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Charting by machines

Author

Listed:
  • Murray, Scott
  • Xia, Yusen
  • Xiao, Houping

Abstract

We test the efficient market hypothesis by using machine learning to forecast stock returns from historical performance. These forecasts strongly predict the cross-section of future stock returns. The predictive power holds in most subperiods and is strong among the largest 500 stocks. The forecasting function has important nonlinearities and interactions, is remarkably stable through time, and captures effects distinct from momentum, reversal, and extant technical signals. These findings question the efficient market hypothesis and indicate that technical analysis and charting have merit. We also demonstrate that machine learning models that perform well in optimization continue to perform well out-of-sample.

Suggested Citation

  • Murray, Scott & Xia, Yusen & Xiao, Houping, 2024. "Charting by machines," Journal of Financial Economics, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:jfinec:v:153:y:2024:i:c:s0304405x2400014x
    DOI: 10.1016/j.jfineco.2024.103791
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    More about this item

    Keywords

    Efficient market hypothesis; Machine learning; Deep learning; Charting; Technical analysis; Cross-section of stock returns;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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