Advertisement

A Time-frequency search for stock market anomalies

  • Sudeshna Adak
  • Abhinanda Sarkar
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)

Abstract

We carry out a time-frequency analysis of daily values of the Dow Jones Industrial Average and the S&P 500 Index of stock prices based on 62 years of data. The algorithm used is based on pruning dyadic trees to obtain optimal stationary segmentations of nonstationary time series. The resulting time-frequency representations yield insights into the frequency-domain evolution of the US stock market. Details concerning computational speed and recombination are briefly addressed.

Keywords

Stock Market Segmentation Algorithm Adjacent Segment Data Segment Efficient Market Hypothesis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Birkhäuser Boston 1998

Authors and Affiliations

  • Sudeshna Adak
    • 1
  • Abhinanda Sarkar
    • 2
  1. 1.Department of BiostatisticsHarvard School of Public HealthBostonUSA
  2. 2.Department of MathematicsM.I.TCambridgeUSA

Personalised recommendations