Firm’s quality increases and the cross-section of stock returns: Evidence from China

Journal Pre-proof Firm's Quality Increases and the Cross-Section of Stock Returns: Evidence from China

Libo Yin, Huiyi Liao PII:

S1059-0560(19)30828-7

DOI:

https://doi.org/10.1016/j.iref.2019.12.001

Reference:

REVECO 1875

To appear in:

International Review of Economics and Finance

Received Date:

08 September 2019

Accepted Date:

02 December 2019

Please cite this article as: Libo Yin, Huiyi Liao, Firm's Quality Increases and the Cross-Section of Stock Returns: Evidence from China, International Review of Economics and Finance (2019), https://doi.org/10.1016/j.iref.2019.12.001

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Journal Pre-proof

Firm's Quality Increases and the Cross-Section of Stock Returns: Evidence from China Author information: Libo Yin, Corresponding author. Associate Professor, School of Finance, Central University of Finance and Economics Address: 39South College Road, Haidian District, Beijing, 100081, China. Email: [email protected]. Huiyi Liao. Ph.D. Candidate, School of Finance, Central University of Finance and Economics

Acknowledgement This research is financially supported by the National Natural Science Foundation of China under projects No. 71671193, Program for Innovation Research in Central University of Finance and Economics, and the "Young talents" Support Program in Central University of Finance and Economics (QYP1901).

Journal Pre-proof

Firm's Quality Increases and the Cross-Section of Stock Returns: Evidence from China Abstract: Using a quality increase factor from a dynamic perspective to capture the changes in the quality of firms over a period of time, we explore the impact of firm’s quality increases on the crosssection of stock returns. The empirical results show that there is a significant positive quality increase premium in Chinese stock market; i.e. the firms with high quality increases generate higher returns than those with low quality increases. Quality increases have both short-term and long-term predictability on stock future returns. Moreover, the predictive information contained in the quality increases is not subsumed by the quality level, or other well-known firm characteristics and risks. We further show that underreaction caused by the positive feedback trading and investor’s negligence contributes substantially to this premium, which is consistent with the implications of irrational mispricing rather than rational pricing based on risk compensation. Keywords: Quality; Quality increases premium; Chinese stock market; Cross-section of stock returns; Mispricing

1. Introduction Legendary investors1 such as Benjamin Graham, Warren Buffett and Charlie Munger pay great attention to quality of firms when making investment decisions (Frazzini, Kabiller and Pedersen, 2018; Lee, 2014). Of the seven “quality and quantity criteria” that Graham suggested a firm should meet for inclusion in an investor’s portfolio, five screens of them attempt to ensure that one buys only high quality firms, while the other two ensure that one buys them only at reasonable prices. Practitioners now are increasingly gravitating to this style as a target for factor indexes, MSCI, FTSE Russell, Standard & Poor’s, and Deutsche Bank, among others.

1

Berkshire Hathaway has realized a Sharpe ratio of 0.76, higher than any other stock or mutual fund

with a history of more than 30 years. The standard academic factors that capture the market, size, value, and momentum premiums cannot explain Buffett’s performance, making his success a mystery (Martin and Puthenpurackal, 2008). 1

Journal Pre-proof Unlike standard factors such as value and size who have clear and accepted definitions, no standard definition for the quality factor has been agreed on for researchers (Hsu, Kalesnik and Kose, 2019). Moreover, although an extensive literature is dedicated to a few specific facets of quality, little rigorous empirical analysis has been conducted to explore the overall strength of the quality performance, as well as the explanation for the quality premium from a theoretical standpoint. In this paper, we aim to examine this issue in light of some of the latest research on the determinants of returns. Recently, Asness, Frazzini and Pedersen (2019) provide a tractable valuation model that shows how stock prices increase in their quality characteristics: profitability, growth, and safety. Empirically, they show that the quality-minus-junk factor earns significant risk-adjusted returns in the United States and across 24 developed countries. Frazzini, Kabiller and Pedersen (2018) use a quantitative investment method to reproduce Buffett's performance and affirm the important explanatory power of the firm’s quality to Buffett's value investment strategy, indicating that the strategy of longing high-quality stocks can reflect the Buffett's concept of value investment. In this paper, we contribute to these two pioneers by showing that the current level of a firm’s quality cannot give a full picture of the firm’s prospects for future quality and stock returns, and investors should care much more about the recent trajectory of a firm’s quality. According to its definition, the level of quality, integrating the market valuation and the fundamental of firm itself, can help investors judge whether a company is good or not in terms of profitability, safety, growth, and payout in the context of the current competitive environment. However, the overall competitive environment in which they operate is not static. As time goes by, firms have ups and downs in their performance due to swings in their own competitive strength as does the overall competitive environment. For example, strong earnings in one quarter do not necessarily suggest that the firm’s profitability is strong, but consistent strong earnings do (Loh and Warachka, 2012). Huang (2009) use standard deviation of cash flow over a period of time to measure the firm’s cash flow volatility and shows the firms with high cash flow volatility yield lower future stock returns than those with low cash flow volatility. According to Campbell, Giglio, Polk and Turley (2018), if firm profits suffer from cash flow shocks, they are likely to be permanent in the sense that rational investors have no reason to expect the stock price to rebound to previous levels. The recent path of quality, however, can reveal the struggles and successes that a firm has encountered in arriving at its current 2

Journal Pre-proof level of quality, and the change in this path may shed additional valuable light on the prospects for future quality and stock returns. The following example illustrates our motivation intuitively. Consider a famous firm in China stock market, Gui Zhou Mao Tai. Its net profit growth rate in the third quarter of 2017 was significantly higher than that in the third quarter of 2016 (60.31%, 9.11%, respectively)2. Then its stock price rose significantly during the fourth quarter of 2017 (from 517 yuan at the end of September 2017 to 697 yuan at the end of December 2017). Surprisingly, during the fourth quarter of 2018, its stock price performed weakly (from 730 yuan at the end of September 2018 to 590 yuan at the end of December 2018) when its net profit growth rate in the third quarter of 2018 substantially underperformed than that in the third quarter of 2017(23.77%, 60.31%, respectively). Although Gui Zhou Mao Tai as a firm is typically a very good investment target, as long as the current net profit growth rate was lower than that in the same period last year, the future stock price would plunge, and vice versa. Therefore, we conjecture that changes of quality during a certain period of time will contain incremental predictive information for future stock returns, beyond the level of quality at the end of the same period. Specifically, the quality level compares the quality of different companies in the cross-section, while the quality increase compares the same company’s current quality with the past from the time dimension. Our empirical analysis proceeds in four steps. Firstly, we examine the quality increase premium in Chinese stock market by testing whether quality increase can predict future stock returns. Following Asness, Frazzini and Pedersen (2019), we calculate the firm’s quality based on the firm’s profitability, growth, safety and payout. On this basis, we construct the quality increase by subtracting the quality level of the same period last year from the current quality level. A positive value of quality increase means that the firm has recently experienced an increase in quality level. By single sort, we find the firms with the high quality increase yield higher returns than those with the low quality increase. A hedge portfolio which long stocks with the highest quality increase and short stocks with the lowest quality increase can generate an annual return about 15.6%. This positive predictive power of quality increase for future returns is robust when well-known factors which influence stock returns are controlled in double sort and Fama-Macbeth regression.

2

The data comes from the website: http://data.eastmoney.com/bbsj/600519.html. 3

Journal Pre-proof Secondly, we further explore the source of the quality increases premium by testing whether the premium is caused by rational pricing based on risk compensation theory or irrational mispricing. Particularly, following Daniel and Titman (1997), we examine whether the stock returns can be predicted by the loading of mimicking portfolios. If premium stems from rational pricing, the relation between the quality increase and stock returns should reflect compensation for some form of risk associated with the quality increase and the factor loading can predict stock returns positively and significantly. We find that empirical evidence support the argument about mispricing, rather than risk factors-based explanations. Thirdly, we examine whether behavioral mispricing associated with overreaction or underreaction dominates the premium. Specifically, we build overlapping hedge portfolios with different holding periods to test whether the returns reversal happens in the long run. Empirical evidence suggests that quality increases premium is attributed to the investors’ underreaction as no reversal appears in the long run. Because a large number of retail investors who are prone to behavioral biases gather in Chinese market, we infer that the irrational behaviors, or rather, behavioral biases of investors, may be the deeper source of underreaction to quality increases. Thus combined with the typical characteristics of Chinese market, we concentrate on two behavioral biases: the positive feedback trading and investor’s negligence, which are widely discussed in both practice and academic studies. Empirical evidence confirms their important explanation power on underreaction to quality increases. Lastly, we conduct a large number of additional tests to check the robustness of our findings: 1) we adopt different measures of quality to check the sensitivity of our results to the measures of quality and empirical results show that our finding still hold under alternative measures of quality; 2) we explore whether quality increases factor is redundant by regressing mimicking portfolio of quality increases on the Fama-French five factor and q-factor model and we find that the quality increases premium cannot be subsumed by these models; 3) we use Fama-MacBeth regression to test whether quality increases have long-term predictive power on stock future returns and the tests suggest that the quality increases have a long-term positive predictability at least five years; 4) we wonder whether the quality increases possess predictive information of returns beyond the quality level, and results show that even controlling the quality level, the firm’s quality increases can also predict subsequent stock returns positively; 5) we conduct one-way sorting analysis to explore how 4

