Patent Thickets, Stock Returns, and Conditional CAPM

Patent thickets, a phenomenon of fragmented ownership of overlapping patent rights, hamper firms’ commercialization of patents and thus deliver asset pricing implications. We show that firms with deeper patent thickets are involved in more patent litigations, launch fewer new products, and become less profitable in the future. These firms are also associated with lower subsequent stock returns, which can be explained by a conditional Capital Asset Pricing Model (CAPM) based on a general equilibrium model that features heterogeneous market betas conditional on time-varying aggregate productivity. This explanation is supported by further evidence from factor regressions and stochastic discount factor tests. This paper was accepted by Karl Diether, finance.

The Distortion in Prices Due to Passive Investing

In the capital asset pricing model (CAPM), it is ex post optimal to index. To examine the implications of market indexing, we develop a conditional CAPM with costless private information in which some investors are, for exogenous reasons, ex ante indexers. We show that, as more nonindexers become indexers, the price efficiency of stocks diminishes, asset prices comove, and the statistical fit (measured by R2) of the CAPM regression decreases. We also report asset prices at the limit, when 100% of the investors are market indexers. This paper was accepted by Tyler Shumway, finance.

Betas in the time of corona: a conditional CAPM approach using multivariate GARCH model for India

PurposeThis paper empirically investigates the effect of the coronavirus pandemic (COVID-19) on the Indian financial market and firm betas, perhaps the first paper to do so. The results will be helpful for investors tracking betas during future the coronavirus waves.Design/methodology/approachA conditional capital asset pricing model (CAPM) and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model is used to estimate time-varying daily betas of the 50 largest Indian stocks spread across 16 industries over five years (Nov 2017 to May 2021), including the two waves of COVID-19 in India.FindingsThe results show that the betas increased during the COVID wave-1 (2020) but not during COVID wave-2 (2021). Moreover, the increase is more pronounced for consumer goods, infrastructure, insurance and information technology, unlike energy (oil and gas, power and mining) industries. Further, there are positive abnormal residual returns during the COVID waves. The results will be helpful for investors tracking betas during future COVID-19 waves.Originality/valueThis is perhaps the first paper to study the firm betas in light of the COVID-19 pandemic.

Evaluating the Conditional CAPM using Consumption-based State Variables: Evidence from the Korean Stock Market

In this study, we evaluate the empirical performance of conditional asset pricing models using consumption-based measures as state variables. We incorporate three consumption variables known to forecast the equity risk premium as conditioning variables to capture time variations in the risk premium. These three variables are the consumption-aggregate wealth ratio, the surplus consumption ratio, and the labor income to consumption ratio. The asset pricing models evaluated in this study are the CAPM, the CAPM with human capital, the consumption CAPM, and the Fama-French three-factor model. We compare the unconditional and conditional specifications of these four asset pricing models using the two-pass cross-sectional regression methodology, using the size, book-to-market, turnover, and idiosyncratic risk sorted portfolios and sector portfolios as test assets. We demonstrate that the conditional CAPM with human capital performs far better than the unconditional specifications and about as well as the Fama and French three-factor model in explaining the crosssection of average stock returns in Korea.

Nuclear Hazard and Asset Prices: Implications of Nuclear Disasters in the Cross-Sectional Behavior of Stock Returns

Using stock return data for the Japanese equity market, for the period from July 1983 to June 2018, we analyze the effect of major nuclear disasters worldwide on Japanese discount rates. For that purpose, we compare the performance of the capital asset pricing model (CAPM) conditional on the event of nuclear disasters with that of the classic CAPM and the Fama–French three- and five-factor models. In order to control for nuclear disasters, we use an instrument that allows us to parameterize the linear stochastic discount factor of the conditional CAPM and transform the classic CAPM into a three-factor model. In this regard, the use of nuclear disasters as an explanatory variable for the cross-sectional behavior of stock returns is a novel contribution of this research. Our results suggest that nuclear disasters account for a large fraction of the variation of stock returns, allowing the CAPM to perform similarly to the Fama–French three- and five-factor models. Furthermore, our results show that, in general, nuclear disasters are positively related to the expected returns of a large number of assets under study. Our results have important implications for the task of estimating the cost of equity and constitute a step forward in understanding the relationship between equity risk premiums and nuclear disasters.

The Conditional Capital Asset Pricing Model Revisited: Evidence from High-Frequency Betas

When using high-frequency data, the conditional capital asset pricing model (CAPM) can explain asset-pricing anomalies. Using conditional betas based on daily data, the model works reasonably well for a recent sample period. However, it fails to explain the size anomaly as well as three out of six of the anomaly component excess returns. Using high-frequency betas, the conditional CAPM is able to explain the size, value, and momentum anomalies. We further show that high-frequency betas provide more accurate predictions of future betas than those based on daily data. This result holds for both the time-series and the cross-sectional dimensions. This paper was accepted by Karl Diether, finance.