Financial market prediction system with Evolino neural network and Delphi method

By Nijolė Maknickienė, Algirdas Antano Maknickas. 2013

27 citations recorded by the source corpus.

Use of artificial intelligence systems in forecasting financial markets requires a reliable and simple model that would ensure profitable growth. The model presented in the paper combines Evolino recurrent neural networks with orthogonal data inputs and the Delphi expert evaluation method for its investment portfolio decision making process. A statistical study demonstrates the reliability of the model and describes its accuracy. Capabilities of the model are demonstrated using a trading simulation.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research, Economics & Operations Research, Time-Series & Forecasting

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Charu C. Aggarwal
2. Liheng Zhang
3. Guo-Jun Qi
4. Tyson R. Browning
5. Martin Palecek
6. Leo Liberti

In Management Science and Operations Research

1. Charu C. Aggarwal
2. Liheng Zhang
3. Guo-Jun Qi
4. Weiwei Jiang
5. Bryan Hooi
6. Ailin Deng
7. Ralf Klinkenberg
8. Stephen Coggeshall

In Economics & Operations Research

1. Charu C. Aggarwal
2. Liheng Zhang
3. Guo-Jun Qi
4. Weiwei Jiang

In Time-Series & Forecasting

1. Charu C. Aggarwal
2. Liheng Zhang
3. Guo-Jun Qi
4. Weiwei Jiang

Citations in the corpus

• Stock Price Prediction via Discovering Multi-Frequency Trading Patterns (2017, methodology): “While there are many approaches to stock price prediction [2, 18, 23, 30], none of existing models, to our best knowledge, explicitly decompose trading patterns into various frequency components, and seamlessly integrate the discovered multi-frequency patterns into price predictions.”