Learning Financial Time Series for Prediction of the Stock Exchange Market

10 pages•Published: March 13, 2019

Roberto Rosas-Romero and Juan-Pablo Medina-Ochoa

Abstract

This paper presents the extension and application of three predictive models to time series within the financial sector, specifically data from 75 companies on the Mexican stock exchange market. A tool, which generates awareness of the potential benefits obtained from using formal financial services, would encourage more participation in a formal system. The three statistical models used for prediction of financial time series are a regression model, multi-layer perceptron with linear activation function at the output, and a Hidden Markov Model. Experiments were conducted by finding the optimal set of parameters for each predicting model while applying a model to 75 companies. Theory, issues, challenges and results related to the application of artificial predicting systems to financial time series, and performance of the methods are presented.

Keyphrases: artificial financial predictors, financial time series, hidden markov models, multi layer perceptron, regression

In: Gordon Lee and Ying Jin (editors). Proceedings of 34th International Conference on Computers and Their Applications, vol 58, pages 418-427.

Links:	https://easychair.org/publications/paper/STHc
	https://doi.org/10.29007/mh4m

BibTeX entry

@inproceedings{CATA2019:Learning_Financial_Time_Series,
  author    = {Roberto Rosas-Romero and Juan-Pablo Medina-Ochoa},
  title     = {Learning Financial Time Series for Prediction of the Stock Exchange Market},
  booktitle = {Proceedings of 34th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {58},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/STHc},
  doi       = {10.29007/mh4m},
  pages     = {418-427},
  year      = {2019}}

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