We examine stock return performance of Chinese family-firms vs. Nonfamily-firms, and the impact on family-firm returns of firm size, having a founder as CEO, and levels of family ownership. We model returns with the Capital Asset Pricing Model (CAPM), the Fama-French 3-factor model, and a new 5-factor model we modified from Miralles-Marcelo, Miralles-Quirós, which adds factors for debt and illiquidity. In previous studies, though the prevalence is for family-firm outperformance, the results have been mixed. Using CAPM or the Fama-French 3-factor model, we find that family-firms outperform nonfamily-firms, but with the 5-factor model that outperformance vanishes, and this is robust to controlling for the GFC. For the founder CEO, firm size and family ownership considerations, again previous research has been mixed. With CAPM and Fama-French 3-factor models, we find that founder CEOs outperform nonfounder, large firms outperform small firms, and higher family ownership outperforms lower ownership. However, with the 5-factor model, only size remains as having significant impact. This study helps to reconcile conflicting results in previous research.
Since 1978, China’s economy has undergone a huge transformation from being a planned economy to a market-oriented economy. According to the Chinese Family Business Survey and Analysis Report (2014), this market-oriented economy has encouraged development of a free market and the number of private firms increased dramatically between 2009 and 2015, from 6.64 million to 70 million. Furthermore, the percentage of family-owned firms in the Chinese Shanghai-A-share market increased from 22% to 32% between 2007 and 2016. Expectations are that the number of listed and non-listed private enterprise companies will continue to increase in the future. This was emphasized in the October 2017, 19th Communist Party Congress meeting. Chairman Xi stated that China will push ahead with market-oriented reforms, and that the government will support the development of private firms and stimulate the vitality of all types of market entities [
There are two main motivations for why family firms might outperform non-family firms. First, according to behavioral theory regarding the ownership and management of family and non-family-owned firms, family-owned firms control higher cash flow rights than non-family-owned firms. This is driven by singular leadership and sustainable development [
Secondly, based on agency theory, the separation of ownership and control in public firms can result in managers pursuing their own interests at the expense of shareholders’ interests [
There have been quite a few family vs. non-family studies done in various countries, both within and outside of China. In general, the results are quite mixed due to different sample countries, different time-periods, and different methodologies in particular return generation models.
It is the purpose of this paper to reconcile some of these results, with a common time frame that includes the GFC, incorporates control variables that have been used in other studies, and compare across three different methodologies. To our knowledge, this is the first study to be similarly comprehensive. The main issues we address are: Family firms vs. Non-family performance, the impact of firm size, the impact of the CEO being the founder, and the impact of the level of family share ownership.
Most of the studies find that family-run firms outperform non-family firms. [
As to size, the results are quite mixed. Isakov and Weisskopf [
These and other studies have found a few other interesting effects with respect to social awareness and risk. Dyer and Whetten [
Within China, the studies are similarly mixed in results. Ding, Zhang [
Chinese family-owned firms may also be influenced by religious belief. Du [
Cai, Luo [
Agency costs have been addressed in some of the studies. Chang, Wu [
A study by Cao, Cumming [
From all of these studies, it is clear there is a wide variety of findings. Some of this disparity is due to different regions, but even in the Chinese studies there is ambiguity. Some of the studies have included the GFC, some have controlled for firm size, but others have not (shown by Xie and Qu [
Anderson and Reeb [
Chang, Wu [
In this study, we define a firm as family-owned whenever a founder and/or family members control at least 51% of voting rights and 10% of ownership rights.
The data used in this research is from firms listed on the Chinese shanghai-A share market obtained from the CSMAR’s database, with monthly data from December 2006 to November 2016. We require at least 24 months of returns, and that a firm is consistently either family or non-family during our sample period. Following Barontini and Caprio [
As noted above, family-owned firms have higher ownership by family members and most have singular leadership [
Hypothesis 1: Family-owned firms outperform non-family-owned firms.
The owners of family-owned firms, particularly founder-CEOs, treat their firms’ wealth as a symbol of personal wealth [
Hypothesis 2: Founder-CEOs of family-owned firms outperform non-founder-CEOs of family-owned firms.
Of the studies we have reviewed on the Chinese stock market, most find that larger-size has a positive effect on a firms’ performance. The expectation is that larger family-owned firms have higher stock performance than smaller family-owned firms. Therefore, the third hypothesis is:
Hypothesis 3: Bigger family-owned firms outperform smaller family-owned firms.
