Using intelligent computing and data stream mining for behavioral finance associated with market profile and financial physics

Chien Cheng Lin, Chun Sheng Chen, An-Pin Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Day trading has become an important topic of discussion in the last decades, especially with regard to computer program trading or the increasing trend of high-frequency transactions. However, due to the high level of complexity regarding the forecasting of day trading trends, the use of traditional financial analysis or technical indicators for the forecasting of short-term market trends is often ineffective. The main reason is that in addition to the technical analysis of market physical trends, financial market trading behaviors are also often affected by psychological factors such as greed and fear, which are emotions displayed by investors during the transaction process. For this reason, this study will use the neural network to integrate into the financial engineering technology analysis of the physical momentum behavior and market profile theory to quantify controlled learning. The goal is to be able to provide an empirical explanation of the discoveries related to trading behaviors by using trading strategies. Our experiments showed that trading behaviors in the financial market could be explained by the physical trends of a quantitative and technical analysis of the market profile theory. It has also been proven that the financial trading market follows the existence of a certain trading logic.

Original languageEnglish
Pages (from-to)756-764
Number of pages9
JournalApplied Soft Computing Journal
Volume68
DOIs
StatePublished - 1 Jul 2018

Keywords

  • Data stream mining
  • Financial physics
  • Market profile theory
  • Neural networks
  • Taiwan futures exchange
  • Trading analysis

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