Tendencias recientes en el pronóstico de series de tiempo financieras usando máquinas de vectores de soporte

Resumen: El pronóstico de las series de tiempo financieras es un área de trabajo intensiva para investigadores y profesionales. En este estudio, analizamos 59 artículos y discutimos sobe el progreso en el análisis de series de tiempo financieras usando máquinas de vectores de soporte. Las principales conclusiones a las que llegamos son: (a) el pronóstico se hace con datos de frecuencia diaria y los estudios con otras frecuencias de tiempo son escasos; (b) la mayoría de los artículos están enfocados en mejorar el proceso de estimación de los parámetros o en el tratamiento previo de las series de tiempo; (c) la mayor parte de los artículos se concentran en el pronóstico de un índice financiero del mercado; (d) los casos experimentales están dispersos, lo que no hace posible comparar entre diferentes estudios.Abstract: Forecasting of financial time series is an intensive working area for researchers and practitioners. In this study, we analyze 59 articles and discuss the progress in financial time series analysis using support vector machines. Our main conclusions are: (a) forecasting is doing in a daily basis and studies in other time scales are scarce; (b) most of works are devoted to improve the parameter estimation process or to preprocessing the time series; (c) most of the work is concerned to forecast market financial index; (d) experimental cases are disperse and it is no possible to compare between different studiesMaestrí

Forecasting Foreign Exchange Rates with the use of Artificial Neural Networks/Learning Machines and comparison with Traditional Concepts and Linear Models

2014 dissertation for MSc in Finance & Risk. Selected by academic staff as a good example of a masters level dissertation. The prediction of Foreign Exchange has been an ever-going learning process. The development of methods of prediction has come a long way, from the beginning where the though there was no ability to predict the future, and behavior is an unpredictable entity to the development of simple statistical linear models that has come a long way to todays technology world where computers and their computational powers have made it possible for Artificial Intelligence to be born. This paper will be going through previous studies on these Neural Networks to forecast the EUR/USD, GBP/USD and USD/JPY to test and review their ability to forecast one day ahead

The Forecast of Exchange Rates using Artificial Neural Networks, and their comparison to classic models

2014 dissertation for MSc in Financial Management. Selected by academic staff as a good example of a masters level dissertation. Predicting Foreign Exchange rates has forever been a task of great importance to any individual, business or organization having to deal with a foreign currency. In the wake of a world where global transactions are an everyday activity, readiness and skill when dealing with the forecasting of international monetary movements is a key factor in the success of any operation; be it that of an individual investor, or that of multi-national index listed company. The motivation behind the desire of conquering the skill of forecasting may range from the simple desire to hedge one‟s investments and dealings in a foreign currency, to that of a speculative investor, looking for arbitrage opportunities in trading foreign exchange markets. This paper had for motivation to test and compare various models in their ability to forecast the return generated by price movements of three globally available and traded currencies; notable the Euro – US Dollar, the Euro-Swiss Franc and the Pound Sterling – US Dollar. Recent studies have been showing great promise in the use of Artificial Neural Networks in the field of forecasting exchange traded assets and currencies; which is why this paper has discussed the performance of 4 Learning Machine models in comparison to 3 base models and 2 linear models. The learning machine models being studied are the Multi-Layer Perceptron, the Higher Order Neural Network, Gene Expression and Rolling Genetic-Support Vector Regression. These models were compared using various methods of statistical evaluation, in order to measure the discrepancy of the forecasted values from the actual values, as well as the annualized return and the risk to return ratio. It was concluded that modern forecasting technique do outweigh the classic base and linear models in terms of forecasting accuracy as well as potential gain and risk to return

Machine learning for financial market prediction

The usage of machine learning techniques for the prediction of financial time series is investigated. Both discriminative and generative methods are considered and compared to more standard financial prediction techniques. Generative methods such as Switching Autoregressive Hidden Markov and changepoint models are found to be unsuccessful at predicting daily and minutely prices from a wide range of asset classes. Committees of discriminative techniques (Support Vector Machines (SVM), Relevance Vector Machines and Neural Networks) are found to perform well when incorporating sophisticated exogenous financial information in order to predict daily FX carry basket returns. The higher dimensionality that Electronic Communication Networks make available through order book data is transformed into simple features. These volume-based features, along with other price-based ones motivated by common trading rules, are used by Multiple Kernel Learning (MKL) to classify the direction of price movement for a currency over a range of time horizons. Outperformance relative to both individual SVM and benchmarks is found, along with an indication of which features are the most informative for financial prediction tasks. Fisher kernels based on three popular market microstructural models are added to the MKL set. Two subsets of this full set, constructed from the most frequently selected and highest performing individual kernels are also investigated. Furthermore, kernel learning is employed - optimising hyperparameter and Fisher feature parameters with the aim of improving predictive performance. Significant improvements in out-of-sample predictive accuracy relative to both individual SVM and standard MKL is found using these various novel enhancements to the MKL algorithm

Foreign exchange trading with support vector machines

This paper analyzes and examines the general ability of Support Vector Machine (SVM) models to correctly predict and trade daily EUR exchange rate directions. Seven models with varying kernel functions are considered. Each SVM model is benchmarked against traditional forecasting techniques in order to ascertain its potential value as out-of-sample forecasting and quantitative trading tool. It is found that hyperbolic SVMs perform well in terms of forecasting accuracy and trading results via a simulated strategy. This supports the idea that SVMs are promising learning systems for coping with nonlinear classification tasks in the field of financial time series applications