Causality between Cash Flow and Earnings: Evidence from Tehran (Iran) Stock Exchange.

This article employs data from 155 companies from 27 different industries listed on the Tehran Stock Exchange (TSE) for the period from 2000 to 2009 to examine the direction of causality between cash flow and earnings after taking consideration of stationarity and co-integration. The results indicate that there is a bidirectional causal relationship between cash flow and earnings at the level of all individual companies, so that cash flow variables caused earning variables and vice versa. However, at the level of industrial sectors, causality exists only between earning before interest and taxation (EBIT) and cash flow from operating activities (CFOA)

Mining

Abstract—nowadays, there are plenty of online websites related to news. Hence, new technologies, tools and special search engines are created for having access to the news on these websites. Online news is a special type of public information which has exclusive characteristics. These characteristics contribute news engines tasks such as discovering, collecting and searching to be different with similar tasks in traditional web search engines. Clustering plays conspicuous role in news engines tasks. In this paper we study various tasks in news engines and also focusing on clustering applications in them

Authority Flow-based Ranking in Heterogeneous Networks: Prediction, Personalization, and Learning to Rank

Many real-world datasets, including biological networks, the Web, and social media, can be effectively modeled as networks or graphs, in which nodes represent entities of interest and links mimic the interactions or relationships among them. Such networks often contain multiple entity or relationship types, which are referred to as heterogeneous networks. Networks also evolve due to the existence of temporal features that characterize the entities or to the temporal relationships among them. Finding important/authoritative entities in real-world networks is a long-standing and well-defined challenge. In this dissertation, I focus on two variants of the problem. The first is the prediction of the ranking of scientific publications in a future state of a citation network. I introduce a new measure labeled the future PageRank score. I develop FutureRank, a prediction algorithm for predicting the future PageRank scores from the historical network structure, and evaluate the FutureRank algorithm on multiple bibliographic dataset. Next, I focus on personalized ranking in social media. I extend a social media dataset to include relationships (edge types) between authors, blog posts, categories (topics) of the posts, and events (collections of posts). I then apply personalized ranking algorithms over the historical posts and events that have been visited by a user and use the ranking to recommend additional posts. I evaluate the personalized recommendations through an experiment with real users, as well as an extensive study of synthetic users whose preferences are defined based on intuitive criteria. Finally, I present an approach for learning to rank (algorithms) applied to heterogeneous networks. Existing methods for learning to rank are typically limited to content-based features, while many real world problems correspond to relational features. I develop a framework for learning to rank, which targets authority flow-based ranking models on heterogeneous networks. I propose algorithms for both pointwise and pairwise learning. However, this framework can easily utilize any loss function from a non-relational learning domain. Experiments show that even with a small amount of training data, both pointwise and pairwise algorithms perform successfully and converge very fast. In addition, these solutions are shown to be robust against noise

A method for focused crawling using combination of link structure and content similarity

Abstract — The rapid growth of the world-wide web poses unprecedented scaling challenges for general-purpose crawlers and search engines. A focused crawler aims at selectively seek out pages that are relevant to a pre-defined set of topics. Besides specifying topics by some keywords, it is customary also to use some exemplary documents to compute the similarity of a given web document to the topic. In this paper we introduce a new hybride focused crawler, which uses link structure of documents as well as similarity of pages to the topic to crawl the web I