Efficient Tests of Stock Return Predictability

Empirical studies have suggested that stock returns can be predicted by ï¬nancial variables such as the dividend-price ratio. However, these studies typically ignore the high persistence of predictor variables, which can make ï¬rst-order asymptotics a poor approximation in ï¬nite samples. Using a more accurate asymptotic approximation, we propose two methods to deal with the persistence problem. First, we develop a pretest that determines when the conventional t-test for predictability is misleading. Second, we develop a new test of predictability that results in correct inference regardless of the degree of persistence and is efficient compared to existing methods. Applying our methods to US data, we ï¬nd that the dividend-price ratio and the smoothed earningsprice ratio are sufficiently persistent for conventional inference to be highly misleading. However, we ï¬nd some evidence for predictability using our test, particularly with the earnings-price ratio. We also ï¬nd evidence for predictability with the short-term interest rate and the long-short yield spread, for which the conventional t-test leads to correct inference.

Efficient Tests of Stock Return Predictability

Conventional tests of the predictability of stock returns could be invalid, that is reject the null too frequently, when the predictor variable is persistent and its innovations are highly correlated with returns. We develop a pretest to determine whether the conventional t-test leads to invalid inference and an efficient test of predictability that corrects this problem. Although the conventional t-test is invalid for the dividend-price and smoothed earnings-price ratios, our test finds evidence for predictability. We also find evidence for predictability with the short rate and the long-short yield spread, for which the conventional t-test leads to valid inference.

Efficient Tests of Stock Return Predictability

Conventional tests of the predictability of stock returns could be invalid, that is reject the null too frequently, when the predictor variable is persistent and its innovations are highly correlated with returns. We develop a pretest to determine whether the conventional t-test leads to invalid inference and an efficient test of predictability that corrects this problem. Although the conventional t-test is invalid for the dividend–price and smoothed earnings–price ratios, our test finds evidence for predictability. We also find evidence for predictability with the short rate and the long-short yield spread, for which the conventional t-test leads to valid inference

Updated full range of Eliciting Dose values for Cow’s milk for use in food allergen risk assessment

Access to Eliciting Doses (ED) for allergens enables advanced food allergen risk assessment. Previously, the full ED range for 14 allergenic foods, including milk, and recommendations for their use were provided (Houben et al., 2020). Additional food challenge studies with cow’s milk-allergic patients added 247 data points to the original dataset. Using the Stacked Model Averaging statistical method for interval-censored data on the 697 individual NOAELs and LOAELs for milk generated an updated full ED distribution. The ED01 and ED05, the doses at which 1% and 5% of the milk-allergic population would be predicted to experience any objective allergic reaction, were 0.3 and 3.2 mg milk protein for the discrete and 0.4 mg and 4.3 mg milk protein for the cumulative dose distribution, respectively. These values are slightly higher but remain within the 95% confidence interval of previously published EDs. We recommend using the updated EDs for future characterization of risks of exposure of milk-allergic individuals to milk protein. This paper contributes to the discussion on the Reference Dose for milk in the recent Ad hoc Joint FAO/WHO Expert Consultation on Risk Assessment of Food Allergens. It will also benefit harmonization of food allergen risk assessment and risk management globally

XPS Spectral Simulation of Chitosan in Thermal Decomposition Process,

金沢大学大学院自然科学研究科物質情報解

Theoretical analysis of Auger electron spectra of 2nd periodic element containing substances,

金沢大学大学院自然科学研究科物質情報解

X-Ray Photoelectron Spectral Analysis for Carbon Allotropes,

金沢大学大学院自然科学研究科物質情報解

Numerical Galaxy Catalog -I. A Semi-analytic Model of Galaxy Formation with N-body simulations

We construct the Numerical Galaxy Catalog ($\nu$GC), based on a semi-analytic model of galaxy formation combined with high-resolution N-body simulations in a $\Lambda$-dominated flat cold dark matter ($\Lambda$CDM) cosmological model. The model includes several essential ingredients for galaxy formation, such as merging histories of dark halos directly taken from N-body simulations, radiative gas cooling, star formation, heating by supernova explosions (supernova feedback), mergers of galaxies, population synthesis, and extinction by internal dust and intervening HI clouds. As the first paper in a series using this model, we focus on basic photometric, structural and kinematical properties of galaxies at present and high redshifts. Two sets of model parameters are examined, strong and weak supernova feedback models, which are in good agreement with observational luminosity functions of local galaxies in a range of observational uncertainty. Both models agree well with many observations such as cold gas mass-to-stellar luminosity ratios of spiral galaxies, HI mass functions, galaxy sizes, faint galaxy number counts and photometric redshift distributions in optical pass-bands, isophotal angular sizes, and cosmic star formation rates. In particular, the strong supernova feedback model is in much better agreement with near-infrared (K'-band) faint galaxy number counts and redshift distribution than the weak feedback model and our previous semi-analytic models based on the extended Press-Schechter formalism. (Abridged)Comment: 26 pages including 27 figures, accepted for publication in ApJ, full-resolution version is available at http://grape.astron.s.u-tokyo.ac.jp/~yahagi/nugc

Thin-Film Metamaterials called Sculptured Thin Films

Morphology and performance are conjointed attributes of metamaterials, of which sculptured thin films (STFs) are examples. STFs are assemblies of nanowires that can be fabricated from many different materials, typically via physical vapor deposition onto rotating substrates. The curvilinear--nanowire morphology of STFs is determined by the substrate motions during fabrication. The optical properties, especially, can be tailored by varying the morphology of STFs. In many cases prototype devices have been fabricated for various optical, thermal, chemical, and biological applications.Comment: to be published in Proc. ICTP School on Metamaterials (Augsut 2009, Sibiu, Romania