Journal Pre-proof the market reacts when investors are faced with the increases or decreases of firm’s quality and the analysis show that the quality increases premium is asymmetric and the investors underreact to decreases of firm’s quality more than the increases of firm’s quality. The contribution of this paper can be summarized as follows. Firstly, existing studies only focus on the quality level at a certain point in time from a static perspective (Asness, Frazzini and Pedersen, 2019; Frazzini, Kabiller and Pedersen, 2018) while our work takes quality change during some period into account from a dynamic view. The firm’s quality increase which measures changes in quality over a certain period of time contains predictive power for stock returns beyond the quality level. This result indicates that in order to evaluate the intrinsic value of the company more comprehensively, investors should paying attention to both the quality level and the quality change path. Our paper also relates to a large literature on pricing factors. Voluminous studies have designed to exploit some specific facet of the quality factor and established some of them is a significant determinant of future stock returns, such as profitability (Ball, Gerakos, Linnainmaa, and Nikolaev, 2015; Fama and French, 2015, 2018; Hou, Xue, and Zhang, 2015, 2018; Jiang, Qi and Tang, 2016; Novy-Marx, 2013), safety (Ang et al., 2006; Frazzini and Pedersen, 2014), growth (Cooper, Gulen and Schill, 2008; Fama and French, 2016; Hou, Xue, and Zhang, 2015) and payout (Ap Gwilym, Seaton, Suddason, Thomas, 2006; Boudoukh, Michaely, Richardson and Roberts, 2007; Pontiff and Woodgate, 2008). Our paper illustrates a unifying theme, and provides comprehensive evidence about the outperformance of high quality increases stocks. Secondly, our work fulfills the gap in evidence of quality premium for emerging markets. China has been the second-largest stock market in the world. However, the different political and economic environments from those in the US and other developed economies, as well as the separated market and investors from the rest of the world determine Chinese mainland stock market has its unique features. The advisability of applying the conclusions coming from U.S. and other developed stock markets to Chinese market is questionable, even is absolutely different when replicating the investigation which documented in US (Cheema and Nartea, 2017; Hsu et al., 2018; Liu, Stambaugh and Yuan, 2019). Therefore an additional exploration is necessary for gaining a better understanding of quality effect in Chinese stock market. Thirdly, none of literature provide solid explanation for the quality premium. Taking profitability which tend to capture most of the quality return premium (Hsu, Kalesnik and Kose, 5

Journal Pre-proof 2019) as an example, the profitability effect can be potentially attributed to both rational explanation based on q-theory with investment frictions and behavioral mispricing-based explanation (Hou et al., 2015; Lam et al., 2016; Wang and Yu, 2013). We therefore provide a powerful test for the competing theories in exploring the sources of the quality premium. The findings have important policy implications because understanding what causes the quality increase premium provides the possible remedies to resume the quality premium and let the capital flow to its most productive uses. Our work also contributes to the growing asset pricing literature on the Chinese market. The Chinese stock market has long been criticized as speculative, lacking a strong link between equity valuation and fundamentals (Fong and Toh, 2014; Gu, Kang and Xu, 2018; Ng and Wu, 2007). Surprisingly, we find that quality increase can predict future stock returns positively and this premium can attribute to irrational mispricing. These findings provide some enlightenment for understanding the asset pricing mechanism of Chinese stock market and promoting the effectiveness of Chinese market. The rest of this paper is organized as follows. Section 2 discusses the data and how to calculate the indicator of quality increases. Section 3 examines the predictive power of quality increases for stock returns by portfolio tests and Fama-MacBeth regressions. Section 4 explores the source of quality increases premium. Section 5 conducts several test to check the robustness of quality increases premium. Section 6 draws conclusion.

2. Data and variable definitions 2.1 Data Our sample includes all common shares for Chinese A-share stocks. The accounting data and stock returns come from the China Stock Market and Accounting Research Database (CSMAR). We compute the Fama-French three factors data in Chinese market according to the standard construction process in Fama and French (1993). According to the common sample screening procedures, we exclude the data within one month of listing for each stock. We also exclude financial firms which have large market capitalization and different balance sheet structures from nonfinancial firms. Besides, we exclude the firms with special treatment (st) which are distressed and have different price limits from normal stocks. Because the information quality of accounting statements before 2001 is not convincible 6

Journal Pre-proof enough (Carpenter, Lu and Whitelaw, 2015) and quarterly accounting data before 2002 is unavailable, sample period of our empirical test spans from January 2002 to March 2019. Since some measures are computed based on the previous two-year data, the actual analysis interval is from September 2004 to March 2019. We use single quarterly data which comes from quarterly reports from the first quarter of 2002 to the third quarter of 2018. In order to avoid the look-ahead bias, we use the quarterly data to construct relevant indicators at the end of April, August and October respectively. 2.2 Construction of quality score According to the evaluation of the company's intrinsic value in Asness, Frazzini and Pedersen (2019) and combined with the requirements of rational investment that our target companies should exhibit good performance, low risk and stable growth, we use four sub-factors (profitability, growth, safety and payout) to build quality score. In order to make the results convincible, we have adopted multiple proxy indicators for each sub-factor. See Table 1 for the specific method of construction. [Insert Table 1 Here] After completing the calculation of each proxy indicators we rank each indicators and calculate their z-score (

rank-mean of rank ) to reduce the impact of outliers and allocate equal weight standard deviation of rank

to each indicator, following Asness, Frazzini and Pedersen (2019). After that, we sum scores of corresponding proxy indicators and calculate the z-score of these sums to obtain the score of quality’s sub-factors. For example, profitability  z  z  ROE   z  ROA   z  GPOA   z  GMAR   and the calculations of other sub-factors are similar. Finally, the quality score is calculated according to the formula: quality  z  profitability  growth  payout  safety  . 2.3 Construction of quality increase To avoid the autocorrelation and seasonal effect in the quality score, we calculate the quality changes relative to the same quarter last year to obtain the quality increase each quarter. Specifically, the quality increase at the end of the quarter q of year t equals the quality at the end of the quarter q of year t minus the quality at the end of the quarter q of year (t-1). 2.4 Control variables We control various well-known firm characteristics which have been proved to influence stock 7

Journal Pre-proof returns, including market risk (Frazzini and Pedersen, 2014; Hsu et al., 2018), size (Wang and Xu, 2004; Carpenter et al., 2015), B/M ratio (Huang et al., 2013; Hilliard and Zhang, 2015), operating profitability (Fama and French, 2015), asset growth (Titman et al., 2004, Wang et al., 2015), maximum daily returns (Bali et al., 2011), momentum (Jegadeesh and Titman, 1993), idiosyncratic volatility (Ang et al., 2006; Gu, et al., 2018), turnover (Jiang et al., 2017) and illiquid (Amihud, 2002). The control variables are defined in Table 2. [Insert Table 2 Here] 2.5 Summary statistic Table 3 presents the summary statistics and the correlations between quality increases and other variables. In Panel A, the mean and medium of quality increases are both -0.02, which are roughly close to 0, showing that firms with positive quality increases almost offset the firms with negative quality increases. Additionally, there is a significant variation in quality increases, ranging from 3.13 to 3.05, indicating that there is a tremendous cross-sectional difference in quality increases across firms. The mean and standard deviation of quality is 0 and 1 respectively because quality is standardized in the building process. In Panel B, the correlation between quality increases and size is close to 0, at -0.01. Similarly, the correlation between quality increases and B/M ratio is also very small in magnitude, only at 0.00. Therefore, quality increases may not be related to size or B/M ratio. In addition, one important note is that the correlation between quality increase and quality is 0.50, indicating that the level of quality may have important bearing on whether quality has been increasing or decreasing. [Insert Table 3 Here]

3. The predictive power of quality increase to stock returns In this section, we explore whether quality increases possess the predictive power on future stock returns. We start our analysis by one-way sorting portfolio approach. Then we test whether the correlations between quality increases and stock returns exist stably when some firm’s characteristics which can influence stock future returns are controlled in two-way sorting analysis. Further, using all individual firms without imposing breakpoints, we conduct the Fama-MacBeth regression to check the robustness of the predictability of quality increases on stock future returns.

8

Journal Pre-proof 3.1 One-Way Sorting Analysis In the univariate sorting analysis, we sort the stocks into deciles based on quality increase at the end of April, August and October each year, which makes us avoid look-ahead bias. Each decile portfolio is held until the next adjustment and the monthly portfolio returns are value weighted. We obtain the three-factor adjusted returns (FF3 alpha) and the factor loadings by regressing portfolios returns on Fama-French three factors. The results are shown in Table 4. [Insert Table 4 Here] From the results of the second column of Table 4, it can be seen that there is a significant positive correlation between the stock returns and the quality increase. The firms with high quality increase outperforms those with low quality increase (1.75%, 0.45%, respectively). And the hedge portfolio which is long high quality increase firms and short low quality increase firms earns an average value weighted monthly returns of 1.30% with the t statistic of 5.36. The third column of Table 4 shows that three factor alphas increase almost monotonically as quality increase increases, which indicate the above positive relationship cannot be explained by the Fama-French three-factor model. The average risk-adjusted return of hedge portfolio is 1.27% per month, which is statistically significant at 1% level as well. There are no monotonic patterns in factor loadings, which is consistent with the finding in the analysis of summary statistics that there is no obvious relationship between quality increases and market capitalization (B/M ratio). In addition, the three-factor adjusted return of the decile with lowest quality increases is -1.01%, whereas the adjusted return of the decile with highest quality increases is only 0.26%, indicating that quality increases premium stems from the short leg rather than long leg. This finding implies the mispricing may be the source of the premium because of the heavy short constraints in Chinese market. In other words, the stocks with lowest quality increases may be overpriced severely by investors and this overpricing cannot be eliminated by arbitragers owing to short constraints, thus these stocks generate very low future returns. Besides, if mispricing is real source of the quality increases premium, the high premium from short leg will indicate that investors may not react in time when they are faced with the decrease in firm’s quality. In summary, quality increases appear to be a strong and positive predictor of future crosssectional stock returns in the Chinese stock market, with the raw profitability premium up to 1.30% per month. In addition, this premium mainly stems from the short leg. 9

