آنالیز شاخص های بازار سهام از طریق شاخص گذاری چندوجهی

We propose a graphical method to visualize possible time-varying correlations between fifteen stock market values. The method is useful for observing stable or emerging clusters of stock markets with similar behaviour. The graphs, originated from applying multidimensional scaling techniques (MDS), may also guide the construction of multivariate econometric models.

Economical indexes measure the performance of segments of the stock market and are normally used to benchmark the performance of stock portfolios. This paper proposes a descriptive method which analyses possible correlations/similarities in international stock markets. Its results are expected to guide the design of statistical models aiming to test hypotheses of interest. Ultimately, the method can even lead to the postulation of new hypotheses. The study of the correlation of international stock markets may have different motivations. Economic motivations to identify the main factors which affect the behaviour of stock markets across different exchanges and countries. Statistical motivations to visualize correlations in order to suggest some potentially plausible parameter relations and restrictions.The understanding of such correlations would be helpful to the design good portfolios [16] and [18]. Bearing these ideas in mind the outline of our paper is as follows. In Section 2 we give the fundamentals of the multidimensional scaling (MDS) technique, which is the core of our method, and we discuss the details that are relevant for our specific application. In Section 3 we apply our method for daily data on fifteen stock markets, including major American, Asian/Pacific, and European stock markets. In Section 4 we conclude the paper with some final remarks and potential topics for further research.

In this paper, we proposed simple graphical tools to visualize time-varying correlations between stock market behaviour. We illustrated our MDS-based method daily close values of fifteen stock markets. There are several issues relevant for further research. A first issue concerns applying our method to alternative data sets, with perhaps different sampling frequencies or returns and absolute returns, to see how informative the method can be in other cases. A second issue concerns taking the graphical evidence seriously and incorporating it in an econometric time series model to see if it can improve empirical specification strategies.