Stock market analysis using clustering techniques: the impact of foreign ownership on stock volatility in Vietnam

ABSTRACT

Data mining techniques have been used for various aspects of the financial market, such as prediction on stock index and price, portfolio risk management, and trend detection. In the stock market, there are a huge amount of data, including firms' profile, characteristics, and historical trading data. This paper investigates the impact of foreign ownership on stock market volatility in Vietnam using daily live trading of 100 major stocks on Ho Chi Minh Stock Exchange (HOSE) over the period from September 1^st, 2015 to September 1^st, 2016. Such companies are often categorized according to their business operations, but this is not necessarily reflected in the way their market valuations fluctuate. It is therefore interesting to analyze stock prices to identify companies that are trading in a similar way. K-mean cluster algorithm and hierarchical clustering methods are used to visualize the analysis on net trading volume, price volatility and return volatility ratio. Based on the visualized patterns of the stock market, we can observe the impact of foreign capital on the market volatility. Furthermore, those patterns indicate the investment behaviors that can help optimize the portfolio investment management.

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Index Terms

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Unsupervised learning

        • Cluster analysis