Journal Pre-proof 3.2 Two-Way Sorting Analysis In this subsection, we use the two-way dependent sorting portfolio approach to examine whether the quality increase effect prevails when we control the firm characteristics which have been shown to influence stock returns, including market risk, size, B/M ratio, operating profitability and asset growth3. Specifically, we sort all stocks into quintiles based on firm characteristics at the end of April, August, and October every year. Then within each group, we further sort stocks into quintiles by the quality increases. Thus we obtain 25 portfolios and calculate their value-weighted monthly returns. In addition, in order to check whether the quality increases effect is related to the firm characteristics, we subtract the hedge portfolio returns in the quintile with low firm characteristics from those in the quintile with high firm characteristics (see the results for the last row of each panel). Table 5 shows the risk adjusted returns by Fama and French three factor model (FF3 α, in percentage) of the two-way sorting portfolios controlling for size, B/M ratio and operating profitability. [Insert Table 5 Here] In Panel A and Panel B in Table 5, hedge portfolios results suggest that quality increases effect remains statistically significant within almost all size or B/M groups (except the group with lowest BM ratio), indicating that the predictive information contained in quality increases still remains after controlling size or B/M ratio. For example, in panel A, the long-short hedge portfolio earns an average monthly risk-adjusted return of 0.54% in small size group and 0.83% in big size group. Both returns are statistically significant at 1% level and similar results are also shown in other size groups (hedge portfolio returns are significant with 0.80%, 0.95%, 1.22%, respectively). In addition, quality increases effect did not show a significant linear correlation with size or B/M ratio based on the results of the last row of each panel. For example, in Panel B, the difference between hedge portfolio returns in highest B/M ratio group and hedge portfolio returns in lowest B/M ratio group

3

To save space, we only report the results about controlling size, B/M ratio and operating

profitability. When market risk and asset growth are controlled, quality increases premium is still statistically and economically significant. The results about controlling market risk and asset growth are available upon request. 10

Journal Pre-proof is 0.20% per month with the t statistic of 0.40. These results support the previous finding that quality increases are not correlated with size or B/M ratio. Since profitability is an important sub-factor of quality, we are concerned about whether quality increases premium disappears when we control firm’s profitability. In Panel C, the quality increases premium is prevalent across firms with all profitability levels, indicating that quality increase provides incremental predictive information about future stock returns beyond profitability. In other words, the other sub-factors play important roles in quality premium. Additionally, the difference between hedge portfolio returns in highest profitability group and hedge portfolio returns in lowest profitability group is 0.40% per month with the t statistic of 1.10, which is indistinguishable from 0. Thus quality increases effect is not correlated with profitability either. In short, the predictability of quality increases on stock returns cannot be subsumed by other firm characteristics such as size, B/M ratio and profitability. 3.3 Fama-MacBeth Regression The portfolio approach analysis above can only capture the impact of limited firm characteristics on stock returns. Quality increases may be associated with several firm characteristics potentially. Therefore, to further test the robustness of predictability of quality increases, we use Fama-MacBeth regression to extend portfolio approach analysis by regressing future stock returns on more firm characteristics which have been confirmed to have predictive ability for returns in this section. Our regression model is set as follows: ri ,t 1   0  1del _ qualityi ,t   2 betai ,t   3 sizei ,t   4 bmi ,t   5 opi ,t   6 invi ,t   7 maxi ,t  8 momi ,t   9 ivoli ,t  10 turni ,t  11illidi ,t   i ,t 1

,

(1)

where ri ,t 1 is the future return for firm i in month t  1 , del _ qualityi ,t is the key variable measuring quality increase. The rests are all control variables and the construction can be found in the Table 2. We estimate the coefficients according to the model above each month. After obtaining the coefficients time series, we calculate the mean coefficients and the Newey-West t-statistics. The results are shown in Table 6. [Insert Table 6 Here] In the first column of Table 6, the univariate regression result suggests that quality increases 11

Journal Pre-proof have a positive predictive power on future stock returns. The regression coefficient on the quality increase is 0.0031 with the t-statistics of 7.35. The subsequent results of the other columns show that this positive relation remains robustly when well-known characteristics are controlled. In model 5 where all firm characteristics are controlled, regression coefficients on the quality increases is 0.0026 with the t-statistics of 6.14, which are significant at 1% level. Overall, the Fama-Macbeth regressions provide more convincing and robust results which is consistent with portfolio approach analysis. The quality increase effect cannot be subsumed by other return predictors.

4. The sources of quality increase premium We have found that quality increase effect exists stably in Chinese market. However, we have not provided evidence and explanations for the quality increase effect so far. Therefore, we further conduct empirical test to investigate whether the quality increase effect is caused by rational pricing or irrational mispricing. 4.1 Rational pricing based on risk explanation Based on the risk compensation explanation, if the pricing is rational, the high returns of firms with high quality increase should stem from bearing the high risk. Daniel and Titman (1997) put forward an approach to test whether anomaly obeys risk explanation: if the risk is the main driver of quality increase effect, then the loading of quality increase factor-mimicking portfolio should have a significant positive predictive power for stock returns. If not, the factor loading will have little predictive power while the predictive power of firm characteristic will be significant. Following Daniel and Titman (1997), we first independently sort all stocks into two size groups and three quality increase groups to obtain six value-weighted portfolios. Then we construct the quality increase mimicking factor (DQMJ) as the average of the two high quality increase portfolio returns minus the average of the two low quality increase portfolio returns. Next, we calculate the factor loadings by regressing the daily individual stock returns on the mimicking portfolio as well as Fama-French three factors. Last, we employ Fama-MacBeth regression to examine the predictive power of factor loading when characteristics are controlled. Our regression model is set as follows: ri ,t 1   0  1del _ qualityi ,t   2 sizei ,t   3bmi ,t   4 beta _ dqmji ,t   5beta _ smbi ,t   6 beta _ hmli ,t   i ,t 1

12

,

(2)

Journal Pre-proof where del _ qualityi ,t is the firm’s quality increases, and the factor loading of quality increases mimicking factor is denoted as beta _ dqmji ,t . The regression results are shown in Table 7. [Insert Table 7 Here] In Table 7, the factor loading of the quality increases factor does not show a significant predictive power for stock returns, with the t statistics ranging from -0.42 to 1.20, regardless of the univariate and multivariate regression. Instead, the characteristic of quality increases can always predict the stock returns positively, with the t statistics ranging from 6.53 to 7.16. Therefore, the empirical evidence does not support the risk explanation. In other words, irrational mispricing may be the main cause of quality increase effect. 4.2 Irrational mispricing base on underreaction From the above analysis, we know that the quality increases effect may be mainly caused by irrational mispricing. In this section, we investigate whether behavioral mispricing helps to explain the quality increases premium in the Chinese stock market. Specifically, we would like to explore whether mispricing related to underreaction or overreaction dominates the quality increases effect. If investors overreact positive (negative) information about high (low) quality increases, they will push price far higher (lower) than firms’ fundamentals in the short run, which will cause a positive relation between quality increase and future stock returns in the short run. Thus the hedging strategy of going long firms with high quality increase and short firms with low quality increase can earn a significant positive return. However, in the long run, with correction of stock price by arbitrage activities, the stock price of the original firms with high (low) quality increases will fall (rise), which makes the hedge strategy earns a significant negative returns. Conversely, if investors underreact the information, then hedging strategy will earn a positive return in the short run and there will be no return reversal in the long run. Following Jegadeesh and Titman (1993), we sort all stocks into deciles using the most recently available quality increase at the beginning of each month. Then we build overlapping portfolios with different holding periods. For example, when the holding period is from the first month to the sixth month (denoted as [1, 6]), the overlapping portfolio in each decile is the average of the six portfolios constructed based on the sorting from the beginning of the month (t-5) to the beginning of the month t. The returns of hedging strategy are presented in Table 8. 13

Journal Pre-proof [Insert Table 8 Here] Table 8 shows that after sorting at the month t, the hedging strategy which is long firms with high quality increase and short firms with low quality increase earns significant positive returns regardless of raw returns or risk-adjusted returns in the next half year (returns are 0.79% and 0.82% with t statistics of 3.67 and 3.88, respectively). The results of the raw returns suggest that in the medium and long run (holding period begins after 7th month), although the hedging strategy earns both positive and negative returns, the returns are all insignificant, which indicates that there is no significant returns reversal. From the results of the risk-adjusted returns, we find that there is no significant returns reversal in the risk-adjusted returns of hedging strategy. And the fact that the risk-adjusted returns are significantly positive in the holding period [19, 24] and [25, 30] also confirms that the investors’ underreaction to the information. In other words, the investors only react part of the information of the quality increases in the short run and as time goes on, the investors further react the information contained in quality increases into the price adjustment completely in the long run. To investigate whether there is a return reversal in the hedge strategy of quality increases in the long run more visually, we present the both short-term and long-term performance of hedge strategy. Specifically, we calculate the cumulative returns of hedge portfolios of quality increases in different holding periods ranging from 1 month to 60 month. Following Akbas et al. (2017), the cumulative returns are calculated as the value of 1 yuan which is invested in month t and held for each of the following 60 month. Figure 1 plots the raw returns, FF3 alpha of hedge portfolios respectively. [Insert Figure 1 Here] In Figure 1, the raw return of hedge strategy appreciates about 6.3% in the short run (over 12 months following portfolio formation). Although the cumulative returns decrease from 7.7% to 4.7% with the difference of 3.0% when holding period increases from 30 months to 51 months, the magnitude of decrease is much smaller than the magnitude of increase (6.3%) in the short run. Moreover, even if the holding period reaches 60 months, the cumulative return is still positive with the return of 6.4%. Therefore, overall quality increases have a positive long-term predictability on stock future returns and there is no obvious reversal in returns of hedge portfolios in the long run. Similar pattern can be seen in FF3 alpha of hedge portfolios: the risk-adjusted returns appreciate 14

