An intelligent system for risk classification of stock investment projects

The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

An Exploratory Study of Patient Falls

Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body

Telling the market story through organic information interaction design and broadcast media : submitted to the College of Creative Arts as requirement for the degree of Master of Design, Massey University, Wellington, New Zealand, 2007

Interaction Design, which is essentially story-creating and telling, is at once both and ancient art and a new technology. Media have always effected the telling of stories and the creation of experiences. (Shedroff, N., 1994, p. 2) Advances with visual representations within broadcast design have been applied to areas such as weather simulations, sporting events, and historical reconstruction's. However, financial market information presentation is fairly uniform in television news broadcasting, showing little progression in pace with other news information catego­ries. While stock market news segments make limited use of supporting graphics, addi­ tional information that may assist the viewer is filtered out, effecting viewers interest, understanding and decision making process often associated with market related stories. Research to date has been limited to single visualisations. There has been little re­search into the use of multiple information views that are composed to support news presentations. People use many different information sources on a daily basis. News sources are used to stay informed about events, to some sources, viewer evaluation of informa­tion is a part of that process. News information and other data commodity sources are now more accessible, allowing designers to look at ways of transforming them into new or improved information services. This research explores the display of stock market information by looking at ap­propriate media delivery methods combined with Organic Information Interaction Design to enhance information relationships. Organic Design and Information Inter­action Design 1 principles are combined. This denotes a 'living' relationship between elements, incorporating hierarchy principles with enhanced information delivery and user experiences. Four themes are tied together through the use of a conceptual prototype. [FROM INTRO

Modelling and trading the Greek stock market with gene expression and genetic programing algorithms

This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter