Using artificial neural networks and market profile theory to construct the trading analysis in stock market

Chien Cheng Lin*, Mu Yen Chen, Min Chih Hung, An-Pin Chen, Hsiu Sen Chiang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The empirical research on the financial trading market in the past few years is quite important and complex. In addition to discover the characteristics of the market trend movement, that buying and selling timing in the market has a great impact on profitability performance. Therefore, through the extension study of previously published literature, there should be existing relatively low risk buying and selling points in the trading market. Using market profile indicators and financial engineering physical quantities to find trade signals, and using reverse-operation trading strategies to verify whether it is a relatively low-risk buying and selling point. The results of this study show that by statistically significant differences in profitability performance, it proves that there exist relatively low risk buying and selling points in the financial trading market. There are three contributions to this study: 1).This study refutes both the Efficient-market Theory and the Random Walk Theory and there is the existence of relatively low-risk buying and selling points in the market. 2). Verify the financial physics of the trading market 3). Verify the applicability of the new indicator definition for the market profile.

Original languageEnglish
Title of host publicationProceedings of 2018 the 4th International Conference on Frontiers of Educational Technologies, ICFET 2018 - Workshop 2018 3rd International Conference on Knowledge Engineering and Applications, ICKEA 2018
PublisherAssociation for Computing Machinery
Pages122-126
Number of pages5
ISBN (Print)9781450364720
DOIs
StatePublished - 25 Jun 2018
Event4th International Conference on Frontiers of Educational Technologies, ICFET 2018, Jointly with its Workshop the 3rd International Conference on Knowledge Engineering and Applications, ICKEA 2018 - Moscow, Russian Federation
Duration: 25 Jun 201827 Jun 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference4th International Conference on Frontiers of Educational Technologies, ICFET 2018, Jointly with its Workshop the 3rd International Conference on Knowledge Engineering and Applications, ICKEA 2018
CountryRussian Federation
CityMoscow
Period25/06/1827/06/18

Keywords

  • Financial Physics
  • Market Profile Theory
  • Neural Networks
  • Trading Analysis

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