EVIDENCE FROM THE FINANCE INDUSTRY
2. Literature review
Two strands of research are related to assets impairment. One examines market reactions to the announcement of the asset impairment loss. Another strand of research investigates the motivations of firms recognizing an impairment loss. Empirical findings are mixed in the first strand. For example, Francis, Hanna, and Vincent (1996) find that announcement of an impairment loss conveys information about decreases in economic values of assets. However, Strong and Meyer (1987) find negative average cumulative abnormal returns around the impairment announcement date, but the negative returns are reversed in six days after the impairment announcement. Finally, some studies do not find market reactions to be significant (Hogan & Jeter, 1998; Zucca & Campbell, 1992).
Subsequent studies have attempted to improve research design to resolve the inconsistent findings.
Bunsis (1997) partitions the write-offs based on how the events underlying the write-off are expected to affect cash flows: decreasing, increasing or no effect on future cash flows. The results show that the market reacts negatively (positively) to events that are expected to decrease (increase) expected future cash flows. Alciatore, Easton, and Spear (2000) examine the case of the fall in oil and gas prices in late 1985 and early 1986. They find no significant correlation between the write-down amounts and contemporaneous returns because write-downs tend to be reported after the associated decline in share prices-- the market already knew at least some of the information implicit in the write-down amounts.
Collins and Henning (2004) argue that many studies document associations between poor earnings performance for the firm as a whole and subsequent write-downs of only certain assets of the firm, which is indirect evidence. They examine the context of write-downs accompanying segment divestitures.
Results show that the write-down magnitude is strongly associated with segment earnings declines, with earlier declines in segment earnings more
heavily weighted in write-down measurement than more recent declines.
The second line of research focuses on the motivations of firms that recognize an impairment loss. Francis, Hanna, and Vincent (1996) examine factors driving write-down decisions during 1989-1992 that predate SFAS No.121 and find that the decisions can be accounted for by both impairment and manipulation factors. Loh and Tan (2002) examine firm-specific and macro-economic factors that are likely to influence the asset write-off decision in Singapore. They find that macroeconomic factors, such as unemployment rate, GDP growth rate and occupancy rate of properties, and firm-specific factors, including profitability and a change of board chairman, are the determinants. Elliott and Shaw (1988) and Strong and Meyer (1987) indicate that write-off decision is associated with manager‟s incentives.
Rees, Gill, and Gore (1996) find that abnormal accruals in the year of the asset write-down are significantly negative; however, the abnormal accruals in the write-down year do not reverse in subsequent years, suggesting that the firms have experienced a permanent shift in their accrual balances in the write-down year. The authors argue that managers provide credible signals to investors regarding future firm performance. Riedl (2004) contrasts the characteristics of write-offs reported prior versus subsequent to the issuance of SFAS No.121. Empirical results reveal that economic factors have a weaker association with write-offs relative to that between “big bath” reporting behavior and write-offs. In addition, this “big bath” reporting behavior more likely reflects opportunistic reporting by managers than the provision of their private information. Overall, the results suggest that the reporting quality of write-offs under SFAS No.121 has decreased.
Empirical studies related to assets impairment in Taiwan include Hsieh and Wu (2005) and Chao (2006). Hsieh and Wu (2005) investigate determinants of the timing and the amount of assets impairment decisions of Taiwan‟s SFAS No. 35. They also examine market reactions to impairment announcement. Empirical results show that determinants for early adopters include taking a “big bath” (the reporting motivation) and factors reflecting the accrual-based and cash flow-based recoverability of long-lived assets (operational motivations). The amount of an impairment loss is associated with only reporting motivations (the taking a “big bath” purpose, the income smoothing purpose, and the change in top management) for early adopters. For non-early adopters, the amounts of assets impairment are associated with not only the reporting (income smoothing) but also operational motivations. Market reactions to announcements of an impairment loss reveal that the stock market reacts significantly and negatively to fourth-quarter impairment loss. In addition, the stock market does not react significantly to first-quarter impairment loss. Chao (2006) has a similar finding.
--- Insert Table 5 about Here ---
We further plot the mean and median values of board size over nine years around large board size changes in Figure 2.