Family-owned firms show stronger performance when family owners have high levels of ownership. This is because family members in such firms have the incentive to make optimal development strategies for the long-term [
Hypothesis 4: Family-owned firms with high levels of family member ownership outperform family-owned firms with low levels of ownership.
Many empirical studies of the U.S., European and other markets have used the Capital Asset Pricing Model (CAPM) and the Fama-French Three-Factor model (e.g. Fama and French [
Miralles-Marcelo, Miralles-Quirós [
1To conserve space we do not report it here, but we also ran time series regressions using these models on portfolios, in a similar vein to 2. Miralles-Marcelo JL, Miralles-Quirós MdM, Lisboa I. The stock performance of family firms in the Portuguese market. Applied Financial Economics, 2013; 23(22): 1721-32. Results are consistent with the panel data conclusions. Our results are available on request.
To examine our hypotheses, we compare outperformance using three models: CAPM, the Fama-French 3-factor model, and our new 5-factor model. We use panel data methodology to allow investigation into individual securities, rather than potentially inducing bias with portfolios of firms stratified in some way1. We employ this with random firm effects, as random effects models can include time invariant variables, and in our sample the family or nonfamily designation is time invariant; with fixed firm effects these variables are absorbed by the intercept.
As noted earlier, we use three different models to examine the question of whether family firms outperform non-family―CAPM, Fama-French 3-Factor, New 5-Factor.
The Capital Asset Pricing Model (CAPM)
r i , t = α i N o n F a m + α i F a m D i F a m + β i R m , t + ε i , t (1)
Fama-French Three-Factor Model
r i , t = α i N o n F a m + α i F a m D i F a m + β i R m , t + s i S M B t + h i H M L t + ε ′ i , t (2)
New Five-Factor Model
r i , t = α i N o n F a m + α i F a m D i F a m + β i R m , t + s i S M B t + h i H M L t + δ i D e b t i , t + λ i I l l i q i , t + ε ″ i , t (3)
where:
rit: Rit - Rft is the excess stock return at time t,
Rm,t: Rm,t - Rft is the excess market return at time t,
SMBt is the difference between returns on small- and large-stocks with approximately the same weighted-average book-to-market equity at time t,
HMLt is the difference between returns on high- and low-BE/ME stock with approximately the same weighted-average size at time t [
Debtit is the total debt/total market value at time t,
Illiqit is the ratio of absolute stock return divided by its volume at time t [
D i F a m is a dummy variable which equals 1 when the firm is a family-owned firm and otherwise 0, and
α i F a m is the risk-adjusted return difference between family and nonfamily.
Our next step is to examine the issues posed in hypotheses 2-4. For this we replace the family dummy in the above regressions with the appropriate dummy, as in: Founder-CEO (=1 if CEO is the founder), Size (=1 if firm size greater than median), Ownership (=1 if family owns more than 50% of shares).
r i , t = α i Other + α i F a m ( Founder/Size/Ownership ) D i F a m ( Founder/Size/Ownership ) + β i R m , t + ε i , t (4)
r i , t = α i Other + α i F a m ( Founder/Size/Ownership ) D i F a m ( Founder/Size/Ownership ) + β i R m , t + s i S M B t + h i H M L t + ε ′ i , t (5)
r i , t = α i Other + α i F a m ( Founder/Size/Ownership ) D i F a m ( Founder/Size/Ownership ) + β i R m , t + s i S M B t + h i H M L t + δ i D e b t i , t + λ i I l l i q i , t + ε ″ i , t (6)
Then α i F a m ( Founder/Size/Ownership ) is the risk-adjusted return difference between these family firms and other family firms.
For each of equations above, we add a dummy for GFC (=1 for years 2008-2010) and an interaction term for family and GFC. The GFC dummy is a control; the interaction term, Fam*GFC, will reveal how family firms contribute during the GFC.
