Gaining Competitive Advantage for Trading in Emerging Capital Markets with Neural Networks
Document Type
Article
Publication Date
1999
Keywords
emerging markets, trading, neural networks, Pacific Rim
Digital Object Identifier (DOI)
https://doi.org/10.1080/07421222.1999.11518251
Abstract
Emerging capital markets may not be as efficient as the more established equity markets. Because of the possible inefficiency in these markets, various indica- tors that are external to the emerging capital market may provide a significant trading advantage. A preliminary analysis suggests that the Singapore market appears to be efficient. Neural network models are used to evaluate the claim that emerging equity markets, specifically the Singapore exchange, are affected by external signals and attempt to exploit any trading advantage imparted by these signals. The neural network technique as it is applied to trading on market indices in the "emerging" Singapore market is compared with the more established Dow Jones market index. Results indicate that external market signals can significantly improve forecasting on the Singapore DBS50 index but have little or no effect on forecasts for the more established Dow Jones Industrial Average index. The research demonstrates the efficacy of using neural network methods to capitalize on discovered market ineffi- ciencies. Utilizing external market signals, a neural network forecasting model achieved a 63 percent trading prediction accuracy.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
Journal of Management Information Systems, v. 16, no. 2, p. 177-192
Scholar Commons Citation
Walczak, Steven, "Gaining Competitive Advantage for Trading in Emerging Capital Markets with Neural Networks" (1999). School of Information Faculty Publications. 210.
https://digitalcommons.usf.edu/si_facpub/210
