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• W2765995896 abstract "Abstract This paper provides an empirical evaluation of the U.S. aggregate stock market predictability based on a new technical analysis index that eliminates the idiosyncratic noise component in technical indicators. I find that the new index exhibits statistically and economically significant in-sample and out-of-sample predictive power and outperforms the well-known technical indicators and macroeconomic variables. In addition, it can predict cross-sectional stock portfolio returns sorted by size, value, momentum, and industry and generate substantial utility gains for a mean-variance investor. A vector autoregression-based stock return decomposition shows that the economic source of the predictive power predominantly comes from time variations in future cash flows (i.e., the cash flow channel)." @default.
• W2765995896 created "2017-11-10" @default.
• W2765995896 creator A5029580390 @default.
• W2765995896 date "2018-03-01" @default.
• W2765995896 modified "2023-10-17" @default.
• W2765995896 title "Technical analysis and stock return predictability: An aligned approach" @default.
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• W2765995896 doi "https://doi.org/10.1016/j.finmar.2017.09.003" @default.
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