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Eliminating the 20-F reconciliation from IFRS to U.S. GAAP: Short-term and long-term liquidity effects
Research in Accounting Regulation 24 (2012) 90–95
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Research Report
Eliminating the 20-F reconciliation from IFRS to U.S. GAAP: Short-term and long-term liquidity effects q Bidisha Chakrabarty a, Kenneth W. Shaw b,⇑ a b
John Cook School of Business, Saint Louis University, St. Louis, MO 63108, USA Robert J. Trulaske, Sr. College of Business, University of Missouri, Columbia, MO 65211, USA
a r t i c l e
i n f o
Article history: Available online 8 June 2012 Keywords: IFRS 20-F Liquidity International accounting
a b s t r a c t This paper examines the effects of the SEC’s 2008 decision to no longer require foreign private issuers using IFRS and trading on U.S. exchanges to reconcile their financial statements to U.S. GAAP. Extant research has found conflicting results using short event windows, while studies using longer event windows have found limited capital market impact from eliminating the reconciliation. Motivated by the SEC’s interest in understanding how disclosure rules impact market liquidity, we examine changes in effective bid-ask spreads, the price impact of trades, and quoted depth around 20-F filing dates for a sample of foreign private issuers. We find that effective spreads increase more around 20-F filing dates for filers using IFRS than for filers using U.S. GAAP, suggesting the 20-F report is more informative for filers using IFRS. We then find, in a subsample of filers using IFRS, that the increase in effective spreads for IFRS firms around 20-F filing dates is directly related to the magnitude of differences in book values between IFRS and U.S. GAAP. In sum, our results suggest a loss of useful information after the SEC’s rule change. Ó 2012 Elsevier Ltd. All rights reserved.
Introduction In early 2008 the Securities and Exchange Commission (SEC) issued a rule that allows foreign private issuers that use International Financial Reporting Standards (IFRS) to file financial statements without reconciliation to United States generally accepted accounting principles (U.S. GAAP).1 Previously, foreign private issuers using IFRS disclosed what their earnings and book values would have been under U.S. GAAP, and provided details on the accounting areas that generated the IFRS–U.S. GAAP differences. In com-
q Helpful comments from workshop participants at Seoul National University, Southern Illinois University-Carbondale, the 2009 Eastern Finance Association meeting, Dave Farber, Ayesha Malhotra, and Andriy Shkilko are appreciated. ⇑ Corresponding author. Tel.: +1 573 882 5939. E-mail address: [email protected] (K.W. Shaw). 1 This rule is effective for fiscal years ending after November 15, 2007. Form 20-F is the form that foreign private issuers file with the SEC. This form is due within six months after fiscal year-end.
1052-0457/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.racreg.2012.05.008
ment letters, proponents of the new rule asserted that IFRS were acceptable as high-quality accounting standards and reconciliation to U.S. GAAP was unnecessary. Dissenters argued that important differences remained between IFRS and U.S. GAAP, such that reconciliation to U.S. GAAP remained useful. In this paper we review research that examines the impact of the SEC rule change and present new empirical evidence on the impact of the rule change on market liquidity.
Extant evidence Here we summarize findings in related research on the capital market effects of eliminating the 20-F reconciliation. We group studies into those that examine short-term effects, over a small number of days around 20-F filings or earnings announcements, and those that examine more long-term effects using longer event windows. Chen and Sami (2012) study a sample of firms that use IFRS and reconcile to U.S. GAAP in 2005 and 2006 filings.
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The authors find that trading volume in the U.S. equity market increases in the days { 1, +1} around the 20-F filing date relative to trading volume in a benchmark nonannouncement period. These results are driven by firms with low levels of institutional holdings and first-time IFRS filers. In contrast, Jiang, Petroni, and Wang (2010) find no evidence of increased trading volume or stock return volatility over days { 1, +1}. Likewise, Jiang et al. (2010) find no evidence of changes in closing quoted bid-ask spreads in the period {+2, +10} around the 20-F date as compared to average bid-ask spreads over days { 10, 2}. Byard, Mashruwala, and Suh (2011) examine ‘‘information transfers’’ around quarterly earnings announcements for 36 foreign private issuers over January 2006 through December 2009. They examine how the release of 20-F information by a foreign private issuer impacts stock returns or trading volume of a nonannouncing, but otherwise similar U.S. GAAP firm. The authors find positive information transfer effects over their full sample period, but the effect drops dramatically after the reconciliation is no longer required.2 The above studies focus on fairly short time windows around 20-F filings. In contrast, Kim, Li, and Li (2012) examine longer time periods, namely, 90-day periods that begin one day before 20-F filing dates. Kim et al.’s (2012) sample includes 78 foreign private issuers that use IFRS in 2006 and 2007, with a control sample of 162 cross-listed firms that do not use IFRS. Kim et al. (2012) find no evidence that removing the 20-F reconciliation had any significant impact on cost of capital, the probability of informed trading, properties of analysts’ earnings forecasts, institutional ownership, stock price efficiency, or stock price synchronicity. Henry, Lin, and Yang (2009) study a sample of 75 crosslisted EU firms over 2004–2006. They find that while the magnitude of IFRS–U.S. GAAP book value and net income differences declined over their sample period, the difference in net income remained significant. Henry et al. (2009) also find pension and goodwill accounting to be the dominant reconciling items. However, both Henry et al. (2009), and Kim et al. (2012) find no relation between the 12-month stock returns after 20-F filings and IFRS–U.S. GAAP income or book value differences.3 Several observations emerge from a review of related research. First, capital market effects of removing the 20F reconciliation requirement, if any, are likely to manifest in short-time periods around important information events (such as 20-F filings and earnings announcements). This is consistent with research that suggests that bid-ask spreads and quoted depths change significantly around earnings announcements (Lee, Mucklow, & Ready, 1993) and management earnings forecasts (Coller & Yohn, 1997), but fairly quickly return to their preannouncement levels. This phenomenon, in conjunction with using a relatively long window around the 20-F date might explain the lack of results when using bid-ask spread changes in Jiang
2 Specifically, information transfers drop by 55% when using stock returns and to zero when using trading volume. 3 Henry et al. (2009) do find that market value, measured six months after the 20-filing, is significantly related to both earnings and book IFRSU.S. GAAP differences.
