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

Understanding economical outcomes with the mental number line

With this study we provide evidence that the cognitive processes involved in addition/subtraction, mapped along the mental number line, seem to mediate our understanding of trading verbs. When left-to-right culture participants read "loss" verbs, cognitive activation moves "leftward" as in arithmetical subtraction, while reading "gain" verbs activates a mental rightward space as in addition. 
 We test this hypothesis by asking to a group of participants to use their left and right hand in judging (as correct of not) the syntactic form of several verbs meaning financial outcomes. Results show that processing “gain verbs” was associated with shorter latencies when responding with the right hand similarly when performing an addition task, while processing “loss verbs” was associated with shorter latencies when responding with the left, similarly when performing a subtraction task. This finding suggests that understanding language denoting economics outcomes covertly engages the arithmetical system in a spatially left-right dimension. 
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Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

Comparing policy gradient and value function based reinforcement learning methods in simulated electrical power trade

In electrical power engineering, reinforcement learning algorithms can be used to model the strategies of electricity market participants. However, traditional value function based reinforcement learning algorithms suffer from convergence issues when used with value function approximators. Function approximation is required in this domain to capture the characteristics of the complex and continuous multivariate problem space. The contribution of this paper is the comparison of policy gradient reinforcement learning methods, using artificial neural networks for policy function approximation, with traditional value function based methods in simulations of electricity trade. The methods are compared using an AC optimal power flow based power exchange auction market model and a reference electric power system model

A validated computational framework to evaluate the stiffness of 3D printed ankle foot orthoses

The purpose of this study was to create and validate a standardized framework for the evaluation of the ankle stiffness of two designs of 3D printed ankle foot orthoses (AFOs). The creation of four finite element (FE) models allowed patient-specific quantification of the stiffness and stress distribution over their specific range of motion during the second rocker of the gait. Validation was performed by comparing the model outputs with the results obtained from a dedicated experimental setup, which showed an overall good agreement with a maximum relative error of 10.38% in plantarflexion and 10.66% in dorsiflexion. The combination of advanced computer modelling algorithms and 3D printing techniques clearly shows potential to further improve the manufacturing process of AFOs

The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.