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

Actionable insights through association mining of exchange rates: a case study

Association mining is the methodology within data mining that researches associations among the elements of a given set, based on how they appear together in multiple subsets of that set. Extensive literature exists on the development of efficient algorithms for association mining computations, and the fundamental motivation for this literature is that association mining reveals actionable insights and enables better policies. This motivation is proven valid for domains such as retailing, healthcare and software engineering, where elements of the analyzed set are physical or virtual items that appear in transactions. However, the literature does not prove this motivation for databases where items are “derived items”, rather than actual items. This study investigates the association patterns in changes of exchange rates of US Dollar, Euro and Gold in the Turkish economy, by representing the percentage changes as “derived items” that appear in “derived market baskets”, the day on which the observations are made. The study is one of the few in literature that applies such a mapping and applies association mining in exchange rate analysis, and the first one that considers the Turkish case. Actionable insights, along with their policy implications, demonstrate the usability of the developed analysis approach

Predicting Exchange Rate under UIRP Framework with Support Vector Regression

This study aimed to forecast the exchange rate between the Vietnamese dong and the US dollar for the following month in the context of the COVID-19 pandemic. It used the Support Vector Regression (SVR) algorithm under the Uncovered Interest Rate Parity (UIRP) theoretical framework; the results are compared with the Ordinary Least Square (OLS) regression model and the Random Walk (RW) model under the rolling window method. The data included the VND/USD exchange rate, the bank interest rate for the 1-month term, and the 1-month T-bill from January 01, 2020, to September 11, 2021. The research discovered a linear link between the two nations' exchange rates and interest rate differentials. Interest rate differentials are input variables to forecast interest rate differentials. Furthermore, the connection between the exchange rate and interest rate differentials during this era does not support the UIRP hypothesis; hence, the error for OLS predictions remains large. The study provided a model to forecast future exchange rates by combining the UIRP theoretical framework and the SVR algorithm. The UIRP theoretical framework can anticipate exchange rate differentials using the input variable and the interest rates between two nations. Meanwhile, the SVR algorithm is a robust machine learning technique that enhances prediction accuracy. Doi: 10.28991/ESJ-2022-06-03-014 Full Text: PD

Understanding SLL / US$ exchange rate dynamics in Sierra Leone using Box-Jenkins ARIMA approach

This study was carried out with the purpose of producing twelve out-of-sample forecast for a univariate exchange rate variable as a way of addressing challenges faced around dollarization issues in the domestic economy. In pursuit of this, the ARIMA model was utilised, with the best model [1,4,7] indicating that the Sierra Leone - Leone [SLL] currency will continue to depreciate against the United States Dollar [US$] throughout most part of the year 2020. This was done on the assumption of Ceteris Paribus condition, and most importantly on the view that past events of the univariate exchange rate variable is a determinant of future outcomes or performances. In a bid to moving forward, policy recommendations have suggested high level collaboration between relevant policy institutions like the Bank of Sierra Leone and the Ministry of Finance to address issues of concern, for example, a boost to the real sector and many more

