DocumentCode :
693854
Title :
A Trend Tracking Strategy for Gold Future: An Artificial Neutral Network Analysis
Author :
Chen, Chaoteng Jordan ; Ying Huang ; Kin Keung Lai
Author_Institution :
Dept. of Manage. Sci., City Univ. of Hong Kong, Hong Kong, China
fYear :
2013
fDate :
14-16 Nov. 2013
Firstpage :
31
Lastpage :
35
Abstract :
In this paper, we construct a simple data-driven trend tracking strategy for gold future in a view of contrarians. The artificial neutral network (ANN) is adopted to determine the price trend signal, by which the degree of tightness could be adjusted based on observed data. We attempt to capture the small profits when the price is deviated from the Bollinger band in the gold future market by intraday trading. High frequency data of gold future is used to train and test the strategy. Despite of the trading cost, the back-tests show that our strategy has delivered positive returns and is adaptive to different price trends. Finally, we evaluate the profitability with the consideration of trading cost, revealing that the strategy is applicable in practice.
Keywords :
gold; industrial economics; neural nets; pricing; profitability; Bollinger band; artificial neutral network analysis; back-tests; data-driven trend tracking strategy; degree of tightness; gold future market; intraday trading; price trend signal; profitability; trading cost; Algorithm design and analysis; Artificial neural networks; Forecasting; Gold; Market research; Profitability; algorithmic trading; gold future; neural network; technical analysis; trend tracking;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Business Intelligence and Financial Engineering (BIFE), 2013 Sixth International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-4778-2
Type :
conf
DOI :
10.1109/BIFE.2013.8
Filename :
6961085
Link To Document :
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