“…Following Berk et al. (), Zhang (), and Kuehn and Schmid (), I do not close the model in general equilibrium. Rather, I take advantage of a parametric stochastic discount factor because the focus of this paper is on the production of the economy.…”
Section: The Modelmentioning
confidence: 88%
“…Adjustment cost parameters are from Cooper and Haltiwanger () and Bloom (). Finally, external equity financing cost parameters are from Hennessy and Whited () and Kuehn and Schmid ().…”
Section: Computing the Modelmentioning
confidence: 99%
“…Firms can raise funds by issuing new equity whenever the cash flow is short of required physical and intangible investment in return for equity flotation costs. Following Hennessy and Whited () and Kuehn and Schmid (), I assume that equity issuance incurs both fixed cost () and variable cost ().…”
This paper extends the canonical, neoclassical investment‐based asset‐pricing model through the incorporation of intangible capital and the formulation of a joint productivity distribution with economic uncertainty shocks at the firm level. The distinctive evolutionary dynamics of intangible capital as opposed to that of physical capital mitigate the negative impact of temporary uncertainty shock on production and serve well to explain the value premium with modest assumptions. The value premium is unconditionally positive, but the realized value spread plummets to negative after major transient second‐moment shocks, for example, the Loma Prieta Earthquake and the 9/11 terrorist attack.
“…Following Berk et al. (), Zhang (), and Kuehn and Schmid (), I do not close the model in general equilibrium. Rather, I take advantage of a parametric stochastic discount factor because the focus of this paper is on the production of the economy.…”
Section: The Modelmentioning
confidence: 88%
“…Adjustment cost parameters are from Cooper and Haltiwanger () and Bloom (). Finally, external equity financing cost parameters are from Hennessy and Whited () and Kuehn and Schmid ().…”
Section: Computing the Modelmentioning
confidence: 99%
“…Firms can raise funds by issuing new equity whenever the cash flow is short of required physical and intangible investment in return for equity flotation costs. Following Hennessy and Whited () and Kuehn and Schmid (), I assume that equity issuance incurs both fixed cost () and variable cost ().…”
This paper extends the canonical, neoclassical investment‐based asset‐pricing model through the incorporation of intangible capital and the formulation of a joint productivity distribution with economic uncertainty shocks at the firm level. The distinctive evolutionary dynamics of intangible capital as opposed to that of physical capital mitigate the negative impact of temporary uncertainty shock on production and serve well to explain the value premium with modest assumptions. The value premium is unconditionally positive, but the realized value spread plummets to negative after major transient second‐moment shocks, for example, the Loma Prieta Earthquake and the 9/11 terrorist attack.
“…We pick the fixed and proportional equity flotation costs, λ 0 and λ 1 , to be 0.5 and 0.025. As, for example, Kuehn and Schmid (2012) and Bolton, Wang, and Yang (2021), we choose λ 0 to approximately match the frequency of equity issuance in the data. For the proportional component λ 1 , we pick the same value as in Gomes and Schmid (2010), who also study levered returns.…”
“…Related work in production‐based asset pricing includes Jermann (, , ), Yogo (), Gomes, Kogan, and Yogo (), Kuehn (), Lochstoer (), Belo (), Gomes and Schmid (), Kuehn and Schmid (), Kogan, Papanikolaou, and Stoffman (2017), and Binsbergen ().…”
In this paper, I examine asset pricing in a multisector model with sectors connected through an input‐output network. Changes in the network are sources of systematic risk reflected in equilibrium asset prices. Two characteristics of the network matter for asset prices: network concentration and network sparsity. These two production‐based asset pricing factors are determined by the structure of the network and are computed from input‐output data. Consistent with the model predictions, I find return spreads of 4.6% and −3.2% per year on sparsity and concentration beta‐sorted portfolios, respectively.
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