Report on Stock Market
Report on Stock Market
List of Tables and Figures
Table 1 Stock Indexes Correlation Matrix ......................................................................................3
Figure 1 All Indexes Correlation Graph ..........................................................................................4 Figure 2 Group A Indexes Correlation Graph .................................................................................5 Figure 3 Group B Indexes Correlation Graph .................................................................................6 Figure 4 Average Monthly Returns .................................................................................................7
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Ten Stock Indexes. Analysis of Correlation. Over the past years the correlation of stock markets has increased mainly due to globalisation and financial market liberalisation across various countries (Eom and Park, 2017). Cross-country integration of businesses, the spread of multinational enterprises (MNEs), and international diversification opportunities available to investors may align the movements in stock markets (Vermuelen, 2013). This assumption is tested by analysing the correlations between stock prices of ten global indexes. The results are presented in correlation matrix (Table 1). Table 1 Stock Indexes Correlation Matrix
INDEX FTSE S&P500 Nikkei225 Sensex HIS CSI300 NZX50 AX200 Kospi RTSI
FTSE S&P500 Nikkei225 Sensex 1.000 0.829 1.000 0.390 0.248 1.000 0.787 0.578 0.348 1.000 0.642 0.295 0.660 0.709 0.341 0.061 0.715 0.375 0.346 0.668 -0.434 0.120 0.841 0.611 0.551 0.832 0.719 0.697 0.486 0.555 0.795 0.877 0.242 0.440
HIS
1.000 0.882 -0.303 0.784 0.672 0.353
CSI300 NZX50 AX200
1.000 -0.459 0.560 0.558 0.178
1.000 0.037 0.269 0.591
Kospi
RTSI
1.000 0.662 1.000 0.556 0.751 1.000
The analysis shows that the highest correlation is observed between S&P500 and RTSI indexes. However, the correlation of these two indexes with Kospi, AX200, and Sensex is rather significant as well, since Pearson correlation coefficients for all combinations of these pairs are above 71%. At the same time, the research finds another group of indexes that are characterised by a relatively high correlation. Specifically, the study shows that Nikkei225, HIS, and CSI300 are correlated significantly as well. Besides, the Pearson correlation coefficients between Sensex and AX200, as well as HIS, CSI300, and AX200 are found to be rather high. Further analysis provides a more detailed overview of index price changes and unveils possible reasons for the co-movement of indexes.
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Graphical representation of the stock prices over the period from January 2015 to April 2017 is shown in Figure 1. Figure 1 All Indexes Correlation Graph
The analysis indeed demonstrates some co-movement of stock prices over the period. The research transformed index prices into comparable index-like variables, where January-2015 index price equals 100 for each index. This allows the researcher to ensure that the graphical representation of the changes in index prices is relevant, and there is no need to convert index prices into a single currency. The analysis demonstrates that most indexes followed similar patterns during some time spans. For example, a general decline in the indexes was observed in Summer and Autumn 2015. The decline in prices of some indexes, such as RTSI, CSI300, Kospi and HIS started in May 2015. However, other indexes, namely S&P500 and Nikkei did not show any decline movements over this sub-period. This suggests that while correlations between the indexes are observed for longer terms, some discrepancies in the movements of index prices can be captured over shorter periods. Nevertheless, the decline of such indexes as Nikkei and S&P500 started in August 2015, which may indicate at a possibility of cointegration between the indexes (Cavenaile et al., 2014). The declines in index prices in 2015-2016 can be attributed to a substantial decline in assets around the globe. The fall in assets was observed overnight and could be attributed to different economic and political factors. One of such factors could be a slowdown of Chinese economic growth. Other factors could be renewed uncertainty in terms of Greece and the Eurozone, increasing strength of the US dollar, and the possibility of interest rates growth (Ro and Udland, 2015). 4
The major general trend captured for all indexes is noted for the period after March 2016. In April 2016 all indexes demonstrated a growth pattern that has been consistent until April 2017, while further data is not yet available. A more detailed presentation of the changes in index prices is provided in Figure 2. Figure 2 Group A Indexes Correlation Graph
The analysis shows a considerably higher volatility of the RTSI index compared to other indexes, which can be explained by oil price volatility and the dependence of Russian economics and markets on oil exports (Amin et al., 2016). Volatility of oil prices led to the fluctuation of Russian stock market index volatility. However, this effect does not influence the long-term correlation between RTIS and other indexes. On the one hand, higher volatility is associated with higher risks (Khan et al., 2015). On the other hand, the research reveals that RTSI index achieved the highest growth over the entire period. The index demonstrated the most significant fall over May 2015 – May 2016, but then demonstrated an impressive growth to the level that was 45% higher than its initial January 2015 price. Despite this peculiarity of RTSI, the same pattern of development is observed for other indexes as well. The indexes’ changes are characterised by a lower volatility, but the changes in the direction of index price movements are similar.
