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Hybrid Investment Strategy Based on Momentum and Macroeconomic Approach

Author

Listed:
  • Kamil Korzeń

    (Faculty of Economic Sciences, University of Warsaw)

  • Robert Ślepaczuk

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

The purpose of this research is to test the potential returns and robustness of an automated investment strategy. The strategy is based on momentum and macroeconomic approach, that consists of the technical core – momentum, and the additional macro screening, which is used to determine whether investment signals generate relevant investment opportunities or just technical noise. In order to check whether the macroeconomic factor is the value added to the momentum strategy, the hybrid approach is tested and compared with the simple momentum and the macroeconomic strategy alone and then assessed on a risk-adjusted return basis. The main aim of this paper is to answer the question, whether an investor can gain surplus risk-adjusted returns from merging short-term momentum strategy with the long-term macroeconomic approach. Strategies are based on the data for the selected companies from the S&P500 index in the period ranging from 02/01/1990 to 31/12/2018.

Suggested Citation

  • Kamil Korzeń & Robert Ślepaczuk, 2019. "Hybrid Investment Strategy Based on Momentum and Macroeconomic Approach," Working Papers 2019-17, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2019-17
    as

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    File URL: https://www.wne.uw.edu.pl/index.php/download_file/5084/
    File Function: First version, 2019
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    References listed on IDEAS

    as
    1. Robert Ślepaczuk & Grzegorz Zakrzewski & Paweł Sakowski, 2012. "Investment strategies beating the market. What can we squeeze from the market?," Working Papers 2012-04, Faculty of Economic Sciences, University of Warsaw.
    2. Evan Gatev & William N. Goetzmann & K. Geert Rouwenhorst, 2006. "Pairs Trading: Performance of a Relative-Value Arbitrage Rule," The Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 797-827.
    3. Phil Maguire & Karl Moffett & Rebecca Maguire, 2018. "Combining Independent Smart Beta Strategies for Portfolio Optimization," Papers 1808.02505, arXiv.org, revised Aug 2018.
    4. Kang, Joseph & Liu, Ming-Hua & Ni, Sophie Xiaoyan, 2002. "Contrarian and momentum strategies in the China stock market: 1993-2000," Pacific-Basin Finance Journal, Elsevier, vol. 10(3), pages 243-265, June.
    5. Narasimhan Jegadeesh & Sheridan Titman, 1999. "Profitability of Momentum Strategies: An Evaluation of Alternative Explanations," NBER Working Papers 7159, National Bureau of Economic Research, Inc.
    6. Przemysław Ryś & Robert Ślepaczuk, 2018. "Machine learning in algorithmic trading strategy optimization - implementation and efficiency," Working Papers 2018-25, Faculty of Economic Sciences, University of Warsaw.
    7. Laura Xiaolei Liu & Lu Zhang, 2008. "Momentum Profits, Factor Pricing, and Macroeconomic Risk," The Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2417-2448, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    investment strategy; momentum; macroeconomic indicators; algorithmic trading; risk adjusted returns;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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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