28 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 30 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 33 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 34 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. 35 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 36 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. 37 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 References Affleck-Graves, J., Callahan, C. M., & Chipalkatti, N. (2002). Earnings Predictability, Information Asymmetry Market Liquidity. 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