목차
1. Introduction
2. Analysis for Individual stocks
3. Portfolio Analysis
4. Conclusion
2. Analysis for Individual stocks
3. Portfolio Analysis
4. Conclusion
본문내용
1. Introduction
Our group has interest in the performance of stocks of Korean company in US stock market. In order to analyze it, we selected five Korean companies listed in NYSE (see Exhibit 1) and gathered their monthly stock prices and returns data. Our objects are to make an analysis for the performance of individual stocks and portfolio and to find the best regression model for forecasting. OLS method is to be used for simple and multiple regressions.
2. Analysis for individual stocks Descriptive statistics
During the 5-year period from 2002 through 2006, all five stocks outperformed S&P 500 index. However, when we look at the graph we can find that each stock has had higher volatility than S&P 500. It is interpreted that each stock yields returns higher than market average and thus is suitable for long-term investment, but might be riskier in short-term.
The following table shows the distribution of monthly returns of individual stocks. Benchmark is S&P 500 index, of which distribution is bell-shaped. On the other hand, individual stock returns are dispersed over a wider range. See Exhibit 2 for the detailed descriptive statistics.
Covariance and Correlation (see Exhibit 2)
According to covariance and correlation matrices, no pair of different stocks shows strong correlation. Instead, all stocks have positive correlation with each other, within a range between 0.30 and 0.58.
Regression for Beta (see Exhibit 3)
It is revealed that S&P 500 index is not a good variable for explaining the performance
Our group has interest in the performance of stocks of Korean company in US stock market. In order to analyze it, we selected five Korean companies listed in NYSE (see Exhibit 1) and gathered their monthly stock prices and returns data. Our objects are to make an analysis for the performance of individual stocks and portfolio and to find the best regression model for forecasting. OLS method is to be used for simple and multiple regressions.
2. Analysis for individual stocks Descriptive statistics
During the 5-year period from 2002 through 2006, all five stocks outperformed S&P 500 index. However, when we look at the graph we can find that each stock has had higher volatility than S&P 500. It is interpreted that each stock yields returns higher than market average and thus is suitable for long-term investment, but might be riskier in short-term.
The following table shows the distribution of monthly returns of individual stocks. Benchmark is S&P 500 index, of which distribution is bell-shaped. On the other hand, individual stock returns are dispersed over a wider range. See Exhibit 2 for the detailed descriptive statistics.
Covariance and Correlation (see Exhibit 2)
According to covariance and correlation matrices, no pair of different stocks shows strong correlation. Instead, all stocks have positive correlation with each other, within a range between 0.30 and 0.58.
Regression for Beta (see Exhibit 3)
It is revealed that S&P 500 index is not a good variable for explaining the performance
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