Drivers of Stock Prices in Ghana: An Empirical Mode Decomposition Approach

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.

A Nonlinear Approach to Quantifying Investor Fear in Stock Markets of BRIC

“…e current study considered the outcome of each VMF but did not account for the aggregated impact of the VMFs based on multi-frequencies via the cluster analysis approach as employed by extant literature [14,90]. Studies can also be conducted on other emerging markets whose financial markets are integrated for comparison within the emerging markets region.…”

Multi-frequency information transmission among constituents and global equity returns: a sustainable and conventional way of investing

“…The IMFs were classified in this work into multi-frequencies (high, medium and low frequencies) using the Cluster analysis technique. The multi-frequencies were discovered by Multifrequency information transmission looking at the mean periods of each IMF (Zhu et al, 2015;Gyamfi et al, 2021;Adam et al, 2022;Asafo-Adjei et al, 2022a). In this instance, the mean period was calculated using the average frequency of each IMF.…”

“…The techniques are the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (I-CEEMDAN)-based cluster analysis and entropy. Although a plethora of literature has utilised most of these techniques on several other financial assets (Zhu et al, 2015;Adam et al, 2022;Owusu Junior et al, 2021b;Gyamfi et al, 2021;Asafo-Adjei et al, 2022a etc. ), limited attention has been extended to SE returns.…”