Catálogo de publicaciones - libros

Recommendation is to offer information which fits user’s interests and tastes to provide better services and to reduce information overload. It recently draws attention upon Internet users and information providers. Collaborative filtering is one of the widely used methods for recommendation. It recommends an item to a user based on the reference users’ preferences for the target item or the target user’s preferences for the reference items. In this paper, we propose a neural network based collaborative filtering method. Our method builds a model by learning correlation between users or items using a multi-layer perceptron. We also investigate integration of diverse information to solve the sparsity problem and selecting the reference users or items based on similarity to improve performance. We finally demonstrate that our method outperforms the existing methods through experiments using the EachMovie data.

We provide a framework for learning to price complex options by learning risk-neutral measures (Martingale measures). In a simple geometric Brownian motion model, the price volatility, fixed interest rate and a no-arbitrage condition suffice to determine a unique risk-neutral measure. On the other hand, in our framework, we relax some of these assumptions to obtain a of allowable risk-neutral measures. We then propose a framework for learning the appropriate risk-neural measure. In particular, we provide an efficient algorithm for backpropagating gradients through multinomial pricing trees. Since the risk-neutral measure prices all options simultaneously, we can use all the option contracts on a particular stock for learning. We demonstrate the performance of these models on historical data. Finally, we illustrate the power of such a framework by developing a real time trading system based upon these pricing methods.

is a process to obtain the theoretical fair value of an option based on the factors affecting its price. Currently, the nonparametric and computational methods of option valuation are able to construct a model of the pricing formula from historical data. However, these models are generally based on a global learning paradigm, which may not be able to efficiently and accurately capture the dynamics and time-varying characteristics of the option data. This paper proposes a novel brain-inspired cerebellar associative memory model for pricing American-style option on currency futures. The proposed model, called PSECMAC, constitute a local learning model that is inspired by the neurophysiological aspects of the human cerebellum. The PSECMAC-based option pricing model is subsequently applied in a mis-priced option arbitrage trading system. Simulation results show a return on investment as high as 23.1% for a relatively risk-free investment.

In this study, a reliability-based RBF neural network ensemble forecasting model is proposed to overcome the shortcomings of the existing neural ensemble methods and ameliorate forecasting performance. In this model, the ensemble weights are determined by the reliability measure of RBF network output. For testing purposes, we compare the new ensemble model’s performance with some existing network ensemble approaches in terms of three exchange rates series. Experimental results reveal that the prediction using the proposed approach is consistently better than those obtained using the other methods presented in this study in terms of the same measurements.

Support vector machine (SVM) has appeared as a powerful tool for time series forecasting and demonstrated better performance over other methods. This paper proposes a novel hybrid model which combines time-scale feature extractions with SVM models for stock index forecasting. The time series of explanatory variables are decomposed by the wavelet basis, and the extracted time-scale features then serve as inputs of a SVM model which performs the nonparametric forecasting. Compared with pure SVM models or traditional GARCH models, the performance of the new method is the best. The root-mean-squared forecasting errors are significantly reduced. The results of this study can help investors for controlling and reducing their risks in international investments.

Recently independent component analysis (ICA) has been proposed for discovery of linear, non-Gaussian, and acyclic causal models (LiNGAM). As in practice the LiNGAM assumption usually does not exactly hold, in this paper we propose some methods to perform causality discovery even when LiNGAM is violated. The first method is ICA with a sparse separation matrix. By incorporating a suitable penalty term, the separation matrix produced by this method tends to satisfy the LiNGAM assumption. The other two methods are proposed to tackle nonlinearity in the data generation procedure, which violates the LiNGAM assumption. In the second method, the post-nonlinear mixing ICA model is exploited to do causality discovery when the nonlinearity is component-wise. The third method is proposed for the case where the nonlinear distortion in data generation is of arbitrary form, but smooth and weak. The separation system for such data is a linear transformation coupled with a nonlinear one, and the nonlinear one is as weak as possible such that it can be neglected when performing causality discovery. The linear causal relations in the data are then revealed. The proposed methods are applied to discover the causal relations in the Hong Kong stock market, and the last method works very well. The resulting causal diagram shows some interesting information in the stock market.

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 becoming popular because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In addition, they don’t require huge training samples and have little possibility of overfitting. However, in order to use SVM, a user should determine several factors such as the parameters of a kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders accurate prediction results when using SVM. In this study, we propose a novel approach to enhance the prediction performance of SVM for the prediction of financial distress. Our suggestion is the simultaneous optimization of the feature selection and the instance selection as well as the parameters of a kernel function for SVM by using genetic algorithms (GAs). We apply our model to a real-world case. Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.

This paper employs pattern classification methods for assisting investors in making financial decisions. Specifically, the problem entails the categorization of investment recommendations. Based on the forecasted performance of certain indices, the Stock Quantity Selection Component is to recommend to the investor to purchase stocks, hold the current investment position or sell stocks in possession. Three designs of the component were implemented and compared in terms of their complexity as well as scalability. Designs that utilized 1, 4 and 16 classifiers, respectively, were developed. These designs were implemented using Artificial Neural Networks, Fuzzy Inference Systems as well as Adaptive Neuro-Fuzzy Inference Systems. The design that employed 4 classifiers achieved low complexity and high scalability. As a result, this design is most appropriate for the application of concern.

In this paper we present a particular implementation of the Learn++ algorithm: we investigate the predictability of financial movement direction with Learn++ by forecasting the daily movement direction of the Dow Jones. The Learn++ algorithm is derived from the Adaboost algorithm, which is denominated by sub-sampling. The goal of concept learning, according to the probably approximately correct weak model, is to generate a description of another function, called the hypothesis, which is close to the concept, by using a set of examples. The hypothesis which is derived from weak learning is boosted to provide a better composite hypothesis in generalizing the establishment of the final classification boundary. The framework is implemented using multi-layer Perceptron (MLP) as a weak Learner. First, a weak learning algorithm, which tries to learn a class concept with a single input Perceptron, is established. The Learn++ algorithm is then applied to improve the weak MLP learning capacity and introduces the concept of online incremental learning. The proposed framework is able to adapt as new data are introduced and is able to classify.

This paper describes four currency options volatility forecasting models. These models are based on shift-invariant wavelet transform and neural networks techniques. The algorithm is used to realize the shift-invariant wavelet transform. Wavelets provide a decomposition of the volatility in a nonlinear feature space. Neural networks are used to infer future volatility from the feature space. The individual wavelet domain forecasts are recombined by different techniques to form the accurate overall forecast. The proposed models have been tested with the USD/Yen options volatility market data. Experimental results show that wavelet prediction scheme has the best forecasting performance on testing dataset among four models, with regards to the least error values. Therefore, wavelet prediction scheme outperforms the other three models and avoids effectively over-fitting problems.