Online Forecasting of Stock Market Movement Direction Using the Improved Incremental Algorithm

  • Dalton Lunga
  • Tshilidzi Marwala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


In this paper we present a particular implementation of the Learn++ algorithm: we investigate the predictability of financial movement direction with Learn++ by forecasting the daily movement direction of the Dow Jones. The Learn++ algorithm is derived from the Adaboost algorithm, which is denominated by sub-sampling. The goal of concept learning, according to the probably approximately correct weak model, is to generate a description of another function, called the hypothesis, which is close to the concept, by using a set of examples. The hypothesis which is derived from weak learning is boosted to provide a better composite hypothesis in generalizing the establishment of the final classification boundary. The framework is implemented using multi-layer Perceptron (MLP) as a weak Learner. First, a weak learning algorithm, which tries to learn a class concept with a single input Perceptron, is established. The Learn++ algorithm is then applied to improve the weak MLP learning capacity and introduces the concept of online incremental learning. The proposed framework is able to adapt as new data are introduced and is able to classify.


Incremental Learning Weak Learning Very High Testing Subset Training Subset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dalton Lunga
    • 1
  • Tshilidzi Marwala
    • 1
  1. 1.School of Electrical and Information EngineeringUniversity of the WitwatersrandJohannesburgSouth Africa

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