Abstract:The positive return differential between high and low book-to-market stocks is the value effect, having been documented in many markets around the world. A great number of researches have shown that the market-to-book (MB) factor performs poorly in tests on China’s portfolios. The mechanism for why the MB effect is not significant in China needs a more detailed description, but further study in this area is deficient. As an emerging market, the volatility of China’s stock market is larger than that of developed countries. The high return volatility leads us to suspect that the market behavior of investors may interfere with the measurement of the MB effect. We decompose MB into market-to-value and value-to-book components by using the model introduced by Rhodes-Kropf, Robinson, and Viswanathan (2005). By analyzing the dynamic adjustment behavior between the value-to-book component and the convenience MB by using the partial adjustment model proposed by Flannery and Rangan (2006), we find that MB fluctuates up and down around the value-to-book component, and we define this finding as an anchor-twisting motion. After excluding the anchor-twisting component, we re-test the three-factor pricing model proposed by Fama and French by using the Chinese portfolio, and find that the MB effect changes from insignificant (t value=-1.202) to significant (t value=-3.561). The finding of our research provides a standard data preprocessing program for the follow-up research of Fama and French’s factor pricing test on China’s portfolios. The result of the partial adjustment model shows that MB’s adjustment speed is 0.85, which indicates that the typical firm completes more than half of its proper market capital adjustment in less than one year. The adjustment parameter is significant at the 99 confidence level, which suggests that the anchor-twisting motion is widespread in Chinese stock markets. Over the 1998 to 2017 period, a long-short portfolio strategy based on the conventional MB ratio produces an average return of -2.3% per year. The same strategy based on value-to-book produces an average return of 8.4%, whereas the market-to-value produces an average return of -10.3%. Our baseline results show that the entire value premium concentrates on the value-to-book component. The market behavioral component (market-to-value) interferes with the measurement of the conventional MB effect. The formal pricing tests show that the value-to-book component can explain the cross-section of returns. The parameter of the three-factor regression rises by 42 basis points from MB’s -0.017 (t=-1.202) to M*B’s -0.059 (t=-3.561), significant at 99% confidence level. We give a specific explanation for the source of the anchor-twisting motion. The M (market capital) and B (book value) have different information transmission efficiency. M’s information transmission efficiency is higher because the price is transmitted in real-time, while B’s information transmission efficiency is lower because the financial information is disclosed quarterly. Investors always optimistically or pessimistically predict the future based on past information, when the past B rises, their expectation for future B will also rise. Once they find that their optimistic or pessimistic expectations are not in line with the new disclosed B, they will overbuy or sell stocks in the opposite direction. Under this view, the anchor-twisting motion represents reversals of expectation errors, which tend to occur around earnings announcement dates following portfolio formation. The anchor-twisting motion reflects systematically optimistic and pessimistic expectations. The widespread anchor-twisting motion is due to the inefficiency of information transmission in the Chinese stock market. We suggest that China continuously improve the information disclosure system of listed companies. The contributions of this paper are summarized as follows. (1) We define the anchor-twisting motion and find it is widespread in the Chinese market. (2) By decomposing MB into market-to-value and value-to-book components, we find that the entire value premium concentrates on the value-to-book components, and the market-to-value component interferes with the measurement of the conventional MB effect. (3) We provide a standard data preprocessing program for the follow-up research of Fama and French’s factor pricing test on China’s portfolios.
刘彬, 肖文. 中国的市值账面比效应为什么不显著?[J]. 浙江大学学报(人文社会科学版), 2021, 51(3): 117-131.
Liu Bin, Xiao Wen. Why Is the Market-to-Book Effect Not Significant in China?. JOURNAL OF ZHEJIANG UNIVERSITY, 2021, 51(3): 117-131.
