Count (and count-like) data in finance

Cohn, Jonathan B.; Liu, Zack; Wardlaw, Malcolm

doi:10.1016/j.jfineco.2022.08.004

Cited by 467 publications

(35 citation statements)

References 25 publications

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“…We estimate equation (5) using Poisson regressions, which have two advantages (Cohn, Liu, and Wardlaw (2022)). First, Poisson regressions account for the distributional characteristics of our count‐based outcomes (they provide unbiased estimates for dependent variables with a large mass of values at zero combined with severe skewness).…”

Section: Economic Applicationsmentioning

confidence: 99%

Firm‐Level Climate Change Exposure

Sautner¹,

Lent²,

Vilkov³

et al. 2023

The Journal of Finance

418

134

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We develop a method that identifies the attention paid by earnings call participants to firms' climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. We show that the measures are useful in predicting important real outcomes related to the net-zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets. CLIMATE CHANGE WILL PROFOUNDLY AFFECT the way business is conducted. Scientists have developed complex models that estimate the effect of greenhouse gas emissions on the global climate. At the same time, however, little

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Section: Economic Applicationsmentioning

confidence: 99%

Firm‐Level Climate Change Exposure

Sautner¹,

Lent²,

Vilkov³

et al. 2023

The Journal of Finance

418

134

View full text Add to dashboard Cite

show abstract

“… 5 We follow the estimation procedure proposed by Cohn et al (2022) to tackle with count data, and document similar evidence that the number of bonds does not respond significantly to pandemic shocks (not reported). …”

mentioning

confidence: 60%

Sovereign debt responses to the COVID-19 pandemic

Zheng

2023

Journal of International Economics

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“…In light of the mathematical issues discussed by Cohn et al (2022), in particular, to have an appropriate economic interpretation, we test Hypothesis 1 by estimating Poisson specifications.…”

Section: Resultsmentioning

confidence: 99%

How top management team social status impacts innovation

Radfard,

Pistilli,

Paccagnini

2023

R & D Management

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Scholars have investigated the effect that top management team (TMT) status has on several organizational dimensions, including strategic decision, risk propensity, and, ultimately, performance. However, the existing literature is relatively silent on the effect of TMT status on innovation. Our scope is to cover that research gap. Grounding our reasoning on two different yet intertwined literature streams – one on the TMT status and the other on innovation – we predict that TMT status should be positively correlated with innovation and its market value, but not with its scientific value. Relying on a unique, hand‐crafted dataset composed of 833 firm‐years' observations for the period 2005–2010, we can validate our hypotheses. Our study contributes to a better understanding of the relationship between TMT status and innovation generated by the respective firm. Finally, the study discusses limitations and recommendations for further research.

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Count (and count-like) data in finance

Cited by 467 publications

References 25 publications

Firm‐Level Climate Change Exposure

Firm‐Level Climate Change Exposure

Sovereign debt responses to the COVID-19 pandemic

How top management team social status impacts innovation

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