--- Insert Figure 2 about Here --- Both the results in Table 5 and Figure 2 indicate that firm value does not improve substantially following large board structure changes. We employ Wilcoxon Sign test of Tobin‟s Q values for five selected years, Year 0 vs. Year -3, Year 0 vs. Year -1, Year +1 vs. Year -1, Year +3 vs. Year -1, and Year +3 vs. Year -1, and find no significant evidence that firm value changes following large board expansions or reductions. The one-way ANOVA test of Tobin‟s Q across all nine years from Year -3 to Year +5 provides further evidence that both large decreases and increases in board size impose an insignificant impact on the value of the firm.
Additionally, we follow Yermack (1996) to use three additional financial variables (return on assets, sales over assets, and return on sales) to measure firm performance to conduct a robustness test of the results.
Similarly, no significant evidence has been found that these financial ratios are significantly improved following large board size movements. This may provide some evidence supporting Ning, Davidson, and Wang (2007), who argue that firms are motivated by more than just firm-value maximization when selecting board size given the trade-off of the likely costs and benefits associated with small and large boards. A firm tends to adjust its board size to the target board size zone which is influenced by various industry and firm characteristics.
5. Conclusions
Several studies have been done regarding the statistical inverse relation of firm value and the number of directors on the firm‟s board (Yermack, 1996; Gertner & Kaplan, 1996; Eisenberg, Sundgren,
& Wells, 1998; Denis & Sarin, 1999; Ning, Davidson,
& Wang, 2007), but few studies investigate the impact of large changes in board size, defined as an increase or decrease at least three directors at one time, on the firm‟s board composition, CEO characteristics, ownership structure, firm characteristics, and firm value. Our study intends to fill this void using a randomly-selected sample of 88 companies with large changes of board size from 1988 to 1999 to explore the issue.
First, we find strong evidence that large changes in board size are persistent movements rather than temporary changes, which is consistent with Denis and Sarin (1999). However, we also find evidence of small reversals following large board size decreases and significant reversal after large increases in board size, which is different from their findings.
Second, we explore the changes of board structure, CEO, ownership, and firm characteristics around large board size changes, and find strong evidence that the number of directors of all types (inside, affiliated, and independent) moves in the same direction as the movements of board size. Large board size changes provide a good opportunity for a firm to improve its board structure through increasing board independence and make board members younger. Empirical evidence also shows that large changes in board size are associated with more board meetings and committees, a greater probability of CEO turnovers, the higher presence of a new CEO whose tenure is less than 3 years, and large increases in total assets. These findings provide further evidence supporting Yermack (1996) and Denis and Sarin (1999). They argue that large changes in board size often result from a firm‟s fundamental changes in business conditions, large changes in ownership structure, CEO transitions, or assets restructurings.
Finally, we delve into the issue of short-term and long-term effects of large changes of board size on firm value and firm performance. After examining nine years‟ Tobin‟s Q values from Year -3 to Year 5, we conclude that both large decreases and large increases in board size do not seem to add (or destroy) firm value measured by Tobin‟s Q. Further analysis of three financial ratios (return on assets, sales over assets, and return on sales) draw a similar conclusion.
Firms may be motivated by more than just firm value-maximization when selecting board size. They consider the trade-off of benefits and costs associated with large boards (Ning, Davidson, & Wang, 2007).
They have incentives to move their board size towards an optimal board size zone over time.
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Figure 1: The Permanence of Large Changes in Board Size
0 2 4 6 8 10 12 14 16
Year -1 Year 0 Year +1 Year +2 Year +3
Number of Directors
Mean (Change ≥ 3) Median (Change ≥ 3) Mean (Change ≤ -3) Median (Change ≤ -3)
Figure 2: The Effetcs of Large Changes in Board Size on Firm Value
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
Year -3 Year -2 Year -1 Year 0 Year 1 Year 2 Year 3 Year 4 Year 5
Tobin Q
Mean (Change ≥ 3) Median (Change ≥ 3) Mean (Change ≤ -3) Median (Change ≤ -3)
Table 1. Yearly Distribution of Large Changes in Board Size from 1988~1999
We first identify all CRSP-listed firms on January 1, 1988, and obtain a total of 7086 firms. Second, we randomly choose 2000 firms from this list. To make sure at there is at least one-year of board data for each firm, we delete the 209 firms that disappeared from CRSP in 1988. Then from the remaining 1791 firms, we proportionally select 26.5% of firms from each surviving-year category to construct the sample. The sample consists of 3858 firm-years over a 12-year period from 1988 to 1999. Following Denis and Sarin (1999), we define large changes in board size as either an increase or a decrease of at least three directors at one time, and identify a final sample of 88 firms with large changes in board size at one time.
