Factor Models for Asset Returns Based on Transformed Factors

10.1016/j.jeconom.2018.09.001Journal of Econometrics2072432-44

Previsão da direção de índices da Bovespa por intermédio de Máquina de Suporte Vetorial

Monografia (graduação)—Universidade de Brasília, Faculdade de Economia, Administração e Contabilidade, Departamento de Administração, 2015.Esta pesquisa tem por objetivo analisar a aplicação de Máquinas de Suporte Vetorial com o intuito de prever o movimento de índices de ações da BOVESPA. Os dados da pesquisa abrangem o período de 22/01/2001 até 22/09/2015. Os dados de entrada da máquina são os Log-Retornos dos índices e dois indicadores de análise técnica - Índice de Força Relativa e Médias Móveis Convergentes Divergentes. Esses dados são utilizados para determinar o movimento do índice (subir ou descer) e a probabilidade de ocorrência da previsão. Uma validação cruzada (k-fold) é realizada para a escolha dos melhores parâmetros, onde o melhor desempenho da máquina é uma acurácia de 70% na previsão. ________________________________________________________________________________ ABSTRACTThis research aims to examine the application of Support Vector Machines in order to predict the movement of the Bovespa stock index. This survey data cover the period from 01/22/2001 to 09/22/2015. Machine input data is the log-returns of the indices and two technical analysis indicators - Relative Strength Index and Moving Average Convergence Divergence. These data are used to determine the movement (up or down) of the indices and the probability of the forecast. A cross-validation (k-fold) is performed to choose the best parameters, where the best machine performance in forecasting is a hit ratio of 70%

Unconstrained Learning Machines

With the use of information technology in industries, a new need has arisen in analyzing large scale data sets and automating data analysis that was once performed by human intuition and simple analog processing machines. The new generation of computer programs now has to outperform their predecessors in detecting complex and non-trivial patterns buried in data warehouses. Improved Machines Learning (ML) techniques such as Neural Networks (NNs) and Support Vector Machines (SVMs) have shown remarkable performances on supervised learning problems for the past couple of decades (e.g. anomaly detection, classification and identification, interpolation and extrapolation, etc.).Nevertheless, many such techniques have ill-conditioned structures which lack adaptability for processing exotic data or very large amounts of data. Some techniques cannot even process data in an on-line fashion. Furthermore, as the processing power of computers increases, there is a pressing need for ML algorithms to perform supervised learning tasks in less time than previously required over even larger sets of data, which means that time and memory complexities of these algorithms must be improved.The aims of this research is to construct an improved type of SVM-like algorithms for tasks such as nonlinear classification and interpolation that is more scalable, error-tolerant and accurate. Additionally, this family of algorithms must be able to compute solutions in a controlled timing, preferably small with respect to modern computational technologies. These new algorithms should also be versatile enough to have useful applications in engineering, meteorology or quality control.This dissertation introduces a family of SVM-based algorithms named Unconstrained Learning Machines (ULMs) which attempt to solve the robustness, scalability and timing issues of traditional supervised learning algorithms. ULMs are not based on geometrical analogies (e.g. SVMs) or on the replication of biological models (e.g. NNs). Their construction is strictly based on statistical considerations taken from the recently developed statistical learning theory. Like SVMs, ULMS are using kernel methods extensively in order to process exotic and/or non-numerical objects stored in databases and search for hidden patterns in data with tailored measures of similarities.ULMs are applied to a variety of problems in manufacturing engineering and in meteorology. The robust nonlinear nonparametric interpolation abilities of ULMs allow for the representation of sub-millimetric deformations on the surface of manufactured parts, the selection of conforming objects and the diagnostic and modeling of manufacturing processes. ULMs play a role in assimilating the system states of computational weather models, removing the intrinsic noise without any knowledge of the underlying mathematical models and helping the establishment of more accurate forecasts

Applications of hybrid neural networks and genetic programming in financial forecasting

This thesis explores the utility of computational intelligent techniques and aims to contribute to the growing literature of hybrid neural networks and genetic programming applications in financial forecasting. The theoretical background and the description of the forecasting techniques are given in the first part of the thesis (chapters 1-3), while the contribution is provided through the last five self-contained chapters (chapters 4-8). Chapter 4 investigates the utility of the Psi Sigma neural network when applied to the task of forecasting and trading the Euro/Dollar exchange rate, while Kalman Filter estimation is tested in combining neural network forecasts. A time-varying leverage trading strategy based on volatility forecasts is also introduced. In chapter 5 three neural networks are used to forecast an exchange rate, while Kalman Filter, Genetic Programming and Support Vector Regression are implemented to provide stochastic and genetic forecast combinations. In addition, a hybrid leverage trading strategy tests if volatility forecasts and market shocks can be combined to boost the trading performance of the models. Chapter 6 presents a hybrid Genetic Algorithm – Support Vector Regression model for optimal parameter selection and feature subset combination. The model is applied to the task of forecasting and trading three euro exchange rates. The results of these chapters suggest that the stochastic and genetic neural network forecast combinations present superior forecasts and high profitability. In that way, more light is shed in the demanding issue of achieving statistical and trading efficiency in the foreign exchange markets. The focus of the next two chapters shifts from exchange rate forecasting to inflation and unemployment prediction through optimal macroeconomic variable selection. Chapter 7 focuses on forecasting the US inflation and unemployment, while chapter 8 presents the Rolling Genetic – Support Vector Regression model. The latter is applied to several forecasting exercises of inflation and unemployment of EMU members. Both chapters provide information on which set of macroeconomic indicators is found relevant to inflation and unemployment targeting on a monthly basis. The proposed models statistically outperform traditional ones. Hence, the voluminous literature, suggesting that non-linear time-varying approaches are more efficient and realistic in similar applications, is extended. From a technical point of view, these algorithms are superior to non-adaptive algorithms; avoid time consuming optimization approaches and efficiently cope with dimensionality and data-snooping issues