Showing papers in "International Journal of Intelligent Systems in Accounting, Finance & Management in 2015"
TL;DR: A number of the applications that employ Twitter Mining are reviewed, investigating Twitter information for prediction, discovery and as an informational basis of causation.
Abstract: Twitter has found substantial use in a number of settings. For example, Twitter played a major role in the 'Arab Spring' and has been adopted by a large number of the Fortune 100. All of these and other events have led to a large database of Twitter tweets that has attracted the attention of a number of companies and researchers through what has become known as 'Twitter mining' also known as 'TwitterMining'. This paper analyses some of the approaches used to gather information and knowledge from Twitter for Twitter mining. In addition, this paper reviews a number of the applications that employ Twitter Mining, investigating Twitter information for prediction, discovery and as an informational basis of causation. Copyright © 2015 John Wiley & Sons, Ltd.
46 citations
TL;DR: An innovative fuzzy logic approach is proposed which identifies and categorizes technical rules performance across different regions in the trend and volatility space and dynamically prioritizes higher performing regions at an intraday level and adapts money management policies with the objective to maximize global risk-adjusted performance.
Abstract: The majority of existing artificial intelligence AI studies in computational finance literature are devoted solely to predicting market movements. In this paper we shift the attention to how AI can be applied to control risk-based money management decisions. We propose an innovative fuzzy logic approach which identifies and categorizes technical rules performance across different regions in the trend and volatility space. The model dynamically prioritizes higher performing regions at an intraday level and adapts money management policies with the objective to maximize global risk-adjusted performance. By adopting a hybrid method in conjunction with a popular neural network NN trend prediction model, our results show significant performance improvements compared with both standard NN and buy-and-hold approaches. Copyright © 2014 John Wiley & Sons, Ltd.
14 citations
TL;DR: In this paper, a simulator was used to visualize complex quantitative aspects that affect economic dynamics, and the main conclusions were that relatively simple economic settings produce complex nonlinear dynamics and therefore, linear regressions are often unsuitable to capture complex economic dynamics.
Abstract: In complex systems, many different parts interact in nonobvious ways. Traditional research focuses on a few or a single aspect of the problem so as to analyse it with the tools available. To get a better insight of phenomena that emerge from complex interactions, we need instruments that can analyse simultaneously complex interactions between many parts. Here, a simulator modelling different types of economies is used to visualize complex quantitative aspects that affect economic dynamics. The main conclusions are: 1 relatively simple economic settings produce complex nonlinear dynamics and, therefore, linear regressions are often unsuitable to capture complex economic dynamics; 2 flexible pricing of goods by individual agents according to their microenvironment increases the health and wealth of the society, but asymmetries in price sensitivity between buyers and sellers increase price inflation; 3 prices for goods conferring risky long-term benefits are not tracked efficiently by simple market forces; 4 division of labour creates synergies that improve enormously the health and wealth of the society by increasing the efficiency of economic activity; 5 stochastic modelling improves our understanding of real economies, and didactic games based on them might help policy-makers and nonspecialists in grasping the complex dynamics underlying even simple economic settings. Copyright © 2015 John Wiley & Sons, Ltd.
12 citations
TL;DR: This work focuses oncontent ODPs, which form small ontologies themselves and thus can be subject to ontology quality metrics in general, and investigates the use of such metrics for content ODP evaluation in terms of metrics applicability and validity.
Abstract: Ontology design patterns ODPs provide best-practice solutions for common or recurring ontology design problems This work focuses on content ODPs, which form small ontologies themselves and thus can be subject to ontology quality metrics in general We investigate the use of such metrics for content ODP evaluation in terms of metrics applicability and validity The quality metrics used for this investigation are taken from existing work in the area of ontology quality evaluation We discuss the general applicability to content ODP of each metric considering its definition, ODP characteristics, and the defined goals of ODPs The research process presented in this paper has two phases In the first phase, we conducted a literature research in the area of metrics for assessing ontology quality The second phase consisted of a two-step evaluation of the ontology metrics identified in the literature analysis During the first step, we investigated whether the metrics are appropriate to differentiate between content ODPs of different quality Metrics that proved to be applicable were calculated for a random set of 14 content ODPs In the second step, a controlled experiment, the quality indicated by the metric value was contrasted with the perception of ontology engineers; that is, do 'measured quality' and 'perceived quality' match? Copyright © 2015 John Wiley & Sons, Ltd
11 citations
TL;DR: In this study, PSO is adopted in lieu of the social or individual evolutionary learning algorithms as a model of individual adaptation in an agent-based computational model and the dynamics and convergence properties associated with this model are compared with those where evolutionarylearning algorithms are employed.