Journal Pre-proof slowly over the 60 months following portfolio formation. These findings support the previous finding that quality increases premium may be caused by mispricing related to underreaction. In summary, we argue that the quality increase effect is attributed to the investors’ underreaction. Investors underreact the positive (negative) information about high (low) quality increases, which makes the stocks underpriced (overpriced) and generate high (low) returns in the future. 4.3 Further investigation of underreaction The mispricing-based underreaction explanations for the quality increase premium suggest that the stocks of high (low) quality increase firms tends to be relatively underpriced (overpriced), leading to return predictability. In this subsection, we further provide additional evidence to explanations for quality increases effect based on some features of the Chinese market. It is widely known that high speculation, irrationality and inefficiency are the typical characteristics of the Chinese market, and the large numbers of retail investors who are prone to behavioral biases are documented as the main reasons for the formation of these features of Chinese market (Chang et al., 2014; Hsu et al., 2018). Therefore, we concentrate on two behavioral biases in this sub-section: the positive feedback trading and investor’s negligence, which are widely discussed in both practice and academic study. 4.3.1 Positive feedback trading Positive feedback trading is an investment behavior that investors find the trend in stock price over the past period and trade based on the belief that that this trend will persist. Positive feedback trading widely exists in the emerging markets including Chinese markets and this behavior is more obvious in the retail investors (Bange, 2000; Ng and Wu, 2007; Bohl and Siklos, 2008) Therefore, we infer that positive feedback trading may be the cause of underreaction in quality increases effect. Specifically, because positive feedback traders are too focused on the past trend of stock prices to pay attention to the fundamentals of the firm itself, they will not react adequately to the negative (positive) information about low (high) quality increases from stocks that have performed well (poor) in the past. Thus, the negative (positive) information about low (high) quality increases may not be reflect in the current price in time because positive feedback traders push (pull) the price to a higher (lower) level in the stocks that have performed well (poor) in the past. Therefore, the current price 15

Journal Pre-proof of the stocks which have good (bad) past performance and low (high) quality increases is overpriced (underpriced) while the current price of the stocks which have good (bad) past performance and high (low) quality increase is not likely to be mispricing. Then hedging strategy which is long firms with high quality increase and short firms with low quality increase can earn significantly positive returns. However, due to the short-selling restrictions in China, positive feedback traders will focus on stocks that have performed well in the past more, so stocks that have a good past performance will be more attractive to positive feedback traders and then induce more positive feedback trading. According to this logic, we conjecture that the quality increase effect will be stronger among the stock with a good past performance. The first hypothesis is developed below: Hypothesis 1: The quality increases effect will be stronger among stocks with a good past performance. 4.3.2 Investor’s negligence Investor may neglect some important information when they are faced with much and various information because their attention is limited (Hong and Stein, 1999). From this point of view, we conjecture that if the investors with limited-attention neglect the fundamental information such as firm’s quality increases, they will be underreact this kind of information. According to Chou et al. (2019), we adopt price delay in order to measure the extent of investor’s negligence. If price delay is high, the current market information will reflect into the future price adjustment rather than current price adjustment. Stocks with high price delay will exhibit more investor’s negligence and thus are unable to react market information in time (Hou and Moskowitz, 2005). Here, we assume that the investors who neglect the market information will also ignore the fundamental information such as quality increases, which is more complicated and harder to obtain than market information. Hence, investors may underreact the quality increases of stocks with high price delay and we develop the second hypothesis: Hypothesis 2: The quality increases effect will be stronger among stocks with high price delay. 4.3.3 Empirical tests on the mispricing-based explanation In this subsection, we adopt the two-way sorting approach to test the hypothesis mentioned above using the history cumulative returns (Mom) and price delay measure (Pd) as the proxies for the past performance and price delay. Each month we first sort stocks into quintile based on the 16

Journal Pre-proof value of controlling variables (Mom or Pd), then we also sort stocks into quintile according to the quality increase within each portfolio. After obtaining the 25 portfolios, we compute the valueweighted monthly returns. Besides, in order to investigate whether the quality increase effect is related to the relative variables, we subtract the hedge portfolio returns in the quintile with low firm characteristics from those in the quintile with high firm characteristics (see the results for the last row of each panel). Table 9 presents the risk-adjusted returns by Fama-French three factor model (in percentage) of the two-way sorting portfolios controlling for Mom and Pd. [Insert Table 9 Here] In the Panel A of Table 9, the risk-adjusted returns of hedge portfolios which is long firms with high quality increase and short firms with low quality increase is higher in the stocks with good past performance than those in the stocks with poor past performance. The difference between the two hedge portfolios is 1.32% with the t statistics of 3.01. Similar results can be seen in the Panel B, the risk-adjusted returns of hedge portfolios increase almost monotonically as the quality increase increases. The difference between the two hedge portfolios is 0.89% with the t statistics of 2.32. The results both support the hypothesis mentioned above, indicating that underreaction caused by the positive feedback trading and price delay is the main source of mispricing for quality increase effect.

5. Additional tests 5.1 Alternative measures of quality In this sub-section, we conduct a comprehensive analysis of the sensitivity of our results to the measures of quality. Our baseline measure of quality is aggregating four sub-factors with equal weight. Hence, the first alternative measure of quality is weighting four sub-factors to obtain the quality. The weight on each sub-factor is the correlation coefficient between it and baseline quality divided by the sum of correlation coefficients of these four sub-factors. Another measure of quality is using the principal components analysis. We conduct principal components analysis on all proxy indicators for four sub-factors, such as return on equity, idiosyncratic volatility and so on. Then we choose the first principal component as the quality. After constructing new quality, we calculate the quality changes relative to the same quarter last year to obtain the quality increases each quarter. Then we conduct the Fama-MacBeth regression 17

Journal Pre-proof of Equation (1) to check the predictability of new quality increases on stock future returns. The regression results are shown in Table 10 where quality in model 1 is constructed by weighting four sub-factors and quality in model 2 is constructed by principal components analysis. [Insert Table 10 Here] In Table 10, the regression coefficients on quality increases are both significantly positive. The coefficient in model 1 is 0.0033 with the t statistic of 6.84 and the coefficient in model 2 is 0.0033 with the t statistic of 7.61. Therefore, the quality increases possess the positive predictive power on stock future returns even if other firm characteristics which have influence on stock returns are controlled. In sum, our findings still hold under alternative measures of quality. 5.2 Redundancy tests: Regression of mimicking portfolio of quality increases on the factor model From the one-way sorting analysis, we know that quality increases effect cannot be subsumed by Fama-French three factor model. In this sub-section, we further want to know whether quality increases anomaly can be explained by Fama-French five factor model or the q-factor model, which can both explain the cross-sectional stock returns very well. We first create the mimicking portfolio of quality increases similar to the construction of HML in Fama and French (1993). Then we construct the Fama-French model following Fama and French (2015) and q-factor model following Hou et al. (2015). We regress the returns of quality increases mimicking portfolio on FF5 and q-factor model, respectively. If our quality increases factor can provide incremental predictive power on stock future returns, the intercept will be distinguish from 0. The regression results of FF5 model and q-factor model are shown in Table 11. [Insert Table 11 Here] In Panel A, the regression intercept is 0.0066 with the t statistic of 5.29, which is statistically significant at 1% level. From the results of Panel B, it can be seen that the loadings of the factor in q-factor model are more significant than that in FF5 model. Thus the intercept in Panel B is less than that in Panel A. Nevertheless, the intercept is 0.0047 with the t statistic of 3.75, still significant at 1% level. Therefore, the quality increases premium cannot be subsumed by FF5 model and qfactor model. 5.3 The long-term predictability of quality increases: Fama-MacBeth regression analysis 18

Journal Pre-proof Different from the portfolio approach in Section 4.1, we further conduct the Fama-MacBeth regression to check the long-term predictability of quality increases on future returns, using individual returns without imposing breakpoints and controlling other firm characteristics. Specifically, we estimate the Fama-MacBeth regression of Equation (1), regressing the future 1-month returns, future 12-month cumulative returns, future 36-month cumulative returns and future 60-month cumulative returns on quality increases and other control variables, respectively (denoted as model (1), model (2), model (3) and model (4), respectively). The results are shown in Table 12. [Insert Table 12 Here] In all models, the coefficients on quality increases are positive and significant. For example, in model 4, the coefficient is 0.0127 with the t statistic of 2.92, which is significant at 1% level. These results show that quality increases exhibit the long-term predictability on future returns, consistent with the previous finding. 5.4. Analysis of controlling for quality level Although quality increases effect can exist stably after controlling the well-known predictors for stock returns, what we want to know more is how the quality increases effect will behave after controlling the quality level. In order to check whether the dynamic information contained in the quality increases has been reflected in the quality level, that is, whether the quality increases are redundant, we further investigate the predictive power of quality increases when quality level is controlled, using Fama-Macbeth regression approaches. We estimate the Fama-MacBeth regression to test whether the quality increase can predict future stock returns significantly after controlling the quality level. The regression results are shown in Table 13. [Insert Table 13 Here] In Table 13, it can be seen that even after controlling the quality level and other relevant variables, quality increases still have a significant positive impact on the future stock returns. In model 1, the coefficient on quality increases is 0.0020 with t statistics of 3.24. In models 2 and 3 where more firm characteristics are controlled, the coefficients of quality increases are still distinguishable from 0 at 10% level with t statistics of 1.76 and 1.71 respectively, though the significance of coefficients on quality increases are weaker than that in model 1. Therefore, the

