Abstract
This study utilized the empirical mode decomposition (EMD) technique and examined which group of investors based on their trading frequencies influence stock prices in Ghana. We applied this technique to a dataset of daily closing prices of GSE Financial Stock Index for the period 04/01/2011 to 28/08/2015. The daily closing prices were decomposed into six intrinsic mode functions (IMFs) and a residue. We used the hierarchical clustering method to reconstruct the IMFs into high frequency, low frequency, and trend components. Using statistical measures such as Pearson product moment correlation coefficient and the Kendall rank correlation, we found that the low frequency and trend components of stock prices are the main drivers of the GSE stock index. These low-frequency traders are the institutional investors. Therefore, stock prices on the GSE are affected by real economic growth but not short-lived market fluctuations.
Highlights
In contrast to previous methods such as wavelet analysis and spectrum analysis, empirical mode decomposition (EMD) is intuitive, direct, posterior, and adaptive [21]. e EMD can be used to decompose a series into a finite and often small number of intrinsic mode functions (IMFs) [6]
Similar to EMD, wavelet has been used to study nonstationary signal analysis. Studies such as Tiwari et al [22, 23]; Jammazi et al [24]; Jiang et al [25]; Ferrer et al [26]; Yang et al [27]; Wang et al [28]; Boubaker and Raza [29]; and Frimpong et al [30] used wavelet-based methods to study the behaviour of financial variables such as oil prices, exchange rates, inflation, and stock prices at different frequencies
EMD has not been widely used in the analysis of stock prices
Summary
Similar to EMD, wavelet has been used to study nonstationary signal analysis. We apply EMD in time-frequency analysis for GSE index for the period from 04/01/2011 to 28/08/2015. E main objective of this study is to determine which group of investors based on their trading frequencies influence stock prices in Ghana. E following contributions are made to the literature It provides a detailed analysis of the use of EMD to decompose the stock price into several IMFs and one residue. These IMFs and the residue are reconstructed into high frequency, low frequency, and trend components using the hierarchical clustering method. Data e data which were obtained from DataStream are the daily closing prices of the GSE Financial Stock (GSE-FSI) index coded in DataStream as GSEFSII (Price Index). e data period is between January 4, 2011, and August 28, 2015. e period of study was chosen because of data availability. e closing prices were transformed into returns which were calculated by taking the natural log first difference of prices
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