Q-Theory, Mispricing, and Profitability Premium: Evidence from China

Jiang, Fuwei; Qi, Xinlin; Tang, Guohao

doi:10.2139/ssrn.2635345

Cited by 7 publications

(8 citation statements)

References 21 publications

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“…The Sharpe ratios of different fundamental timing spreads are similar, which vary from 1.26 of BMbased spread portfolio to 1.35 of EP-based spread portfolio. These results are similar to those studies by studying the predictability of individual firm fundamentals in Chinese market (Jiang et al, 2018;Jiang, Tang, et al, 2019).…”

Section: Value-weighted Returns Of Different Strategiessupporting

confidence: 91%

“…We obtain the Chinese risk-free rate and Chinese Fama-French (1993) three-factor data from the CSMAR as well. Following Jiang, Rapach, Strauss, Tu, and Zhou (2011), Jiang et al (2018), Jiang, Tang, and Zhou (2019 and others, our sample consists of all of the Chinese A-share stocks with accounting and returns' data available traded on the main boards of Shanghai and Shenzhen, SME Board, and Chinext Board, to cover different levels of Chinese stock markets.…”

Section: Data Descriptionmentioning

confidence: 99%

“…Fundamental or value investing is first advocated by Graham and Dodd (1934), which suggests purchasing cheap stocks and quality stocks while selling expensive stocks and junk stocks. In Chinese market, Jiang, Qi, and Tang (2018) test the cross-sectional predictability of Chinese stock market with three profitability variables, and Liu, Stambaugh, and Yuan (2018) study the value premiums. Complementary to existing research, we explore the less studied time-series timing dimension of these fundamentals-based investment strategies.…”

Section: Introductionmentioning

confidence: 99%

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It takes two to tango: Fundamental timing in stock market

Tang

Jiang

et al. 2020

Int. J Fin Econ

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In this paper, we propose a fundamental timing strategy in both U.S. and Chinese stock markets to timing the fundamentals sorted portfolios such as value and profitability portfolios in the time-series dimension. We find that fundamental timing strategies based on moving average (MA) timing signals could generate substantial performance gains relative to buy-and-hold strategies. The annualized average return of fundamental timing strategies reaches about 37% with Sharpe ratio nearly 1.30. These findings are robust to Fama-French factor model adjustment, alternative lag lengths of MA signals, holding days, and transaction costs. Moreover, the fundamental timing premium cannot be explained away by market timing or business cycle, and fundamental timing is more profitable among firms with high idiosyncratic volatility and high illiquidity.

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Section: Value-weighted Returns Of Different Strategiessupporting

confidence: 91%

Section: Data Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

It takes two to tango: Fundamental timing in stock market

Tang

Jiang

et al. 2020

Int. J Fin Econ

Self Cite

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“…In addition, idiosyncratic volatility is positively correlated to mispricing [ 29 ]. Idiosyncratic volatility can also be used as proxy for mispricing, and findings show that valuation uncertainty does not amplify the profitability premium in the Chinese stock market [ 30 ]. Meanwhile, idiosyncratic risk can also be used as the proxy for arbitrage cost, because arbitrageurs are unable to hedge this risk [ 31 , 32 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Leverage structure and stock price synchronicity: Evidence from China

Zhang

Zhou

2020

PLoS ONE

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This paper investigate the impact of leverage structure on stock price synchronicity. To better understand the mechanism of the impact of leverage, we break leverage into operating leverage and financing leverage. This breakdown reveals the impact of different component of leverage. Moreover, in this paper, we employ the quantile regression model to investigate the impact of leverage on different level of stock price synchronicity, which provides us a more comprehensive picture. Our empirical results show, operating and financing leverage have negative impact on stock price synchronicity. Moreover, the higher the stock price synchronicity becomes, the higher this impact is. Furthermore, the marginal effect of financing leverage will be attenuated as the profitability of firms becomes higher, but the interaction effect doesn't exist for operating leverage. On the contrary, the marginal effect of financing leverage will be enhanced as the market capitalization of firms becomes larger, again, it doesn't exist for operating leverage. Finally, as firms are state-owned, the marginal effect of both operating and financing leverage will be higher.

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“…The model is built based on factor pool and dynamic time window design. a Refer to Hu & Gu, 2018;Zhang et al, 2014;Amihud, 2002;Goyenko et al, 2009;Jiang et al, 2018;Pastor & Stambaugh, 2003;Roll, 1984; Refer to Wu & Wu, 2003;Yang & Huang, 2005;Yang et al, 2020;Zhang et al, 2020;Zhao, 1998;Chan & Jegadeesh, 1996; Refer to Hu & Gu, 2018;Jiang et al, 2018;Loughran & Wellman, 2011; Refer to Li & Liao, 2007;Zhang et al, 2020. From Figure 3, the number of effective samples in China A-share market shows an overall upward trend, which is related to the number of listed companies, the proportion of ST and *ST companies, and the data quality of company factors. Considering this dynamic feature, this paper buys or holds the top 1% of predicted returns in each round of portfolio construction.…”

Section: Portfolio Performance Analysismentioning

confidence: 99%

Can Machine Learning Unlock the Continuous Alpha? Empirical Study Based on China A-Share Market

Lin¹,

Ye²

2021

OJBM

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With the development of fintech and artificial intelligence, machine learning algorithms are widely used in quantitative investment. Based on the listed companies in China A-share market from February 2005 to July 2020, quantitative stock selection models with machine learning algorithms are established to obtain continuous alpha returns. The results show that machine learning algorithms can effectively identify the relationship between factors and returns and then improve the performance of the quantitative stock selection model. China A-share market is a weak-form efficient market. By mining the factors that are not fully digested by the market, continuous alpha returns can be obtained. The ensemble algorithms represented by the extremely randomized tree (ET) and light gradient boosting machine (LGBM) perform best in stock market prediction.

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Q-Theory, Mispricing, and Profitability Premium: Evidence from China

Cited by 7 publications

References 21 publications

It takes two to tango: Fundamental timing in stock market

It takes two to tango: Fundamental timing in stock market

Leverage structure and stock price synchronicity: Evidence from China

Can Machine Learning Unlock the Continuous Alpha? Empirical Study Based on China A-Share Market

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