Period # of firms with large increase in board size
# of firms with large decreases in board size
Total
1988 ~ 1989 4 5 9
1989 ~ 1990 8 6 14
1990 ~ 1991 3 8 11
1991 ~ 1992 2 4 6
1992 ~ 1993 4 5 9
1993 ~ 1994 0 5 5
1994 ~ 1995 1 7 8
1995 ~ 1996 3 4 7
1996 ~ 1997 3 3 6
1997 ~ 1998 2 4 6
1998 ~ 1999 3 4 7
Total 32 56 88
Table 2. The Permanence of Large Changes in Board Size
The final sample consists of 88 large changes (32 large increases, and 56 large decreases) in board size over the 12-year period from 1988 to 1999. Following Denis and Sarin (1999), we define large changes in board size as either an increase or a decrease of at least three directors at one time.
Large increases in board size Large decreases in board size Panel A: Summary Statistics
Mean Median S.D. Mean Median S.D.
Year -1 10.72 10.00 4.81 15.21 15.00 4.33
Year 0 14.88 13.50 5.99 11.61 11.00 4.10
Year +1 13.55 12.00 4.52 11.43 10.00 4.13
Year +2 12.25 12.00 4.82 12.31 12.00 3.44
Year +3 12.89 12.00 4.94 11.97 12.00 3.11
Panel B: Tests of Board Size Changes Mean difference
t statistic Mean
difference
t statistic
Year 0 Vs. Year -1 4.16 (9.59)*** -3.60 (-23.37)***
Year +1 Vs. Year -1 2.68 (9.09)*** -3.64 (-11.54)***
Year +2 Vs. Year -1 1.83 (5.61)*** -3.43 (-9.16)***
Year +3 Vs. Year -1 2.04 (4.58)*** -3.55 (-8.10)***
Year +1 Vs. Year 0 -1.48 (-2.91)** -0.18 (-0.07)
Year +2 Vs. Year 0 -1.90 (-4.73)*** 0.17 (0.59)
Year +3 Vs. Year 0 -1.74 (-3.94)*** 0.03 (0.09)
***, **, and *denote significance at the 0.01, 0.05, and 0.10 levels, respectively.
Table 3. Board of Directors and CEO Characteristics around Large Changes in Board Size
The final sample consists of 88 large changes in board size over the 12-year period from 1988 to 1999. We define large changes in board size as either an increase or a decrease of at least three directors at one time. CEO duality (= 1, if CEO is the chairman simultaneously); CEO is founder (= 1, if CEO is the founder of the company); CEO succession (= 1, if CEO succession occurs in the year); Presence of new CEO (= 1, if CEO tenure is 3 years or less), and CEO involvement in director selection (= 1, if CEO is involved in director selection). We define that CEO is involved in director selection if the board has a nominating committee and CEO serves in the committee, or if the board has no nominating committee (Shivdasani & Yermack, 1999).
Large increases in board size Large decreases in board size
Before-change
After-change
Changes t-statistic Before-change
After-change
Changes t-statistic
Panel A: Board Of Director
Board meeting 7.00 7.87 0.87 1.26 7.81 8.62 0.81 1.90*
Number of inside directors
2.19 2.90 0.71 3.93*** 3.39 2.48 -0.91 -4.26***
Number of affiliated directors
1.26 1.61 0.35 1.78* 1.57 1.07 -0.50 -3.47***
Number of independent directors
7.45 10.58 3.13 6.09*** 10.27 8.09 -2.18 -10.11***
Percent of inside directors 21.15 19.83 -1.32 -1.04 22.80 22.57 -0.23 -0.17 Percent of affiliated
directors
11.88 11.43 -0.45 -0.28 10.47 9.44 -1.03 -0.85
Percent of independent directors
67.03 68.73 1.70 0.98 66.66 68.15 1.49 1.11
% of directors (age ≥ 62 years)