1 Lakonishok J., Shleifer A. & Vishny R. W., “Contrarian investment, extrapolation, and risk,” The Journal of Finance, Vol. 49, No. 5 (1994), pp. 1541-1578. 2 Golubov A. & Konstantinidi T., “Where is the risk in value? evidence from a market-to-book decomposition,” The Journal of Finance, Vol. 74, No. 6 (2019), pp. 3135-3186. 3 Rhodes-Kropf M., Robinson D. T. & Viswanathan S., “Valuation waves and merger activity: the empirical evidence,” Journal of Financial Economics, Vol. 77, No. 9 (2005), pp. 561-603. 4 肖作平: 《资本结构影响因素和双向效应动态模型——来自中国上市公司面板数据的证据》,《会计研究》2004年第2期,第36-41页。 5 连玉君、钟经樊: 《中国上市公司资本结构动态调整机制研究》,《南方经济》2007年第1期,第23-38页。 6 Fama E. F. & French K. R., “Size, value, and momentum in international stock returns,” Journal of Financial Economics, Vol. 105, No. 3 (2012), pp. 457-472. 7 Fama E. F. & French K. R., “A five-factor asset pricing model,” Journal of Financial Economics, Vol. 116, No. 1 (2015), pp. 1-22. 8 Fama E. F. & French K. R., “Value versus growth: the international evidence,” The Journal of Finance, Vol. 53, No. 6 (1998), pp. 1975-1999. 9 Kogan L. & Papanikolaou D., “Growth opportunities, technology shocks, and asset prices,” The Journal of Finance, Vol. 69, No. 2 (2014), pp. 675-718. 10 Liew J. & Vassalou M., “Can book-to-market, size and momentum be risk factors that predict economic growth,” Journal of Financial Economics, Vol. 57, No. 2 (2000), pp. 221-245. 11 Lettau M. & Wachter J. A., “Why is long-horizon equity less risky? a duration based explanation of the value premium,” The Journal of Finance, Vol. 62, No. 1 (2007), pp. 55-92. 12 Amit R. & Livnat J., “Diversification and the risk-return trade-off,” Academy of Management Journal, Vol. 31, No. 1 (1988), pp. 154-166. 13 Kumar M. V. S., “Are joint ventures positive sum games? the relative effects of cooperative and noncooperative behavior,” Strategic Management Journal, Vol. 32, No. 1 (2010), pp. 32-54. 14 Shleifer A. & Vishny R. W., “Stock market driven acquisitions,” Journal of Financial Economics, Vol. 70, No. 3 (2003), pp. 295-311. 15 Lui D., Markov S. & Tamayo A., “What makes a stock risky? evidence from sell-side analysts’ risk ratings,” Journal of Accounting Research, Vol. 45, No. 3 (2007), pp. 629-665. 16 Da Z. & Warachka M. C., “Cashflow risk, systematic earnings revisions, and the cross-section of stock returns,” Journal of Financial Economics, Vol. 94, No. 3 (2009), pp. 448-468. 17 Iyer D. N. & Miller K. D., “Performance feedback, slack, and the timing of acquisitions,” The Academy of Management Journal, Vol. 51, No. 4 (2008), pp. 808-822. 18 Fama E. F. & French K. R., “Common risk factors in the returns on the stocks and bonds,” Journal of Financial Economics, Vol. 33, No. 1 (1993), pp. 3-56. 19 Daniel K. & Titman S., “Market reactions to tangible and intangible information,” The Journal of Finance, Vol. 61, No. 4 (2006), pp. 1605-1643. 20 Daniel K. & Titman S., “Testing factor-model explanations of market anomalies,” Critical Finance Review, Vol. 1, No. 1 (2013), pp. 103-139. 21 Alti A. & Titman S., “A dynamic model of characteristic-based return predictability,” The Journal of Finance, Vol. 74, No. 6 (2019), pp. 3187-3216. 22 Kleibergen F. & Zhan Z., “Robust inference for consumption-based asset pricing,” The Journal of Finance, Vol. 75, No. 1 (2020), pp. 507-550. 23 Kubota K. & Takehara H., “Does the Fama and French five-factor model work well in Japan,” International Review of Finance, Vol. 18, No. 1 (2017), pp. 137-146. 24 Chui A. C. W. & Wei K. C. J., “Book-to-market, firm size, and the turn-of-the-year effect: evidence from Pacific-Basin emerging markets,” Pacific-Basin Finance Journal, Vol. 6, No. 3-4 (1998), pp. 275-293. 25 Griffin J. M., “Are the Fama and French factors global or country specific,” The Review of Financial Studies, Vol. 15, No. 3 (2002), pp. 783-803. 26 Carhart M. M., “On persistence in mutual fund performance,” The Journal of Finance, Vol. 52, No. 1 (1997), pp. 57-82. 27 黄兴旺、胡四修、郭军: 《中国股票市场的二因素模型》,《当代经济科学》2002年第5期,第50-57,95页。 28 顾娟、丁楹: 《中国证券市场价值成长效应的实证研究》,《经济评论》2003年第2期,第101-105页。 29 陈学胜: 《中国股票市场的三因子时变风险溢价模型研究》,《南方经济》2007年第4期,第67-73页。 30 贺炎林: 《基于状态转移信息对FF三因子模型的改进》,《中国管理科学》2008年第1期,第7-15页。 31 潘莉、徐建国: 《A股市场的风险与特征因子》,《金融研究》2011年第10期,第140-154页。 32 田利辉、王冠英、张伟: 《三因素模型定价:中国与美国有何不同?》,《国际金融研究》2014年第7期,第 37-45页。 33 李倩、梅婷: 《三因素模型方法探析及适用性再检验:基于上证A股的经验数据》,《管理世界》2015年第4期,第184-185页。 34 熊明达: 《Fama-French三因素模型在中国股市的应用——基于A股市场的实证检验》,《当代经济》2015年第26期,第130-131页。 35 杨炘、陈展辉: 《中国股市三因子资产定价模型实证研究》,《数量经济技术经济研究》2003年第12期,第 137-141页。 36 李志冰、杨光艺、冯永昌等: 《Fama-French五因子模型在中国股票市场的实证检验》,《金融研究》2017年第6期,第191-206页。 37 Goranova M., Dharwadkar R. & Brandes P., “Owners on both sides of the deal: mergers and acquisitions and overlapping institutional ownership,” Strategic Management Journal, Vol. 31, No. 10 (2010), pp. 1114-1135.