19

Journal Pre-proof quality increases have incremental predictive information beyond the quality level. This result indicates that we should consider both the quality level and the quality increases when we analyze the impact of quality on stock returns in the future researches. 5.5. Asymmetry of quality increases effect Our quality increases measure the change of firm’s quality over the past period of time, which are positive when firm’s quality increases relative to the past and negative when firm’s quality decreases relative to the past. We are concerned about how the market reacts when investors are facing with the increases or decreases of firms’ quality. Therefore, we divide firms into two groups based on whether firms’ quality increases exceed 0. The group where firms’ quality increases exceeds and equals 0 is denoted as “dq_positive” and the group where firms’ quality increases do not exceeds 0 is denoted as “dq_negative”. We conduct the one-way sorting analysis to examine the performance of quality increases in these two groups. Table 14 shows the raw and abnormal returns on portfolios of one-way sorting. [Insert Table 14 Here] In Panel A, the return of hedge portfolio that is long the firms with high quality increases and short the firms with low quality increases in the group “dq_negative” is 1.21%, which is distinguish from 0. However, the returns of hedge portfolio in the group “dq_positive” is insignificant (0.36% with the t statistic of 1.21). Similar results are shows in the FF3-adjusted returns in Panel B. The last two rows in each panel present the difference between the hedge portfolio return in the group “dg_negative” and the hedge portfolio return in the group “dg_positive” and corresponding t statistics. The difference is 1.96% with the t statistic of 4.42 in Panel B, indicating that the quality increases premium is much higher in the group “dg_negative” than that in the group “dg_positive”. In short, portfolio analyses show that the asymmetry of quality increases effect is clear enough and the premium is much superior in the group “dg_negative”. Considering that quality increases premium may stem from the investors’ underreaction, this finding indicates that the investors may pay more attention to the increase of firms’ quality, but relatively neglect the decrease of firms’ quality. Thus they can reflect the increases of firms’ quality into price in time, but underreact the decreases of firms’ quality. This finding also support the previous results that quality increase premium comes from the short leg rather than long leg.

20

Journal Pre-proof 6. Conclusion In this paper, we show that there is a significant positive quality increases premium in the Chinese stock market, that is, the firms with the high quality increases yield higher returns than those with the low quality increases. A hedge portfolio which is long stocks with the highest quality increases and short lowest quality increases can generate an annual return about 15.6%. And quality increases even possess long-term predictability on stock future returns, at least five years. The quality increases premium exists stably after controlling the well-known factors which have impact on the future stock returns. This premium also cannot be subsumed by Fama-French five factor and q-factor model. Even though the quality level of the firm is controlled, the predictive power of firm’s quality increase is still robust. Moreover, the premium still holds under alternative measures of quality. Furthermore, we find that the quality increases premium may be caused by mispricing instead of risk compensation. The high return of stocks with the high quality increases do not stem from high risk. Further empirical evidence documents that underreaction caused by the positive feedback trading and investor’s negligence is the main reason of mispricing for quality increases premium. Besides, we find that investors underreact the decreases of firms’ quality (firm’s quality increases are negative) more than the increases of firm’s quality (firm’s quality increases are positive), which causes the asymmetry of quality increases effect.

Reference Akbas F, Jiang C, Koch P D. The trend in firm profitability and the cross-section of stock returns[J]. The Accounting Review, 2017, 92(5): 1-32 Amihud Y. Illiquidity and stock returns: cross-section and time-series effects [J]. Journal of Financial Markets, 2002, 5(1): 31-56. Ang A, Hodrick R J, Xing Y, Zhang X. The Cross-section of volatility and expected returns[J]. Journal of Finance, 2006, 61(1): 259-299. Ap Gwilym O, Seaton J, Suddason K, Thomas S. International evidence on the payout ratio, earnings, dividends, and, returns[J]. Financial Analysts Journal, 2006, 62(1): 36-53. Asness C S, Frazzini A, Pedersen L H. Quality minus junk[J]. Review of Accounting Studies, 2019, 24(1): 34-112. 21

Journal Pre-proof Bali T G, Cakici N, Whitelaw R F. Maxing out: Stocks as lotteries and the cross-section of expected returns[J]. Journal of Financial Economics, 2011, 99(2): 427-446. Ball R, Gerakos J, Linnainmaa J T, Nikolaev V. Deflating profitability[J]. Journal of Financial Economics, 2015, 117(2): 225-248. Bange M. Do the portfolios of small investors reflect positive feedback trading?[J]. Journal of Financial and Quantitative analysis, 2000, 35(2): 239-255. Bohl M, Siklos, P. Empirical evidence on feedback trading in mature and emerging stock market[J]. Applied Financial Economics, 2008, 18(17):1397-1389. Boudoukh J, Michaely R, Richardson M, Roberts M R. On the importance of measuring payout yield: Implications for empirical asset pricing[J]. Journal of Finance, 2007, 62(2): 877-915. Campbell J Y, Giglio S, Polk C, Turley R. An intertemporal CAPM with stochastic volatility[J]. Journal of Financial Economics, 2018, 128(2): 207-233. Carpenter J N, Lu F, Whitelaw R F. The real value of China's stock market[R]. Working paper, National Bureau of Economic Research, 2015. Chang E C, Luo Y, Ren J. Short-selling, margin-trading, and price efficiency: Evidence from the Chinese market[J]. Journal of Banking & Finance, 2014, 48: 411-424. Cheema M A, Nartea G V. Momentum returns, market states, and market dynamics: Is China different?[J]. International Review of Economics & Finance, 2017, 50: 85-97. Chou P, Ko K, Yang N. Asset growth, style investing and momentum[J]. Journal of Banking & Finance, 2019, 98: 108-124. Cooper M J, Gulen H, Schill M J. Asset growth and the cross-section of stock returns[J]. Journal of Finance, 2008, 63(4): 1609-1651. Daniel K, Titman S. Evidence on the characteristics of cross sectional variation in stock returns[J]. Journal of Finance, 1997, 52(1): 1-33. Fama E F, French K R. Common risk factors in the returns on stocks and bonds[J]. Journal of Financial Economics, 1993, 33, 3-56. Fama E F, French K R. A five-factor asset pricing model[J]. Journal of Financial Economics, 2015, 116(1): 1-22. Fama E F, French K R. Dissecting anomalies with a five-factor model[J]. The Review of Financial Studies, 2016, 29(1): 69-103. 22

Journal Pre-proof Fama E F, French K R. Choosing factors[J]. Journal of Financial Economics, 2018, 128(2): 234252. Fong W M, Toh B. Investor sentiment and the MAX effect[J]. Journal of Banking & Finance, 2014, 46:190-201. Frazzini A, Kabiller D, Pedersen L H. Buffett's alpha[J]. Financial Analysts Journal, 2018, 74 (4): 35-55. Frazzini A, Pedersen L. Betting against beta[J]. Journal of Financial Economics, 2014, 111: 1-25. Gu M, Kang W, Xu B. Limits of arbitrage and idiosyncratic volatility: Evidence from China stock market[J]. Journal of Banking & Finance, 2018, 86: 240-258. Hilliard J, H Zhang. Size and price-to-book effects: Evidence from the Chinese stock markets[J]. Pacific Basin Finance Journal, 2015, 32: 40-55 Hong H, Stein J C. A unified theory of under reaction, momentum trading, and overreaction in asset markets[J]. Journal of Finance, 1999, 54(6): 2143-2184. Hou K, Moskowitz T J. Market Frictions, Price Delay, and the Cross-Section of Expected Returns[J]. Review of Financial Studies, 2005, 18(3): 981-1020. Hou K, Xue C, Zhang L. Digesting anomalies: An investment approach[J]. Review of Financial Studies, 2015, 28, 650-705. Hou K, Xue C, Zhang L. Replicating anomalies[J]. Review of Financial Studies, 2018. Forthcoming. Hsu J, Kalesnik V, Kose E. What is quality?[J]. Financial Analysts Journal, 2019. Forthcoming. Hsu J C, Viswanathan V, Wang C, Wool P. Anomalies in Chinese A-Shares [J]. Journal of Portfolio Management, 2018, 44(7): 108-123. Huang A G. The cross section of cashflow volatility and expected stock returns [J]. Journal of Empirical Finance, 2009, 16(3): 409-429. Huang Y, J Yang, Y Zhang. Value premium in the Chinese stock market: Free lunch or paid lunch[J]? Applied Financial Economics, 2013, 23 (4): 315-324 Jegadeesh N, Titman S. Returns to buying winners and selling losers: Implications for stock market efficiency[J]. Journal of Finance, 1993, 48(1): 65-91 Jiang F, Qi X, Tang G. Q-theory, mispricing, and profitability premium: evidence from China[J]. Journal of Banking & Finance, 2017, 87: 135-149. Lam F E, Wang S, Wei K C. The profitability premium: Macroeconomic risks or expectation 23

Journal Pre-proof errors?[J]. Working Paper. 2016. Available at SSRN: https://ssrn.com/abstract=2726027 or http://dx.doi.org/10.2139/ssrn.2726027. Lee C M C. Value investing: Bridging theory and practice[J]. China Accounting and Finance Review, 2014, 16(2): 1-29. Liu J, Stambaugh R F, Yuan Y. Size and value in China[J]. Journal of Financial Economics, 2019. Forthcoming. Loh R K, Warachka M. Streaks in earnings surprises and the cross-section of stock returns[J]. Management Science, 2012, 58(7): 1305-1321. Martin G S, Puthenpurackal J. Imitation is the sincerest form of flattery: Warren Buffett and Berkshire

Hathaway[J].

Working

paper.

2008.