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(2010). Second, extant short-window studies provide conflicting evidence, particularly with respect to trading volume. Third, related research often combines days before and after the 20-F filing, making it difficult to obtain a clean identification of the impact of 20-F filings. Our study differs by examining measures (e.g. effective spreads and depths) motivated by theory (Diamond & Verrecchia, 1991; Kyle, 1985) that more precisely capture information effects than do alternative proxies such as trading volume. In addition, we separately study short time periods before and after 20-F filing dates. Research questions We examine two research questions: (1) Do changes in market liquidity around 20-F filing dates differ between foreign private issuers reporting under IFRS and those reporting under U.S. GAAP? (2) For foreign private issuers reporting under IFRS, do earnings and/or book value reconciliation amounts help explain changes in liquidity around 20-F dates? Our liquidity measures include effective bidask spreads, depths, and the price impact of trades, all computed from intra-day trade and quote data.4 From a policy standpoint, the SEC considers effective spreads to be ‘‘one of the most important measures of market quality’’ and uses this metric in its own analysis of trade execution cost.5 Ceteris paribus, lower effective spreads, higher depths, and lower price impact of trades imply greater market liquidity. Extant literature provides opposing predictions regarding changes in market liquidity after a 20-F filing. The disclosures made in 20-F filings could reduce the information advantage of informed traders, thus leveling the playing field between informed and uninformed investors (Lev, 1988). This line of reasoning suggests that changes in market liquidity after 20-F filings should not differ between IFRS and U.S. GAAP firms. In this case we also would not expect to find any relation between disclosed 20-F earnings and book value differences and changes in liquidity. In contrast, Kim and Verrecchia (1994) show that when an announcement is noisy and certain traders have superior ability in processing news, market liquidity will decrease after the announcement. If larger differences between IFRS and U.S. GAAP results require more expert interpretation, investors with superior processing skills might be better able to infer private information, and thus liquidity might decrease after the 20-F. In this case, we would expect to observe positive relations between the 4 The spread is the difference between the price at which specialists or market makers will buy shares (bid price) and the price at which they offer to sell shares (ask price). Depth is the quantity of shares the specialist or market maker is willing to trade at each of the posted bid and ask prices. A liquid market can be considered one in which investors can buy or sell the number of shares they want, when they want, at low transaction cost, and causing prices to move little as a direct result of their trades (low price impact). 5 Unlike quoted spreads, effective spreads are computed by comparing actual prices at which trades occur to quoted prices. Many trades occur within the quoted bid-ask spread, i.e. the effective cost to trade is lower than that quoted. See the SEC Execution cost analysis document at http:// www.sec.gov/spotlight/regnms/companalysis121504.pdf for a full discussion of the appropriate statistics to measure trade execution cost for investors.
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earnings or book value differences and changes in market liquidity measures around 20-F filings.
computed under U.S. GAAP (disclosed in a 20-F reconciliation table). These variables are scaled by the number of common shares outstanding.
Sample selection and descriptive statistics Control variables Sample selection An initial sample of 670 20-F filings in 2006 and 2007 is obtained from Chakrabarty and Shaw (2011). This sample resulted from applying data requirements to a list of 917 distinct foreign private issuers that filed 20-F reports with the SEC in 2006 (i.e., for the fiscal 2005 year-end).6 A total of 606 firm-years have necessary data to estimate changes models around 20-F filing dates in 2006 and 2007. This sample of 606 firm-year observations contains both IFRS filers and U.S. GAAP filers and is used to test our first research question.7 Our second research question focuses on the relevance of 20-F earnings and book value reconciling amounts to changes in market liquidity around 20-F dates. As U.S. GAAP filers do not reconcile their results to IFRS, the sample is reduced to the 203 IFRS firm-years for which we can collect earnings and book value reconciling amounts from IFRS to U.S. GAAP in 20-F reports. This sample is used to test our second research question.
Following prior research (e.g. Corwin & Lipson, 2000; Heflin & Shaw, 2000; Heflin, Shaw, & Wild, 2005) we control for other cross-sectional determinants of liquidity, including stock price per share, firm size, trade size, the average daily number of trades, the standard deviation of daily stock returns, and an indicator variable for stock exchange. To control for the possibility that differential enforcement of accounting rules across countries impacts our results, we include the rule of law variable from Kaufman, Kraay, and Mastruzzi (2008).9 We compute changes in those control variables (share price, trade size, and number of trades) that reasonably can be expected to change over short time periods; we compute these changes in a manner similar to that used for our dependent variables. For continuous variables that do not vary over short time periods (firm size and standard deviation of stock returns) we compute averages for each firmyear across all days with available data in that firm-year.10 Regression model
Research methods and results Dependent variables Appendix A lists our variables and their measurement.8 Our dependent variables include effective spread, the price impact of trades, average quoted depth and average dollar depth. For these tests we define the 20-F filing date per EDGAR as day 0, and days 20 through 11 relative to the 20-F filing date as a non-announcement reference period. For each firm-year and each liquidity measure, we examine the difference between the announcement day values on each of the days ( 2, 1) and (0, +1) and their nonannouncement period mean, scaled by the non-announcement mean. This yields the percentage abnormal change in liquidity on a given day. We then cumulate these percentage changes over days 2 through 1 (0 and + 1) to capture the change in market liquidity before (in response to) the 20-F filing. Test variables To test our first research question we read 20-F reports and code an independent variable to capture whether the 20-F filer uses U.S. GAAP or IFRS. This indicator variable, labeled IFRS, equals 1 (0) if the firm uses IFRS (U.S. GAAP). To test our second research question we compute differences between earnings and book values under IFRS (included in the 20-F financial statements) and their counterparts 6 See Chakrabarty and Shaw (2011) for a detailed discussion of the sample filtering procedures. 7 As the revised SEC reporting rules only apply to firms that use IFRS, we exclude firms which use forms of ‘‘local’’ GAAP from our analyses. 8 Refer to Appendix B for further details on collecting TAQ data.
