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Predicting And Beating The Stock Market With Machine Learning And Technical Analysis

Author

Listed:
  • MACCHIARULO, ANTHONY

    (Morgan Stanley and Co LLC NYC, NY, USA)

Abstract
The paper studies whether machine learning or technical analysis best predicts the stock market and in turn generates the best return. The research back tests machine learning and technical analysis methods ten years in the past to predict ten years in the future. After prediction stage, the research incorporates the main findings into trading strategies to beat the S&P 500 index. To further this analysis, the paper examines all market periods and then examines the results specifically in up market and down-market periods. The sampling period is January 1995 through December 2005, and the trading period is January 2006 through December 2016. The null hypothesis is that machine learning and technical analysis would generate returns with no statistically significant difference. The study uses State Street’s SPDR® SPY ETF as the benchmark. Data is retrieved from Bloomberg and Yahoo Finance. Outputs are calculated in R, MATLAB, SPSS, EVIEWS, Python, and SAS languages.

Suggested Citation

  • Macchiarulo, Anthony, 2018. "Predicting And Beating The Stock Market With Machine Learning And Technical Analysis," Journal of Internet Banking and Commerce, , vol. 23(01), pages 01-22, April.
  • Handle: RePEc:ris:joibac:0054
    as

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    More about this item

    Keywords

    Machine Learning; Technical Analysis; Statistics; Predicting; Stock Market; Analysis; Investing; Trading; Securities;
    All these keywords.

    JEL classification:

    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists

    Statistics

    Access and download statistics

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