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On the Returns of Trend-Following Trading Strategies

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Paper [I] tests the success rate of trades and the returns of the Opening Range Breakout (ORB) strategy. A trader that trades on the ORB strategy seeks to identify large intraday price movements and trades only when the price moves beyond some predetermined threshold. We present an ORB strategy based on normally distributed returns to identify such days and find that our ORB trading strategy result in significantly higher returns than zero as well as an increased success rate in relation to a fair game. The characteristics of such an approach over conventional statistical tests is that it involves the joint distribution of low; high; open and close over a given time horizon. Paper [II] measures the returns of a popular day trading strategy; the Opening Range Breakout strategy (ORB); across volatility states. We calculate the average daily returns of the ORB strategy for each volatility state of the underlying asset when applied on long time series of crude oil and S&P 500 futures contracts. We find an average difference in returns between the highest and the lowest volatility state of around 200 basis points per day for crude oil; and of around 150 basis points per day for the S&P 500. This finding suggests that the success in day trading can depend to a large extent on the volatility of the underlying asset. Paper [III] performs empirical analysis on short-term and long-term Commodity Trading Advisor (CTA) strategies regarding their exposures to unanticipated risk shocks. Previous research documents that CTA strategies offer diversification opportunities during equity market crisis situations when evaluated as a group; but do not separate between short-term and long-term CTA strategies. When separating between short-term and long-term CTA strategies; this paper finds that only short-term CTA strategies provide a significant; and consistent; exposure to unanticipated risk shocks while long-term CTA strategies do not. For the purpose of diversifying a portfolio during equity market crisis situations; this result suggests that an investor should allocate to short-term CTA strategies rather than to long-term CTA strategies.

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  • Lundström, Christian, 2017. "On the Returns of Trend-Following Trading Strategies," Umeå Economic Studies 948, Umeå University, Department of Economics.
  • Handle: RePEc:hhs:umnees:0948
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    1. Widerstedt, Barbro, 1998. "Moving or Staying? Job Mobility as a Sorting Process," Umeå Economic Studies 464, Umeå University, Department of Economics.
    2. Rashid, Saman, 2004. "Immigrants' Income and Family Migration," Umeå Economic Studies 625, Umeå University, Department of Economics.
    3. Fung, William & Hsieh, David A, 2001. "The Risk in Hedge Fund Strategies: Theory and Evidence from Trend Followers," Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 313-341.
    4. Hossein Kazemi & Ying Li, 2009. "Market timing of CTAs: An examination of systematic CTAs vs. discretionary CTAs," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 29(11), pages 1067-1099, November.
    5. Quoreshi, Shahiduzzaman, 2006. "Time Series Modelling Of High Frequency Stock Transaction Data," Umeå Economic Studies 675, Umeå University, Department of Economics.
    6. Bask, Mikael, 1998. "Essays on Exchange Rates: Deterministic Chaos and Technical Analysis," Umeå Economic Studies 465, Umeå University, Department of Economics.
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    More about this item

    Keywords

    Bootstrap; Commodity Trading Advisor funds; Contraction-Expansion principle; Crude oil futures; Futures trading; Opening Range Breakout strategies; S&P 500 futures; Technical analysis; Time series momentum; Time-varying market inefficiency;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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