We estimate regressions of each of our separate liquidity change variables on our test and control variables. For each liquidity measure we estimate these regressions over periods { 2, 1} relative to the 20-F filing date and {0, +1} relative to the 20-F filing date. Following related research we use logarithmic values of all continuous variables in our regressions. Significance tests are based on standard errors corrected for heteroscedasticity (Huber, 1967; White, 1980). Descriptive statistics Table 1 presents descriptive statistics on our full sample of 606 firm-years, computed using data from all days with available data in each firm-year. The typical (median) firm has an effective spread of 5.21 cents per share, depth of about $28,979, and experiences 258 trades per day, with an average trade size of 310 shares. Median firm size is $408 million with a median share price of $22.88. Consistent with evidence in Plumlee and Plumlee (2008) and Henry et al. (2009), our sample firms’ earnings are on average higher and book value lower under IFRS relative to their amounts under U.S. GAAP (untabulated, available upon request). Results Table 2 reports regression results. Over days {0, +1} relative to the 20-F filing date, effective spreads increase more for filers using IFRS than for filers using U.S. GAAP. 9
See Kaufman et al. (2008, p. 6). In untabulated analyses we obtain qualitatively similar results when all control variables are measured in levels. 10
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B. Chakrabarty, K.W. Shaw / Research in Accounting Regulation 24 (2012) 90–95 Table 1 Descriptive statistics. Variable
Mean
Std. Dev.
Q1
Median
Q3
Effective spread (cents per share) Depth (# of shares) Dollar depth ($) Price impact (cents per share) Price ($ per share) Size ($ millions) Trade size (# of shares) Number of trades (daily average) Standard deviation of stock return Rule of law
8.97 1798 39,336 3.51 32.17 2259 375 780 0.025 1.14
11.52 2123 36,388 5.52 31.52 5625 205 1230 0.013 0.72
3.36 713 9139 1.41 9.67 94 245 73 0.017 0.78
5.21 1132 28,979 1.87 22.88 408 310 258 0.022 1.38
9.33 1990 56,160 3.22 45.96 1773 434 973 0.028 1.73
Variables are defined in Appendix A. N = 606 firm-years.
Table 2 Regressions of changes in liquidity around days {0, +1} after 20-F filing dates on accounting type and controls. Variable
Full Sample of IFRS and U.S. GAAP filers (N = 606)
DEFFSPRD
DIMPACT
DDOLDEPTH
DIFF_E
0.050 0.079 0.121⁄⁄ ( 1.09) ( 0.89) ( 2.24) 0.251 1.584⁄⁄⁄ 1.010⁄⁄⁄ ( 1.28) ( 4.15) (4.34) 0.002 0.002 0.77 ( 0.77) ( 0.77) (0.44) 0.024 0.256⁄⁄ 0.247⁄⁄⁄ (0.45) (2.50) (3.96) 0.025 0.013 0.017 ( 1.55) ( 0.43) ( 0.85) ⁄ 1.554 0.411 3.682⁄⁄⁄ (1.70) ( 0.23) (3.39) 0.038 0.016 0.075⁄⁄ (1.54) (0.33) (2.55) 0.007 0.024 0.015 (0.45) (0.84) (0.90) ⁄⁄ 0.050 0.021 0.023 (2.00) (0.47) ( 0.85) n/a, not available for U.S. GAAP firms
DIFF_BV
n/a, not available for U.S. GAAP firms
Adjusted R2
0.012
Intercept
DPRICE SIZE
DTRDSIZ DNUMTRD SDRET EXCH RULE IFRS
0.031
0.065
Subsample of IFRS filers (N = 203)
DSHRDEPTH
DEFFSPRD
0.043 ( 0.87) 0.070 (0.33) 0.003 ( 1.11) 0.273⁄⁄⁄ (4.77) 0.022 ( 1.23) 1.280 (1.29) 0.057⁄⁄ (2.16) 0.012 (0.75) 0.037 ( 1.49)
0.285⁄⁄⁄ (2.79) 0.164 ( 0.71) 0.029⁄⁄ ( 2.31) 0.101 (1.06) 0.019 ( 1.10) 3.953⁄⁄ ( 2.51) 0.049 ( 1.07) 0.020 (1.42) n/a, IFRS filers
0.039
DIMPACT 0.262 (1.39) 1.601⁄⁄ ( 2.00) 0.023 ( 0.98) 0.101 ( 0.31) 0.009 (0.22) 4.285 ( 1.45) 0.008 (0.08) 0.068⁄ (1.76) only
DDOLDEPTH
DSHRDEPTH
0.122 ( 0.63) 0.599 (1.21) 0.015 (1.20) 0.295⁄ (1.73) 0.001 (0.02) 1.638 (0.55) 0.004 (0.06) 0.018 ( 0.65)
0.041 (0.38) 0.060 ( 0.15) 0.006 (0.67) 0.254 (1.62) 0.013 (0.38) 1.406 ( 0.71) 0.019 ( 0.38) 0.020 ( 0.86)
0.058 ( 1.34) 0.059⁄⁄ (2.41)
0.150 ( 1.38) 0.001 (0.02)
0.025 (0.49) 0.010 (0.40)
0.026 (0.53) 0.025 ( 1.38)
0.027
0.057
0.029
0.021
The table reports coefficient estimates and Huber–White robust heteroscedasticity-consistent t-statistics (in parentheses) from ordinary least squares regressions. t-Statistics are computed from observations clustered by country. DEFFSPRD, DIMPACT, DDOLDEPTH, DSHRDEPTH, DTRDSIZ, and DNUMTRD are measured as changes from days {0, +1} relative to a non-announcement period mean, computed over days { 20, 11} relative to the 20-F filing date, then scaled by the non-announcement period mean. Other variables are as defined in Appendix A. ,, denotes the coefficient estimate is different from zero at the 0.01, 0.05, or 0.10 significance level (two-tailed tests).
Specifically, the coefficient on the IFRS indicator variable in the regression with DEFFSPRD as the dependent variable is positive (a8 = 0.05) and significant at p < 0.05. Changes in the other liquidity measures we examine are not different between IFRS and U.S. GAAP. Likewise, in untabulated regressions we find no relations between liquidity changes and our IFRS variables in the period { 2, 1} before the 20F filing. The results thus far suggest that 20-F reports prepared under IFRS are more informative than those prepared under U.S. GAAP. We next examine whether a difference in the information content of 20-F’s between IFRS and U.S. GAAP users, namely the earnings and book value reconciliations, explains (in part) the difference in liquidity reactions across IFRS and U.S. GAAP users. Again, in untabulated regressions we find no relations between
liquidity changes and earnings or book value differences in the period { 2, 1} before the 20-F. However, we find that the increase in effective spreads after 20-F filings is positively related to the size of the difference in book value computed under IFRS versus U.S. GAAP. The coefficient on DIFF_BV equals 0.059 and it is significant at p < 0.05. Taken together, the results in Table 2 suggest effective spreads increase after 20-F filings for firms that use IFRS, due in part to significant differences in book values computed under IFRS and U.S. GAAP.
