Global Journal of Management and Business Research, A: Administration and Management, Volume 21 Issue 12

Table 4: Results of Component Matrix Component Matrix a Component 1 2 3 4 ASX 200(Australia) .801 Nikkei 225(Japan) .737 KOSPI (South Korea) .723 -.390 Hang Seng (Hong Kong) .782 Jakarta Composite Index (Indonesia) .535 .352 SSE Composite Index (China) .307 Taiwan Capitalization Weighted Stock Index .632 -.385 Sensex (India) .666 Amman SE General (Jordan) .514 BLOM (Lebanon) .780 QE General (Qatar) .518 MSM 30 (Oman) .590 .446 Tadawul (Saudi Arabia) .541 .404 Tel Aviv (Israel) .520 CAC 40 (France) .893 DAX 30 (Germany) .876 BEL 20 (Belgium) .816 Euronext 100 .906 DJIA (United States) .806 TSE (Canada) .807 IBOVSPA (Brazil) .697 BMV (Mexico) .743 Extraction Method: Principal Component Analysis. a. 4 components extracted. Source: Authors calculations The component matrix results in table 4 above; many of the stock markets results are highly correlated with factor 1 followed by factor 2, factor 3, and factor 4. In factor 1, Indian stock market returns are correlated with many world stock market returns covering South Asia and European countries. Table 5: Results of Communalities Communalities Initial Extraction ASX 200(Australia) 1.000 .645 Nikkei 225(Japan) 1.000 .579 KOSPI (South Korea) 1.000 .690 Hang Seng (Hong Kong) 1.000 .723 Jakarta Composite Index (Indonesia) 1.000 .469 SSE Composite Index (China) 1.000 .280 Taiwan Capitalization Weighted Stock Index 1.000 .577 Sensex (India) 1.000 .518 19 Global Journal of Management and Business Research Volume XXI Issue XII Version I Year 2021 ( ) A © 2021 Global Journals Design of Portfolio using Multivariate Analysis