Stock Prices Predictability at Long-horizons: Two Tales from the Time-Frequency Domain

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Credit and Capital Markets – Kredit und Kapital ; ISSN: 2199-1235 ; Volume: 50 ; Year: 2017 ; Issue: 1 ; Pages: 37-61

Classification and Topics

• Classification: Wirtschaft ; General Financial Markets: General (includes Measurement and Data) ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Single Equation Models; Single Variables: Other
• Subject: Stock prices and dividends ; Time-frequency decomposition

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Mitianoudis, Nikolaos ; Dergiades, Theologos
• Event: Veröffentlichung
• (who): Duncker & Humblot
• (where): Berlin
• (when): 2017

Further information

• DOI: doi:10.3790/ccm.50.1.37
• Last update: 10.03.2025, 11:42 AM CET