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Using trading mechanisms to investigate large futures data and their implications to market trends

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Abstract

Market trends have been one of the highly debated phenomena in the financial industries and academia. Prior works show the profitability in exploiting transactions via market trend quantification; on the other hand, traders’ behaviors and effects on the market trends can be better understood by market trend studies. In general, the trading strategies on the market trend include trend following strategies and contrarian strategies. Following the trend, trading strategies exploit the momentum effects. The momentum strategies profit in a long position with the rising market prices, as well as in a short position with the decreasing market prices. On the contrary, the view of contrarian trading strategy is based on the mean-reversion property, i.e., a long position is taken when the price moves down and a short position is taken when the price moves up. In this paper, we apply the stop-loss and stop-profit mechanisms to verify the market trends based on two new simple strategies, i.e., the BuyOp. strategy and the BuyHi.SellLo. strategy. We back-test these two strategies on the Taiwan Stock Exchange Capitalization Weighted Stock Index Futures (TAIEX Futures) during the period from May 25, 2010 to August 19, 2015. We compare the numerical results of its profits and losses through various stop-loss thresholds and stop-profit thresholds, and verify the existence of the momentum effect via applying these two new trading strategies. Besides, we analyze the market trends through the repeated simulations of random trades with the stop-loss and stop-profit mechanisms. Our numerical results reveal that there exist momentum effects in TAIEX Futures, which verifies the market inefficiency and the market profitability in exploiting the market inefficiency. In addition, the techniques of random trades are also applied to the other commodities, such as AAPL in NASDAQ, IBM, GOOG in NYSE, and, TSMC in TPE, and so on. Surprisingly, not all the stocks have the momentum effects. Our experimental results show that some stocks or markets are more suitable for the mean-reverse strategy. Finally, we propose a technique to quantify the momentum effect of a financial market by using Jensen–Shannon divergence.

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Acknowledgments

The work of Mu-En Wu was funded by the Ministry of Science and Technology, Taiwan (MOST-104-2221-E-031 -004). The work of Wei-Ho Chung was funded by the Ministry of Science and Technology, Taiwan (MOST-104-2221-E-001-008-MY3).

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Authors and Affiliations

  1. Department of Mathematics, Soochow University, Taipei, Taiwan

    Mu-En Wu

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, People’s Republic of China

    Chia-Hung Wang

  3. Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan

    Wei-Ho Chung

Authors
  1. Mu-En Wu
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  2. Chia-Hung Wang
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  3. Wei-Ho Chung
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Correspondence to Wei-Ho Chung.

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Conflict of interest

Mu-En Wu declares that he has no conflict of interest. Chia-Hung Wang declares that he has no conflict of interest. Wei-Ho Chung declares that he has no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by C.-H. Chen.

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Wu, ME., Wang, CH. & Chung, WH. Using trading mechanisms to investigate large futures data and their implications to market trends. Soft Comput 21, 2821–2834 (2017). https://doi.org/10.1007/s00500-016-2162-6

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  • DOI: https://doi.org/10.1007/s00500-016-2162-6

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