Endogenous technological progress and the cross section of stock returns

I study the cross sectional variation of stock returns and technological progress using a dynamic equilibrium model with production. In the model, technological progress is endogenously driven by R&D investment and is composed of two parts. One part is product innovation devoted to creating new products; the other part is dedicated to increasing the productivity of physical investment and is embodied in new tangible capital (e.g., structures and equipment). The model breaks the symmetry assumed in standard models between intangible capital and tangible capital, in which the accumulation processes of tangible capital stock and intangible capital stock do not affect each other. The model explains qualitatively and in many cases quantitatively well-documented empirical regularities: (i) the positive relation between R&D investment and the average stock returns; (ii) the negative relation between physical investment and the average stock returns; and (iii) the positive relation between book-to-market ratio and the average stock returns.Technological Progress, R&D Investment, Physical Investment, Stock Return

Labor hiring, investment and stock return predictability in the cross section

We document that the firm level hiring rate predicts stock returns in the cross-section of US publicly traded firms even after controlling for investment, size, book-to-market and momentum as well as other known predictors of stock returns. The predictability shows up in both Fama-MacBeth cross sectional regressions and in portfolio sorts and it is robust to the exclusion of micro cap firms from the sample. We propose a production-based asset pricing model with adjustment costs in labor and capital that replicates the main empirical findings well. Labor adjustment costs makes hiring decisions forward looking in nature and thus informative about the firms’ expectations about future cash-flows and risk-adjusted discount rates. The model implies that the investment rate and the hiring rate predicts stock returns because these variables proxy for the firm’s time-varying conditional beta

Covariances versus Characteristics in General Equilibrium

We question a deep-ingrained doctrine in asset pricing: If an empirical characteristic-return relation is consistent with investor "rationality," the relation must be "explained" by a risk factor model. The investment approach changes the big picture of asset pricing. Factors formed on characteristics are not necessarily risk factors: Characteristics-based factor models are linear approximations of firm-level investment returns. The evidence that characteristics dominate covariances in horse races does not necessarily mean mispricing: Measurement errors in covariances are more likely to blame. Most important, the investment approach completes the consumption approach in general equilibrium, especially for cross-sectional asset pricing.

Resonant sequential scattering in two-frequency-pumping superradiance from a Bose-Einstein condensate

We study sequential scattering in superradiance from a Bose-Einstein condensate pumped by a two-frequency laser beam. We find that the distribution of atomic side modes presents highly different patterns for various frequency difference between the two pump components. A novel distribution is observed, with a frequency difference of eight times the recoil frequency. These observations reveal that the frequency overlap between the end-fire modes related to different side modes plays an essential role in the dynamics of sequential superradiant scattering. The numerical results from a semiclassical model qualitatively agree with our observations.Comment: Submitted to PR

Revisiting the Growth of Black Phosphorus in Sn-I Assisted Reactions

Black phosphorus, an emerging layered material, exhibits promising applications in diverse fields, ranging from electronics to optics. However, controlled synthesis of black phosphorus, particularly its few-layered counterparts, is still challenging, which should be due to the unclear growth mechanism of black phosphorus. Here, taking the most commonly used Sn-I assisted synthesis of black phosphorus as an example, we propose a growth mechanism of black phosphorus crystals by monitoring the reactions and analyzing the as-synthesized products. In the proposed mechanism, Sn24P19.3I8 is the active site for the growth of black phosphorus, and the black phosphorus crystals are formed with the assistance of SnI2, following a polymerization-like process. In addition, we suggest that all Sn-I assisted synthesis of black phosphorus should share the same reaction mechanism despite the differences among Sn-I containing additives. Our results shown here should shed light on the controlled synthesis of black phosphorus and facilitate further applications of black phosphorus

