Exchange rate forecasting: an application of radial basis function neural networks

The purpose of this research is to investigate the forecasting performance of Artificial Neural Network models applied to foreign exchange rates. The study concentrates on the behavior of forecasts of exchange rates generated from the radial basis function (RBF) network models where little previous work exists;Exchange rates examined are the German mark/US dollar, Japanese yen/US dollar, and Italian lira/US dollar. One-step-ahead forecasts from univariate and multivariate RBF models are compared with those generated from ARIMA models, random walk forecasts and the forward rates. Interest rates and the money supply (M1) are used as explanatory variables in the multivariate analyses;Out-of-sample evaluation criteria include root mean squared error, correct direction , and speculative direction . Hypothesis tests are used to assess if differences in forecast accuracy from different models are significant and to assess if models can predict the direction of change with statistical significance. The tests employed are the Modified Diebold Marino test [Harvey et al. (1997)], the Pesaran-Timmerman (1992, 1994) non-parametric market timing test, and the chi2 test of independence [see Swanson and White (1997)];The main results of the study indicate that RBF models may be a useful alternative to the other models considered for forecasting exchange rates. In particular, out-of-sample forecasting results indicate that some multivariate RBF models using interest rates as economic variables do have forecasting value for some exchange rates. In the presence of interest rates, the M1 variable does not seem to possess much explanatory power for forecasting the three exchange rates

Applications of artificial neural networks in financial market forecasting

This thesis evaluates the utility of Artificial Neural Networks (ANNs) applied to financial market and macroeconomic forecasting. In application, ANNs are evaluated in comparison to traditional forecasting models to evaluate if their nonlinear and adaptive properties yield superior forecasting performance in terms of robustness and accuracy. Furthermore, as ANNs are data-driven models, an emphasis is placed on the data collection stage by compiling extensive candidate input variable pools, a task frequently underperformed by prior research. In evaluating their performance, ANNs are applied to the domains of: exchange rate forecasting, volatility forecasting, and macroeconomic forecasting. Regarding exchange rate forecasting, ANNs are applied to forecast the daily logarithmic returns of the EUR/USD over a short-term forecast horizon of one period. Initially, the analytic method of Technical Analysis (TA) and its sub-section of technical indicators are utilized to compile an extensive candidate input variable pool featuring standard and advanced technical indicators measuring all technical aspects of the EUR/USD time series. The candidate input variable pool is then subjected to a two-stage Input Variable Selection (IVS) process, producing an informative subset of technical indicators to serve as inputs to the ANNs. A collection of ANNs is then trained and tested on the EUR/USD time series data with their performance evaluated over a 5-year sample period (2012 to 2016), reserving the last two years for out of sample testing. A Moving Average Convergence Divergence (MACD) model serves as a benchmark with the in-sample and out-of-sample empirical results demonstrating the MACD is a superior forecasting model across most forecast evaluation metrics. For volatility forecasting, ANNs are applied to forecast the volatility of the Nikkei 225 Index over a short-term forecast horizon of one period. Initially, an extensive candidate input variable pool is compiled consisting of implied volatility models and historical volatility models. The candidate input variable pool is then subjected to a two-stage IVS process. A collection of ANNs is then trained and tested on the Nikkei 225 Index time series data with their performance evaluated over a 4-year sample period (2014 to 2017), reserving the last year for out-of-sample testing. A GARCH (1,1) model serves as a benchmark with the out-of-sample empirical results finding the GARCH (1,1) model to be the superior volatility forecasting model. The research concludes with ANNs applied to macroeconomic forecasting, where ANNs are applied to forecast the monthly per cent-change in U.S. civilian unemployment and the quarterly per cent-change in U.S. Gross Domestic Product (GDP). For both studies, an extensive candidate input variable pool is compiled using relevant macroeconomic indicator data sourced from the Federal Bank of St Louis. The candidate input variable pools are then subjected to a two-stage IVS process. A collection of ANNs is trained and tested on the U.S. unemployment time series data (UNEMPLOY) and U.S. GDP time series data. The sample periods are (1972 to 2017) and (1960 to 2016) respectively, reserving the last 20% of data for out of sample testing. In both studies, the performance of the ANNs is benchmarked against a Support Vector Regression (SVR) model and a Naïve forecast. In both studies, the ANNs outperform the SVR benchmark model. The empirical results demonstrate that ANNs are superior forecasting models in the domain of macroeconomic forecasting, with the Modular Neural Network performing notably well. However, the empirical results question the utility of ANNs in the domains of exchange rate forecasting and volatility forecasting. A MACD model outperforms ANNs in exchange rate forecasting both in-sample and out-of-sample, and a GARCH (1,1) model outperforms ANNs in volatility forecasting

