Stock Prices Predictability at Long-horizons: Two Tales from the Time-Frequency Domain
Nikolaos Mitinanoudis () and
Theologos Dergiades ()
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Nikolaos Mitinanoudis: Image Processing and Mulitmedia Laboratory, Dept. of Electrical and Computer Engineering, Democritus University of Thrace
Credit and Capital Markets, 2017, vol. 50, issue 1, 37-61
Accepting non-linearities as an endemic feature of financial data, this paper re-examines Cochrane's “new fact in finance” hypothesis (Cochrane, Economic Perspectives-FRB of Chicago 23, 36-58, 1999). By implementing two methods, frequently encountered in digital signal processing analysis, (Undecimated Wavelet Transform and Empirical Mode Decomposition both methods extract components in the time-frequency domain), we decompose the real stock prices and the real dividends, for the US economy, into signals that correspond to distinctive frequency bands. Armed with the decomposed signals and acting within a non-linear framework, the predictability of stock prices through the use of dividends is assessed at alternative horizons. It is shown that the “new fact in finance” hypothesis is a valid proposition, provided that dividends contribute significantly to predicting stock prices at horizons spanning beyond 32 months. The identified predictability is entirely non-linear in nature.
JEL-codes: G10 C14 C22 C29 (search for similar items in EconPapers)
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Working Paper: Stock Prices Predictability at Long-horizons: Two Tales from the Time-Frequency Domain (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:kuk:journl:v:50:y:2017:i:1:p:37-61
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