Concluding comments We examine how market liquidity changes around 20-F filings of foreign private issuers. We find that effective
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spreads increase more for filers using IFRS relative to those using U.S. GAAP. We also find that the increase in effective spreads for filers using IFRS is positively related to the magnitude of differences between IFRS and U.S. GAAP. Our results are consistent with a loss of information around 20-F filings after the SEC’s rule eliminating reconciliation between IFRS and U.S. GAAP for foreign private issuers. In a similar vein, Christensen, Lee, and Walker (2009) find that reconciliations from UK GAAP to IFRS convey value-relevant information due to an impact on debt contracting. While our focus is on IFRS to U.S. GAAP reconciliations, Christensen et al.’s (2009) results, in conjunction with our results, suggest the relevance of reconciliations across accounting regimes to capital market participants. Our findings are also consistent with evidence in Yu (2011). Yu (2011) finds that after elimination of the reconciliation requirement, IFRS firms significantly increase their level of voluntary disclosures in annual reports and earnings announcements. Yu (2011) further finds that these voluntary disclosures often pertain to the previously disclosed reconciling items. This suggests that foreign private issuers increased their voluntary disclosures to mitigate the loss of information after the SEC no longer mandated disclosure of reconciliation information. Appendix A. Variable definitions
Variable
Abbreviation Measurement
Dependent variables Effective spread EFFSPRD (cents per share)
Depth (# of shares)
SHRDEPTH
Dollar depth ($) DOLDEPTH Price impact (cents per share)
Test variables Filer uses IFRS
IMPACT
IFRS
Twice the absolute difference between a trade’s price and the quote midpoint in effect at the time of the trade Number of shares quoted at the best ask price plus number of shares quoted at the best bid price SHRDEPTH times bidask quote midpoint Absolute value of the difference in bid-ask quote midpoint, measured from the standing quote before a trade and the quote mid-point 10 minutes after a trade An indicator which equals 1 for firms that use IFRS, and 0 for firms that use U.S.
Appendix A (continued) Variable
Abbreviation Measurement GAAP
20-F earnings DIFF_E reconciliation (per share)
Difference between earnings per IFRS and U.S. GAAP, scaled by the number of outstanding shares
20-F book value DIFF_BV reconciliation (per share)
Difference between total shareholders’ equity per IFRS and U.S. GAAP, scaled by the number of outstanding shares
Control variables Share price ($ per share) Trade size (# of shares) Number of trades Firm size ($ million)
PRICE TRDSIZ NUMTRD SIZE
SDRET
Standard deviation of daily stock returns Exchange
EXCH
Rule of law
RULE
Bid-ask quote midpoint Average daily number of shares traded Average daily number of trades Number of shares outstanding times share price, both measured at the beginning of the year Standard deviation of daily stock return, including dividends, from CRSP Primary exchange on which shares are listed. An indicator which equals 1 for NYSE or AMEX and 0 for NASDAQ. Country-level measure of the rule of law, based on survey evidence in Kaufman et al. (2008). This variable ranges from 2.5 to +2.5. Higher values indicate better enforcement.
Data to compute dependent variables are from the 2006– 2007 Trades and Quotes (TAQ) database. All control variables except for SDRET and RULE are also computed using TAQ data. SDRET is computed using the 2006–2007 CRSP daily stock return file. RULE is obtained from Kaufman et al. (2008). EFFSPRD, SHRDEPTH, DOLDEPTH, IMPACT, TRDSIZ, and NUMTRD are computed for each firm for each trading day within the year, and then averaged by firm over all that firm’s trading days in that year with available data. Test variables (IFRS, DIFF_E and DIFF_BV) are from form 20-F reports filed with the Securities Exchange
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Commission during 2006–2007. By definition, DIFF_E and DIFF_BV are not available for 20-F filers that use U.S. GAAP.
Appendix B. Data cleaning procedure for the NYSE Trade and Quote (TAQ) database We apply the following filters to clean the trade and quote data: We use trades and quotes from regular-hours trading only. We use only trades for which TAQ’s CORR field is equal to zero, one, or two and for which the COND field is either blank or equal to @, E, F, I, J, or K. We eliminate trades with non-positive prices or quantities, with prices more than 150% or less than 50% of the previous trade price. We use only quotes for which TAQ’s MODE field is equal to 1, 2, 6, 10, 12, 21, 22, 23, 24, 25, or 26. We eliminate quotes with non-positive price or size, with bid price greater than ask price, when the quoted spread is greater than 50% of the quote midpoint, or when the ask price is more than 150% of the bid price. We calculate all spreads two ways: (i) using trades and quotes from all exchanges; and (ii) using trades and quotes from NYSE, ARCA, and Nasdaq (these three exchanges account for over 98% of the trading volume in our sample stocks during our sample period). Reported results are based on spreads calculated using method (ii) above to avoid issues related to stale quotes.
References Byard, D., Mashruwala, S., & Suh, J. (2011). The economic consequences of eliminating the reconciliation of IFRS to U.S. GAAP: An information transfer analysis. Working paper, Baruch College-CUNY. Chakrabarty, B., & Shaw, K. (2011). Market liquidity for foreign private issuers using IFRS. Working paper, University of Missouri. Chen, L., & Sami, H. (2012). Trading volume reactions to the earnings reconciliations from IFRS to US GAAP. Contemporary Accounting Research, forthcoming.
