Explore the vast range of titles available.

Price movements in industrial metals such as copper and aluminum predict stock returns. Increasing industrial metal prices are good news for equity markets in recessions and bad news in expansions. A one-standard-deviation increase in industrial metal returns predicts a price drop of one and a half percent in monthly stock market returns in expansions and an increase of around a half percent during recessions. The predictability is distinct to and compares favorably with that from more established predictors. This paper was accepted by Lauren Cohen, finance.

Collaborative tagging is a useful and effective way for classifying items with respect to search, sharing information so that users can be tagged via online social networking. This article proposes a novel recommender system for collaborative tagging in which the genre interestingness measure and gradual decay are utilized with diffusion similarity. The comparison has been done on the benchmark recommender system datasets namely MovieLens, Amazon datasets against the existing approaches such as collaborative filtering based on tagging using E-FCM, and E-GK clustering algorithms, hybrid recommender systems based on tagging using GA and collaborative tagging using incremental clustering with trust. The experimental results ensure that the proposed approach achieves maximum prediction accuracy ratio of 9.25% for average of various splits data of 100 users, which is higher than the existing approaches obtained only prediction accuracy of 5.76%.

The study of innovation diffusion offers an insight into its adoption by a particular community, which has attracted the attention of many researchers. However, most of proposed models do not take all the fundamental elements for simulating the diffusion process into account. The main contribution of this article is proposing an original model founded on the evolutionary algorithm. The model simulates the adoption decision as a process of gradual acceptance and focuses on the representation of (1) the innovation features (2) the individuals' heterogeneity, (3) the social network (4) the communication influence. For this purpose, different simulation scenarios were carried out using a probabilistic foundation. The results validated the model's ability to determine the earlier adopters and therefore, demonstrated an explicit diffusion pattern without the need of historical data.