Manipulating the 3D organization of the largest synthetic yeast chromosome

Whether synthetic genomes can power life has attracted broad interest in the synthetic biology field. Here, we report de novo synthesis of the largest eukaryotic chromosome thus far, synIV, a 1,454,621-bp yeast chromosome resulting from extensive genome streamlining and modification. We developed megachunk assembly combined with a hierarchical integration strategy, which significantly increased the accuracy and flexibility of synthetic chromosome construction. Besides the drastic sequence changes, we further manipulated the 3D structure of synIV to explore spatial gene regulation. Surprisingly, we found few gene expression changes, suggesting that positioning inside the yeast nucleoplasm plays a minor role in gene regulation. Lastly, we tethered synIV to the inner nuclear membrane via its hundreds of loxPsym sites and observed transcriptional repression of the entire chromosome, demonstrating chromosome-wide transcription manipulation without changing the DNA sequences. Our manipulation of the spatial structure of synIV sheds light on higher-order architectural design of the synthetic genomes. </p

Endogenous technological progress and the cross section of stock returns

I study the cross sectional variation of stock returns and technological progress using a dynamic equilibrium model with production. In the model, technological progress is en- dogenously driven by R&D investment and is composed of two parts. One part is product innovation devoted to creating new products; the other part is dedicated to increasing the productivity of physical investment and is embodied in new tangible capital (e.g., structures and equipment). The model breaks the symmetry assumed in standard models between in- tangible capital and tangible capital, in which the accumulation processes of tangible capital stock and intangible capital stock do not a¤ect each other. The model explains qualitatively and in many cases quantitatively well-documented empirical regularities: (i) the positive relation between R&D investment and the average stock returns; (ii) the negative relation between physical investment and the average stock returns; and (iii) the positive relation between book-to-market ratio and the average stock returns

Technology Adoption, Vintage Capital and Asset Prices

We study technology adoption, risk and expected returns using a dynamic equilibrium model with production. The central insight is that optimal technology adoption is an important driving force of the cross section of stock returns. The model predicts that technology adopting \u85rms are less risky than non-adopting \u85rms. Intuitively, by restricting \u85rms from freely upgrading existing capital to the technology frontier, costly technology adoption reduces the exibility of \u85rms in smoothing dividends, and hence generates the risk dispersion between technology adopting rms and non-adopting \u85rms. The model explains qualitatively and in many cases quantitatively empirical regularities: (i) The positive relation between \u85rm age and stock returns; (ii) \u85 rms with high investment on average are younger and earn lower returns than \u85rms with low investment; and (iii) growth \u85rms on average are younger than value \u85rms, and the value premium is increasing in \u85rm age

Technology Adoption, Vintage Capital and Asset Prices

We study technology adoption, risk and expected returns using a dynamic equilibrium model with production. The central insight is that optimal technology adoption is an important driving force of the cross section of stock returns. The model predicts that technology adopting firms are less risky than non-adopting firms. Intuitively, by preventing firms from freely upgrading existing capital to the technology frontier, costly technology adoption reduces the flexibility of firms in smoothing dividends, and hence generates the risk dispersion between technology adopting firms and non-adopting firms. The model explains qualitatively and in many cases quantitatively empirical regularities: (i) The positive relation between firm age and stock returns; (ii) firms with high investment on average are younger and earn lower returns than firms with low investment; and (iii) growth firms on average are younger than value firms, and the value premium is increasing in firm age.

Endogenous Technological Progress and the Cross Section of Stock Returns

I study the cross sectional variation of stock returns and technological progress using a dynamic equilibrium model with production. In the model, technological progress is endogenously driven by R&D investment and is composed of two parts. One part is product innovation devoted to creating new products; the other part is dedicated to increasing the productivity of physical investment and is embodied in new tangible capital (e.g., structures and equipment). The model breaks the symmetry assumed in standard models between in- tangible capital and tangible capital, in which the accumulation processes of tangible capital stock and intangible capital stock do not affect each other. The model explains qualitatively and in many cases quantitatively well-documented empirical regularities: (i) the positive relation between R&D investment and the average stock returns; (ii) the negative relation between physical investment and the average stock returns; and (iii) the positive relation between book-to-market ratio and the average stock returns.