percent and 1 percent levels, respectively. Panel 

A displays descriptive statistics for sample firms by time period. Panel B displays the mean and 

median for the two time periods by firm activity. Variables Defined: BA = bid-ask spread (i.e. Ask-

Bid)/((Ask+Bid)/2); ST = share turnover (i.e. daily volume shares traded divided by average shares 

outstanding for the quarter); PI = price impact (i.e. daily absolute return divided by average trading 

volume); TQ = Tobin's q ratio (i.e. market value of equity divided by book value of equity); EV = 

enterprise value (i.e. sum of market capitalization, long-term debt, minority interests and preferred 

stock reduced by cash and cash equivalents); Size = market value at the end of the previous period, 

log transformed; Turnover = median quarterly turnover ratio, log transformed; Volatility = standard 

deviation of quarterly return in a year, log transformed; Nasdaq = 1 if listed on the nasdaq stock 

exchange, 0 otherwise; Growth = percentage change in slaes growth from previous period (i.e. 

(Currrent Period Sales Revenue - Prior Period Sales Revenue)/Current Period Sales Revenue); 

Leverage = Total Debt/Total Assets; ROA = Net Profit/Total Assets; ROE = Net Income/Total Equity. 



 

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where Xit indicates one of the proxies for liquidity, bid-ask spread, share turnover, and price impact, detailed 
above. ASC is a dummy variable coded as 1 if calendar quarter is after Q4 2009, 0 otherwise; fv3_trns 
is a dummy variable coded as 1 if transfer activity to/from level 3, 0 otherwise; asc x fv3_trns is the 
interaction term coded 1 if both asc and fv3_trns equal 1, 0 otherwise. 
To test H2, the following model is used: 

Zit = β0 + β1ascit + β2fv3_trnsit + β3asc x fv3_trnsit + β4growthit + β5sizeit + β6leverageit + 
β7roeit/roait +eit          (2) 

where Ze indicates one of the proxies for firm value, Tobin’s Q and Enterprise Value referenced above. 
ASC is a dummy variable coded as 1 if calendar quarter is after Q4 2009, 0 otherwise; fv3_trns is a 
dummy variable coded as 1 if transfer activity to/from level 3, 0 otherwise; asc x fv3_trns is the 
interaction term coded 1 if both asc and fv3_trns equal 1, 0 otherwise. 
 

Results 
 Table 3 presents the correlation coefficients for the liquidity and firm value proxies and 
associated control variables. The results follow expectations and prior literature with the liquidity 
proxies being significantly related to size, turnover, volatility, nasdaq, leverage, and roa variables. The firm 
value proxy, ev, is negative and significantly related to turnover, volatility, and roa while being positive and 
significantly related to nasdaq and leverage.  
 
Table 3 – Correlation Matrix 
 

 BA ST PI EV Size Turnover Volatility Nasdaq Growth Leverage ROA ROE 

BA 1            

ST -0.100 1           

PI -0.125 0.022 1          

TQ -0.261 0.204 0.054          

EV 0.298 -0.108 -0.034 1         

Size -0.040 0.048 -0.012 0.010 1        

Turnover -0.252 0.355 0.060 -0.149 0.018 1       

Volatility 0.208 0.108 0.080 -0.035 0.020 0.377 1      

Nasdag 0.161 -0.116 -0.039 0.177 -0.003 -0.214 -0.073 1     

Growth 0.022 0.010 0.001 0.007 0.003 0.006 0.031 -0.019 1    

Leverage 0.237 -0.066 -0.093 0.307 0.004 -0.061 0.007 0.086 0.006 1   

ROA -0.125 0.059 0.037 -0.123 -0.040 -0.014 -0.115 -0.035 -0.006 -0.113 1  

ROE -0.013 0.013 0.005 -0.018 0.018 0.028 -0.002 0.013 -0.01 -0.007 0.039 1 
             
Table 3 displays correlation coefficients. Bold indicates significance at the 5 percent level. Variables previously defined. 

 
 Table 4 presents the results for the analysis of the relationship between liquidity and ASC 820-
10. The significant negative coefficient for asc, when liquidity is proxied by ba indicates that bid-ask 
spreads decreased, and information asymmetry decreased as a result of ASC 820-10 however the 
coefficients for asc when liquidity is proxied by st and pi imply a different interpretation. The significant  
negative coefficients for asc when proxied by st and pi indicate that share turnover decreased and price 
impact increased, a result of increased information asymmetry. The explanation for this may be in that 
the increased requirements imposed by ASC 820-10 increased the available information, but investors 
discounted this data embedding the uncertainty in increased price impact and decreased share 
turnover. The marginally significant and insignificant coefficients for fv3_trns suggest that investors 
may view transfers to/from level 3 similarly and disclosure of this information provides no 
informational advantage. The interaction term, asc x fv3_trns, is significant at the 1% level when 



 

