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On the Profitability of Momentum Strategies and Optimal Leverage Rules

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Paper [I] tests the success rate of trades and the returns of the Opening Range Breakout (ORB) day trading strategy. A trader that trades 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 when applied to a long time series of crude oil futures contracts. 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] assesses the returns of the Opening Range Breakout (ORB) day trading strategy across volatility states of the underlying asset. We calculate the average daily returns of the ORB strategy for each volatility state when applied on long time series of crude oil and S&P 500 index futures contracts. We find an average difference in returns between the highest and 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. Our result suggests that ORB strategy traders can be profitable, even in the long-run, but that the success in day trading to a large extent depend 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 in general 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, our result suggests that an investor should allocate to short-term CTA strategies rather than to long-term CTA strategies. Paper [IV] posits that it is possible to obtain an optimal leverage factor for financial instruments equipped with embedded leverage. By applying the Kelly criterion for optimal leverage, we show that there exists a uniquely optimal level of leverage for maximizing the long-run profit of embedded leverage instruments. The implication of an existing unique optimum is that a smaller leverage factor than optimal leads to a lower long-term profit than is feasible, but also that a larger leverage factor leads to a lower long-term profit than is feasible. Our empirical analysis shows how an optimal level of embedded leverage can increase the profitability of Exchange Traded Products. Paper [V] systematically analyses the effect of leverage on long-run profit when trading the Opening Range Breakout (ORB) day trading strategy. This paper clarifies the relation to two optimal leverage rules proposed for maximizing trading profit; the Kelly criterion and the Optimal fraction criterion. Our empirical analysis shows how leverage can increase day trading profit in-sample and out-of-sample when applied to a long time series of DAX 30 index futures contracts.

Suggested Citation

  • Lundström, Christian, 2020. "On the Profitability of Momentum Strategies and Optimal Leverage Rules," Umeå Economic Studies 974, Umeå University, Department of Economics.
  • Handle: RePEc:hhs:umnees:0974
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    More about this item

    Keywords

    Bootstrap; Exchange Traded Products; Kelly criterion; Money management; Opening Range Breakout strategies; Optimal fraction criterion; Time series momentum;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • 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
    • G40 - Financial Economics - - Behavioral Finance - - - General

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