Abstract: The numerous variations of the particle swarm optimization PSO algorithm originally proposed by Kennedy and Eberhart . Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks IV. IEEE: Piscataway, NJ; 1942-1948 have proven to be powerful optimization methods that rely on exploiting simple analogues of social interaction. In this study, PSO is adopted in lieu of the social or individual evolutionary learning algorithms as a model of individual adaptation in an agent-based computational model. In this examination of the simple Cournot market framework, each agent's individual strategy evolves according to the PSO algorithm. The model is one in which agents' strategies must adapt interdependently. That is, a change in one particle may not only affect its performance but also other particles within the same swarm simultaneously. The dynamics and convergence properties associated with this model are compared with those where evolutionary learning algorithms are employed. Similar to evolutionary learning, convergence to equilibrium is dependent on the scope of learning, social or individual. While convergence is dependent on some of the algorithm parameters, prices resulting from the individual PSO are nearest the Cournot equilibrium and those from social PSO are nearest the Walrasian equilibrium in all cases. For particular parameterizations, certain advantages over evolutionary algorithms exist: in the main, decreasing volatility in market prices does not require an election operator or the addition of free parameters through two-level learning. Copyright © 2015 John Wiley & Sons, Ltd.
8 citations
TL;DR: A flexible approach that supports heterogeneous requirements on systems for the semantic annotation of web content by providing a set of default handlers, which can be customized by filling their extension points.
Abstract: In this paper we propose a flexible approach that supports heterogeneous requirements on systems for the semantic annotation of web content. The flexibility of the approach originates from a model based on the definition of abstract events, which captures at the logical level the main interactions occurring in a system for combined management of ontologies and web content. Application-specific semantics is then provided operationally as an assignment of handlers to these events. While the abstract events are rather coarse-grained to reduce prior commitment, preconditions on the handlers express application-specific distinctions based on contextual information associated with each specific event. Although the possibility to define completely new handlers guarantees the generality of our approach, we foster convention over configuration by providing a set of default handlers, which can be customized by filling their extension points. The use of customizable handlers, whether or not the default ones, reduces the development effort and guarantees consistent user experience despite evolving requirements. A comprehensive framework for semantic annotation of web content has been realized and will be hereafter introduced. Copyright © 2015 John Wiley & Sons, Ltd.
5 citations
TL;DR: A novel conceptual model is presented that systematizes the integrated management and adaptation of enterprise models, their representations, their underlying meta-models and their abstract syntax and the representation rules i.e. concrete syntax for the respective models.
Abstract: In this paper we present a novel conceptual model that systematizes the integrated management and adaptation of: 1 enterprise models, 2 their representations, 3 their underlying meta-models i.e. their abstract syntax and 4 the representation rules i.e. concrete syntax for the respective models. All this for different modelling languages and also different versions of these languages. Thanks to our original use of the adaptive object model and type square patterns-normally applied in the context of software engineering, but here applied for enterprise engineering-we manage to provide a strong conceptual foundation for the development of software tools that allow a precise and coherent specification of models and their evolution and also of meta-models and their evolution. We also present a prototype of such a tool currently being developed to enable collaborative enterprise ontology model management using the Semantic MediaWiki as a base framework. This solution is solidly grounded on the theoretical foundations of organizational self-awareness and Ψ-theory of enterprise ontology and is a valuable contribution to facilitate general and distributed enterprise model management and also concrete and abstract syntax specification i.e. the specification of a language's meta-model. This allows flexibility and ease of use in creation and adaptation of organizational models and also the use of semantic queries to detect and inform users on any violation of meta-model rules. Copyright © 2015 John Wiley & Sons, Ltd.
5 citations
TL;DR: The Rasch model via mental accounting theory is applied to identify unobservable and latent difficulties in adopting noncash payment instruments for lottery participation and the moderating effect between income and age positively significantly influences non cash payment adoption and payment framing behaviour.
Abstract: This study analyses the difficulties of using stored-value cards for noncash payment adoption and payment framing behaviour development. This study applies the Rasch model via mental accounting theory to identify unobservable and latent difficulties in adopting noncash payment instruments for lottery participation. Anonymity, a reduction in cash carrying, and convenient purchases have discouraged noncash payment adoptions. However, consumers prefer stored-value cards because they are easy to carry and reduce payment time and long waits. Consumers also develop payment framing behaviour difficulties from crediting up to the maximum stored value and fear of insufficient cash, with preferences for period purchases. Lower income significantly discourages noncash payment adoption and payment framing behaviour development, whereas being of a younger age causes significant payment framing difficulties. Regional variations differ in convenience, anonymity, stored value, balance checking, and indirect donations. The moderating effect between income and age also positively significantly influences noncash payment adoption and payment framing behaviour. Copyright © 2015 John Wiley & Sons, Ltd.
