Which Factors?*

Hou, Kewei; Mo, Haitao; Chen, Xue; Zhang, Lu

doi:10.1093/rof/rfy032

Cited by 146 publications

(52 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These anomalies can be broadly classified as strategies related with value, momentum, investment, profitability, and intangibles. We test conditional versions of the CAPM, 4-factor model of HXZ (2015) and Hou, Mo, Xue, and Zhang (2019) (HXZ), and the 5-factor model of Fama and French (FF) 2015, (2016). We estimate a conditional HXZ model that contains the value spread, T-bill rate, investment-to-capital ratio, and stock return dispersion as instruments.…”

Section: Introductionmentioning

confidence: 99%

New Evidence on Conditional Factor Models

Cooper¹,

Maio²

2018

J. Financ. Quant. Anal.

View full text Add to dashboard Cite

We estimate conditional multifactor models over a large cross section of stock returns matching 25 CAPM anomalies. Using conditioning information associated with different instruments improves the performance of the Hou, Xue, and Zhang (HXZ) (2015) and Fama and French (FF) (2015), (2016) models. The largest increase in performance holds for momentum, investment, and intangibles-based anomalies. Yet, there are significant differences in the performance of scaled models: HXZ clearly dominates FF in explaining momentum and profitability anomalies, while the converse holds for value–growth anomalies. Thus, the asset pricing implications of alternative investment and profitability factors (in a conditional setting) differ in a nontrivial way.

show abstract

Section: Introductionmentioning

confidence: 99%

New Evidence on Conditional Factor Models

Cooper¹,

Maio²

2018

J. Financ. Quant. Anal.

View full text Add to dashboard Cite

show abstract

“…That study proposed a two-coefficient model by adding an intercept coefficient alpha (α) to the original CAPM to represent the stock's expected excess return when the market risk premium is zero (α equals zero in an efficient market). Recent studies confirmed the alpha existed for the real stocks ( Barillas and Shanken, 2017 ; Fama and French, 1996b , 2015 ; 2018 ; Hou et al., 2015 , 2020b ; Hou et al., 2019 , 2020a ; Pham and Phuoc, 2020 ; Phuoc, 2018 ; Zhang, 2017 ). However, that study does not explore and capture other risks such as the firm's financial ratios and investment, stock momentum, and macroeconomic risks.…”

Section: Introductionmentioning

confidence: 81%

“…Importantly, even Fama and French (2004) admitted and other studies ( Berk, 1995 ; Ferson et al., 1999 ; Kim et al., 2011 ; Kothari et al., 1995 ; Lo and MacKinlay, 1990 ; MacKinlay, 1995 ; Wang and Wu, 2011 ) also pointed out that the traded factors, SMB, HML, and others employed in the FF3 (in FF5 and FF6 as well) do not have a solid background but brute-force ideas. So, these FF3, FF5, and FF6 are just ad-hoc models ( Hou et al., 2019 ). Also, the FF5 is not driven by the valuation theory as claimed ( Hou et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…So, these FF3, FF5, and FF6 are just ad-hoc models ( Hou et al., 2019 ). Also, the FF5 is not driven by the valuation theory as claimed ( Hou et al., 2019 ). Also, the HML (value), RMW (profitability), and CMA (investment) factors were shown to be non-significant in explaining the stock returns ( Fama and French, 2015 ; Hou et al., 2015 , 2020a,b; Kim et al., 2011 ; Kothari et al., 1995 ; Kubota and Takehara, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

“…Also, in many cases, the q -factor model outperformed the FF3 and Carhart (1997) 4-factor model in capturing the significant anomalies. Similarly, Hou et al. (2019) revised the

by adding one more factor, the expected investment growth.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An augmented capital asset pricing model using new macroeconomic determinants

Pham¹,

Phuoc

2020

Heliyon

View full text Add to dashboard Cite

Using the interview results of 26 experienced scholars, managers, and professional stock traders in conjunction with findings of recent studies in economics, we proposed an augmented asset pricing model using the macroeconomic determinants representing the macroeconomic state variables to explain the nexus between these risks and the U.S. stock returns. This non-traded factor model (MAPM) is inspired by and based on the macroeconomic theory and models and consists of the market return, U.S. prime rate, U.S. government long-term bond rate, and exchange rate of USD/EUR as in Eq. (1). Using the Bayesian approach (via two Bayes and t.Bayes estimators) and monthly returns of the S&P 500 stocks from 2007- 2019, our results showed the MAPM consistently yielded a statistically significant greater forecasting, explanatory power, and model adequacy compared to the most used capital asset pricing model (CAPM) in practice. Interestingly, our study found and confirmed ( t -statistic > 3) that the last two macroeconomic determinants have a statistically significant positive effect on the stock returns, which also supports the MAPM. These findings suggest the MAPM is a more efficient and advantageous model compared to the CAPM. So, practitioners would be better off employing the MAPM over CAPM in practice and research.

show abstract

Identifying factors via automatic debiased machine learning

Maasoumi,

Wang,

Wang

et al. 2024

J of Applied Econometrics

View full text Add to dashboard Cite

SummaryIdentifying risk factors that have significant explanatory power for the cross‐sectional asset returns is fundamental in asset pricing. We adopt a novel automatic debiased machine learning (ADML) method proposed by Chernozhukov, Newey, and Singh (2022) to robustly estimate partial pricing effect of a certain factor controlling for a large number of confounding factors under a nonlinear stochastic discount factor (SDF) assumption. The ADML resolves biased estimation, non‐robustness, and overfitting issues that are common to traditional machine learning approaches. We find that the most significant factors selected by the ADML outperform the Fama–French sparse factors and factors identified via the double‐selection LASSO method under a linear factor model assumption. Out of a high‐dimensional zoo of US stock market factors commonly tested in the finance literature, we identify approximately 30 to 50 factors having significant but declining pricing power in explaining the cross‐section of stock returns. Our findings are robust to hyperparameter settings and choices of test assets and machine learning methods.

show abstract

Which Factors?*

Cited by 146 publications

References 33 publications

New Evidence on Conditional Factor Models

New Evidence on Conditional Factor Models

An augmented capital asset pricing model using new macroeconomic determinants

Identifying factors via automatic debiased machine learning

Contact Info

Product

Resources

About