Intelligent technical analysis based equivolume charting for stock trading using neural networks

By Thira Chavarnakul, David Enke. 2008

126 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. Ali Mohammad Kimiagari
2. Azizollah Memariani
3. Arash Bahrammirzaee
4. Milad Jasemi
5. Anthony Grace

In Management Science and Operations Research

1. Anthony Grace
2. Ali Mohammad Kimiagari
3. Azizollah Memariani
4. Arash Bahrammirzaee
5. Milad Jasemi
6. Jacinta Chan Phooi M’ng
7. M. Mehralizadeh

Citations in the corpus

• A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “[225] Stock trading VAMA and EMV indicator with GRNN VAMA and EMV indicator Hybrid system performs better”
• A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick (2011, methodology): “This factor makes them completely suitable for classification, recognition and forecasting of financial data (Chavarnakul & Enke, 2008).”
• Can Deep Learning Techniques Improve the Risk Adjusted Returns from Enhanced Indexing Investment Strategies (2017, methodology): “The use of volume measures in the context of neural network based prediction models is specifically considered by Chavaranakul and Enke (2008).”