The discovery of evolutionary laws of financial market is always built on the basis of financial data. Any financial market must be controlled by some basic laws, including macroscopic level, submicroscopic level and microscopic level laws. How to discover its necessity-laws from financial data is the most important task of financial market analysis and prediction. Based on the evolutionary com...

Stock market decision making is a very challenging and difficult task of financial data prediction. Prediction about stock market with high accuracy movement yield profit for investors of the stocks. Because of the complexity of stock market financial data, development of efficient models for prediction decision is very difficult, and it must be accurate. This study attempted to develop models ...

One of the most important research issues in finance is building accurate corporate bankruptcy prediction models since they are essential for the risk management of financial institutions. Thus, researchers have applied various data-driven approaches to enhance prediction performance including statistical and artificial intelligence techniques. Recently, support vector machines (SVMs) are becom...

The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. Second, we develop an NN bankruptcy prediction model. Inspired by one of the traditional credit ri...

Financial modeling is one application that benefits substantially from Grid's multiprocessing capabilities. Together with the Centre for Financial Engineering, TCG@NUS’s project team worked on identifying suitable applications and enabling them on the TCG@NUS. The first application identified was Structured Product Pricing (SPP) a Monte Carlo-style simulation application which uses the historic...

We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network...

This article presents a financial scoring model estimated on Czech corporate accounting data. Seven financial indicators capable of explaining business failure at a 1-year prediction horizon are identified. Using the model estimated in this way, an aggregate indicator of the creditworthiness of the Czech corporate sector is then constructed and its evolution over time is shown. This indicator a...

This paper presents a new method for company failure prediction using probabilistic neural networks. The method extracts templates through a supervised learning. Each template represents the companies having similar financial performance. A comparison between a company and a template can find out some financial problems occurring to a company and an early warning can be given if necessary. The ...

We consider the scenario where the parameters of a probabilistic model are expected to vary over time. We construct a novel prior distribution that promotes sparsity and adapts the strength of correlation between parameters at successive timesteps, based on the data. We derive approximate variational inference procedures for learning and prediction with this prior. We test the approach on two t...