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Christensen, H., Lee, E., & Walker, M. (2009). Do IFRS reconciliations convey information: The effect of debt contracting. Journal of Accounting Research, 5, 1167–1199. Coller, M., & Yohn, T. (1997). Management forecasts and information asymmetry: An examination of bid-ask spreads. Journal of Accounting Research, 35, 181–191. Corwin, S., & Lipson, M. (2000). Order flow and liquidity around NYSE trading halts. Journal of Finance, 55, 1771–1801. Diamond, D. W., & Verrecchia, R. E. (1991). Disclosure, liquidity, and the cost of capital. Journal of Finance, 46, 1325–1359. Henry, E., Lin, S., & Yang, Y. (2009). The European-U.S. ‘‘GAAP gap’’: IFRS to US GAAP form 20-F reconciliations. Accounting Horizons, 23, 121–150. Heflin, F., & Shaw, K. (2000). Blockholder ownership and market liquidity. Journal of Financial and Quantitative Analysis, 35, 621–633. Heflin, F., Shaw, K., & Wild, J. (2005). Disclosure policy and market liquidity: Impact of depth quotes and order sizes. Contemporary Accounting Research, 22, 829–865. Huber, P. (1967). The behavior of maximum likelihood estimates under nonstandard conditions. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Berkeley, CA (pp. 221–223). University of California Press. Jiang, J., Petroni, K., & Wang, I. (2010). Did eliminating the 20-F reconciliation between IFRS and U.S. GAAP matter? Working paper, Michigan State University. Kaufman, D., Kraay, A., & Mastruzzi, M. (2008). Governance matters VI: Aggregate and individual governance indicators 1996–2006. The World Bank. Kim, O., & Verrecchia, R. (1994). Market liquidity and volume around earnings announcements. Journal of Accounting and Economics, 17, 41–67. Kim, Y., Li, H., & Li, S. (2012). Does eliminating the Form 20-F reconciliation from IFRS to U.S. GAAP have capital market consequences?. Journal of Accounting and Economics, 53, 249–270. Kyle, A. (1985). Continuous auctions and insider trading. Econometrica, 15, 1315–1335. Lee, C., Mucklow, B., & Ready, M. (1993). Spreads, depths, and the impact of earnings information: An intraday analysis. Review of Financial Studies, 6, 345–374. Lev, B. (1988). Toward a theory of equitable and efficient accounting policy. The Accounting Review, 63, 1–22. Plumlee, M., & Plumlee, D. (2008). Information lost: A descriptive analysis of IFRS firms’ 20-F reconciliations. Journal of Applied Research in Accounting and Finance, 3, 15–31. White, H. (1980). A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica, 48, 817–838. Yu, J. (2011). The interaction of voluntary and mandatory disclosures: Evidence from the SEC’s elimination of the IFRS–U.S. GAAP reconciliation. Working paper, University of Georgia.
Contents lists available at SciVerse ScienceDirect
Research in Accounting Regulation journal homepage: www.elsevier.com/locate/racreg
Research Report
Eliminating the 20-F reconciliation from IFRS to U.S. GAAP: Short-term and long-term liquidity effects q Bidisha Chakrabarty a, Kenneth W. Shaw b,⇑ a b
John Cook School of Business, Saint Louis University, St. Louis, MO 63108, USA Robert J. Trulaske, Sr. College of Business, University of Missouri, Columbia, MO 65211, USA
a r t i c l e
i n f o
Article history: Available online 8 June 2012 Keywords: IFRS 20-F Liquidity International accounting
a b s t r a c t This paper examines the effects of the SEC’s 2008 decision to no longer require foreign private issuers using IFRS and trading on U.S. exchanges to reconcile their financial statements to U.S. GAAP. Extant research has found conflicting results using short event windows, while studies using longer event windows have found limited capital market impact from eliminating the reconciliation. Motivated by the SEC’s interest in understanding how disclosure rules impact market liquidity, we examine changes in effective bid-ask spreads, the price impact of trades, and quoted depth around 20-F filing dates for a sample of foreign private issuers. We find that effective spreads increase more around 20-F filing dates for filers using IFRS than for filers using U.S. GAAP, suggesting the 20-F report is more informative for filers using IFRS. We then find, in a subsample of filers using IFRS, that the increase in effective spreads for IFRS firms around 20-F filing dates is directly related to the magnitude of differences in book values between IFRS and U.S. GAAP. In sum, our results suggest a loss of useful information after the SEC’s rule change. Ó 2012 Elsevier Ltd. All rights reserved.
Introduction In early 2008 the Securities and Exchange Commission (SEC) issued a rule that allows foreign private issuers that use International Financial Reporting Standards (IFRS) to file financial statements without reconciliation to United States generally accepted accounting principles (U.S. GAAP).1 Previously, foreign private issuers using IFRS disclosed what their earnings and book values would have been under U.S. GAAP, and provided details on the accounting areas that generated the IFRS–U.S. GAAP differences. In com-
q Helpful comments from workshop participants at Seoul National University, Southern Illinois University-Carbondale, the 2009 Eastern Finance Association meeting, Dave Farber, Ayesha Malhotra, and Andriy Shkilko are appreciated. ⇑ Corresponding author. Tel.: +1 573 882 5939. E-mail address: [email protected] (K.W. Shaw). 1 This rule is effective for fiscal years ending after November 15, 2007. Form 20-F is the form that foreign private issuers file with the SEC. This form is due within six months after fiscal year-end.
1052-0457/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.racreg.2012.05.008
ment letters, proponents of the new rule asserted that IFRS were acceptable as high-quality accounting standards and reconciliation to U.S. GAAP was unnecessary. Dissenters argued that important differences remained between IFRS and U.S. GAAP, such that reconciliation to U.S. GAAP remained useful. In this paper we review research that examines the impact of the SEC rule change and present new empirical evidence on the impact of the rule change on market liquidity.
Extant evidence Here we summarize findings in related research on the capital market effects of eliminating the 20-F reconciliation. We group studies into those that examine short-term effects, over a small number of days around 20-F filings or earnings announcements, and those that examine more long-term effects using longer event windows. Chen and Sami (2012) study a sample of firms that use IFRS and reconcile to U.S. GAAP in 2005 and 2006 filings.
