FAST: Fundamental Analysis Support for Financial Statements. Using semantics for trading recommendations

Abstract

Trading systems are tools to aid financial analysts in the investment process in companies. This process is highly complex because a big number of variables take part in it. Furthermore, huge sets of data must be taken into account to perform a grounded investment, making the process even more complicated. In this paper we present a real trading system that has been developed using semantic technologies. These cutting-edge technologies are very useful in this context because they enable the definition of schemes that can be used for storing financial information, which, in turn, can be easily accessed and queried. Additionally, the inference capabilities of the existing reasoning engines enable the generation of a set of rules supporting this investment analysis process.

Semantic Management of Enterprise Information Systems through Ontologies

“…Ontologies aim to determine "semantic agreements", reducing language ambiguity and knowledge variations between agents, which can cause errors, misunderstandings and inefficiencies [2]. Given their importance, ontologies are seen as the cornerstone of many promising technologies such as, for example, the semantic web and related data, reporting an abundant implementation in literature like [9], [22]. The scope of IT/OT service management is very far from this trend, although there are several attempts to use ontologies in some areas such as the life cycle of cloud services [15], software system development and IT service management processes [30], quality of service -security metrics [6], facilitation of operational procedures in public administration [24], service management in the Internet of Things [23] or IT service management for business-IT integration [31].…”

Towards a social and context-aware mobile recommendation system for tourism

“…Due to the importance of the topic and the increasing applicability due to the expansion of internet penetration and use, recommender systems appear in various fields of application including movie recommendation studies [29], e-business recommendations [30], expert recommendation [31,32], smart homes management [33], financial recommendations [34,35], social networks [36] or consumer electronics [37] citing just some of the most relevant cases.…”

Recommendations based on Social Relationships in Mobile Services

“…A variety of recommendation researches have been conducted, involving pictures (Gürsel and Sen, ), music (Nanopoulos et al ., ), blogs (Li and Chen, ), e‐commerce (Felfernig and Gula, ; Li and Karahanna, ), finance (Rodríguez‐González et al ., ), engineering (Ren et al , ; Li et al ., ), marketing (Li et al ., ), business process (Gao et al , ; Tan et al , ; Li et al ., ), agriculture (Xu et al ., ), industry operations (Wilamowski and Kaynak, ; Fritzsche et al , ; Xing et al , ), IT management (Jiang et al , ; Wan and Jones, ) and others (Shi et al , ; Duan and Xu, ; Hunter et al , ; Lykourentzou et al , ; Wang et al , ).…”