Abstract
A firm’s equity price on the stock-market is reported to be closely related to the Macroeconomic Variable (MVs) of the country in which the firm trades. For this reason, researchers, market traders, financial analysts and forecasters to examine the association between MVs and stock-price have carried out numerous studies, using time-series statistical analysis methods like Autoregressive Integrated Moving Average (ARIMA), Autoregressive Moving Average (ARMA) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH). However, these techniques are reported to suffer from limited predictive power and restrictive assumptions. Besides, in pursuit of ways to remedy these paucities and limitations within these techniques, some researchers have examined uncountable machine learning techniques for measuring the stock-markets trends and making trading decisions using macroeconomic variables. On the other hand, a higher percentage of these studies paid attention to the stock index prediction and neglected the diversity of MVs that influence different sector indices. In addressing the issues above, this study seeks to examine the degree of significance between different sectors stock-price and MVs and predict a 30-day head stock-price using Random Forest (RF) with an improve leave-one-out cross-validation tactic and Long Short-Term Memory Recurrent Neural Network (LSTMRNN). An empirical analysis of the proposed model over the Ghana Stock Exchange (GSE) exhibits high prediction accuracy and better mean absolute error compared with other time-series techniques. It can, therefore, be inferred from the fallouts that the proposed stock-market prediction with MVs, provides an efficient approach to automatic identification and extraction of MVs that affect diverse sector stock and offer an accurate prediction of a stock's future price.
Highlights
In every country, the financial markets play an essential role in the economy, in areas such as allocation, re-allocation, mobilisation, and pricing of capital resources to several stages of monetary intermediation (Asekome and Agbonkhese, 2015; Dosdoğru et al, 2018)
The results reveal that out of the top twenty (20) selected feature from 42 initial feature in each sector understudied in this study, only two (2) features British Pound Sterling (PD) and the Canadian Dollar (CD) representing 10% are associated with stock price movement in all studied sectors of Ghana’s economy
Conclusion and Recommendations for Several previous studies aimed at finding the relationship between stock market volatility and Macroeconomic Variables (MVs)
Summary
The financial markets play an essential role in the economy, in areas such as allocation, re-allocation, mobilisation, and pricing of capital resources to several stages of monetary intermediation (Asekome and Agbonkhese, 2015; Dosdoğru et al, 2018). Macroeconomic Variables (MVs) such as the inflation rate and Gross Domestic Product (GDP) are reported to determine a country’s growth (Ratti and Vespignani, 2015; 2016). The instability in a country’s inflation rate or prices leads to the poor performance of the overall economy of a country (Siok et al, 2015). One can infer that the stock-prices are associated with economic changes happing or anticipated to happen within a country.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.