Exchange rate prediction using hybrid neural networks and trading indicators

This paper describes a hybrid model formed by a mixture of various regressive neural network models, such as temporal self-organising maps and support vector regressions, for modelling and prediction of foreign exchange rate time series. A selected set of influential trading indicators, including the moving average convergence/divergence and relative strength index, are also utilised in the proposed method. A genetic algorithm is applied to fuse all the information from the mixture regression models and the economical indicators. Experimental results and comparisons show that the proposed method outperforms the global modelling techniques such as generalised autoregressive conditional heteroscedasticity in terms of profit returns. A virtual trading system is built to examine the performance of the methods under study.     

Design of enhanced MIA-GMDH learning networks

The article presents an enhanced multilayered iterative algorithm-group method of data handling (MIA-GMDH)-type network, discusses a comprehensive design methodology and carries out some numerical experiments which encompass system prediction and modelling. The method presented in this article is an enhancement of self-organising polynomial GMDH with several specific improved features – coefficient rounding and thresholding schemes and semi-randomised selection approach to pruning. The experiments carried out include representative time series prediction (gas furnace process data) and process modelling (investigating the milligrams of vitamin B2 per gram of turnip greens and drilling cutting force modelling). The results in this article show promising potential of self-organising network methodology in the field of both prediction and modelling applications.     

A self-organising mixture autoregressive network for FX time series modelling and prediction

Nowadays a great deal of effort has been made in order to gain advantages in foreign exchange (FX) rates predictions. However, most existing techniques seldom excel the simple random walk model in practical applications. This paper describes a self-organising network formed on the basis of a mixture of adaptive autoregressive models. The proposed network, termed self-organising mixture autoregressive (SOMAR) model, can be used to describe and model nonstationary, nonlinear time series by means of a number of underlying local regressive models. An autocorrelation coefficient-based measure is proposed as the similarity measure for assigning input samples to the underlying local models. Experiments on both benchmark time series and several FX rates have been conducted. The results show that the proposed method consistently outperforms other local time series modelling techniques on a range of performance measures including the mean-square-error, correct trend predication percentage, accumulated profit and model variance.     

Data depth based support vector machines for predicting corporate bankruptcy

In financial distress analysis, the diagnosis of firms at risk for bankruptcy is crucial in preparing to hedge against any financial damage the at-risk firms stand to inflict. Some pre-alarm signals that indicate a potential financial crisis exist when a firm faces a default risk. Early studies on corporate bankruptcy prediction include parametric and nonparametric approaches, such as artificial intelligence (AI), for detecting pre-alarm signals. Among nonparametric techniques, the methods involving support vector machine (SVM) have shown potential in predicting corporate bankruptcy. We propose a hybrid method that combines data depths and nonlinear SVM for the prediction of corporate bankruptcy. We employed data depth functions to condense multivariate financial data with nonlinear and non-normal characteristics into one-dimensional space. The SVM method was introduced to classify the data points on a depth versus depth plot (DD-plot). Based on data set that records failed and non-failed manufacturing firms in Korea over 10 years, the empirical results demonstrated that the proposed method offers a higher level of accuracy in corporate bankruptcy prediction than existing methods. The proposed method is expected to provide a guidance in corporate investing for investors or other interested parties.     

A Fuzzy Modelling Approach Using Hierarchical Neural Networks

A simple and effective fuzzy modelling approach is presented in this paper. A three-layer hierarchical clustering neural network is developed to build fuzzy rule-based models from numerical data. Differing from existing clustering-based methods, in this approach the structure identification of the fuzzy model is implemented on the basis of a class of sub-clusters created by a self-organising network instead of on raw data. By combined use of unsupervised and supervised learning, both structure identification and parameter optimisation of the fuzzy model can be carried out automatically. The simulation results show that the proposed method can provide good model structure for fuzzy modelling and has high computing efficiency.     

Water pipe condition assessment: a hierarchical beta process approach for sparse incident data

Prediction of water pipe condition through statistical modelling is an important element for the risk management strategy of water distribution systems. In this work a hierarchical nonparametric model has been used to enhance the performance of pipe condition assessment. The main aims of this work are three-fold: (1) For sparse incident data, develop an efficient approximate inference algorithm based on hierarchical beta process. (2) Apply the hierarchical beta process based method to water pipe condition assessment. (3) Interpret the outcomes in financial terms usable by the water utilities. The experimental results show superior performance of the proposed method compared to current best practice methods, leading to substantial savings on reactive repairs and maintenance, as well as improved prioritization for capital expenditure.     