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The authors find that trading volume in the U.S. equity market increases in the days { 1, +1} around the 20-F filing date relative to trading volume in a benchmark nonannouncement period. These results are driven by firms with low levels of institutional holdings and first-time IFRS filers. In contrast, Jiang, Petroni, and Wang (2010) find no evidence of increased trading volume or stock return volatility over days { 1, +1}. Likewise, Jiang et al. (2010) find no evidence of changes in closing quoted bid-ask spreads in the period {+2, +10} around the 20-F date as compared to average bid-ask spreads over days { 10, 2}. Byard, Mashruwala, and Suh (2011) examine ‘‘information transfers’’ around quarterly earnings announcements for 36 foreign private issuers over January 2006 through December 2009. They examine how the release of 20-F information by a foreign private issuer impacts stock returns or trading volume of a nonannouncing, but otherwise similar U.S. GAAP firm. The authors find positive information transfer effects over their full sample period, but the effect drops dramatically after the reconciliation is no longer required.2 The above studies focus on fairly short time windows around 20-F filings. In contrast, Kim, Li, and Li (2012) examine longer time periods, namely, 90-day periods that begin one day before 20-F filing dates. Kim et al.’s (2012) sample includes 78 foreign private issuers that use IFRS in 2006 and 2007, with a control sample of 162 cross-listed firms that do not use IFRS. Kim et al. (2012) find no evidence that removing the 20-F reconciliation had any significant impact on cost of capital, the probability of informed trading, properties of analysts’ earnings forecasts, institutional ownership, stock price efficiency, or stock price synchronicity. Henry, Lin, and Yang (2009) study a sample of 75 crosslisted EU firms over 2004–2006. They find that while the magnitude of IFRS–U.S. GAAP book value and net income differences declined over their sample period, the difference in net income remained significant. Henry et al. (2009) also find pension and goodwill accounting to be the dominant reconciling items. However, both Henry et al. (2009), and Kim et al. (2012) find no relation between the 12-month stock returns after 20-F filings and IFRS–U.S. GAAP income or book value differences.3 Several observations emerge from a review of related research. First, capital market effects of removing the 20F reconciliation requirement, if any, are likely to manifest in short-time periods around important information events (such as 20-F filings and earnings announcements). This is consistent with research that suggests that bid-ask spreads and quoted depths change significantly around earnings announcements (Lee, Mucklow, & Ready, 1993) and management earnings forecasts (Coller & Yohn, 1997), but fairly quickly return to their preannouncement levels. This phenomenon, in conjunction with using a relatively long window around the 20-F date might explain the lack of results when using bid-ask spread changes in Jiang
2 Specifically, information transfers drop by 55% when using stock returns and to zero when using trading volume. 3 Henry et al. (2009) do find that market value, measured six months after the 20-filing, is significantly related to both earnings and book IFRSU.S. GAAP differences.
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(2010). Second, extant short-window studies provide conflicting evidence, particularly with respect to trading volume. Third, related research often combines days before and after the 20-F filing, making it difficult to obtain a clean identification of the impact of 20-F filings. Our study differs by examining measures (e.g. effective spreads and depths) motivated by theory (Diamond & Verrecchia, 1991; Kyle, 1985) that more precisely capture information effects than do alternative proxies such as trading volume. In addition, we separately study short time periods before and after 20-F filing dates. Research questions We examine two research questions: (1) Do changes in market liquidity around 20-F filing dates differ between foreign private issuers reporting under IFRS and those reporting under U.S. GAAP? (2) For foreign private issuers reporting under IFRS, do earnings and/or book value reconciliation amounts help explain changes in liquidity around 20-F dates? Our liquidity measures include effective bidask spreads, depths, and the price impact of trades, all computed from intra-day trade and quote data.4 From a policy standpoint, the SEC considers effective spreads to be ‘‘one of the most important measures of market quality’’ and uses this metric in its own analysis of trade execution cost.5 Ceteris paribus, lower effective spreads, higher depths, and lower price impact of trades imply greater market liquidity. Extant literature provides opposing predictions regarding changes in market liquidity after a 20-F filing. The disclosures made in 20-F filings could reduce the information advantage of informed traders, thus leveling the playing field between informed and uninformed investors (Lev, 1988). This line of reasoning suggests that changes in market liquidity after 20-F filings should not differ between IFRS and U.S. GAAP firms. In this case we also would not expect to find any relation between disclosed 20-F earnings and book value differences and changes in liquidity. In contrast, Kim and Verrecchia (1994) show that when an announcement is noisy and certain traders have superior ability in processing news, market liquidity will decrease after the announcement. If larger differences between IFRS and U.S. GAAP results require more expert interpretation, investors with superior processing skills might be better able to infer private information, and thus liquidity might decrease after the 20-F. In this case, we would expect to observe positive relations between the 4 The spread is the difference between the price at which specialists or market makers will buy shares (bid price) and the price at which they offer to sell shares (ask price). Depth is the quantity of shares the specialist or market maker is willing to trade at each of the posted bid and ask prices. A liquid market can be considered one in which investors can buy or sell the number of shares they want, when they want, at low transaction cost, and causing prices to move little as a direct result of their trades (low price impact). 5 Unlike quoted spreads, effective spreads are computed by comparing actual prices at which trades occur to quoted prices. Many trades occur within the quoted bid-ask spread, i.e. the effective cost to trade is lower than that quoted. See the SEC Execution cost analysis document at http:// www.sec.gov/spotlight/regnms/companalysis121504.pdf for a full discussion of the appropriate statistics to measure trade execution cost for investors.
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earnings or book value differences and changes in market liquidity measures around 20-F filings.
computed under U.S. GAAP (disclosed in a 20-F reconciliation table). These variables are scaled by the number of common shares outstanding.
Sample selection and descriptive statistics Control variables Sample selection An initial sample of 670 20-F filings in 2006 and 2007 is obtained from Chakrabarty and Shaw (2011). This sample resulted from applying data requirements to a list of 917 distinct foreign private issuers that filed 20-F reports with the SEC in 2006 (i.e., for the fiscal 2005 year-end).6 A total of 606 firm-years have necessary data to estimate changes models around 20-F filing dates in 2006 and 2007. This sample of 606 firm-year observations contains both IFRS filers and U.S. GAAP filers and is used to test our first research question.7 Our second research question focuses on the relevance of 20-F earnings and book value reconciling amounts to changes in market liquidity around 20-F dates. As U.S. GAAP filers do not reconcile their results to IFRS, the sample is reduced to the 203 IFRS firm-years for which we can collect earnings and book value reconciling amounts from IFRS to U.S. GAAP in 20-F reports. This sample is used to test our second research question.
Following prior research (e.g. Corwin & Lipson, 2000; Heflin & Shaw, 2000; Heflin, Shaw, & Wild, 2005) we control for other cross-sectional determinants of liquidity, including stock price per share, firm size, trade size, the average daily number of trades, the standard deviation of daily stock returns, and an indicator variable for stock exchange. To control for the possibility that differential enforcement of accounting rules across countries impacts our results, we include the rule of law variable from Kaufman, Kraay, and Mastruzzi (2008).9 We compute changes in those control variables (share price, trade size, and number of trades) that reasonably can be expected to change over short time periods; we compute these changes in a manner similar to that used for our dependent variables. For continuous variables that do not vary over short time periods (firm size and standard deviation of stock returns) we compute averages for each firmyear across all days with available data in that firm-year.10 Regression model
Research methods and results Dependent variables Appendix A lists our variables and their measurement.8 Our dependent variables include effective spread, the price impact of trades, average quoted depth and average dollar depth. For these tests we define the 20-F filing date per EDGAR as day 0, and days 20 through 11 relative to the 20-F filing date as a non-announcement reference period. For each firm-year and each liquidity measure, we examine the difference between the announcement day values on each of the days ( 2, 1) and (0, +1) and their nonannouncement period mean, scaled by the non-announcement mean. This yields the percentage abnormal change in liquidity on a given day. We then cumulate these percentage changes over days 2 through 1 (0 and + 1) to capture the change in market liquidity before (in response to) the 20-F filing. Test variables To test our first research question we read 20-F reports and code an independent variable to capture whether the 20-F filer uses U.S. GAAP or IFRS. This indicator variable, labeled IFRS, equals 1 (0) if the firm uses IFRS (U.S. GAAP). To test our second research question we compute differences between earnings and book values under IFRS (included in the 20-F financial statements) and their counterparts 6 See Chakrabarty and Shaw (2011) for a detailed discussion of the sample filtering procedures. 7 As the revised SEC reporting rules only apply to firms that use IFRS, we exclude firms which use forms of ‘‘local’’ GAAP from our analyses. 8 Refer to Appendix B for further details on collecting TAQ data.
