A Spectral Approach to Stock Market Performance
First published: 2023
Brief summary
Reviews and extends the application of harmonic and spectral analysis techniques to stock market indices, building on earlier work applying harmonic analysis to the Dow Jones Industrial Average and cross-periodogram analysis of US market volatility and correlation.
Article
A Spectral Approach to Stock Market Performance is a preprint published by arXiv in 2023. It reviews and extends the application of harmonic and spectral analysis techniques to stock market indices, building on earlier work applying harmonic analysis to the Dow Jones Industrial Average and cross-periodogram analysis of US market volatility and correlation.
The study references prior harmonic analysis of the Dow Jones Industrial Average (Escanuela 2011) and cross-periodogram volatility/correlation analysis (Chaudhuri & Lo 2015). It also considers methodology-focused. This gives the cycle claim a specific numerical and evidential setting rather than presenting periodicity only as a visual impression.
The article reports the following result: Escanuela (2011) applied harmonic analysis to the Dow Jones Industrial Stock Average series in the United States, while Chaudhuri and Lo (2015) used the cross-periodogram to analyse volatility and correlation in the U.S. stock market. The interpretation is strongest when trend, volatility, structural breaks, data-snooping and out-of-sample performance are addressed.
For cycles researchers, the article brings together stock market spectral analysis, harmonic analysis, market volatility, dow jones. It is relevant to financial-cycle research because multiple periodicities can overlap with trend, volatility, structural breaks and changing investor behaviour.
Because it is a preprint, the work should be read alongside later peer-reviewed publications and independent replications. It remains useful because the proposed cycle, dataset and analytical approach are stated clearly enough to be scrutinised.
Source details and credits
- Source / publisher: arXiv
- Source type: Preprint
- URL type: PDF
- Credits: arXiv
- URL: https://arxiv.org/pdf/2305.05762