Abstract
The present study evaluates the prediction performance of the multi-machine learning models (MLMs) on high-volatile financial markets data sets since 2007 to 2020. The linear and nonlinear empirical data sets are comprised on stock price returns of Karachi stock exchange (KSE) 100-Index of Pakistan and currencies exchange rates of Pakistani Rupees (PKR) against five major currencies (USD, Euro, GBP, CHF & JPY). In the present study, the support vector regression (SVR), random forest (RF), and machine learning-linear regression model (ML-LRM) are under-evaluated for comparative prediction performance. Moreover, the findings demonstrated that the SVR comparatively gives optimal prediction performance on group1. Similarly, the RF relatively gives the best prediction performance on group2. The findings of study concludes that the algorithm of RF is most appropriate for nonlinear approximation/evaluation and the algorithm of SVR is most useful for high-frequency time-series data estimation. The present study is contributed by exploring comparative enthusiastic/optimistic machine learning model on multi-nature data sets. This empirical study would be helpful for finance and machine-learning pupils, data analysts and researchers, especially for those who are deploying machine-learning approaches for financial analysis.
Highlights
Since several decades, a lot of researchers and data analysts have been applying traditional econometric models (TEM) for hypothetical testing and financial-nonfinancial market evaluation
The present study evaluates the prediction performance of the multi-machine learning models (MLMs) on high-volatile financial markets data sets since 2007 to 2020
The group1 is contained on daily stock price returns of Karachi stock exchange (KSE) 100-index of Pakistan and group2 is contained on Pakistani Rupees (PKR) currency exchange rate against five major currencies of the world
Summary
A lot of researchers and data analysts have been applying traditional econometric models (TEM) for hypothetical testing and financial-nonfinancial market evaluation. Nowadays, the supervised and unsupervised machine learning approaches have quite famous to precisely evaluate the different nature of big data. The machine learning approaches are successfully employed into different domains with respect to their applications. It is usually used for speech recognition, image processing, wind-speed frequency scaling, network filtering and financial markets prediction [1,2,3,4]. The evaluation of stock market and forex market returns with machine learning approaches are very hot topic nowadays
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More From: International Journal of Advanced Computer Science and Applications
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