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

Regulatory motif discovery using a population clustering evolutionary algorithm

This paper describes a novel evolutionary algorithm for regulatory motif discovery in DNA promoter sequences. The algorithm uses data clustering to logically distribute the evolving population across the search space. Mating then takes place within local regions of the population, promoting overall solution diversity and encouraging discovery of multiple solutions. Experiments using synthetic data sets have demonstrated the algorithm's capacity to find position frequency matrix models of known regulatory motifs in relatively long promoter sequences. These experiments have also shown the algorithm's ability to maintain diversity during search and discover multiple motifs within a single population. The utility of the algorithm for discovering motifs in real biological data is demonstrated by its ability to find meaningful motifs within muscle-specific regulatory sequences

Credit scoring data for information asset analysis

Risk assessment is an important topic for financial institution nowadays, especially in the context of loan applications. Some of these institutions have already implemented their own credit scoring mechanisms to evaluate their clients’ risk and decide based in this indicator. In fact, the information gathered by financial institutions constitutes a valuable source of data for the creation of information assets from which credit scoring mechanisms can be developed. The purpose of this paper is to, from information assets, create a decision mechanism that is able to evaluate a client’s risk. Furthermore, upon this decision mechanism, a suggestive algorithm is presented to better explain and give insights on how the decision mechanism values attributes.The work described in this paper is part of TIARAC -Telematics and Artificial Intelligence in Alternative Conflict Resolution Project (PTDC/JUR/71354/2006), research project supported by FCT (Science & Technology Foundation), Portuga