Combining NeuroEvolution and Principal Component Analysis to trade in the financial markets. (1st August 2018)
- Record Type:
- Journal Article
- Title:
- Combining NeuroEvolution and Principal Component Analysis to trade in the financial markets. (1st August 2018)
- Main Title:
- Combining NeuroEvolution and Principal Component Analysis to trade in the financial markets
- Authors:
- Nadkarni, João
Ferreira Neves, Rui - Abstract:
- Highlights: A system that uses PCA and NEAT to generate a lucrative trading signal is proposed. PCA is used to reduce the dimensionality of the input financial data. NEAT creates and evolves the neural network that generates the trading signal. The proposed system outperforms the B&H strategy in multiple markets. The PCA method has a big influence in the performance of the system. Abstract: When investing in the financial market, determining a trading signal that can fulfill the financial performance demands of an investor is a difficult task and a very popular research topic in the financial investment area. This paper presents an approach combining the principal component analysis (PCA) with the NeuroEvolution of Augmenting Topologies (NEAT) to generate a trading signal capable of achieving high returns and daily profits with low associated risk. The proposed approach is tested with real daily data from four financial markets of different sectors and with very different characteristics. Three different fitness functions are considered in the NEAT algorithm and the most robust results are produced by a fitness function that measures the mean daily profit obtained by the generated trading signal. The results achieved show that this approach outperforms the Buy and Hold (B&H) strategy in the markets tested (in the S&P 500 index this system achieves a rate of return of 18.89% while the B&H achieves 15.71% and in the Brent Crude futures contract this system achieves a rate ofHighlights: A system that uses PCA and NEAT to generate a lucrative trading signal is proposed. PCA is used to reduce the dimensionality of the input financial data. NEAT creates and evolves the neural network that generates the trading signal. The proposed system outperforms the B&H strategy in multiple markets. The PCA method has a big influence in the performance of the system. Abstract: When investing in the financial market, determining a trading signal that can fulfill the financial performance demands of an investor is a difficult task and a very popular research topic in the financial investment area. This paper presents an approach combining the principal component analysis (PCA) with the NeuroEvolution of Augmenting Topologies (NEAT) to generate a trading signal capable of achieving high returns and daily profits with low associated risk. The proposed approach is tested with real daily data from four financial markets of different sectors and with very different characteristics. Three different fitness functions are considered in the NEAT algorithm and the most robust results are produced by a fitness function that measures the mean daily profit obtained by the generated trading signal. The results achieved show that this approach outperforms the Buy and Hold (B&H) strategy in the markets tested (in the S&P 500 index this system achieves a rate of return of 18.89% while the B&H achieves 15.71% and in the Brent Crude futures contract this system achieves a rate of return of 37.91% while the B&H achieves −9.94%). Furthermore, it's concluded that the PCA method is vital for the good performance of the proposed approach. … (more)
- Is Part Of:
- Expert systems with applications. Volume 103(2018)
- Journal:
- Expert systems with applications
- Issue:
- Volume 103(2018)
- Issue Display:
- Volume 103, Issue 2018 (2018)
- Year:
- 2018
- Volume:
- 103
- Issue:
- 2018
- Issue Sort Value:
- 2018-0103-2018-0000
- Page Start:
- 184
- Page End:
- 195
- Publication Date:
- 2018-08-01
- Subjects:
- Financial markets -- Trading signal -- Technical analysis -- Principal Component Analysis (PCA) -- NeuroEvolution of Augmenting Topologies (NEAT)
Expert systems (Computer science) -- Periodicals
Systèmes experts (Informatique) -- Périodiques
Electronic journals
006.33 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09574174 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.eswa.2018.03.012 ↗
- Languages:
- English
- ISSNs:
- 0957-4174
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3842.004220
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 6227.xml