We estimate regressions of each of our separate liquidity change variables on our test and control variables. For each liquidity measure we estimate these regressions over periods { 2, 1} relative to the 20-F filing date and {0, +1} relative to the 20-F filing date. Following related research we use logarithmic values of all continuous variables in our regressions. Significance tests are based on standard errors corrected for heteroscedasticity (Huber, 1967; White, 1980). Descriptive statistics Table 1 presents descriptive statistics on our full sample of 606 firm-years, computed using data from all days with available data in each firm-year. The typical (median) firm has an effective spread of 5.21 cents per share, depth of about $28,979, and experiences 258 trades per day, with an average trade size of 310 shares. Median firm size is $408 million with a median share price of $22.88. Consistent with evidence in Plumlee and Plumlee (2008) and Henry et al. (2009), our sample firms’ earnings are on average higher and book value lower under IFRS relative to their amounts under U.S. GAAP (untabulated, available upon request). Results Table 2 reports regression results. Over days {0, +1} relative to the 20-F filing date, effective spreads increase more for filers using IFRS than for filers using U.S. GAAP. 9
See Kaufman et al. (2008, p. 6). In untabulated analyses we obtain qualitatively similar results when all control variables are measured in levels. 10
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B. Chakrabarty, K.W. Shaw / Research in Accounting Regulation 24 (2012) 90–95 Table 1 Descriptive statistics. Variable
Mean
Std. Dev.
Q1
Median
Q3
Effective spread (cents per share) Depth (# of shares) Dollar depth ($) Price impact (cents per share) Price ($ per share) Size ($ millions) Trade size (# of shares) Number of trades (daily average) Standard deviation of stock return Rule of law
8.97 1798 39,336 3.51 32.17 2259 375 780 0.025 1.14
11.52 2123 36,388 5.52 31.52 5625 205 1230 0.013 0.72
3.36 713 9139 1.41 9.67 94 245 73 0.017 0.78
5.21 1132 28,979 1.87 22.88 408 310 258 0.022 1.38
9.33 1990 56,160 3.22 45.96 1773 434 973 0.028 1.73
Variables are defined in Appendix A. N = 606 firm-years.
Table 2 Regressions of changes in liquidity around days {0, +1} after 20-F filing dates on accounting type and controls. Variable
Full Sample of IFRS and U.S. GAAP filers (N = 606)
DEFFSPRD
DIMPACT
DDOLDEPTH
DIFF_E
0.050 0.079 0.121⁄⁄ ( 1.09) ( 0.89) ( 2.24) 0.251 1.584⁄⁄⁄ 1.010⁄⁄⁄ ( 1.28) ( 4.15) (4.34) 0.002 0.002 0.77 ( 0.77) ( 0.77) (0.44) 0.024 0.256⁄⁄ 0.247⁄⁄⁄ (0.45) (2.50) (3.96) 0.025 0.013 0.017 ( 1.55) ( 0.43) ( 0.85) ⁄ 1.554 0.411 3.682⁄⁄⁄ (1.70) ( 0.23) (3.39) 0.038 0.016 0.075⁄⁄ (1.54) (0.33) (2.55) 0.007 0.024 0.015 (0.45) (0.84) (0.90) ⁄⁄ 0.050 0.021 0.023 (2.00) (0.47) ( 0.85) n/a, not available for U.S. GAAP firms
DIFF_BV
n/a, not available for U.S. GAAP firms
Adjusted R2
0.012
Intercept
DPRICE SIZE
DTRDSIZ DNUMTRD SDRET EXCH RULE IFRS
0.031
0.065
Subsample of IFRS filers (N = 203)
DSHRDEPTH
DEFFSPRD
0.043 ( 0.87) 0.070 (0.33) 0.003 ( 1.11) 0.273⁄⁄⁄ (4.77) 0.022 ( 1.23) 1.280 (1.29) 0.057⁄⁄ (2.16) 0.012 (0.75) 0.037 ( 1.49)
0.285⁄⁄⁄ (2.79) 0.164 ( 0.71) 0.029⁄⁄ ( 2.31) 0.101 (1.06) 0.019 ( 1.10) 3.953⁄⁄ ( 2.51) 0.049 ( 1.07) 0.020 (1.42) n/a, IFRS filers
0.039
DIMPACT 0.262 (1.39) 1.601⁄⁄ ( 2.00) 0.023 ( 0.98) 0.101 ( 0.31) 0.009 (0.22) 4.285 ( 1.45) 0.008 (0.08) 0.068⁄ (1.76) only
DDOLDEPTH
DSHRDEPTH
0.122 ( 0.63) 0.599 (1.21) 0.015 (1.20) 0.295⁄ (1.73) 0.001 (0.02) 1.638 (0.55) 0.004 (0.06) 0.018 ( 0.65)
0.041 (0.38) 0.060 ( 0.15) 0.006 (0.67) 0.254 (1.62) 0.013 (0.38) 1.406 ( 0.71) 0.019 ( 0.38) 0.020 ( 0.86)
0.058 ( 1.34) 0.059⁄⁄ (2.41)
0.150 ( 1.38) 0.001 (0.02)
0.025 (0.49) 0.010 (0.40)
0.026 (0.53) 0.025 ( 1.38)
0.027
0.057
0.029
0.021
The table reports coefficient estimates and Huber–White robust heteroscedasticity-consistent t-statistics (in parentheses) from ordinary least squares regressions. t-Statistics are computed from observations clustered by country. DEFFSPRD, DIMPACT, DDOLDEPTH, DSHRDEPTH, DTRDSIZ, and DNUMTRD are measured as changes from days {0, +1} relative to a non-announcement period mean, computed over days { 20, 11} relative to the 20-F filing date, then scaled by the non-announcement period mean. Other variables are as defined in Appendix A. ,, denotes the coefficient estimate is different from zero at the 0.01, 0.05, or 0.10 significance level (two-tailed tests).
