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The Trend is Your Friend: A Note on An Ensemble Learning Approach to Finding It

Author

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  • Tzu-Pu Chang
  • Yu-Cheng Chang
  • Po-Ching Chou

Abstract

The essential goal of trend-following investing is to precisely identify where the uptrend and downtrend are located. This paper thus provides a two-layer stacking technique, which is a novel ensemble learning approach, to predict such trends for the Taiwan Top 50 ETF. The proposed stacking technique stacks the predictors of support vector machine (SVM), multi-layer perception (MLP), adaptive boosting (Adaboost), and extreme gradient boosting (Xgboost), presenting empirical results whereby following the trends obtained from the stacking technique can generate positive returns and beat both conventional moving-average crossover and buy-and-hold strategies.

Suggested Citation

  • Tzu-Pu Chang & Yu-Cheng Chang & Po-Ching Chou, 2022. "The Trend is Your Friend: A Note on An Ensemble Learning Approach to Finding It," Bulletin of Applied Economics, Risk Market Journals, vol. 9(1), pages 19-25.
  • Handle: RePEc:rmk:rmkbae:v:9:y:2022:i:1:p:19-25
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    References listed on IDEAS

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    1. Emilio Colombo & Gianfranco Forte & Roberto Rossignoli, 2019. "Carry Trade Returns with Support Vector Machines," International Review of Finance, International Review of Finance Ltd., vol. 19(3), pages 483-504, September.
    2. Clare, Andrew & Seaton, James & Smith, Peter N. & Thomas, Stephen, 2016. "The trend is our friend: Risk parity, momentum and trend following in global asset allocation," Journal of Behavioral and Experimental Finance, Elsevier, vol. 9(C), pages 63-80.
    3. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    4. Chen, Shiu-Sheng, 2009. "Predicting the bear stock market: Macroeconomic variables as leading indicators," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 211-223, February.
    5. Mahendra Raj & David Thurston, 1996. "Effectiveness of simple technical trading rules in the Hong Kong futures markets," Applied Economics Letters, Taylor & Francis Journals, vol. 3(1), pages 33-36.
    6. Menkhoff, Lukas, 2010. "The use of technical analysis by fund managers: International evidence," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2573-2586, November.
    7. Nyberg, Henri, 2013. "Predicting bear and bull stock markets with dynamic binary time series models," Journal of Banking & Finance, Elsevier, vol. 37(9), pages 3351-3363.
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    More about this item

    Keywords

    Trend-following investing; Stacking technique; Ensemble learning; Machine learning; Taiwan Top 50 ETF;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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