Networks in Production: Asset Pricing Implications

This paper studies asset pricing in a multisector model in which sectors are connected to each other through an input-output network. Changes in the structure of the network are sources of systematic risk reflected in equilibrium asset prices. There are two key characteristics of the network that matter for asset prices: network concentration and network sparsity. Network concentration measures the degree to which equilibrium output is dominated by few large sectors while network sparsity measures the average input specialization of the economy. Furthermore, these two production-based asset pricing factors are determined by the structure of the network of production and can be computed from input-output data. By sorting stocks based on their exposure to the network factors, I find a return spread of 6% per year on portfolios sorted on sparsity-beta and ~4% per year on portfolios sorted on concentration-beta. These return gaps cannot be explained by standard asset pricing models such as the CAPM or the Fama French three-factor model. A calibrated model matches the network factor betas and return spreads alongside other asset pricing moments.