Bankruptcy prediction in firms with statistical and intelligent techniques and a comparison of evolutionary computation approaches

In this paper, we compare some traditional statistical methods for predicting financial distress to some more “unconventional” methods, such as decision tree classification, neural networks, and evolutionary computation techniques, using data collected from 200 Taiwan Stock Exchange Corporation (TSEC) listed companies. Empirical experiments were conducted using a total of 42 ratios including 33 financial, 8 non-financial and 1 combined macroeconomic index, using principle component analysis (PCA) to extract suitable variables.This paper makes four critical contributions: (1) with nearly 80% fewer financial ratios by the PCA method, the prediction performance is still able to provide highly-accurate forecasts of financial bankruptcy; (2) we show that traditional statistical methods are better able to handle large datasets without sacrificing prediction performance, while intelligent techniques achieve better performance with smaller datasets and would be adversely affected by huge datasets; (3) empirical results show that C5.0 and CART provide the best prediction performance for imminent bankruptcies; and (4) Support Vector Machines (SVMs) with evolutionary computation provide a good balance of high-accuracy short- and long-term performance predictions for healthy and distressed firms. Therefore, the experimental results show that the Particle Swarm Optimization (PSO) integrated with SVM (PSO-SVM) approach could be considered for predicting potential financial distress.     

A new hybrid ensemble credit scoring model based on classifiers consensus system approach

During the last few years there has been marked attention towards hybrid and ensemble systems development, having proved their ability to be more accurate than single classifier models. However, among the hybrid and ensemble models developed in the literature there has been little consideration given to: 1) combining data filtering and feature selection methods 2) combining classifiers of different algorithms; and 3) exploring different classifier output combination techniques other than the traditional ones found in the literature. In this paper, the aim is to improve predictive performance by presenting a new hybrid ensemble credit scoring model through the combination of two data pre-processing methods based on Gabriel Neighbourhood Graph editing (GNG) and Multivariate Adaptive Regression Splines (MARS) in the hybrid modelling phase. In addition, a new classifier combination rule based on the consensus approach (ConsA) of different classification algorithms during the ensemble modelling phase is proposed. Several comparisons will be carried out in this paper, as follows: 1) Comparison of individual base classifiers with the GNG and MARS methods applied separately and combined in order to choose the best results for the ensemble modelling phase; 2) Comparison of the proposed approach with all the base classifiers and ensemble classifiers with the traditional combination methods; and 3) Comparison of the proposed approach with recent related studies in the literature. Five of the well-known base classifiers are used, namely, neural networks (NN), support vector machines (SVM), random forests (RF), decision trees (DT), and naïve Bayes (NB). The experimental results, analysis and statistical tests prove the ability of the proposed approach to improve prediction performance against all the base classifiers, hybrid and the traditional combination methods in terms of average accuracy, the area under the curve (AUC) H-measure and the Brier Score. The model was validated over seven real world credit datasets.     

Modelling and asset allocation for financial markets based on a stochastic volatility microstructure model

On the basis of the market microstructure theory, a continuous time microstructure model is proposed for describing the dynamics of financial markets with stochastic volatility property. From the microstructure model, one may obtain the estimates of two state variables, which represent the market excess demand and liquidity respectively but cannot be directly observed. Based on the indirectly obtained excess demand information instead of the prediction of price, a simple asset dynamic allocation approach is investigated. The local linearization method, nonlinear Kalman filter and maximum likelihood method-based estimation approach for the microstructure model proposed is presented. Case studies on the financial markets modelling and the estimated model-based asset dynamic allocation control for the JPY/USD (Japanese Yen/US Dollar) exchange rate and Japan TOPIX (Tokyo stock Price IndeX) show a satisfactory modelling precision and dynamic allocation performance.     

Improving prediction of exchange rates using Differential EMD

Volatility is a key parameter when measuring the size of errors made in modelling returns and other financial variables such as exchanged rates. The autoregressive moving-average (ARMA) model is a linear process in time series; whilst in the nonlinear system, the generalised autoregressive conditional heteroskedasticity (GARCH) and Markov switching GARCH (MS-GARCH) have been widely applied. In statistical learning theory, support vector regression (SVR) plays an important role in predicting nonlinear and nonstationary time series variables. In this paper, we propose a new algorithm, differential Empirical Mode Decomposition (EMD) for improving prediction of exchange rates under support vector regression (SVR). The new algorithm of Differential EMD has the capability of smoothing and reducing the noise, whereas the SVR model with the filtered dataset improves predicting the exchange rates. Simulations results consisting of the Differential EMD and SVR model show that our model outperforms simulations by a state-of-the-art MS-GARCH and Markov switching regression (MSR) models.     