Available

at

SSRN:

https://ssrn.com/abstract=806246 or http://dx.doi.org/10.2139/ssrn.806246. Ng L, Wu F. The trading behavior of institutions and individuals in Chinese equity markets[J]. Journal of Banking & Finance, 2007, 31: 2695-2710. Novy-Marx R. The other side of value: The gross profitability premium[J]. Journal of Financial Economics, 2013, 108(1): 1-28. Pontiff J, Woodgate A. Share issuance and cross-sectional returns[J]. Journal of Finance, 2008, 94(1): 1-17. Titman S, Wei K C J, Xie F. Capital investments and stock returns[J]. Journal of Financial and Quantitative Analysis, 2004, 39(4): 677-700. Wang F, Xu Y. What determines Chinese stock returns?[J]. Financial Analysts Journal, 2004, 60(6): 65-77. Wang Y, Liu C, Lee J S, Wang Y. The relation between asset growth and the cross-section of stock returns: Evidence from the Chinese stock market[J]. Economic Modelling, 2015, 44: 59-67. Wang H, Yu J. Dissecting the profitability premium[C]. AFA 2013 San Diego Meetings Paper. 2013. Available

at

SSRN:

https://ssrn.com/abstract=1711856

http://dx.doi.org/10.2139/ssrn.1711856

24

or

Journal Pre-proof Table 1 Dimensions of the level of quality. Dimension

Profitability

Constructions method 1. Return on equity (ROE), which is the quarterly net income divided by quarterly total shareholders’ equity. 2. Return on assets (ROA), which is the quarterly net income divided by quarterly total toasset. 3. Gross profits over assets (GPOA), which is the quarterly revenue minus costs of goods sold divided by total assets. 4. Gross profit margin (GMAR), which is quarterly revenue minus costs of goods sold divided by total sales.

Growth

1. Growth in return on equity (DROE), which is the changes of net income relative to the same quarter last year divided by total shareholders’ equity at the same quarter last year. 2. Growth in return on assets (DROA), which is the changes of net income relative to the same quarter last year divided by total assets at the same quarter last year. 3. Growth in gross profits over assets (DGPOA), which is the changes of gross profit relative to the same quarter last year divided by total assets at the same quarter last year. 4. Growth in gross profit margin (DGMAR), which is the changes of gross profit relative to the same quarter last year divided by total sales at the same quarter last year.

Payout

1. Net equity issuance (EISS), which is minus one-quarter percent change in number of shares outstanding and equals minus natural logarithm of the ratio of number of shares outstanding at the quarter t to number of shares outstanding at the quarter (t-1). 2. Net debt issuance (DISS), which is minus one-quarter percent change in number of total debt and equals minus natural logarithm of the ratio of number of total debt at the quarter t to number of total debt at the quarter (t-1). 3. Total net payout over profits (NPOP), which is equal the sum of total net payout (net income minus changes in book equity) over the past 4 quarters divided by total gross profits over the past 4 quarters.

Safety

1. Market risk (BETA), which is minus coefficient from regression of daily stock returns on market returns using three-year rolling window. 2. Idiosyncratic volatility (IVOL), which is minus the standard deviation of daily adjusted returns by Fama-French three factor model with one-year rolling window. 3. Leverage (LEV), which is minus quarterly total debt over total assets.

1

Journal Pre-proof Table 2 Definitions of control variables Variables

Definitions

Beta

Market risk, which is coefficient from regressing daily stock returns on market returns using three-year rolling window. Market value of tradable shares. Book-to-market equity ratio, which is the ratio of most recent available book equity to the market value. Operating profitability, which is the revenue minus cost of goods sold, minus interest expense, minus selling, and administrative expenses, divided by book equity. Asset growth, which is the growth rate of total assets from the ending in year t-2 to the ending in year t-1. Momentum, which is calculated as the cumulative stock returns over the previous six month. The maximum returns in the previous one month. Idiosyncratic volatility, which is the standard deviation of daily excess return adjusted by Fama-French 3 factors using one-year rolling window. Turnover rate, which is the average daily turnover (the ratio of the number of shares traded to the number of shares outstanding) over the past month at the end of each month. Illiquid, which is the average daily illiquid (the ratio of absolute daily return to the daily trading volume) over the past 250 trading days at the end of each month. Price delay, which is one minus the ratio of R2 from the regression by regressing weekly return on the market return to the R2 from the regression by regressing weekly return on contemporaneous and four weeks of lagged market returns using one year rolling window.

Size B/M Op

Inv Mom Max Ivol Turn

Illiq Pd

2

Journal Pre-proof Table 3 Summary statistics. Panel A: Summary statistics Variables

Mean

Std

Min

Medium

Max

Observations

Quality increases Quality Beta Size B/M Op Inv Mom Max Ivol Turn Illiq Pd

-0.02 0.00 1.10 21.59 0.38 0.14 0.24 0.09 0.06 0.02 0.03 2.14 0.20

1.03 1.00 0.18 1.01 0.22 0.15 0.36 0.24 0.02 0.02 0.03 8.39 0.17

-3.13 -1.73 -0.59 19.11 0.00 -0.52 -0.31 -0.65 0.02 0.01 0.00 0.02 0.00

-0.02 0.00 1.13 21.49 0.34 0.12 0.14 0.06 0.05 0.02 0.02 1.31 0.15

3.05 1.73 1.79 26.78 1.85 0.69 2.62 1.57 0.10 0.55 0.43 261.78 0.99

1351 1474 1543 2026 2094 2450 2233 1795 1932 1818 2026 1856 1987

Panel B: Correlations Quality increases

Quality

Beta

Size

Bm

Op

Inv

Mom

Max

Ivol

Turn

Illid

Pd

Quality increases

1

0.5 1

0.04

0.01

0.0 0

0.06

0.03

0.1 1

0.03

0.07

0.04

0.0 4

0.0 2

Notes: Panel A presents the time series average of the cross-sectional mean, standard deviation, minimum, median, maximum and number of observations of variables including quality increase, quality and other controlling variables. Panel B shows the correlations between quality increases and other variables. Quality increases and quality are defined in Section 2.2 and 2.3 respectively. All controlling variables are described as Table 2. The sample period covers September 2004 through March 2019.

3

Journal Pre-proof Table 4 Raw and abnormal returns on portfolios of one-way sorting based on the quality increase. Portfolios

Returns (%)

FF3 alpha (%)

 mkt

 smb

 hml

Low 2 3 4 5 6 7 8 9 High

0.45 (0.49) 0.59 (0.62) 0.86 (0.95) 1.02 (1.10) 1.03 (1.16) 1.56 (1.77) 1.31 (1.46) 1.36 (1.50) 1.55 (1.62) 1.75 (1.74)

-1.01***(-5.25) -0.70***(-4.89) -0.23 (-1.59) -0.16 (-0.99) 0.14 (0.91) 0.52***(3.47) 0.18 (1.37) 0.19 (1.57) 0.21 (1.44) 0.26 (1.53)

1.06***(34.40) 1.05***(48.99) 1.01***(38.23) 0.99***(40.79) 0.94***(37.11) 0.97***(43.54) 0.99***(44.73) 0.99***(54.61) 1.04***(45.01) 1.07***(48.75)

0.22***(4.55) 0.08**(2.02) -0.06 (-1.00) 0.02 (0.42) -0.17***(-3.12) -0.06 (-1.03) -0.01 (-0.13) 0.07 (1.62) 0.14***(3.18) 0.23***(4.02)

0.05 (0.76) 0.07 (1.43) 0.10 (0.86) 0.00 (0.01) 0.00 (0.00) -0.07 (-1.00) -0.14***(-2.71) 0.01 (0.12) 0.07 (1.27) 0.06 (0.98)

1.30 ***(5.36)

1.27 ***(5.22)

0.00 (0.13)

0.01 (0.08)

0.01 (0.10)

High-Low

Notes: This table reports the results of one-way sorting. At the end of April, August and October each year, we sort the stocks into deciles based on quality increase. We assume that each decile portfolio is held until the next adjustment and the monthly portfolio returns are value weighted. “Low” refers to the stocks in the lowest quality increase decile while “High” refers to the stocks in the highest quality increase decile. “High-Low” refers to the hedge portfolio which is long firms with high quality increase and short firms with low quality increase. The table presents the monthly average returns (in percentage), returns adjusted by Fama-French three factors (FF3 alpha, in percentage), factor loadings (  mkt ,  smb ,  hml ) and Newey-west t statistics with 6 lags. The sample period spans from September 2004 through March 2019. *, **, *** show significance at the 0.1 level, 0.05 level and 0.01 level, respectively.

4

Journal Pre-proof Table 5 Results of two-way sorting controlling for size, BM ratio and PE ratio. Quality increases Panel A：Size - Quality increases

Low 2 Size

3 4 High

Low

2

3

4

High

HighLow

t value

-0.36

-0.18

-0.28

0.03

0.18

0.54***

3.43

0.11

0.80***

3.71

-0.09

0.95***

4.53

0.02

1.22***

7.19

0.44

0.83***

3.42

0.29

1.12

-0.70 -1.03 -1.19 -0.39

-0.61 -0.51 -0.55 0.19

-0.42 -0.34 -0.10 0.53

-0.19 -0.43 -0.14 0.54

Panel B：Bm - Quality increases

Low 2 Bm

3 4 High

Low

2

3

4

High

HighLow

t value

-1.26

-0.15

0.68

-0.03

-0.62

0.64

1.51

0.34

1.39***

3.69

0.29

1.43***

5.18

0.38

0.96***

3.87

0.62

0.84**

2.34

0.20

0.40

-1.04 -1.15 -0.58 -0.22

-0.27 -0.29 -0.40 0.22

0.15

-0.02

-0.10 0.12

0.08 0.08

0.12

0.58

Panel C：Op - Quality increases

Low 2 Op

3 4 High

Low

2

3

4

High

HighLow

t value

-1.11

-1.04

-0.66

-0.30

-0.17

0.94***

4.16

-0.14

0.72***

2.77

0.12

0.75***

3.17

0.17

1.16***

4.45

0.78

1.34***

3.63

0.40

1.10

-0.87 -0.63 -0.99 -0.57

-0.84 -0.33 -0.30 0.45

-0.53 -0.13 -0.04 1.02

-0.39 0.01 0.00 0.89

Notes: This table reports the results of two-way sorting based on quality increases and other firm characteristics including size, BM ratio, operating profitability. At the end of April, August, and October each year, we sort all stocks into quintiles based on firm characteristics. Then within each portfolio, we further sort stocks into quintiles by the quality increases. Within each group of firm characteristics, “Low” refers to the stocks with the lowest quality increases while “High” refers to the stocks with the highest quality increases and “High-Low” refers to the hedge portfolio which is long firms with high quality increases and short firms with low quality increases. We subtract the hedge portfolio returns in the quintile with low firm characteristics from those in the quintile with high firm characteristics and the results are presents at the last row in each panel. The table presents value-weighted monthly Fama-French three factors alpha in percentage over September 2004 through March 2019. We also report Newey-west t statistics with 6 lags. *, **, *** show significance at the 0.1 level, 0.05 level and 0.01 level, respectively.

