Portfolio Optimization Using ARIMA – Global Minimum Variance Approach

Raiysat, Kamran (2020) Portfolio Optimization Using ARIMA – Global Minimum Variance Approach. Masters thesis, Dublin, National College of Ireland.

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Abstract

The stock price prediction and portfolio optimization have been in the academic sphere over a long period. Several techniques have been studied for this purpose but ARIMA and GMV are the most widely used statistical methods. Advanced machine learning has made statistical techniques more powerful. The study tries to find the performance of the ARIMA and GMV hybrid approach against the GMV approach on the KSE100 index. The purpose of the selection of the KSE100 index is that very limited studies have been conducted on Pakistan Stock Exchanges using advanced machine learning statistical approaches. The data is collected for six most capitalized stocks listed at KSE100 over five years using weekly ending prices. The data is prepared and split into 80% training and 20% test data. The Auto-ARIMA function is used to forecast the future expected values of each stock and KSE100 based on the training data. The validation dataset is created by combining training data and future expected values. Portfolio weights are calculated using GMV on training data and ARIMA-GMV using validation data. The results indicate that the ARIMA – GMV approach performs better than the traditional GMV approach in portfolio optimization. The ARIMA – GMV mainly depends on the performance of the ARIMA for the accuracy of the future expected value. It is also suggested to use non – linear techniques to optimize the portfolio using GMV to build an accurate model in a highly volatile market across borders.
Keywords: ARIMA, GMV, Stock Markets, Forecast, Portfolio optimization

Item Type:	Thesis (Masters)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software
Divisions:	School of Computing > Master of Science in FinTech
Depositing User:	Dan English
Date Deposited:	29 Jan 2021 15:01
Last Modified:	29 Jan 2021 15:01
URI:	https://norma.ncirl.ie/id/eprint/4564

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