• 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