5

Journal Pre-proof Table 6 Fama-MacBeth regressions of stock returns on quality increases and other variables. Model

1

2

3

4

5

Delta_quality

0.0031 (7.35)

0.0028 (6.87) -0.0116 (-3.58) -0.0052 (-3.25) 0.0039 (3.78)

0.0026 (6.05) -0.0047 (-1.32) -0.0062 (-3.70) 0.0024 (2.28)

0.0028 (6.90) -0.0118 (-3.64) -0.0051 (-3.24) 0.0040 (3.72) 0.0001 (0.08) -0.0007 (-0.70)

0.1454 (3.85)

0.0026 (6.14) -0.0047 (-1.32) -0.0061 (-3.65) 0.0024 (2.22) 0.0001 (0.05) -0.0007 (-0.64) -0.0852 (-4.58) -0.0022 (-0.43) 0.0915 (0.70) -0.3840 (-9.62) 0.0016 (1.02) 0.1686 (4.18)

6.16 208906

9.62 208906

Beta Size Bm Op Inv

Intercept

0.0148 (1.53)

0.1462 (3.83)

-0.0841 (-4.48) -0.0020 (-0.40) 0.0953 (0.73) -0.3843 (-9.53) 0.0016 (0.98) 0.1704 (4.21)

R2 observations

0.30 208906

5.97 208906

9.44 208906

Max Mom Ivol Turn Illid

Notes: This table reports the Fama-MacBeth average monthly coefficients for five models. Our main regression model is specified as follows: ri ,t 1   0  1del _ qualityi ,t   2 betai ,t   3 sizei ,t   4 bmi ,t   5 opi ,t   6 invi ,t   7 maxi ,t  8 momi ,t   9 ivoli ,t  10 turni ,t  11illidi ,t   i ,t 1

.

The dependent variable is the future stock returns for firm i in month t  1 ( ri ,t 1 ) and main independent variable is quality increase for firm i in month t ( del _ qualityi ,t ). Other control variables are described as Table 2. The sample period is from September 2004 to March 2019 and the t statistics are adjusted by Newey-West robust standard errors with 6 lags. *, **, *** show significance at the 0.1 level, 0.05 level and 0.01 level, respectively.

6

Journal Pre-proof Table 7 Fama-MacBeth regressions of stock returns on characteristic and factor loadings. Model

1

Del_quality

2

3

4

0.0030***

0.0028***

(7.16)

(6.53) -0.0060*** (-3.26) 0.0029*** (2.48) 0.0004 (1.20)

0.0026*** (6.89) -0.0058*** (-3.05) 0.0028*** (3.02) 0.0003 (0.91) 0.0002 (0.12) -0.0005 (-0.49)

Size Bm Beta_dqmj

-0.0001 (-0.42)

-0.0001 (-0.35)

Beta_smb Beta_hml

Notes: This table presents the results of Fama-MacBeth cross-sectional regression. Our main regression model is specified as follows: ri ,t 1   0  1del _ qualityi ,t   2 sizei ,t   3bmi ,t   4 beta _ dqmji ,t   5beta _ smbi ,t   6 beta _ hmli ,t   i ,t 1

The dependent variable is the future stock returns for firm i in month t  1 . Main independent variables are quality increases for firm i in month t ( del _ qualityi ,t ) and quality increases factor loading for firm i in month t ( beta _ dqmji ,t ). beta _ smbi ,t and beta _ hmli ,t are the factor loadings of Fama-French three factors (SMB and HML, respectively). Other control variables are described as Table 2. We first independently sort all stocks into two size groups and three quality increase groups to obtain six value-weighted portfolios. Then we construct the quality increases mimicking factor (DQMJ) as the average of the two high quality increases portfolio returns minus the average of the two low quality increases portfolio returns. We calculate the quality increases factor loadings by regressing the daily individual stock returns on the mimicking portfolio as well as Fama-French three factors in one-year rolling window each month. The table reports the time series average of coefficients. The sample period is from September 2004 to March 2019 and the t statistics are adjusted by Newey-West robust standard errors with 6 lags. *, **, *** show significance at the 0.1 level, 0.05 level and 0.01 level, respectively.

7

Journal Pre-proof Table 8 The performance of hedge portfolio during different holding periods. Holding period

Raw returns (%)

FF3 alpha (%)

[1,6] [7,12] [13,18] [19,24] [25,30] [31,36] [37,42] [43,48] [49,54] [55,60]

0.79***(3.67)

0.82***(3.88) 0.19 (0.87) -0.03 (-0.16) 0.43**(2.26) 0.42**(2.24) 0.05 (0.24) -0.04 (-0.18) 0.00 (-0.01) 0.01 (0.04) 0.31 (1.42)

0.16 (0.76) -0.12 (-0.63) 0.13 (0.64) 0.20 (1.02) -0.18 (-0.86) -0.01 (-0.04) 0.02 (0.13) 0.00 (-0.02) 0.28 (1.28)

Notes: This table presents the performance of hedge strategy which is long the firms with high quality increases and short the firms with low quality increases during different holding period. At the beginning of each month, we sort all stocks into deciles using the most recently available quality increases. Then we build overlapping portfolios with different holding periods. For example, when the holding period is from the first month to the sixth month (denoted as [1, 6]), the overlapping portfolio in each decile is the average of the six portfolios constructed based on the sorting from the beginning of the month (t-5) to the beginning of the month t. The table reports the monthly average raw returns and Fama-French three factors alpha in percentage. The t statistics are adjusted by Newey-West robust standard errors with 6 lags. *, **, *** show significance at the 0.1 level, 0.05 level and 0.01 level, respectively.

8

Journal Pre-proof Table 9 Two-way sorting controlling the past performance (Mom) and price delay (Pd) Quality increase Panel A：Mom - Quality increase

Low 2 Mom

3 4 High

Low

2

3

4

High

HighLow

t statistics

-0.58

0.20

0.29

0.36

-0.06

0.51*

1.95

0.35

0.92***

3.70

0.55

0.88***

2.77

-0.12

0.63**

2.03

0.29

1.84***

5.18

1.32***

3.01

0.41

0.66***

2.55

0.18

0.93***

3.27

0.23

1.23***

3.90

0.05

0.95***

3.61

0.26

1.55***

3.86

0.89**

2.32

-0.57 -0.32 -0.74 -1.55

-0.05 -0.04 -0.03 -0.69

0.12 0.01 0.14 0.19

0.38 0.50 -0.02 -0.13

Panel B：Pd - Quality increase Low Pd

2 3 4 High

-0.25 -0.75 -0.99 -0.90 -1.28

-0.11 -0.26 -0.59 -0.17 -0.30

-0.09 0.04 0.40 0.28 0.59

0.42 0.07 -0.19 0.24 -0.12

Notes: This table shows the results of two-way sorting based on quality increases and other controlling variables including Mom and Pd. At the end of April, August, and October each year, we sort all stocks into quintiles based on the controlling variables. Within each portfolio, we further sort stocks into quintiles by the quality increase. Within each group of controlling variables, “Low” refers to the stocks with the lowest quality increase while “High” refers to the stocks with the highest quality increase and “High-Low” refers to the hedge portfolio which is long firms with high quality increase and short firms with low quality increase. The last row in each panel presents the returns difference between hedge portfolios in high quintile of controlling variables and hedge portfolios in low quintile of controlling variables. The table presents value-weighted monthly Fama-French three factors alpha in percentage over September 2004 through March 2019. The table also reports Newey-west t statistics with 6 lags.*, **, *** show significance at the 0.1 level, 0.05 level and 0.01 level, respectively.

9

Journal Pre-proof Table 10 Fama-MacBeth regressions of stock returns on quality increases constructed by alternative measures. Model Delta_quality Beta Size Bm Op Inv Max Mom Ivol Turn Illid Intercept R2 observations

1

2

0.0033*** (6.84) -0.0045 (-1.26) -0.0061** (-3.65) 0.0024** (2.24) 0.0002 (0.12) -0.0005 (-0.48) -0.0831*** (-4.43) -0.0028 (-0.55) 0.1115 (0.86) -0.3822*** (-9.61) 0.0015 (0.92) 0.1678*** (4.16)

0.0033*** (7.61) -0.0046 (-1.29) -0.0062*** (-3.67) 0.0023** (2.11) 0.0004 (0.33) -0.0004 (-0.38) -0.0811*** (-4.33) -0.0037 (-0.73) 0.0468 (0.37) -0.3814*** (-9.58) 0.0014 (0.86) 0.1704*** (4.20)

0.0969 208906

0.0973 208906

Notes: This table reports the Fama-MacBeth average monthly coefficients for two models. Our main regression model is specified as follows: ri ,t 1   0  1del _ qualityi ,t   2 betai ,t   3 sizei ,t   4 bmi ,t   5 opi ,t   6 invi ,t   7 maxi ,t  8 momi ,t   9 ivoli ,t  10 turni ,t  11illidi ,t   i ,t 1

.

