Abstract
Abstract In this paper, the dynamics of Standard and Poor’s 500 (S&P 500) stock price index is analysed within a time-frequency framework over a monthly period 1791:08–2015:05. Using the Empirical Mode Decomposition technique, the S&P 500 stock price index is divided into different frequencies known as intrinsic mode functions (IMFs) and one residual. The IMFs and the residual are then reconstructed into high frequency, low frequency and trend components using the hierarchical clustering method. Using different measures, it is shown that the low frequency and trend components of stock prices are relatively important drivers of the S&P 500 index. These results are also robust across various subsamples identified based on structural break tests. Therefore, US stock prices have been driven mostly by fundamental laws rooted in economic growth and long-term returns on investment.
Highlights
In recent years, analyses of stock prices within the time-frequency framework have attracted a lot of attention from academicians and market practitioners
The monthly data on the Standard and Poor's 500 (S&P 500), covering the period 1791:08 to 2015:05 was obtained from the Global Financial Database (GFD)
The data of the S&P 500 index was decomposed into a number of intrinsic mode functions (IMFs) and residuals, using the Ensemble EMD (EEMD)
Summary
Analyses of stock prices within the time-frequency framework have attracted a lot of attention from academicians and market practitioners. The intrinsic complexities of the stock markets have made them least worthy of analysis using the conventional time-domain tools. The obvious reason for this is that stock prices are determined by traders, who deal at different frequencies. While institutional investors and central banks constitute the low-frequency traders, speculators and market makers fall into the category of high-frequency traders in stock markets. Price formation in the stock markets can be attributed to trading by heterogeneous traders within different frequencies. Some appealing events may remain hidden under different frequencies when stock prices are analysed within the time-domain framework
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