Number of firms | Excess return | Capitalization | |||||||
---|---|---|---|---|---|---|---|---|---|
Year | Fam | NFam | Fam | NFam | Difference | t-statistic | Fam | NFam | Difference |
2007 | 184 | 633 | 0.03 | 0.03 | −0.0003 | −0.06 | 4.18 | 12.20 | −8.03 |
2008 | 182 | 640 | −0.12 | −0.12 | −0.004 | −0.78 | 3.81 | 17.13 | −13.32 |
2009 | 183 | 645 | 0.04 | 0.03 | 0.01*** | 2.90 | 3.86 | 14.40 | −10.54 |
2010 | 184 | 660 | −0.03 | −0.04 | 0.006** | 2.04 | 5.78 | 16.62 | −10.84 |
2011 | 214 | 666 | −0.07 | −0.08 | 0.005** | 2.13 | 6.99 | 16.79 | −9.80 |
2012 | 230 | 677 | −0.08 | −0.08 | −0.0004 | −0.13 | 5.72 | 13.53 | −7.81 |
2013 | 231 | 675 | −0.03 | −0.03 | 0.002 | 0.87 | 6.51 | 13.16 | −6.65 |
2014 | 246 | 688 | −0.03 | −0.03 | 0.002 | 0.75 | 7.75 | 13.93 | −6.18 |
2015 | 322 | 700 | 0.01 | −0.01 | 0.02*** | 4.95 | 12.95 | 24.62 | −11.68 |
2016 | 322 | 698 | −0.04 | −0.04 | 0.001 | 0.39 | 12.12 | 20.61 | −8.49 |
Mean | 229.8 | 668.2 | −0.03 | −0.04 | 0.004 | 6.97 | 16.30 | −9.33 |
Notes: *, ** and *** denote significance at the 5% and 1% levels at a two-tail test, respectively. For each year, this table presents the number of family-owned and non-family-owned firms included in the sample, the mean excess return for each group of firms, and their differences. The last row presents the mean values over the sample period. Capitalization units are CNY billion.
Panel A: descriptive statistics | |||||
---|---|---|---|---|---|
Rm | SMB | HML | Debt | Illiquidity | |
Mean | 0.01 | 0.01 | 0.002 | 0.60 | −21.43 |
Minimum | −0.25 | −0.14 | −0.16 | 0.01 | −29.32 |
25th Percentile | −0.05 | −0.01 | −0.03 | 0.14 | −22.37 |
Median | 0.01 | 0.02 | 0.002 | 0.32 | −21.33 |
75th Percentile | 0.06 | 0.05 | 0.02 | 0.72 | −20.40 |
Maximum | 0.27 | 0.13 | 0.18 | 4.37 | −11.57 |
SD | 0.09 | 0.05 | 0.04 | 0.77 | 1.53 |
Skewness | −0.26 | −0.51 | 0.32 | 2.68 | −0.36 |
Kurtosis | 0.83 | 0.77 | 3.65 | 8.26 | 0.52 |
Panel B: correlation coefficients | |||||
Rm | SMB | HML | Debt | Illiquidity | |
Rm | 1.00 | ||||
SMB | 0.02 | 1.00 | |||
HML | 0.19 | −0.37 | 1.00 | ||
Debt | −0.03 | −0.02 | 0.00 | 1.00 | |
Illiquidity | −0.12 | 0.00 | 0.02 | −0.17 | 1.00 |
Panel A presents descriptive statistics, namely mean, median, minimum, SD and skewness and kurtosis, for all five control variables in this research: the market risk factor, the size (SMB), book-to-market (HML), leverage factor (Debt), and aggregate market illiquidity (Illiquidity). Panel B demonstrates the correlation coefficients matrix for all five control variables.
study: market excess return, size, book-to-market ratio, debt ratio and illiquidity. Panel A shows the descriptive statistics of these five factors. Due to the small illiquidity values, this research has used the natural log instead of raw illiquidity. Panel B presents the correlation coefficients between these five control variables.
The first test is to determine in broad terms if family firms outperform nonfamily firms.
For all categories, coefficients on the control variables are as expected, and consistent with previous research (e.g. Xie and Qu [
Large family-owned firms outperform small family firms with all 3 models (
Intercept | Fam | Rm | SMB | HML | Debt | Illiquidity | Adj. R2 | |
---|---|---|---|---|---|---|---|---|
Panel A. CAPM Model | ||||||||
Coefficient | −0.04*** | 0.006*** | 1.02*** | 36.14% | ||||
t-stat. | (−98.85) | (6.27) | (241.28) | |||||
Panel B. Fama-French Three-Factor Model | ||||||||
Coefficient | −0.06*** | 0.005*** | 1.02*** | 1.01*** | −0.17*** | 47.70% | ||
t-stat. | (−139.03) | (6.69) | (260.99) | (131.45) | (−17.95) | |||
Panel C. New Five-Factor Model | ||||||||
Coefficient | 0.11*** | 0.0005 | 1.05*** | 1.01*** | −0.2*** | −0.005*** | 0.008*** | 49.95% |
t-stat. | (22.25) | (0.62) | (269.68) | (133.33) | (−21.36) | (−10.02) | (33.50) |
Notes: *, ** and *** denote significance at the10%, 5% and 1% levels at a two-tail test, respectively. This table reports the panel data regression results on firm performance, using three models. In all cases, the dependent variable is firm return excess of the risk-free rate, Fam is a dummy variable = 1 if the firm is family owned, Rm is market excess return, SMB and HML are the size and growth/value factors for the Fama-French 3-factor model, Debt is total debt divided by the total assets, and Illiquidity is the average return divided by total monthly volume (both at market level). There are 103,410 observations over the period 12/2006 to 11/2016.
