Leverage and the Cross‐Section of Equity Returns

Doshi, Hitesh; Jacobs, Kris; Kumar, Praveen; Rabinovitch, Ramon

doi:10.1111/jofi.12758

Cited by 52 publications

(25 citation statements)

References 106 publications

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“…However, the Capital Asset Pricing Model (CAPM) as in Eq. (1) is very often employed as shown in some studies ( Bartholdy and Peare, 2005 ; Da et al., 2012 ; Fama and French, 1996b ; Jacobs and Shivdasani, 2012 ; Zhang, 2017 ) even some recent studies (e.g., Binsbergen and Opp, 2019 ; Clementi and Palazzo, 2019 ; Doshi et al., 2019 ; Halim et al., 2019 ; Martin and Wagner, 2019 ; Zhang, 2019 ) showed its weaknesses. One of the possible reasons the CAPM is widely used in practice is that this model shows a very simple linear relationship between a stock's Beta and expected returns.…”

Section: Introductionmentioning

confidence: 99%

Is estimating the Capital Asset Pricing Model using monthly and short-horizon data a good choice?

Pham

Phuoc

2020

Heliyon

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This research argued for estimating the Capital Asset Pricing Model (CAPM) using daily and medium-horizon data over monthly and short horizon-data. Using a Gibbs sample, the Bayesian framework via both parametric and non-parametric Bayes estimators, confidence interval approach, and six data sets (two daily, two weekly, and two monthly data) from a sample of 150 randomly selected S&P 500 stocks from 2007 – 2019, the empirical results showed that the CAPM using daily data yielded a statistically significant higher model fit and smaller Beta standard deviation, model error, and Alpha compared with monthly data. The CAPM using medium-horizon data yielded a statistically significant higher model fit, smaller Beta standard deviation and Alpha, and much less zeroed Betas compared with short-horizon data. These findings show 1) daily data is more reliable and efficient, has higher forecasting power, and fits better with the assumption of market efficiency compared with monthly data. 2) Medium-horizon data is more reliable and efficient, has more explanatory power, and fits better with the assumption of market efficiency compared with monthly data. Therefore, these findings challenge the common practices of using monthly (quarterly/annually) and short-horizon data among the practitioners and researchers in asset pricing work.

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Section: Introductionmentioning

confidence: 99%

Is estimating the Capital Asset Pricing Model using monthly and short-horizon data a good choice?

Pham

Phuoc

2020

Heliyon

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“…Second, even if the estimated betas correctly reflect the future riskiness of firms, they subsume the key characteristics examined in the paper. For instance, the CAPM beta is expected to reflect the degree of financial flexibility of a firm (Doshi et al, 2019). Similarly, the ability of firms to profitably alter the scale of operations is expected to be embedded in the firm's exposure to the value factor (Novy‐Marx, 2011).…”

Section: Methodology and Datamentioning

confidence: 99%

Impact of operational fragility on stock returns: Lessons from COVID‐19 crisis

et al. 2022

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We examine how the market valuation of firms varies on account of their operational fragility that makes them vulnerable to the COVID‐19 pandemic. Using the data on plant location that uniquely identifies the vulnerability of firms to operational disruptions, we find that firms with plants located in zones susceptible to higher infections earn significantly lower returns. For firms with high operational fragility, the marginal value of financial flexibility and operating flexibility is higher. The adverse impact of the operational fragility is lower for firms affiliated with the larger business groups. The paper identifies unique channels associated with the pandemic that impact firm value.

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“…Forecasting Realized Volatility via the Cross-Section is indeed not so common in the financial econometrics literature even though the use of aggregate market risks determined by large Cross-Sections is a widely used idea (see, e.g., Bollerslev et al (2018), Doshi et al (2019) and Li et al (2017a)). Our proposed econometric framework is based on statistical estimation of "pooled risks" as well as variable selection via the Lasso norm.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecast Evaluation in Large Cross-Sections of Realized Volatility

Katsouris¹

2021

Preprint

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In this paper, we consider the forecast evaluation of realized volatility measures under cross-section dependence using equal predictive accuracy testing procedures. We evaluate the predictive accuracy of the model based on the augmented cross-section when forecasting Realized Volatility. Under the null hypothesis of equal predictive accuracy the benchmark model employed is a standard HAR model while under the alternative of non-equal predictive accuracy the forecast model is an augmented HAR model estimated via the LASSO shrinkage. We study the sensitivity of forecasts to the model specification by incorporating a measurement error correction as well as cross-sectional jump component measures. The out-of-sample forecast evaluation of the models is assessed with numerical implementations.

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Leverage and the Cross‐Section of Equity Returns

Cited by 52 publications

References 106 publications

Is estimating the Capital Asset Pricing Model using monthly and short-horizon data a good choice?

Is estimating the Capital Asset Pricing Model using monthly and short-horizon data a good choice?

Impact of operational fragility on stock returns: Lessons from COVID‐19 crisis

Forecast Evaluation in Large Cross-Sections of Realized Volatility

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