Asset pricing and nominal price illusion in China

The Capital Asset Pricing Model (CAPM), while foundational, has limitations. Scholars have developed multi-factor models like Fama-French three-factor (FF3) and five-factor (FF5) models to address CAPM's shortcomings by incorporating additional factors beyond market risk. These models, however, still face unexplained anomalies. One such anomaly is the low-price effect, where low-priced stocks exhibit higher returns and volatility. Explanations for this effect range from size premiums and liquidity effects to investor behavioral biases like nominal price illusion. The Chinese stock market, characterized by a high proportion of retail investors, presents a unique setting to examine the low-price premium. While the applicability of Fama's models to the Chinese market is debated, this study investigates whether a low-price premium factor can improve asset pricing models in this context.

The paper reviews the evolution of asset pricing models, starting from CAPM and progressing to multi-factor models like APT, FF3, and FF5. It highlights the limitations of these models and the ongoing debate about their applicability to the Chinese stock market. The literature on the low-price effect is discussed, encompassing various theoretical explanations including size effects, liquidity, and behavioral finance perspectives like nominal price illusion. Existing research on the Chinese stock market's unique investor structure and its impact on asset pricing is also examined, noting the dominance of retail investors and their preference for low-priced stocks. The paper highlights the lack of comprehensive research integrating the low-price premium effect into multi-factor models for the Chinese context.

This study uses monthly data from the China Stock Market & Accounting Research Database (CSMAR) from January 2007 to December 2020. The sample comprises all listed companies in China's A-share market (4392). The FF3 and FF5 models are employed as baselines. A low-price premium (LPP) factor is constructed using the Luo et al. (2017) method (LX method), incorporating factors like stock price, retail investor size, institutional investor shareholding ratio, analyst shareholding, short-selling mechanism, and industry and year fixed effects. The LPP factor is then added to both the FF3 and FF5 models, creating four-factor and six-factor models respectively. The performance of these models is evaluated using regression analysis, focusing on the significance of the LPP factor and the overall explanatory power of the models. The GRS test is utilized to assess the robustness of the models. Additionally, two-dimensional groupings are employed to further analyze the LPP effect across various company characteristics.

The study's descriptive statistics show that while market factors (MKT, SMB) have positive average returns, other factors (HML, RMW, CMA, LPP) have negative average returns. Factor spanning regressions demonstrate that even after controlling for other factors, the A-share market exhibits significant size, book-to-market, profitability, and low-price premium effects. Analysis of two-dimensional groupings (LPP-Size, LPP-EP, LPP-OP, LPP-Inv) reveals a consistent low-priced stock premium effect, where excess returns diminish as LPP increases. Regression results show that the LPP factor is statistically significant across all models (FF3, FF3+LPP, FF5, FF5+LPP), with negative coefficients indicating a negative relationship between LPP and stock returns. The inclusion of LPP improves the explanatory power of both FF3 and FF5, as evidenced by R-squared values. The six-factor model (FF5+LPP) shows the highest explanatory power. The GRS test confirms the robustness of the models including the LPP factor, with lower GRS statistics compared to the baseline FF3 and FF5 models. The FF5 generally outperforms the FF3 in terms of explanatory power and robustness, consistent with previous findings.

The findings confirm the presence of a low-price premium effect in the Chinese A-share market. This effect is linked to the high proportion of retail investors who, due to behavioral biases (nominal price illusion), transaction costs, and wealth constraints, prefer low-priced stocks. This behavior increases the volatility and liquidity of these stocks, potentially lowering overall risk and contributing to high returns. The superior performance of the six-factor model highlights the importance of including the LPP factor in asset pricing models for the Chinese market. The study’s results suggest that investor behavior plays a crucial role in asset pricing in emerging markets like China, highlighting the need for models that incorporate these behavioral aspects.

This study demonstrates that the low-price premium (LPP) is a significant and robust asset pricing factor in the Chinese A-share market. The six-factor model incorporating LPP outperforms the FF3 and FF5 models in explanatory power and robustness. The findings underscore the importance of considering behavioral factors and the unique investor structure of emerging markets when developing asset pricing models. Future research could explore the dynamic relationship between LPP and other market factors, investigate the influence of regulatory changes on LPP, and examine the low-price premium effect in other emerging markets.

The study uses data from a specific period (2007-2020), and the findings might not generalize to other time periods. The LPP factor construction relies on specific variables and methodology, which may affect the results. The study focuses solely on the A-share market in China; different results might be found in other markets or market segments. Further research is needed to delve into the underlying mechanisms driving the low-price premium and to refine the LPP factor construction.