This paper proposes a genetic-based hybrid approach to predict the possibility of corporate failure. We use Genetic Algorithm (GA) to select the critical variables set and optimise the weight of each classifier for integrating the best features of several classification approaches (such as...

Companies in the S&P 500 produce quarterly financial statements that are closely studied by investors who forecast the stock market performance of those companies. This paper describes a Neural Logic Network (NLN) for predicting stock market returns based on financial ratios from financial...

In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Previous research investigated the use of artificial Neural Networks (NNs) to develop models as aids to the insurance underwriter when determining acceptability and price on...

Companies in the S&P 500 produce quarterly financial statements that are closely studied by investors who forecast the stock market performance of those companies. This paper describes a Neural Logic Network (NLN) for predicting stock market returns based on financial ratios from financial...

Reliable stock market movement prediction is a challenging task. The difficulty is mainly due to the close to random-walk behaviour of a stock time series. A number of published techniques have emerged in the trading community for prediction tasks. One of them is neural network, NN. In this...

In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Previous research investigated the use of artificial Neural Networks (NNs) to develop models as aids to the insurance underwriter when determining acceptability and price on...

Reliable stock market movement prediction is a challenging task. The difficulty is mainly due to the close to random-walk behaviour of a stock time series. A number of published techniques have emerged in the trading community for prediction tasks. One of them is neural network, NN. In this...