Application of feature selection methods for automated clustering analysis: a review on synthetic datasets

The effective modelling of high-dimensional data with hundreds to thousands of features remains a challenging task in the field of machine learning. This process is a manually intensive task and requires skilled data scientists to apply exploratory data analysis techniques and statistical methods in pre-processing datasets for meaningful analysis with machine learning methods. However, the massive growth of data has brought about the need for fully automated data analysis methods. One of the key challenges is the accurate selection of a set of relevant features, which can be buried in high-dimensional data along with irrelevant noisy features, by choosing a subset of the complete set of input features that predicts the output with higher accuracy comparable to the performance of the complete input set. Kohonen’s self-organising neural network map has been utilised in various ways for this task, such as with the weighted self-organising map (WSOM) approach and this method is reviewed for its efficacy. The study demonstrates that the WSOM approach can result in different results on different runs on a given dataset due to the inappropriate use of the steepest descent optimisation method to minimise the weighted SOM’s cost function. An alternative feature weighting approach based on analysis of the SOM after training is presented; the proposed approach allows the SOM to converge before analysing the input relevance, unlike the WSOM that aims to apply weighting to the inputs during the training which distorts the SOM’s cost function, resulting in multiple local minimums meaning the SOM does not consistently converge to the same state. We demonstrate the superiority of the proposed method over the WSOM and a standard SOM in feature selection with improved clustering analysis.

     

Combinatorial time series forecasting based on clustering algorithms and neural networks

Time series analysis utilising more than a single forecasting approach is a procedure originated many years ago as an attempt to improve the performance of the individual model forecasts. In the literature there is a wide range of different approaches but their success depends on the forecasting performance of the individual schemes. A clustering algorithm is often employed to distinguish smaller sets of data that share common properties. The application of clustering algorithms in combinatorial forecasting is discussed with an emphasis placed on the formulation of the problem so that better forecasts are generated. Additionally, the hybrid clustering algorithm that assigns data depending on their distance from the hyper-plane that provides their optimal modelling is applied. The developed cluster-based combinatorial forecasting schemes were examined in a single-step ahead prediction of the pound-dollar daily exchange rate and demonstrated an improvement over conventional linear and neural based combinatorial schemes.     

Exploiting performance counters to predict and improve energy performance of HPC systems

Hardware monitoring through performance counters is available on almost all modern processors. Although these counters are originally designed for performance tuning, they have also been used for evaluating power consumption. We propose two approaches for modelling and understanding the behaviour of high performance computing (HPC) systems relying on hardware monitoring counters. We evaluate the effectiveness of our system modelling approach considering both optimizing the energy usage of HPC systems and predicting HPC applications’ energy consumption as target objectives. Although hardware monitoring counters are used for modelling the system, other methods–including partial phase recognition and cross platform energy prediction–are used for energy optimization and prediction. Experimental results for energy prediction demonstrate that we can accurately predict the peak energy consumption of an application on a target platform; whereas, results for energy optimization indicate that with no a priori knowledge of workloads sharing the platform we can save up to 24% of the overall HPC system’s energy consumption under benchmarks and real-life workloads.     

Impact of momentum bias on forecasting through knowledge discovery techniques in the foreign exchange market

To an increasing extent since the late 1980s, software learning methods including neural networks (NN) and case based reasoning (CBR) have been used for prediction in financial markets and other areas. In the past, the prediction of foreign exchange rates has focused on isolated techniques, as exemplified by the use of time series models including regression models or smoothing methods to identify cycles and trends. At best, however, the use of isolated methods can only represent fragmented models of the causative agents, which underlie business cycles. Experience with artificial intelligence applications since the early 1980s points toward a multistrategy approach to discovery and prediction.This paper investigates the impact of momentum bias on forecasting financial markets through knowledge discovery techniques. Different modes of bias are used as input into learning systems using implicit knowledge representation (NNs) and CBR. The concepts are examined in the context of predicting movements in the Japanese yen.     

Classification Based on Hybridization of Parametric and Nonparametric Classifiers

Parametric methods of classification assume specific parametric models for competing population densities (e.g., Gaussian population densities can lead to linear and quadratic discriminant analysis) and they work well when these model assumptions are valid. Violation in one or more of these parametric model assumptions often leads to a poor classifier. On the other hand, nonparametric classifiers (e.g., nearest-neighbor and kernel-based classifiers) are more flexible and free from parametric model assumptions. But, the statistical instability of these classifiers may lead to poor performance when we have small numbers of training sample observations. Nonparametric methods, however, do not use any parametric structure of population densities. Therefore, even when one has some additional information about population densities, that important information is not used to modify the nonparametric classification rule. This paper makes an attempt to overcome these limitations of parametric and nonparametric approaches and combines their strengths to develop some hybrid classification methods. We use some simulated examples and benchmark data sets to examine the performance of these hybrid discriminant analysis tools. Asymptotic results on their misclassification rates have been derived under appropriate regularity conditions.     

