Applications of hybrid neural networks and genetic programming in financial forecasting

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  author =       "Charalampos Stasinakis",
  title =        "Applications of hybrid neural networks and genetic
                 programming in financial forecasting",
  school =       "University of Glasgow",
  year =         "2013",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming, HG Finance",
  URL =          "",
  URL =          "",
  size =         "pages",
  abstract =     "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 optimisation approaches and efficiently cope
                 with dimensionality and data-snooping issues",
  notes =        "",

Genetic Programming entries for Charalampos Stasinakis