Online learning in financial time series

We wish to understand if additional learning forms can be combined with sequential optimisation to provide superior benefit over batch learning in various tasks operating in financial time series. In chapter 4, Online learning with radial basis function networks, we provide multi-horizon forecasts on the returns of financial time series. Our sequentially optimised radial basis function network (RBFNet) outperforms a random-walk baseline and several powerful supervised learners. Our RBFNets naturally measure the similarity between test samples and prototypes that capture the characteristics of the feature space. In chapter 5, Reinforcement learning for systematic FX trading, we perform feature representation transfer from an RBFNet to a direct, recurrent reinforcement learning (DRL) agent. Earlier academic work saw mixed results. We use better features, second-order optimisation methods and adapt our model parameters sequentially. As a result, our DRL agents cope better with statistical changes to the data distribution, achieving higher risk-adjusted returns than a funding and a momentum baseline. In chapter 6, The recurrent reinforcement learning crypto agent, we construct a digital assets trading agent that performs feature space representation transfer from an echo state network to a DRL agent. The agent learns to trade the XBTUSD perpetual swap contract on BitMEX. Our meta-model can process data as a stream and learn sequentially; this helps it cope with the nonstationary environment. In chapter 7, Sequential asset ranking in nonstationary time series, we create an online learning long/short portfolio selection algorithm that can detect the best and worst performing portfolio constituents that change over time; in particular, we successfully handle the higher transaction costs associated with using daily-sampled data, and achieve higher total and risk-adjusted returns than the long-only holding of the S&P 500 index with hindsight

Novel Computationally Intelligent Machine Learning Algorithms for Data Mining and Knowledge Discovery

This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an optimal subset of features in real time, we present an improved hybrid algorithm: SAGA. SAGA combines the ability to avoid being trapped in local minima of Simulated Annealing with the very high convergence rate of the crossover operator of Genetic Algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of generalized regression neural networks (GRNN). For imputing missing values and forecasting univariate time series, we propose a homogeneous neural network ensemble. The proposed ensemble consists of a committee of Generalized Regression Neural Networks (GRNNs) trained on different subsets of features generated by SAGA and the predictions of base classifiers are combined by a fusion rule. This approach makes it possible to discover all important interrelations between the values of the target variable and the input features. The proposed ensemble scheme has two innovative features which make it stand out amongst ensemble learning algorithms: (1) the ensemble makeup is optimized automatically by SAGA; and (2) GRNN is used for both base classifiers and the top level combiner classifier. Because of GRNN, the proposed ensemble is a dynamic weighting scheme. This is in contrast to the existing ensemble approaches which belong to the simple voting and static weighting strategy. The basic idea of the dynamic weighting procedure is to give a higher reliability weight to those scenarios that are similar to the new ones. The simulation results demonstrate the validity of the proposed ensemble model

Automatic detection of relationships between banking operations using machine learning

In their daily business, bank branches should register their operations with several systems in order to share information with other branches and to have a central repository of records. In this way, information can be analysed and processed according to different requisites: fraud detection, accounting or legal requirements. Within this context, there is increasing use of big data and artificial intelligence techniques to improve customer experience. Our research focuses on detecting matches between bank operation records by means of applied intelligence techniques in a big data environment and business intelligence analytics. The business analytics function allows relationships to be established and comparisons to be made between variables from the bank's daily business. Finally, the results obtained show that the framework is able to detect relationships between banking operation records, starting from not homogeneous information and taking into account the large volume of data involved in the process. (C) 2019 Elsevier Inc. All rights reserved.This work was supported by the Research Program of the Ministry of Economy and Competitiveness - Government of Spain, (DeepEMR project TIN2017-87548-C2-1-R)