4 citations
TL;DR: Using a genetic algorithm and Monte Carlo integration solved the agent-based models quicker and provided more precise answers than solving models with particle swarm optimization and using the trapezoidal rule for numerical integration.
Abstract: An agent-based first-price private-value auction and an agent-based posted-price market are developed to compare these selling methods when buyers have private values. If the seller cannot impose a reserve price and has little uncertainty about the item's value, the seller's expected revenue is highest in the posted-price market. Otherwise, the seller is better off selling the item with the auction. Using a genetic algorithm and Monte Carlo integration solved the agent-based models quicker and provided more precise answers than solving models with particle swarm optimization and using the trapezoidal rule for numerical integration. Copyright © 2015 John Wiley & Sons, Ltd.
4 citations
TL;DR: This work demonstrates the advantage of combining knowledge-based and statistical approaches for text retrieval, and establishes the important result that an empirically tuned task-specific ontology performs better than a domain-general resource like WordNet, even on previously unseen examples.
Abstract: This article presents a knowledge-based solution for retrieving English descriptions of images. We analyse the errors made by a baseline system that relies on term frequency, and we find that the task requires deeper semantic representation. Our solution is to perform incremental, task-driven development of an ontology. Ontological features are then applied in a machine-learning algorithm for ranking candidate image descriptions. This work demonstrates the advantage of combining knowledge-based and statistical approaches for text retrieval, and it establishes the important result that an empirically tuned task-specific ontology performs better than a domain-general resource like WordNet, even on previously unseen examples. Copyright © 2015 John Wiley & Sons, Ltd.
3 citations
TL;DR: The proposed standard - eXtensible Business Reporting Language XBRL - provides a means to create a uniform framework for representing corporate and financial information in an easily interpretable, machine-readable and XML-based data format.
Abstract: Availability of business data is an important aspect of effective financial activities. An easy access to financial information has immense influence on actions and decisions regarding investing, trade and operations of companies and firms. The proposed standard - eXtensible Business Reporting Language XBRL - provides a means to create a uniform framework for representing corporate and financial information. XBRL defines an easily interpretable, machine-readable and XML-based data format. Its flexibility allows for representing business data using different languages, as well as following different regulation standards.
TL;DR: A new risk management approach based on the unconditional coverage test to minimize the regulatory capital requirements is introduced and portfolios optimized with the new minimum capital constraint successfully reduce the Basel III market risk capital requirements.
Abstract: The new Basel III framework increases the banks' market risk capital requirements. In this paper, we introduce a new risk management approach based on the unconditional coverage test to minimize the regulatory capital requirements. Portfolios optimized with our new minimum capital constraint successfully reduce the Basel III market risk capital requirements. In general, portfolios with value-at-risk and conditional-value-at-risk objective functions and underlying empirical distribution yield better portfolio risk profiles and have lower capital requirements. For the optimization we use the threshold-accepting heuristic and the common trust-region search method.
TL;DR: A novel conceptual framework to support the creation of knowledge representations based on enriched semantic vectors, using the classical vector space model approach extended with ontological support is introduced, focused on collaborative engineering projects where knowledge plays a key role in the process.
Abstract: This paper introduces a novel conceptual framework to support the creation of knowledge representations based on enriched semantic vectors, using the classical vector space model approach extended with ontological support. This work is focused on collaborative engineering projects where knowledge plays a key role in the process. Collaboration is the arena, engineering projects are the target and knowledge is the currency used to provide harmony into the arena since it can potentially support innovation and, hence, a successful collaboration. The test bed for the assessment of the approach comes from the Building and Construction sector, which is challenged with significant problems for exchanging, sharing and integrating information among actors. Semantic gaps or lack of meaning definition at the conceptual and technical levels, for example, are problems fundamentally originated through the employment of representations to map the 'world' into models in an endeavour to anticipate other actors' views, vocabulary and even motivations. One of the primary research challenges addressed in this work relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word-based information in the document. The research described in this paper explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships semantic associations modelled by domain ontologies with the addition of information presented in documents, by providing a baseline for facilitating knowledge interpretation and sharing between humans and machines. Preliminary results were collected using a clustering algorithm for document classification, which indicates that the proposed approach does improve the precision and recall of classifications. Future work and open issues are also discussed. Copyright © 2015 John Wiley & Sons, Ltd.