Specifically, the coefficient on the IFRS indicator variable in the regression with DEFFSPRD as the dependent variable is positive (a8 = 0.05) and significant at p < 0.05. Changes in the other liquidity measures we examine are not different between IFRS and U.S. GAAP. Likewise, in untabulated regressions we find no relations between liquidity changes and our IFRS variables in the period { 2, 1} before the 20F filing. The results thus far suggest that 20-F reports prepared under IFRS are more informative than those prepared under U.S. GAAP. We next examine whether a difference in the information content of 20-F’s between IFRS and U.S. GAAP users, namely the earnings and book value reconciliations, explains (in part) the difference in liquidity reactions across IFRS and U.S. GAAP users. Again, in untabulated regressions we find no relations between
liquidity changes and earnings or book value differences in the period { 2, 1} before the 20-F. However, we find that the increase in effective spreads after 20-F filings is positively related to the size of the difference in book value computed under IFRS versus U.S. GAAP. The coefficient on DIFF_BV equals 0.059 and it is significant at p < 0.05. Taken together, the results in Table 2 suggest effective spreads increase after 20-F filings for firms that use IFRS, due in part to significant differences in book values computed under IFRS and U.S. GAAP.
Concluding comments We examine how market liquidity changes around 20-F filings of foreign private issuers. We find that effective
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spreads increase more for filers using IFRS relative to those using U.S. GAAP. We also find that the increase in effective spreads for filers using IFRS is positively related to the magnitude of differences between IFRS and U.S. GAAP. Our results are consistent with a loss of information around 20-F filings after the SEC’s rule eliminating reconciliation between IFRS and U.S. GAAP for foreign private issuers. In a similar vein, Christensen, Lee, and Walker (2009) find that reconciliations from UK GAAP to IFRS convey value-relevant information due to an impact on debt contracting. While our focus is on IFRS to U.S. GAAP reconciliations, Christensen et al.’s (2009) results, in conjunction with our results, suggest the relevance of reconciliations across accounting regimes to capital market participants. Our findings are also consistent with evidence in Yu (2011). Yu (2011) finds that after elimination of the reconciliation requirement, IFRS firms significantly increase their level of voluntary disclosures in annual reports and earnings announcements. Yu (2011) further finds that these voluntary disclosures often pertain to the previously disclosed reconciling items. This suggests that foreign private issuers increased their voluntary disclosures to mitigate the loss of information after the SEC no longer mandated disclosure of reconciliation information. Appendix A. Variable definitions
Variable
Abbreviation Measurement
Dependent variables Effective spread EFFSPRD (cents per share)
Depth (# of shares)
SHRDEPTH
Dollar depth ($) DOLDEPTH Price impact (cents per share)
Test variables Filer uses IFRS
IMPACT
IFRS
Twice the absolute difference between a trade’s price and the quote midpoint in effect at the time of the trade Number of shares quoted at the best ask price plus number of shares quoted at the best bid price SHRDEPTH times bidask quote midpoint Absolute value of the difference in bid-ask quote midpoint, measured from the standing quote before a trade and the quote mid-point 10 minutes after a trade An indicator which equals 1 for firms that use IFRS, and 0 for firms that use U.S.
Appendix A (continued) Variable
Abbreviation Measurement GAAP
20-F earnings DIFF_E reconciliation (per share)
Difference between earnings per IFRS and U.S. GAAP, scaled by the number of outstanding shares
20-F book value DIFF_BV reconciliation (per share)
Difference between total shareholders’ equity per IFRS and U.S. GAAP, scaled by the number of outstanding shares
Control variables Share price ($ per share) Trade size (# of shares) Number of trades Firm size ($ million)
PRICE TRDSIZ NUMTRD SIZE
SDRET
Standard deviation of daily stock returns Exchange
EXCH
Rule of law
RULE
Bid-ask quote midpoint Average daily number of shares traded Average daily number of trades Number of shares outstanding times share price, both measured at the beginning of the year Standard deviation of daily stock return, including dividends, from CRSP Primary exchange on which shares are listed. An indicator which equals 1 for NYSE or AMEX and 0 for NASDAQ. Country-level measure of the rule of law, based on survey evidence in Kaufman et al. (2008). This variable ranges from 2.5 to +2.5. Higher values indicate better enforcement.
Data to compute dependent variables are from the 2006– 2007 Trades and Quotes (TAQ) database. All control variables except for SDRET and RULE are also computed using TAQ data. SDRET is computed using the 2006–2007 CRSP daily stock return file. RULE is obtained from Kaufman et al. (2008). EFFSPRD, SHRDEPTH, DOLDEPTH, IMPACT, TRDSIZ, and NUMTRD are computed for each firm for each trading day within the year, and then averaged by firm over all that firm’s trading days in that year with available data. Test variables (IFRS, DIFF_E and DIFF_BV) are from form 20-F reports filed with the Securities Exchange
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Commission during 2006–2007. By definition, DIFF_E and DIFF_BV are not available for 20-F filers that use U.S. GAAP.
Appendix B. Data cleaning procedure for the NYSE Trade and Quote (TAQ) database We apply the following filters to clean the trade and quote data: We use trades and quotes from regular-hours trading only. We use only trades for which TAQ’s CORR field is equal to zero, one, or two and for which the COND field is either blank or equal to @, E, F, I, J, or K. We eliminate trades with non-positive prices or quantities, with prices more than 150% or less than 50% of the previous trade price. We use only quotes for which TAQ’s MODE field is equal to 1, 2, 6, 10, 12, 21, 22, 23, 24, 25, or 26. We eliminate quotes with non-positive price or size, with bid price greater than ask price, when the quoted spread is greater than 50% of the quote midpoint, or when the ask price is more than 150% of the bid price. We calculate all spreads two ways: (i) using trades and quotes from all exchanges; and (ii) using trades and quotes from NYSE, ARCA, and Nasdaq (these three exchanges account for over 98% of the trading volume in our sample stocks during our sample period). Reported results are based on spreads calculated using method (ii) above to avoid issues related to stale quotes.
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