Intercept | FdrCEO | Rm | SMB | HML | Debt | Illiquidity | Adj. R2 | |
---|---|---|---|---|---|---|---|---|
Panel A. CAPM Model | ||||||||
Coefficient | −0.04*** | 0.004** | 1.01*** | 32.19% | ||||
t-stat. | (−28.29) | (2.25) | (110.70) | |||||
Panel B. Fama-French Three-Factor Model | ||||||||
Coefficient | −0.06*** | 0.004** | 1.01*** | 1.12*** | −0.39*** | 47.47% | ||
t-stat. | (−43.28) | (2.19) | (123.31) | (68.04) | (−20.07) | |||
Panel C. New Five-Factor Model | ||||||||
Coefficient | 0.12*** | 0.002 | 1.04*** | 1.11*** | −0.43*** | −0.01*** | 0.008*** | 50.90% |
t-stat. | (12.02) | (1.32) | (130.40) | (70.08) | (−22.82) | (−7.68) | (17.35) |
Notes:*, ** and *** denote significance at the 10%, 5% and 1% levels at a two-tail test, respectively. This table reports the panel data regression results on firm performance, using three models. In all cases, the dependent variable is firm return excess of the risk-free rate, FdrCEO is a dummy variable = 1 if the CEO is also the founder, Rm is market excess return, SMB and HML are the size and growth/value factors for the Fama-French 3-factor model, Debt is total debt divided by the total assets, and Illiquidity is the average return divided by total monthly volume (both at market level). There are 25,905 observations over the period 12/2006 to 11/2016.
Intercept | BigFam | Rm | SMB | HML | Debt | Illiquidity | Adj. R2 | |
---|---|---|---|---|---|---|---|---|
Panel A. CAPM Model | ||||||||
Coefficient | −0.05*** | 0.02*** | 1.01*** | 32.47% | ||||
t-stat. | (−42.61) | (10.65) | (110.96) | |||||
Panel B. Fama-French Three-Factor Model | ||||||||
Coefficient | −0.06*** | 0.01*** | 1.01*** | 1.12*** | −0.39*** | 47.64% | ||
t-stat. | (−61.17) | (9.41) | (123.50) | (68.14) | (−19.90) | |||
Panel C. New Five-Factor Model | ||||||||
Coefficient | 0.15*** | 0.02*** | 1.04*** | 1.10*** | −0.43*** | −0.008*** | 0.01*** | 51.24% |
t-stat. | (14.8) | (13.29) | (131.1) | (70.11) | (−22.73) | (−6.86) | (20.37) |
Notes: *, ** and *** denote significance at the 10%, 5% and 1% levels at a two-tail test, respectively. This table reports the panel data regression results on firm performance, using three models. In all cases, the dependent variable is firm return excess of the risk-free rate, BigFam is a dummy variable = 1 if the firm size is greater than the median of all family firms, Rm is market excess return, SMB and HML are the size and growth/value factors for the Fama-French 3-factor model, Debt is total debt divided by the total assets, and Illiquidity is the average return divided by total monthly volume (both at market level). There are 25,905 observations over the period 12/2006 to 11/2016.