An algorithm for nonparametric GARCH modelling

A simple iterative algorithm for nonparametric first-order GARCH modelling is proposed. This method offers an alternative to fitting one of the many different parametric GARCH specifications that have been proposed in the literature. A theoretical justification for the algorithm is provided and examples of its application to simulated data from various stationary processes showing stochastic volatility, as well as empirical financial return data, are given. The nonparametric procedure is found to often give better estimates of the unobserved latent volatility process than parametric modelling with the standard GARCH(1,1) model, particularly in the presence of asymmetry and other departures from the standard GARCH specification. Extensions of the basic iterative idea to more complex time series models combining ARMA or GARCH features of possibly higher order are suggested.     

An improved boosting based on feature selection for corporate bankruptcy prediction

With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability in corporate bankruptcy prediction. In this paper, a new and improved Boosting, FS-Boosting, is proposed to predict corporate bankruptcy. Through injecting feature selection strategy into Boosting, FS-Booting can get better performance as base learners in FS-Boosting could get more accuracy and diversity. For the testing and illustration purposes, two real world bankruptcy datasets were selected to demonstrate the effectiveness and feasibility of FS-Boosting. Experimental results reveal that FS-Boosting could be used as an alternative method for the corporate bankruptcy prediction.     

Measuring firm performance using financial ratios: A decision tree approach

Determining the firm performance using a set of financial measures/ratios has been an interesting and challenging problem for many researchers and practitioners. Identification of factors (i.e., financial measures/ratios) that can accurately predict the firm performance is of great interest to any decision maker. In this study, we employed a two-step analysis methodology: first, using exploratory factor analysis (EFA) we identified (and validated) underlying dimensions of the financial ratios, followed by using predictive modeling methods to discover the potential relationships between the firm performance and financial ratios. Four popular decision tree algorithms (CHAID, C5.0, QUEST and C&RT) were used to investigate the impact of financial ratios on firm performance. After developing prediction models, information fusion-based sensitivity analyses were performed to measure the relative importance of independent variables. The results showed the CHAID and C5.0 decision tree algorithms produced the best prediction accuracy. Sensitivity analysis results indicated that Earnings Before Tax-to-Equity Ratio and Net Profit Margin are the two most important variables.     

Improving option price forecasts with neural networks and support vector regressions

Options are important financial derivatives that allow investors to control their investment risks in the securities market. Determining the theoretical price for an option, or option pricing, is regarded as one of the most important issues in financial research; a number of parametric and nonparametric option pricing approaches have been presented. While the objective of option pricing is to find the current fair price, for decision making, in contrast, the forecasting activity has to accurately predict the future option price without advance knowledge of the underlying asset price. In this paper, a simple and effective nonparametric method of forecasting option prices based on neural networks (NNs) and support vector regressions (SVRs) is presented. We first modified the improved conventional option pricing methods, allowing them to forecast the option prices. Second, we employed the NNs and SVRs to further decrease the forecasting errors of the parametric methods. Since the conventional methods mimic the trends of movement of the real option prices, using these methods in a first stage allows the NNs and SVRs to concentrate their power in nonlinear curve approximation to further reduce the forecasting errors in a second stage. Finally, extensive experimental studies with data from the Hong Kong option market demonstrated the ability of NNs and SVRs to improve forecast accuracy.     

Detection of Wheat Stripe Rust Using Solar-induced Chlorophyll Fluorescence and Reflectance Spectral Indices

Detection of wheat stripe rust is important for agriculture management and decision,this paper aims to improve detection accuracy of the disease severity of wheat stripe rust by integrating the advantages of reflectance spectroscopy in the detection of crop biochemical parameters and the advantages of chlorophyll fluorescence in photosynthetic physiology diagnosis.Firstly,the solar-induced chlorophyll fluorescence (SIF) at O₂-A band (760 nm) was calculated using the 3FLD algorithm,and seven spectral indices sensitive to wheat stripe rust were investigated for estimating the disease severity.Then,three classic statistical modelling methods,including Support Vector Machine (SVM),Stepwise Regression (SR) and BP neural network (BP),were used to quantitatively investigated the performance of the spectral indices and SIF for detection of winter wheat stripe rust severity.The results show that:(1) there is a significantly negative correlation between SIF and the severity of wheat stripe rust.The relationship between SIF and DI can be effectively applied to detect wheat stripe rust.(2) the spectral models based on SIF combined with spectral indices are more accurate than those based on spectral indices.SIF can significantly improve the detection accuracy of the disease severity of winter wheat stripe rust.(3) compared to the SVM and SR methods,the training model constructed by the BP neural network has the highest prediction accuracy whether using the spectral indices or SIF combined spectral indices.However,the verification results show that the disease severity prediction model constructed by SVM and SR method have a better prediction.