Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization

The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a Neural Network fitness function for financial forecasting purposes. This is done by benchmarking the ARBF-PSO results with those of three different Neural Networks architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model (ARMA), a moving average convergence/divergence model (MACD) plus a naïve strategy. More specifically, the trading and statistical performance of all models is investigated in a forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time series over the period January 1999 to March 2011 using the last two years for out-of-sample testing

Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms: support vector regression forecast combinations

The motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999–2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm

An Improved Stock Price Prediction using Hybrid Market Indicators

In this paper the effect of hybrid market indicators is examined for an improved stock price prediction. The hybrid market indicators consist of technical, fundamental and expert opinion variables as input to artificial neural networks model. The empirical results obtained with published stock data of Dell and Nokia obtained from New York Stock Exchange shows that the proposed model can be effective to improve accuracy of stock price prediction

European exchange trading funds trading with locally weighted support vector regression

In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series

Using a weightless neural network to forecast stock prices: A case study of Nigerian stock exchange

This research work, proposes forecasting stock prices in the stock market industry in Nigeria using a Weightless Neural Network (WNN). A neural network application used to demonstrate the application of the WNN in the forecasting of stock prices in the market is designed and implemented in Visual Foxpro 6.0. The proposed network is tested with stock data obtained from the Nigeria Stock Exchange. This system is compared with Single Exponential Smoothing (SES) model. The WNN error value is found to be 0.39 while that of SES is 9.78, based on these values, forecasting with the WNN is observed to be more accurate and closer to the real data than those using the SES model

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.Comment: Accepted by SIGIR 201

A New Approach to Modeling Early Warning Systems for Currency Crises : can a machine-learning fuzzy expert system predict the currency crises effectively?

This paper presents a hybrid model for predicting the occurrence of currency crises by using the neuro fuzzy modeling approach. The model integrates the learning ability of neural network with the inference mechanism of fuzzy logic. The empirical results show that the proposed neuro fuzzy model leads to a better prediction of crisis. Significantly, the model can also construct a reliable causal relationship among the variables through the obtained knowledge base. Compared to the traditionally used techniques such as logit, the proposed model can thus lead to a somewhat more prescriptive modeling approach towards finding ways to prevent currency crises.