Investment and the Cross‐Section of Equity Returns

Clementi, Gian Luca; Palazzo, Berardino

doi:10.1111/jofi.12730

Cited by 36 publications

(25 citation statements)

References 57 publications

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“…This is notable in itself, since existing models that fit the cross-section of asset returns fail to match the cross-section of firm investment, and vice versa (see, e.g. Clementi and Palazzo, 2015). Further, a notable feature of the data is that firm-level investment and innovation exhibit relatively low persistence, while valuation ratios (Q) are highly persistent.…”

Section: Model Fitmentioning

confidence: 96%

Left Behind: Creative Destruction, Inequality, and the Stock Market

Kogan

Papanikolaou

Stoffman³

2020

Journal of Political Economy

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We develop a general equilibrium model of asset prices in which benefits of technological innovation are distributed asymmetrically. Financial market participants do not capture all economic gains from innovation even when they own shares in innovating firms. Such gains accrue partly to the innovators, who cannot sell claims on proceeds from their future ideas. We show how the resulting inequality among agents can give rise to a high risk premium on the aggregate stock market, return comovement and average return differences among firms, and the failure of traditional representative-agent asset pricing models to account for cross-sectional differences in risk premia. * Earlier versions of this paper circulated under the titles "Technological Innovation: Winners and Losers," and "Winners and Losers: Creative Destruction and the Stock Market." We thank

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Section: Model Fitmentioning

confidence: 96%

Left Behind: Creative Destruction, Inequality, and the Stock Market

Kogan

Papanikolaou

Stoffman³

2020

Journal of Political Economy

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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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“…As described above, trade uncertainty is T P U i,t , that is, the number of mentions of trade uncertainty words divided by the total number of words in the firms' earnings calls. Our investment measure is log k i,t+h − log k i,t−1 , where k i,t is the capital stock of firm i at the start of period t, following Ottonello and Winberry (2018) and Clementi and Palazzo (2019). α i and α s,t denote firm and sector-by-quarter fixed effects, respectively.…”

Section: Firm-level Responses To Trade Policy Uncertaintymentioning

confidence: 99%

Does Trade Policy Uncertainty Affect Global Economic Activity?

et al. 2019

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We study the effects of unexpected changes in trade policy uncertainty (T P U ) on the U.S. economy. We construct three measures of T P U based on newspaper coverage, firms' earnings conference calls, and aggregate data on tariff rates. We document that increases in T P U reduce investment and activity using both firm-level and aggregate macroeconomic data. We interpret the empirical results through the lens of a two-country general equilibrium model with nominal rigidities and firms' export participation decisions. In the model as in the data, news and increased uncertainty about higher future tariffs reduce investment and activity.

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Investment and the Cross‐Section of Equity Returns

Cited by 36 publications

References 57 publications

Left Behind: Creative Destruction, Inequality, and the Stock Market

Left Behind: Creative Destruction, Inequality, and the Stock Market

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

Does Trade Policy Uncertainty Affect Global Economic Activity?

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