also positive and significant for CAPM and the 3-factor model, but not for the 5-factor model (
In terms of our hypotheses, the results are a bit mixed. For hypothesis 1, that family firms outperform nonfamily firms, for CAPM and the 3-factor model, we cannot reject. However, with the 5-factor model we find no significant evidence that family firms outperform. For hypothesis 2, that family firms with a founder CEO outperform other family firms, we cannot reject that family firms outperform under CAPM or the 3-factor model, but again with the 5-factor model we find no significant evidence that firms with founder CEOs outperform. For
Intercept | HiOwner | Rm | SMB | HML | Debt | Illiquidity | Adj. R2 | |
---|---|---|---|---|---|---|---|---|
Panel A. CAPM Model | ||||||||
Coefficient | −0.04*** | 0.005** | 1.01*** | 32.19% | ||||
t-stat. | (−44.16) | (2.18) | (110.70) | |||||
Panel B. Fama-French Three-Factor Model | ||||||||
Coefficient | −0.05*** | 0.004** | 1.01*** | 1.12*** | −0.39*** | 47.47% | ||
t-stat. | (−66.78) | (2.03) | (123.31) | (68.04) | (−20.07) | |||
Panel C. New Five-Factor Model | ||||||||
Coefficient | 0.12*** | 0.001 | 1.04*** | 1.11*** | −0.43*** | −0.01*** | 0.008*** | 50.90% |
t-stat. | (12.12) | (0.63) | (130.62) | (70.20) | (−22.86) | (−7.74) | (17.30) |
Notes: *, ** and *** denote significance at the 10%, 5% and 1% levels at a two-tail test, respectively. This table reports the panel data regression results on firm performance, using three models. In all cases, the dependent variable is firm return excess of the risk-free rate, HiOwner is a dummy variable = 1 if the family share ownership is greater than 50%, Rm is market excess return, SMB and HML are the size and growth/value factors for the Fama-French 3-factor model, Debt is total debt divided by the total assets, and Illiquidity is the average return divided by total monthly volume (both at market level). There are 25,905 observations over the period 12/2006 to 11/2016.
hypothesis 3, that large family firms outperform small family firms, we cannot reject under all 3 models. For hypothesis 4, that higher ownership leads to outperformance, under CAPM and the 3-factor model the ownership impact is significantly positive, but under the 5-factor model it is not significant.
Lipiec [
In nearly all cases, for both the models and categories considered, we find results largely similar to those that do not consider the GFC. As before, the 5-factor model suggests that Debt and Illiquidity are important, but now after addressing the GFC, we reject hypotheses 1, 2 & 4 under the 5-factor model. However for Size, we still find that large family firms outperform small family firms, even after addressing the GFC, with a coefficient of 2%, significant at the 1% level.
We examine monthly returns of family and nonfamily firms on the Shanghai Class-A shares exchange, over the period Dec. 2007 to June 2016, using 3 return generation models: the CAPM, the Fama-French 3-Factor model, and a new model adapted from a model by Miralles-Marcelo, Miralles-Quirós [
We find that family-owned firms significantly outperform nonfamily-owned firms under the CAPM and Fama-French models, but not under the new Five-Factor model, suggesting that illiquidity exposure accounts for most of that outperformance. As family firms on average are nearly 1/3 the size of nonfamily firms, this makes sense.
Interceptor | Fam | Rm | SMB | HML | Debt | Illiquidity | GFC | Fam * GFC | Adj. R2 | |
---|---|---|---|---|---|---|---|---|---|---|
Panel A. CAPM Model | ||||||||||
Coefficient | −0.047*** | 0.007*** | 1.03*** | 0.012*** | −0.003 | 36.24% | ||||
t-stat. | (−89.44) | (6.58) | (241.62) | (11.88) | (−1.38) | |||||
Panel B. Fama-French Three-Factor Model | ||||||||||
Coefficient | −0.06*** | 0.006*** | 1.02*** | 1.01*** | −0.17*** | −0.002** | −0.002 | 47.71% | ||
t-stat. | (−117.09) | (6.14) | (258.85) | (131.31) | (−18.10) | (−2.13) | (−0.98) | |||
Panel C. New Five-Factor Model | ||||||||||
Coefficient | 0.11*** | −0.0001 | 1.04*** | 1.01*** | −0.20*** | −0.005*** | 0.008*** | −0.005*** | 0.001 | 49.96% |
t-stat. | (22.86) | (−0.10) | (267.44) | (133.47) | (−21.73) | (−10.65) | (33.89) | (−5.53) | (0.60) |