The dependent variable is the future stock returns for firm i in month t  1 ( ri ,t 1 ) and main independent variable is quality increase for firm i in month t ( del _ qualityi ,t ). In model 1, the quality is weighting four sub-factors, including profitability, growth, safety and payout. The weight on each sub-factor is the correlation coefficient between it and baseline quality (constructed by aggregating four sub-factors at equal weight) divided by the sum of correlation coefficients of these four sub-factors. In model 2, quality is the first principal component in the principal components analysis on all proxy indicators for four sub-factors, such as return on equity, idiosyncratic volatility and so on. Quality increases are quality changes relative to the same quarter last year. Other control 10

Journal Pre-proof variables are described as Table 2. The sample period is from September 2004 to March 2019 and the t statistics are adjusted by Newey-West robust standard errors with 6 lags. *, **, *** show significance at the 0.1 level, 0.05 level and 0.01 level, respectively. Table 11 Regression of mimicking portfolio of quality increases on the factor model. Panel A Regression of mimicking portfolio of quality increases on FF5 model Estimate t-value

Intercpet

Mkt

Smb

Hml

Rmw

Cma

0.0066*** 5.29

0.0097 0.53

0.0819 1.17

-0.0312 -0.55

0.1181 1.44

0.1120 1.32

Panel B Regression of mimicking portfolio of quality increases on q-factor model Intercept Estimate t-value

0.0047*** 3.75

Mkt

RMe

RI/A

RRoe

0.0130 0.82

0.1141**

0.2116***

2.20

2.83

0.1857*** 3.07

Notes: This table presents the results of time-series regression of mimicking portfolio of quality increases on the factor model. The mimicking portfolio of quality increases is created similar to the construction of HML in Fama and French (1993). The factor models are Fama-French five factors model and q-factor model, which are constructed following Fama and French (2015) and Hou et al. (2015), respectively. The sample period is from September 2004 to March 2019. *, **, *** show significance at the 0.1 level, 0.05 level and 0.01 level, respectively.

11

Journal Pre-proof

Table 12 The Relation between quality increases and long-term future returns: Fama-MacBeth regression analysis. Model Delta_quality Beta Size Bm op inv Max Mom Ivol Turn Illid Intercept R2 observations

1

2

3

4

0.0026***

0.0089***

0.0102***

(6.14) -0.0047 (-1.32) -0.0061*** (-3.65) 0.0024** (2.22) 0.0001 (0.05) -0.0007 (-0.64) -0.0852*** (-4.58) -0.0022 (-0.43) 0.0915 (0.70) -0.3840*** (-9.62) 0.0016 (1.02) 0.1686 (4.18)

(4.23) -0.0615** (-2.32) -0.0502*** (-3.90) 0.0178*** (2.40) -0.0017 (-0.23) -0.0021 (-0.46) -0.2022*** (-2.39) -0.0024 (-0.11) -1.5179*** (-2.65) -1.5243*** (-8.41) 0.0284*** (3.00) 1.4583 (4.90) 10.31 208906

(3.49) -0.0515 (-1.04) -0.1408*** (-6.92) 0.0230 (1.46) 0.0172 (1.65) -0.0113 (-1.00) -0.1548 (-1.13) -0.0752*** (-3.02) -2.3553* (-1.80) -2.5503*** (-8.21) 0.0271*** (2.79) 3.8973 (8.97) 12.97 208906

0.0127*** (2.92) -0.0562 (-1.04) -0.2092*** (-10.09) 0.0432** (2.13) 0.0389*** (2.42) -0.0275** (-2.21) -0.0176 (-0.18) -0.0583* (-1.79) 2.3009 (1.54) -2.6116*** (-7.73) 0.0360*** (2.64) 5.5851 (13.78) 14.00 208906

9.62 208906

Notes: This table reports the Fama-MacBeth average monthly coefficients for four models. Our main regression model is specified as follows: ri ,t  a   0  1del _ qualityi ,t   2 betai ,t   3 sizei ,t   4 bmi ,t   5 opi ,t   6 invi ,t   7 maxi ,t  8 momi ,t   9 ivoli ,t  10 turni ,t  11illidi ,t   i ,t 1

.

The dependent variable ( ri ,t  a ) is the cumulative stock returns for firm i from month t  1 to month t  a , where a equals 1, 12, 36, 60 (denoted as model (1), model (2), model (3) and model (4), respectively). Main independent variable are quality increases for firm i in month t ( del _ qualityi ,t ). Other control variables are described as Table 2. The sample period is from September 2004 to March 2019 and the t statistics are adjusted by Newey-West robust standard 12

Journal Pre-proof errors with 6 lags. *, **, *** show significance at the 0.1 level, 0.05 level and 0.01 level, respectively.

13

Journal Pre-proof Table 13 Fama-MacBeth regressions of stock returns on quality increase controlling the quality level. Model Delta_quality quality

1

2

3

0.0020***

0.0012*

(3.24) 0.0022** (2.07)

(1.76) 0.0031*** (3.08) 0.0004 (0.14) -0.0067*** (-4.04) 0.0026*** (2.59)

0.0011* (1.71) 0.0031*** (3.13) 0.0004 (0.15) -0.0066*** (-3.98) 0.0026*** (2.58) -0.0006 (-0.59) -0.0007 (-0.63) -0.0843*** (-4.72) -0.0041 (-0.84) 0.2336* (1.72) -0.3853*** (-9.44) 0.0018 (1.12) 0.1700*** (4.26)

10.20 208906

Beta Size Bm op inv

Intercept

0.0148 (1.53)

-0.0835*** (-4.62) -0.0039 (-0.81) 0.2367* (1.74) -0.3858*** (-9.36) 0.0017 (1.07) 0.1720*** (4.30)

R2 observations

2.21 208906

10.05 208906

Max Mom Ivol Turn Illid

Notes: This table reports the Fama-MacBeth average monthly coefficients for three models. Our main regression model is specified as follows: ri ,t  a   0  1del _ qualityi ,t   2 qualityi ,t   3betai ,t   4 sizei ,t   5bmi ,t   6 opi ,t   7 invi ,t  8 maxi ,t   9 momi ,t  10 ivoli ,t  11turni ,t  12 illidi ,t   i ,t 1

.

The dependent variable is the future stock returns for firm i in month t  1 and main independent variables are quality increase for firm i in month t ( del _ qualityi ,t ) and quality level for firm i in month t ( qualityi ,t ). Other control variables are described as Table 2. The sample period is from September 2004 to March 2019 and the t statistics are adjusted by Newey-West robust standard errors with 6 lags. *, **, *** show significance at the 0.1 level, 0.05 level and 0.01 level, respectively.

14

Journal Pre-proof Table 14 The raw and abnormal returns on portfolios of one-way sorting in the two groups Low

2

3

4

5

6

7

8

9

High

High-low

Panel A:Raw returns Dq_positive 1.40 T statistics (1.64) Dq_negative 0.35 T statistics (0.39)

1.31 (1.58) 0.47 (0.49)

1.41 (1.53) 0.44 (0.44)

1.21 (1.37) 0.86 (0.91)

1.25 (1.32) 1.03 (1.07)

1.54 (1.59) 0.86 (0.97)

1.62* (1.66) 1.21 (1.26)

1.52 (1.58) 0.90 (1.00)

1.84* (1.82) 0.99 (1.14)

1.76* (1.75) 1.56* (1.67)

0.36 (1.15) 1.21*** (3.20) 0.85 (1.63)

Panel B:Ff3 alphas Dq_positive 0.48*** T statistics (2.89) Dq_negative -1.14*** T statistics (-4.36)

0.32 (1.62) -1.00*** (-5.37)

0.19 (1.22) -0.89*** (-4.50)

0.08 (0.44) -0.38** (-2.03)

0.02 (0.13) -0.16 (-0.85)

0.24 (1.25) -0.22 (-1.07)

0.26** (1.97) -0.01 (-0.07)

0.12 (0.59) -0.34* (-1.67)

0.37* (1.79) -0.01 (-0.04)

0.24 (1.18) 0.58*** (3.00)

-0.24 (-0.97) 1.72*** (4.60) 1.96*** (4.42)

Notes: This table reports the results of one-way sorting in the two groups, “dq_positive” and “dq_negative”. “dq_positive” denotes the group where firms’quality increases exceeds and equals 0. “dq_negative” denotes the group where firms’quality increases do not exceeds 0. At the end of April, August and October each year, we sort the stocks into deciles based on quality increases in each group. We assume that each decile portfolio is held until the next adjustment and the monthly portfolio returns are value weighted. “Low” refers to the stocks in the lowest quality increases decile while “High” refers to the stocks in the highest quality increases decile. “High-Low” refers to the hedge portfolio which is long firms with high quality increase and short firms with low quality increases. The last two rows in each panel present the difference between the hedge portfolio return in the group “dg_negative” and the hedge portfolio return in the group “dg_positive” and corresponding t statistics. The table presents the monthly average returns (in percentage) and returns adjusted by Fama-French three factors (FF3 alpha, in percentage). The sample period spans from September 2004 through March 2019. *, **, *** show significance at the 0.1 level, 0.05 level and 0.01 level, respectively.

15

Journal Pre-proof

1.09 1.08 1.07 1.06 1.05 1.04 1.03

r_quality

1.02

ff3_quality

1.01 1 0.99 0.98 0.97 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60

Figure 1 Cumulative returns of hedge portfolios of quality increases in different holding periods Notes: This figure shows the cumulative returns of hedge portfolios of quality increases in different holding periods ranging from 1 month to 60 month. Following Akbas et al. (2017), the cumulative returns are calculated as the value of 1 yuan which is invested in month t and held for each of the following 60 month. The figure plots the raw returns (r_quality), FF3 alpha (ff3_qualtiy) of hedge portfolios respectively.

16