Temple University

About: Temple University is a education organization based out in Philadelphia, Pennsylvania, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 32154 authors who have published 64375 publications receiving 2219828 citations.

The Superiority of Analyst Forecasts as Measures of Expectations: Evidence from Earnings

Abstract: If both producers and consumers demand forecasts based solely on their forecasting ability, then the equilibrium employment of analysts, a higher cost factor than time series models, implies that analysts must produce better forecasts than time series models. Past studies of comparative earnings forecast accuracy have concluded otherwise. Using nonparametric statistics that provide proper yet powerful tests, we find that Box-Jenkins time series models consistently produce better forecasts than martingale and submartingale earnings models; but Value Line Investment Survey consistently makes significantly better earnings forecasts than the Box-Jenkins models.

Predicting Intrinsic Disorder From Amino Acid Sequence

Abstract: Blind predictions of intrinsic order and disorder were made on 42 proteins subsequently revealed to contain 9,044 ordered residues, 284 disordered residues in 26 segments of length 30 residues or less, and 281 disordered residues in 2 disordered segments of length greater than 30 residues. The accuracies of the six predictors used in this experiment ranged from 77% to 91% for the ordered regions and from 56% to 78% for the disordered segments. The average of the order and disorder predictions ranged from 73% to 77%. The prediction of disorder in the shorter segments was poor, from 25% to 66% correct, while the prediction of disorder in the longer segments was better, from 75% to 95% correct. Four of the predictors were composed of ensembles of neural networks. This enabled them to deal more efficiently with the large asymmetry in the training data through diversified sampling from the significantly larger ordered set and achieve better accuracy on ordered and long disordered regions. The exclusive use of long disordered regions for predictor training likely contributed to the disparity of the predictions on long versus short disordered regions, while averaging the output values over 61-residue windows to eliminate short predictions of order or disorder probably contributed to the even greater disparity for three of the predictors. This experiment supports the predictability of intrinsic disorder from amino acid sequence.

On Product Uncertainty in Online Markets: Theory and Evidence

Abstract: Online markets pose a difficulty for evaluating products, particularly experience goods, such as used cars, that cannot be easily described online. This exacerbates product uncertainty, the buyer’s difficulty in evaluating product characteristics and predicting how a product will perform in the future. However, the IS literature has focused on seller uncertainty and ignored product uncertainty. To address this void, this study conceptualizes product uncertainty and examines its effects and antecedents in online markets for used cars (eBay Motors). Extending the information asymmetry literature from the seller to the product, we first theorize the nature and dimensions – description and performance – of product uncertainty. Second, we propose product uncertainty to be distinct from, yet shaped by, seller uncertainty. Third, we conjecture product uncertainty to negatively affect price premiums in online markets beyond seller uncertainty. Fourth, based on the information signaling literature, we describe how information signals – diagnostic product descriptions and third-party product assurances – reduce product uncertainty. The structural model is validated by a unique dataset comprised of secondary transaction data from used cars on eBay Motors matched with primary data from 331 buyers who bid upon these used cars. The results distinguish between product and seller uncertainty, show that product uncertainty has a stronger effect on price premiums than seller uncertainty, and identify the most influential information signals that reduce product uncertainty. The study’s implications for the emerging role of product uncertainty in online markets are discussed.

Versatile van der Waals Density Functional Based on a Meta-Generalized Gradient Approximation

Abstract: Van der Waals interactions are ubiquitous in different materials yet not always described properly by current theories. Now, researchers have determined how to accurately and efficiently treat long-range Van der Waals interactions together with other chemical bonds, new findings that are important for studies of layered materials.