This paper investigates the profitability of a trading strategy based on training a model to identify stocks with high or low predicted returns. A tail set is defined to be a group of stocks whose volatility-adjusted price change is in the highest or lowest quantile, for example the highest or...

Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the...

Miombo woodlands in Southern Africa are experiencing accelerated changes due to natural and anthropogenic disturbances. In order to formulate sustainable woodland management strategies in the Miombo ecosystem, timely and up-to-date land cover information is required. Recent advances in remote...

By using smart meters, more data about how businesses use energy is becoming available to energy retailers (providers). This is enabling innovation in the structure and type of tariffs on offer in the energy market. We have applied Artificial Neural Networks, Support Vector Machines, and Naive...

Recently, a class of machine learning-inspired procedures, termed kernel machine methods, has been extensively developed in the statistical literature. In this note, we construct a so-called ‘adaptively minimax’ kernel machine. Such a construction highlights the limits on the...

The training of Support Vector Machines may be a very difficult task when dealing with very large datasets. The memory requirement and the time consumption of the SVMs algorithms grow rapidly with the increase of the data. To overcome these drawbacks a lot of parallel algorithms have been...

The application of quantitative techniques for the determination of credit worthiness, i.e., the credit scoring, is a major research field for bankers and academics as it can bring about significant savings to finance institutions whilst minimising their exposure to risk. In the current work,...

In this paper we done a comparison between a Neural Network model and a Support Vector Machine model adapted to predict the exchange rate EUR-LEU. We emphasize the strengths and weakness of these two Artificial Intelligence paradigms and we compare the results of prediction obtained with those...

In ordinal regression, a score function and threshold values are sought to classify a set of objects into a set of ranked classes. Classifying an individual in a class with higher (respectively lower) rank than its actual rank is called an upgrading (respectively downgrading) error. Since...

The purpose of this paper is to propose a hybrid model which combines locally linear embedding (LLE) algorithm and support vector machines (SVM) to predict the failure of firms based on past financial performance data. By making use of the LLE algorithm to perform dimension reduction for feature...