Notes: *, ** and *** denote significance at the 10%, 5% and 1% levels at a two-tail test, respectively. This table, similar to
Intercept | FdrCEO | Rm | SMB | HML | Debt | Illiquidity | GFC | Fam * GFC | Adj. R2 | |
---|---|---|---|---|---|---|---|---|---|---|
Panel A. CAPM Model | ||||||||||
Coefficient | −0.04*** | 0.006*** | 1.02*** | 0.01*** | −0.004 | 32.24% | ||||
t-stat. | (−25.85) | (2.72) | (110.68) | (3.62) | (−1.09) | |||||
Panel B. Fama-French Three-Factor Model | ||||||||||
Coefficient | −0.05*** | 0.004** | 1.01*** | 1.13*** | −0.40*** | −0.006** | −0.002 | 47.51% | ||
t-stat. | (−35.65) | (1.96) | (122.06) | (68.19) | (−20.27) | (−2.11) | (−0.61) | |||
Panel C. New Five-Factor Model | ||||||||||
Coefficient | 0.13*** | 0.001 | 1.04*** | 1.11*** | −0.44*** | −0.01*** | 0.008*** | −0.009*** | 0.002 | 50.94% |
t-stat. | (12.55) | (0.51) | (129.25) | (70.29) | (−23.08) | (−7.83) | (17.73) | (−3.46) | (0.69) |
Notes: *, ** and *** donate significance at the10%, 5% and 1% levels at two-tall test, respectively. This table, similar to
Intercept | BigFam | Rm | SMB | HML | Debt | Illiquidity | GFC | Fam * GFC | Adj. R2 | |
---|---|---|---|---|---|---|---|---|---|---|
Panel A. CAPM Model | ||||||||||
Coefficient | −0.05*** | 0.02*** | 1.02*** | 0.01*** | −0.01 | 32.60% | ||||
t-stat. | (−38.60) | (11.26) | (111.05) | (6.57) | (−1.35) | |||||
Panel B. Fama-French Three-Factor Model | ||||||||||
Coefficient | −0.06*** | 0.01*** | 1.01*** | 1.12*** | −0.39*** | −0.005*** | 0.003 | 47.65% | ||
t-stat. | (−49.30) | (7.44) | (122.41) | (68.13) | (−20.01) | (−2.49) | (0.71) | |||
Panel C. New Five-Factor Model | ||||||||||
Coefficient | 0.15*** | 0.02*** | 1.04*** | 1.11*** | −0.43*** | −0.01*** | 0.01*** | −0.005 | 0.005 | 51.25% |
t-stat. | (14.99) | (10.74) | (130.13) | (70.16) | (−22.84) | (−6.90) | (20.44) | (−2.64) | (1.35) |
Notes: *, ** and *** denote significance at the10%, 5% and 1% levels at a two-tail test, respectively. This table, similar to
Intercept | HiOwner | Rm | SMB | HML | Debt | Illiquidity | GFC | Fam * GFC | Adj. R2 | |
---|---|---|---|---|---|---|---|---|---|---|
Panel A. CAPM Model | ||||||||||
Coefficient | −0.04*** | 0.008*** | 1.02*** | 0.01*** | 0.01 | 32.25% | ||||
t-stat. | (−40.39) | (2.95) | (110.69) | (4.91) | (1.62) | |||||
Panel B. Fama-French Three-Factor Model | ||||||||||
Coefficient | −0.05*** | 0.005** | 1.01*** | 1.13*** | −0.40*** | −0.007*** | 0.007 | 47.51% | ||
t-stat. | (−56.38) | (2.09) | (122.07) | (68.19) | (−20.27) | (−3.71) | (1.25) | |||
Panel C. New Five-Factor Model | ||||||||||
Coefficient | 0.13*** | 0.001 | 1.04*** | 1.11*** | −0.44*** | −0.01*** | 0.008*** | −0.007*** | 0.005 | 50.94% |
t-stat. | (12.12) | (0.63) | (129.26) | (70.29) | (−23.08) | (−7.83) | (17.65) | (−4.37) | (0.90) |
Notes: *, ** and *** denote significance at the 10%, 5% and 1% levels at a two-tail test, respectively. This table, similar to
For the “within family firms” tests, larger firms outperform small firms with all three models. However, the CEO being a firm founder and family ownership level only have significant (and positive) impact under the CAPM and 3-factor models, not in the 5-factor model, again implying a strong impact from debt and illiquidity.
The GFC does not have much impact on our results regarding family and nonfamily firms. The GFC itself was of course significant, but at the margin, the impact of family during the GFC was not significant. For CAPM and the 3-factor model, we find results similar to before considering the GFC, but for the 5-factor model, only size has a significant effect.
Overall, our findings―particularly for the CAPM and Fama-French 3-factor models―corroborate the bulk of previous research, which shows that family firms outperform nonfamily firms, and that founder CEO’s, large firms, and high ownership all play a part in outperformance. However, our addition of the 5-factor model helps to explain some of the contrary findings of other research, showing that size is the dominating factor in family firm outperformance.
Zhang, C.F. and Gregory-Allen, R.B. (2018) Family-Owned Firms and Stock Returns: Evidence from the Chinese Stock Market. Theoretical Economics Letters, 8, 1332-1347. https://doi.org/10.4236/tel.2018.87086