43.45 35.60 -7.85 -3.17*** 41.06 38.32 -2.74 -1.81*
Number of board committees
3.50 3.66 0.16 1.72* 3.91 3.95 0.04 0.47
Panel B: CEO Characteristics
CEO age 54.69 53.78 -0.91 -0.69 55.93 55.04 -0.89 -0.93
CEO duality 75.00 62.50 -12.50 -1.68* 62.50 71.43 8.93 1.53
CEO is founder 25.00 17.85 -7.15 -1.44 10.00 10.00 0.00 --
CEO ownership (%) 8.49 9.88 1.39 0.51 4.91 3.99 -0.92 -1.20
CEO involved in director selection
66.00 66.00 0.00 -- 53.57 51.79 -1.80 -0.57
CEO succession 9.38 28.13 18.80 1.98* 16.07 19.64 3.60 0.47
Presence of new CEO 21.88 40.63 18.75 1.98* 32.14 44.64 12.50 2.18*
***, **, and *denote significance at the 0.01, 0.05, and 0.10 levels, respectively.
Table 4. Ownership Structure and Firm-specific Characteristics around Large Changes in Board Size
The final sample consists of 88 large changes in board size over the 12-year period from 1988 to 1999. We define large changes in board size as either an increase or a decrease of at least three directors at one time.
Ownership data are obtained from annual corporate proxy statement, and financial data come from COMPUSTAT database.
Large increases in board size Large decreases in board size
Before-change
After-change
Changes
t-statistic
Before-change
After-change
Changes
t-statistic Panel A: Ownership
Structure Ownership by officers/directors
16.84 18.21 1.37 1.00 10.92 10.02 -0.90 -1.71*
Percent of block holdings
29.90 30.68 0.78 0.30 23.71 22.89 -0.82 -0.83
Panel B: Firm Characteristics
Total assets ($M) 12,276.43 13,672.97 1,396.54 2.24** 10,387.58 13,176.24 2,788.66 1.14
Return on Assets (%) 1.97 0.59 -1.38 -0.85 1.04 -0.17 -1.21 -1.18
Market-to-book ratio 2.27 2.23 -0.04 -0.17 2.41 1.53 -0.88 -1.35
1-year stock return (%) 16.94 10.03 -6.91 -0.80 8.66 3.85 -4.81 0.64
Total debt to capitalization (%)
68.58 59.47 -9.11 -1.13 65.23 17.09 -48.14 -0.95
PP&E to total assets (%) 26.02 26.78 0.76 0.70 33.59 34.06 0.47 0.74
***, **, and *denote significance at the 0.01, 0.05, and 0.10 levels, respectively.
Table 5. The Effects of Large Changes of Board Size on Tobin‟s Q
The final sample consists of 88 large changes in board size over the 12-year period from 1988 to 1999. We define large changes in board size as either an increase or a decrease of at least three directors at one time.
Tobin‟s Q is calculated based on Chung and Pruitt (1994).We calculate Tobin‟s Q as the sum of the market value of equity and the book value of debt divided by the book value of assets. The book value of debt is the difference between the book value of assets and the book value of equity.
Large increases in board size Large decreases in board size Panel A: Descriptive Statistics
Mean Median S.D. Mean Median S.D.
Year -3 1.39329 1.21973 0.61471 1.50056 1.13655 0.86051
Year -2 1.43582 1.20547 0.66442 1.44846 1.14453 0.79635
Year -1 1.46069 1.16235 0.78866 1.50757 1.19587 0.90703
Year 0 1.47616 1.15601 0.99455 1.44892 1.17010 0.68914
Year +1 1.51649 1.25313 0.73005 1.47374 1.20965 0.77888
Year +2 1.49241 1.24656 0.87458 1.47744 1.21714 0.71161
Year +3 1.51513 1.20426 0.84079 1.41066 1.25662 0.49861
Year +4 1.35670 1.20266 0.50962 1.42946 1.21054 0.51992
Year +5 1.38915 1.17183 0.55720 1.49570 1.20107 0.66691
Panel B: Wilcoxon test for the Selected Years Wilcoxon
Z-score
P value Wilcoxon
Z-score
P value
Year 0 Vs. Year -3 0.296 0.767 -1.206 0.228
Year 0 Vs. Year -1 0.205 0.838 -0.307 0.759
Year +1 Vs. Year -1 0.577 0.564 -0.939 0.347
Year +3 Vs. Year -1 0.094 0.925 -0.277 0.782
Year +5 Vs. Year -1 -0.448 0.654 -0.854 0.393
Panel C: One-way ANOVA test for All Years
F statistic P value F statistic P value
Year -3 to Year +5 0.142 0.997 0.076 0.999