The Fama and French three-factor model in developing markets: evidence from the Chinese markets

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The authors study the Fama and French three-factor (FF-3F) model in relation to a developing market. To this end, they consider Chinese stock markets over the period 1995–2008, which is to say, over a period when these markets are recognized as “developing” markets influenced by speculative activity. The authors find that the model appears to be working as a form of “principal component analysis for the determinants of stock price formation with book-to-market (B/M) as the “variable of choice” on account of that it captures the earnings-to-price (E/P), cash-flow-to-price (C/P) and sales-to-price (S/P) variables while remaining largely uncorrelated with firm size (whereas E/P, C/P and S/P are themselves positively correlated with firm size). The variables, however, are unrelated to risk as represented by market exposure, volatility, or leverage.

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    • Table 1. Descriptive statistics for the Chinese A-share market over the period January 1995 – December 2008
    • Table 2. Basic characteristics for down-markets, up-markets and full sample over the period January 1995 – December 2008
    • Table 3. Average equal-weighted monthly returns on double-sorted portfolios formed on size and book-to-market (B/M) quintiles from 1995 through 2008
    • Table 4. Fama-French three-factor model regressions over the period 1995–2008
    • Table 5. Returns and firm characteristics for various characteristic sorts into quintile portfolios
    • Table 6. Average returns for double-sorted portfolios formed on total volatility, size, and book-to-market ratio (B/M)
    • Table 7. Average characteristics for quintile portfolios formed on total volatility from 1995 through 2008
    • Allen, F., Qian, J., Qian, M., Zhao, M. (2008). A review of China’s financial system and initiatives for the future (Working paper, Wharton Financial Institutions Center).
    • Beltratti, A., Bortolotti, B. (2006). The non-tradable share reform in the Chinese stock market (Working paper, University of Fondazi one Eni Enrico Mattei, Italy).
    • Chan, L., Hamao, Y., Lakonishok, J. (1991). Fundamentals and stock returns in Japan. Journal of Finance, 46, 1739-1789.
    • Drew, M. E., Naughton, T., Veeraraghavan, M. (2003). Firm size, book to market equity and security returns: evidence from the Shanghai Stock Exchange. Australian Journal of Management, 28, 119-140.
    • Eun, C. S., Huang, W. (2007). Asset pricing in China’s domestic stock markets: Is there a logic? Pacific-Basin Finance Journal, 15, 434-451.
    • Fama, E. F., French, K. R. (1992). The cross-section of expected stock returns. Journal of Finance, 47, 427-465.
    • Fama, E. F., French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3-56.
    • Fama, E. F., French, K. R. (1995). Size and book-to-market factors in earnings and returns. Journal of Finance, 50, 131-155.
    • Fama, E. F., French, K. R. (1996). Multifactor explanations of asset pricing anomalies. Journal of Finance, 51, 55-84.
    • Fama, E. F., French, K. R. (2015). A five-factor asset pricing model. Journal of Financial Economics, 116, 1-22.
    • Kang, J., Liu, M., Ni, X. S. (2002). Contrarian and momentum strategies in the China stock market: 1993–2000. Pacific-Basin Finance Journal, 10, 243-265.
    • Lakonishok, J., Shleifer, A., Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49, 1541- 1578.
    • Li, G. (2008). China’s stock market: inefficiencies and institutional implications. China & World Economy, 16, 81-96.
    • Li, Y. M., Yan, D. Y., Greco, J. (2006). Market segmentation and price differentials between A-shares and H-shares in the Chinese stock markets. Journal of Multinational Financial Management, 16, 232-248.
    • Lu, F, Balatbat, M., Czernkowski, R. (2008). The role of consideration in China’s split share structure reform (Working paper, University of New South Wales).
    • Ma, S. G., Faff, R. (2007). Market conditions and the optimal IPO allocation mechanism in China. Pacific-Basin Finance Journal, 15, 121-139.
    • Merton, R. C. (1973). An intertemporal capital asset pricing model. Econometrica, 41, 867-887.
    • Ng, L., Wu, F. (2006). Revealed stock preferences of individual investors: Evidence from Chinese equity markets. Pacific-Basin Finance Journal, 14, 175-192.
    • Ng, L., Wu, F. (2007). The trading behavior of institutions and individuals in Chinese equity markets. Journal of Banking & Finance, 31, 2695-2710.
    • Poon, W., Firth, M., Fung, H. (1998). Asset pricing in segmented capital markets: Preliminary evidence from China-domiciled companies. Pacific-Basin Finance Journal, 6, 307-319.
    • Qi, D., Wu, W., Zhang, H. (2000). Shareholding structure and corporate performance of partially privatized firms: Evidence from listed Chinese companies. Pacific- Basin Finance Journal, 8, 587-610.
    • Ross, S. A. (1976). The Arbitrage Theory of capital asset pricing. Journal of Economic Theory, 13, 341-360.
    • Sun, Q., Chong, T. (2007). Determining the contributions to price discovery for Chinese cross-listed stocks. Pacific-Basin Finance Journal, 15, 140-153.
    • Sun, Q., Tong, W.H.S. (2000). The effect of market segmentation on stock prices: The China syndrome. Journal of Banking and Finance, 24, 1875-1902.
    • Sun, Q., Tong, W.H.S. (2003). China share issue privatization: The extent of its success. Journal of Financial Economics, 70, 183-222.
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