What is it about?
In investment field, stockholders need to manage risk in diversifying stock in order to avoid losses during a crisis. One of the technique to do that is by using low-risk low return or high-risk high return strategy. This study aims to calculate Value-at-Risk of the selected stocks from Indonesia. This research also uses machine learning which is random forest method to apply time series data and shows the degree of similarity between the reference and comparison stock. Returns distribution will be combined using copula method to produce composite index and to determine Value-at-Risk.
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Why is it important?
Stocks have risks that are necessary to measure, and statistic approach such as Value-at-Risk (VaR) is commonly used to estimate risk within the stock market. At the same time diversification is also important to consider in order to reduce the risk of loss. It is hard to determine Value-at-Risk if diversification is conducted at the same time, because there are two stocks used to be analyzed and most likely have different characteristics.
Perspectives
Writing this article was a great pleasure as it has co-authors with whom I respect and has helped me a lot. I hope this article is helpful and easy to understand for people who interested in using different kind of statistical approach in stock investments. And most importantly, I hope you find this article thought-provoking.
Ardhito Utomo
Institut Pertanian Bogor
Read the Original
This page is a summary of: Value-at-risk portfolio estimation with copula on selected stocks using variable importance from random forest, January 2022, American Institute of Physics,
DOI: 10.1063/5.0109435.
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