Machine Learning Approaches for Traffic Flow Forecasting

Intelligent Transport Systems (ITS) as a field has emerged quite rapidly in the recent years. A competitive solution coupled with big data gathered for ITS applications needs the latest AI to drive the ITS for the smart and effective public transport planning and management. Although there is a strong need for ITS applications like Advanced Route Planning (ARP) and Traffic Control Systems (TCS) to take the charge and require the minimum of possible human interventions. This thesis develops the models that can predict the traffic link flows on a junction level such as road traffic flows for a freeway or highway road for all traffic conditions. The research first reviews the state-of-the-art time series data prediction techniques with a deep focus in the field of transport Engineering along with the existing statistical and machine leaning methods and their applications for the freeway traffic flow prediction. This review setup a firm work focussed on the view point to look for the superiority in term of prediction performance of individual statistical or machine learning models over another. A detailed theoretical attention has been given, to learn the structure and working of individual chosen prediction models, in relation to the traffic flow data. In modelling the traffic flows from the real-world Highway England (HE) gathered dataset, a traffic flow objective function for highway road prediction models is proposed in a 3-stage framework including the topological breakdown of traffic network into virtual patches, further into nodes and to the basic links flow profiles behaviour estimations. The proposed objective function is tested with ten different prediction models including the statistical, shallow and deep learning constructed hybrid models for bi-directional links flow prediction methods. The effectiveness of the proposed objective function greatly enhances the accuracy of traffic flow prediction, regardless of the machine learning model used. The proposed prediction objective function base framework gives a new approach to model the traffic network to better understand the unknown traffic flow waves and the resulting congestions caused on a junction level. In addition, the results of applied Machine Learning models indicate that RNN variant LSTMs based models in conjunction with neural networks and Deep CNNs, when applied through the proposed objective function, outperforms other chosen machine learning methods for link flow predictions. The experimentation based practical findings reveal that to arrive at an efficient, robust, offline and accurate prediction model apart from feeding the ML mode with the correct representation of the network data, attention should be paid to the deep learning model structure, data pre-processing (i.e. normalisation) and the error matrices used for data behavioural learning. The proposed framework, in future can be utilised to address one of the main aims of the smart transport systems i.e. to reduce the error rates in network wide congestion predictions and the inflicted general traffic travel time delays in real-time

Contribution to Financial Modeling and Financial Forecasting

This thesis consists of three chapters. Each chapter is independent research that is conducted during my study. This research is concentrated on financial time series modeling and forecasting. On first chapter, the research aims to prove that any abnormal behavior in debt level is a signal of future unexpected return for firms that is listed in indexes in this study, hence it is a signal to buy. In order to prove this theory multiple indexes from around the world were taken into consideration. This behavior is consistent in most of indexes around the word. The second chapter investigate the effect of United State president speech on value of United State Currency in Foreign Exchange Rate market. In this analysis it is shown that during the time the president is delivering a speech there is distinctive changes in USD value and volatility in global markets. This chapter implies that this effect cannot be captured by linear models, and the impact of the presidential speech is short term. Finally, the third chapter which is the major research of this thesis, suggest two new methods that potentially enhance the financial time series forecasting. Firstly, the new ARMA-RNN model is presented. The suggested model is inheriting the process of Autoregressive Moving Average model which is extensively studied, and train a recurrent neural network based on it to benefit from unique ability of ARMA model as well as strength and nonlinearity of artificial neural network. Secondly the research investigates the use of different frequency of data for input layer to predict the same data on output layer. In other words, artificial neural networks are trained on higher frequency data to predict lower frequency. Finally, both stated method is combined to achieve more superior predictive model

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications