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Stock Market Data Prediction Using Machine Learning Techniques

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 918)

Abstract

This paper studies the possibilities of making prediction of stock market prices using historical data and machine learning algorithms. We have experimented with stock market data of the Apple Inc. using random trees and multilayer perceptron algorithms to perform the predictions of closing prices. An accuracy analysis was also conducted to determine how useful can these types of supervised machine learning algorithms could be in the financial field. These types of studies could also be researched with data from the Ecuadorian stock market exchanges i.e. Bolsa de Valores de Quito (BVQ) and Bolsa de Valores de Guayaquil (BVG) to evaluate the effectiveness of the algorithms in less liquid markets and possibly help reduce inefficiency costs for market participants and stakeholders.

Keywords

  • Machine learning
  • Stock market
  • Artificial intelligence
  • Prediction

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  • DOI: 10.1007/978-3-030-11890-7_52
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Correspondence to Edgar P. Torres P. .

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Torres P., E.P., Hernández-Álvarez, M., Torres Hernández, E.A., Yoo, S.G. (2019). Stock Market Data Prediction Using Machine Learning Techniques. In: Rocha, Á., Ferrás, C., Paredes, M. (eds) Information Technology and Systems. ICITS 2019. Advances in Intelligent Systems and Computing, vol 918. Springer, Cham. https://doi.org/10.1007/978-3-030-11890-7_52

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