A contribution to exchange rate forecasting based on machine learning techniques

El propòsit d'aquesta tesi és examinar les aportacions a l'estudi de la predicció de la taxa de canvi basada en l'ús de tècniques d'aprenentatge automàtic. Aquestes aportacions es veuen facilitades i millorades per l'ús de variables econòmiques, indicadors tècnics i variables de tipus ‘business and consumer survey’. Aquesta investigació s’organitza entorn d’una recopilació de quatre articles. L'objectiu de cadascun dels quatre treballs de recerca d'aquesta tesi és el de contribuir a l'avanç del coneixement sobre els efectes i mecanismes mitjançant els quals l'ús de variables econòmiques, indicadors tècnics, variables de tipus ‘business and consumer survey’, i la selecció dels paràmetres de models predictius són capaços de millorar les prediccions de la taxa de canvi. Fent ús d'una tècnica de predicció no lineal, el primer article d'aquesta tesi es centra majoritàriament en l'impacte que tenen l'ús de variables econòmiques i la selecció dels paràmetres dels models en les prediccions de la taxa de canvi per a dos països. L'últim experiment d'aquest primer article fa ús de la taxa de canvi del període anterior i d'indicadors econòmics com a variables d'entrada en els models predictius. El segon article d'aquesta tesi analitza com la combinació de mitjanes mòbils, variables de tipus ‘business and consumer survey’ i la selecció dels paràmetres dels models milloren les prediccions del canvi per a dos països. A diferència del primer article, aquest segon treball de recerca afegeix mitjanes mòbils i variables de tipus ‘business and consumer survey’ com a variables d'entrada en els models predictius, i descarta l'ús de variables econòmiques. Un dels objectius d'aquest segon article és determinar el possible impacte de les variables de tipus ‘business and consumer survey’ en les taxes de canvi. El tercer article d'aquesta tesi té els mateixos objectius que el segon, però amb l'excepció que l'anàlisi abasta les taxes de canvi de set països. El quart article de la tesi compta amb els mateixos objectius que l'article anterior, però amb la diferència que fa ús d'un sol indicador tècnic. En general, l'enfocament d'aquesta tesi pretén examinar diferents alternatives per a millorar les prediccions del tipus de canvi a través de l'ús de màquines de suport vectorial. Una combinació de variables i la selecció dels paràmetres dels models predictius ajudaran a aconseguir aquest propòsit.El propósito de esta tesis es examinar las aportaciones al estudio de la predicción de la tasa de cambio basada en el uso de técnicas de aprendizaje automático. Dichas aportaciones se ven facilitadas y mejoradas por el uso de variables económicas, indicadores técnicos y variables de tipo ‘business and consumer survey’. Esta investigación está organizada en un compendio de cuatro artículos. El objetivo de cada uno de los cuatro trabajos de investigación de esta tesis es el de contribuir al avance del conocimiento sobre los efectos y mecanismos mediante los cuales el uso de variables económicas, indicadores técnicos, variables de tipo ‘business and consumer survey’, y la selección de los parámetros de modelos predictivos son capaces de mejorar las predicciones de la tasa de cambio. Haciendo uso de una técnica de predicción no lineal, el primer artículo de esta tesis se centra mayoritariamente en el impacto que tienen el uso de variables económicas y la selección de los parámetros de los modelos en las predicciones de la tasa de cambio para dos países. El último experimento de este primer artículo hace uso de la tasa de cambio del periodo anterior y de indicadores económicos como variables de entrada en los modelos predictivos. El segundo artículo de esta tesis analiza cómo la combinación de medias móviles, variables de tipo ‘business and consumer survey’ y la selección de los parámetros de los modelos mejoran las predicciones del cambio para dos países. A diferencia del primer artículo, este segundo trabajo de investigación añade medias móviles y variables de tipo ‘business and consumer survey’ como variables de entrada en los modelos predictivos, y descarta el uso de variables económicas. Uno de los objetivos de este segundo artículo es determinar el posible impacto de las variables de tipo ‘business and consumer survey’ en las tasas de cambio. El tercer artículo de esta tesis tiene los mismos objetivos que el segundo, pero con la salvedad de que el análisis abarca las tasas de cambio de siete países. El cuarto artículo de esta tesis cuenta con los mismos objetivos que el artículo anterior, pero con la diferencia de que hace uso de un solo indicador técnico. En general, el enfoque de esta tesis pretende examinar diferentes alternativas para mejorar las predicciones del tipo de cambio a través del uso de máquinas de soporte vectorial. Una combinación de variables y la selección de los parámetros de los modelos predictivos ayudarán a conseguir este propósito.The purpose of this thesis is to examine the contribution made by machine learning techniques on exchange rate forecasting. Such contributions are facilitated and enhanced by the use of fundamental economic variables, technical indicators and business and consumer survey variables as inputs in the forecasting models selected. This research has been organized in a compendium of four articles. The aim of each of these four articles is to contribute to advance our knowledge on the effects and means by which the use of fundamental economic variables, technical indicators, business and consumer surveys, and a model’s free-parameters selection is capable of improving exchange rate predictions. Through the use of a non-linear forecasting technique, one research paper examines the effect of fundamental economic variables and a model’s parameters selection on exchange rate forecasts, whereas the other three articles concentrate on the effect of technical indicators, a model’s parameters selection and business and consumer surveys variables on exchange rate forecasting. The first paper of this thesis has the objective of examining fundamental economic variables and a forecasting model’s parameters in an effort to understand the possible advantages or disadvantages these variables may bring to the exchange rate predictions in terms of forecasting performance and accuracy. The second paper of this thesis analyses how the combination of moving averages, business and consumer surveys and a forecasting model’s parameters improves exchange rate predictions. Compared to the first paper, this second paper adds moving averages and business and consumer surveys variables as inputs to the forecasting model, and disregards the use of fundamental economic variables. One of the goals of this paper is to determine the possible effects of business and consumer surveys on exchange rates. The third paper of this thesis has the same objectives as the second paper, but its analysis is expanded by taking into account the exchange rates of 7 countries. The fourth paper in this thesis takes a similar approach as the second and third papers, but makes use of a single technical indicator. In general, this thesis focuses on the improvement of exchange rate predictions through the use of support vector machines. A combination of variables and a model’s parameters selection enhances the way to achieve this purpose

Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning

The complexity and ambiguity of financial and economic systems, along with frequent changes in the economic environment, have made it difficult to make precise predictions that are supported by theory-consistent explanations. Interpreting the prediction models used for forecasting important macroeconomic indicators is highly valuable for understanding relations among different factors, increasing trust towards the prediction models, and making predictions more actionable. In this study, we develop a fundamental-based model for the Canadian-U.S. dollar exchange rate within an interpretative framework. We propose a comprehensive approach using machine learning to predict the exchange rate and employ interpretability methods to accurately analyze the relationships among macroeconomic variables. Moreover, we implement an ablation study based on the output of the interpretations to improve the predictive accuracy of the models. Our empirical results show that crude oil, as Canada's main commodity export, is the leading factor that determines the exchange rate dynamics with time-varying effects. The changes in the sign and magnitude of the contributions of crude oil to the exchange rate are consistent with significant events in the commodity and energy markets and the evolution of the crude oil trend in Canada. Gold and the TSX stock index are found to be the second and third most important variables that influence the exchange rate. Accordingly, this analysis provides trustworthy and practical insights for policymakers and economists and accurate knowledge about the predictive model's decisions, which are supported by theoretical considerations

Revisión del Estado del Arte en Métodos de Redes Neuronales, Máquinas de Kernel y Computación Evolutiva para Predicción de Precios Financieros

A review of the representative models of machine learning research applied to the foreign exchange rate and stock price prediction problem is conducted. The article is organized as follows: The first section provides a context on the definitions and importance of foreign exchange rate and stock markets. The second section reviews machine learning models for financial prediction focusing on neural networks, SVM and evolutionary methods. Lastly, the third section draws some conclusions.El siguiente artículo revisa algunos de los trabajos de investigación mas representativos relacionados con aprendizaje computacional aplicado al problema de predicción de tipos de cambio y precios de acciones. El artículo esta organizado de la siguiente forma: La primera sección se concentra en contextualizar definiciones relevantes y la importancia del problema de predicción en el mercado de acciones y de tasa de cambio. La segunda sección contiene la revisión de modelos de aprendizaje computacional para predicción de precios financieros enfocándose en tres subareas: Redes Neuronales, SVM y métodos evolutivos. La tercera sección presenta las conclusiones

New input identification and artificial intelligence based techniques for load prediction in commercial building

The accuracy of prediction models for electrical loads are important as the predicted result can affect processes related to energy management such as maintenance planning, decision-making processes, as well as cost and energy savings. The studies on improving load prediction accuracy using Least Squares Support Vector Machine (LSSVM) are widely carried out by optimizing the LSSVM hyper-parameter which includes the Kernel parameter and the regularization parameter. However, studies on the effects of input data determination for the LSSVM have not widely tested by researchers. This research developed an input selection technique using Modified Group Method of Data Handling (MGMDH) to improve the accuracy of buildings load forecasting. In addition, a new cascaded Group Method of Data Handing (GMDH) and LSSVM (GMDH-LSSVM) model is developed for electrical load prediction to improve the prediction accuracy of LSSVM model. To further improve the prediction model ability, a Modified GMDH has been cascaded to the LSSVM model to enhance the accuracy of building electrical load prediction and reduce the complexity of GMDH model. The proposed models are compared with GMDH model, LSSVM model and Artificial Neural Network (ANN) model to observe the prediction performance. The performances of prediction for each tested models are evaluated using the Mean Absolute Percentage Error (MAPE). In this analysis, the proposed prediction model, gives 33.82% improvement of prediction accuracy as compared to LSSVM model. From this research, it can be concluded that cascading the models can improve the prediction accuracy and the proposed models can be used to predict building electrical loads