Time Series Analysis of the Stock Market Data Using Wavelet Analysis and Random Matrix Theory

Neeraj, . (2014) Time Series Analysis of the Stock Market Data Using Wavelet Analysis and Random Matrix Theory. Masters thesis, Indian Institute of Science Education and Research Kolkata.

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Abstract

We have used wavelet transform to study the multi-scale, self similar behaviour and deviations thereof, in the stock prices of large companies, belonging to different economic sectors. The stock market returns exhibit multi-fractal characteristics, with some of the companies showing deviations at small and large scales. We have used daubechis (db) wavelet function to isolate the polynomial trend and thus extracting fluctuations at different levels. We make use of Db4 and Db6 basis sets to respectively isolate local linear and quadratic trends at different scales in order to study the statistical characteristics of these financial time series. Then we invoked random matrix theory to draw some relation between the trend of the market before, during and after the crisis by analysing the eigenvalues and corresponding eigenvectors of the data. We also found the hurst exponent and power law behaviour of the data to check whether the time series data is showing self-persistent or antipersistent nature.

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