Optimal Combination of Trading Rules Using Neural Networks
- Subrata Mitra
Abstract
A large number of trading rules based on technical analysis of prices are being used by investing community for generating trading signals for short term investments. As profitability of these trading rules vary, it is not easy to judge which particular rule really ‘works’. Instead of a single trading rule, combination of rules are likely to offer the portfolio benefits of better risk adjusted return and hence, an experiment is carried out to combine signals generated from of moving averages of different window size using an artificial neural network. It is observed that the risk adjusted performance measure of the artificial neural network based trading model is better than that of simple ‘Buy and Hold’ strategy.- Full Text: PDF
- DOI:10.5539/ibr.v2n1p86
This work is licensed under a Creative Commons Attribution 4.0 License.
Journal Metrics
h-index (January 2024): 102
i10-index (January 2024): 947
h5-index (January 2024): N/A
h5-median(January 2024): N/A
( The data was calculated based on Google Scholar Citations. Click Here to Learn More. )
Index
- Academic Journals Database
- ACNP
- ANVUR (Italian National Agency for the Evaluation of Universities and Research Institutes)
- CNKI Scholar
- COPAC
- CrossRef
- EBSCOhost
- EconBiz
- ECONIS
- EconPapers
- Elektronische Zeitschriftenbibliothek (EZB)
- EuroPub Database
- Excellence in Research for Australia (ERA)
- Genamics JournalSeek
- Google Scholar
- Harvard Library
- IBZ Online
- IDEAS
- Infotrieve
- Kobson
- LOCKSS
- Mendeley
- MIAR
- Norwegian Centre for Research Data (NSD)
- PKP Open Archives Harvester
- Publons
- Qualis/CAPES
- RePEc
- ResearchGate
- ROAD
- Scilit
- SHERPA/RoMEO
- SocioRePEc
- Technische Informationsbibliothek (TIB)
- The Keepers Registry
- UCR Library
- Universe Digital Library
- ZBW-German National Library of Economics
- Zeitschriften Daten Bank (ZDB)
Contact
- Kevin DuranEditorial Assistant
- ibr@ccsenet.org