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liquidity is proxied by pi. Relative to firms without transfer activity, firms with transfers experienced a 
0.119% decrease in price impact. Collectively these results suggest that the increased disclosure 
requirement, albeit increases the information provided to investors, decreases liquidity (increases 
information asymmetry) and transfers between fair value categories do not provide additional relevant 
and reliable information and thus H1 is not supported.     
 Table 5 presents the results for the analysis of the relation between the proxies for firm value 
and ASC 820-10. When the firm value is measured using Tobin's q, none of the coefficients on the 
variables of interest are significant. This is not surprising given that the value of Tobin's q is susceptible 
to speculation and market momentum. Given that enterprise value is a more precise measure of firm 
value, I draw inferences about information asymmetry from the enterprise value regression results.   
When the firm value is proxied by enterprise value asc, fv3_trns and asc x fv3_trns are all significant at 
the 1% level. Likely, the significance difference found on the coefficients for the respective proxies is 
driven by the conceptual difference in Tobin's q and enterprise value. Enterprise value is a more 
precise and comprehensive measure of firm value as it represents the takeover price of a firm and 
includes preferred equity and unfunded pension liabilities.  Overall, the results support H2.  
 
Table 4 – Analysis of Liquidity (H1) 
 

Variables 
Predicted 

Sign 

Dependent Variables 

ba Significance st Significance pi 
asc - -0.480 *** -84.908 *** 0.150 

  -0.105  -21.981  -0.030 

fv3_trns    - 0.036  25.713  0.050 

  -0.115  -27.515  -0.029 

asc x fv3     +/- 0.135  72.380  -0.119 

  -0.155  -62.283  -0.042 

size     - -0.721 *** 84.850 *** 0.034 

  -0.034  -26.055  -0.009 

turnover - -0.684 *** 354.415 *** 0.037 

  -0.048  -76.455  -0.014 

volatility + 3.323 *** -139.463 *** 0.061 

  -0.451  -360.428  -0.204 

nasdaq - -0.232 *** -37.226  0.003 

  -0.091  -46.143  -0.022 

Constant  12.682 *** -1600.703 *** -0.877 

  -0.669  -212.707  -0.151 

       

Observations  6,853  6,853  6,853 

Year Fixed Effects  Yes  Yes  Yes 

Adj R2  21.56%  14.08%  4.21% 

       

*, **, *** indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively. Robust standard errors 

with clustered standard errors are in parentheses. Variables previously defined. 

 
 
 
 
 
 
 



 

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Table 5 – Analysis of Firm Value (H2) 
 

Variables 
Predicted 

Sign 

Dependent Variables 

tq Significance ev Significance 
asc - 1.760  0.731 *** 

  (1.410)  (0.200)  

fv3_trns - 0.403  0.488 *** 

  (1.410)  (0.192)  

asc x fv3 +/- 2.110  -0.706 * 

  (3.380)  (0.313)  

growth + 0.778 *** 24.549 *** 

  0.034  6.043  

size - 10.800 *** -1.745 *** 

  (1.110)  (0.066)  

leverage + 0.005 *** 0.000 ** 

  (0.001)  (0.000)  

roe/roa - -1.120 *** -0.067 *** 

  (0.113)  (0.015)  

Constant  -132.000 *** 31.100 *** 

  (15.800)  (1.066)  

      

Observations  6,853  6,853  

Year Fixed 

Effects 
 Yes  Yes  

Adj R2  23.11%  16.55%  

      

*, **, *** indicate significance at the 10 percent, 5 percent and 1 percent levels, respectively. 

Robust standard errors with clustered standard errors are in parentheses. Variables previously 

defined. 

 
 In an additional untabulated analysis of firms with material transfers, the sample was bifurcated 
by type of transfer activity (i.e assets and/or liabilities transferred in or out of level 3). The results 
reveal that the type of transfer activity is not significantly related to either of the proxies for firm value 
however assets transferred into level 3 are positive and significantly related to the bid-ask spread, while 
liabilities are not. This finding suggests that transfers of assets between level 2 and level 3 categories 
play a significant role in the increase or decrease of information asymmetry while liabilities do not. In 
an additional analysis, the study tests the sensitivity of these results following the model employed in 
Esqueda et.al (2019), and with the inclusion of additional controls; I find that the results are similar 
and statistical inference unchanged. 
 

Conclusion  
This study evaluates the effect of the fair value mandatory disclosure requirement, ASC 820-10, on 
liquidity and firm value. Initial results conducted on a broad sample of 6,853 firm-quarter observations 
for 775 firms suggest that liquidity decreased, and transfer activity has a significant effect on firm value 
for firms affected by the standard, relative to those firms unaffected. Overall, the results of this study 
suggest investors assign value to financial statement information based on relevancy and 
understandability. Theoretically, transfers between categories signal a firm’s certainty about valuation 
inputs however investors generally interpret this signal as insignificant irrespective of the transfer 
orientation. The above findings provide policymakers with empirical evidence of “information 



 

38 
 

overload” and contribute to the motivation to effectively streamline financial disclosures, a priority 
for the IASB and FASB.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 



 

39 
 

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