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Further analysis of the indexes’ development is presented in Figure 3, that reflects the changes in Sensex, Nikkei225, HIS, CSI300, and NZX50. Figure 3 Group B Indexes Correlation Graph
The analysis reveals a significant correlation between some of the indexes, but the correlation can be negative. Specifically, the study shows the co-movement of Sensex, Nikkei225, HIS, and CSI300 indexes. An exception is the growth of CSI300 in February – June 2015, while only Nikkei225 also showed some growth over the period. Meanwhile, a negative correlation between NZX50 and some other indexes is captured. For instance, the study finds that while all indexes declined substantially over December 2015 – March 2016, NZX50 index demonstrated growth over the same period. This observation of negative correlation may be indicative of the opportunities available for investors. Negative correlation between indexes may provide openings for international portfolio diversification (Li, 2007). The growth of the indexes after mid-2016 can be explained by global events and factors. Oil prices increased in August 2016, forcing energy stocks up. The decision of the Fed not to raise interest rates could also contribute to stock price development, while government bond rates were at historically low and sometimes negative levels across the globe (Walden, 2016).
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Figure 4 illustrates average index monthly returns over the period explored. Figure 4 Average Monthly Returns
The findings show that the highest returns were obtained by RTSI, but the study also revealed its significant volatility. The second highest returns were achieved by NZX50, and the research showed that the volatility of this index was not as significant. CSI300 is the only index that had negative returns over the period, while HIS gained the lowest average monthly return, which amounted to 0.1%. The growth of nine out of ten indexes over the period implies a common trend in the index price development, which further confirms the likeliness of correlation between them.
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References Khan, F., Anuar, M. A., Choo, L. G., & Tahir, M. (2015). Trading Effect Emerging Stock Markets Risks-Return Volatility Dynamics and Enterprises Economic Exposure. Journal Transition Studies Review, 21(1), pp.91-112. Cavenaile, L., Gengenbach, C., & Palm, F. (2014). Stock markets, banks and long run economic growth: a panel cointegration-based analysis. De Economist, 162(1), pp.19-40. Vermeulen, R. (2013). International diversification during the financial crisis: A blessing for equity investors?. Journal of International Money and Finance, 35, 104-123. Amin, M. Y., Amin, Z. Y., & Zadeh, H. R. (2016). Oil Price Shocks, Asymmetric Effect and Volatility Spillover; Case of Stock Market in Oil Exporting Countries. Journal of Insurance and Financial Management, 1(2). Leon Li, M. Y. (2007). Volatility states and international diversification of international stock markets. Applied Economics, 39(14), 1867-1876. Eom, C., & Park, J. W. (2017). Effects of common factors on stock correlation networks and portfolio diversification. International Review of Financial Analysis, pp.49, 1-11. Ro, S. and Udland, M. (2015). Market Mayhem. Business Insider, [online], available at: http://www.businessinsider.com/us-markets-sell-off-aug-24-2015-2015-8, [accessed 25 April 2017]. Walden, G. (2016). August 2016 Market Recap: The Markets Take a Summer Vacation. [online], available
at:
https://www.thriventfunds.com/insights/market-update/monthly-recap-aug-
2016.html, [accessed 25 April 2017].
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List of Tables and Figures
Table 1 Stock Indexes Correlation Matrix ......................................................................................3
Figure 1 All Indexes Correlation Graph ..........................................................................................4 Figure 2 Group A Indexes Correlation Graph .................................................................................5 Figure 3 Group B Indexes Correlation Graph .................................................................................6 Figure 4 Average Monthly Returns .................................................................................................7
2
Ten Stock Indexes. Analysis of Correlation. Over the past years the correlation of stock markets has increased mainly due to globalisation and financial market liberalisation across various countries (Eom and Park, 2017). Cross-country integration of businesses, the spread of multinational enterprises (MNEs), and international diversification opportunities available to investors may align the movements in stock markets (Vermuelen, 2013). This assumption is tested by analysing the correlations between stock prices of ten global indexes. The results are presented in correlation matrix (Table 1). Table 1 Stock Indexes Correlation Matrix
INDEX FTSE S&P500 Nikkei225 Sensex HIS CSI300 NZX50 AX200 Kospi RTSI
FTSE S&P500 Nikkei225 Sensex 1.000 0.829 1.000 0.390 0.248 1.000 0.787 0.578 0.348 1.000 0.642 0.295 0.660 0.709 0.341 0.061 0.715 0.375 0.346 0.668 -0.434 0.120 0.841 0.611 0.551 0.832 0.719 0.697 0.486 0.555 0.795 0.877 0.242 0.440
HIS
1.000 0.882 -0.303 0.784 0.672 0.353
CSI300 NZX50 AX200
1.000 -0.459 0.560 0.558 0.178
1.000 0.037 0.269 0.591
Kospi
RTSI
1.000 0.662 1.000 0.556 0.751 1.000
The analysis shows that the highest correlation is observed between S&P500 and RTSI indexes. However, the correlation of these two indexes with Kospi, AX200, and Sensex is rather significant as well, since Pearson correlation coefficients for all combinations of these pairs are above 71%. At the same time, the research finds another group of indexes that are characterised by a relatively high correlation. Specifically, the study shows that Nikkei225, HIS, and CSI300 are correlated significantly as well. Besides, the Pearson correlation coefficients between Sensex and AX200, as well as HIS, CSI300, and AX200 are found to be rather high. Further analysis provides a more detailed overview of index price changes and unveils possible reasons for the co-movement of indexes.
3
Graphical representation of the stock prices over the period from January 2015 to April 2017 is shown in Figure 1. Figure 1 All Indexes Correlation Graph
The analysis indeed demonstrates some co-movement of stock prices over the period. The research transformed index prices into comparable index-like variables, where January-2015 index price equals 100 for each index. This allows the researcher to ensure that the graphical representation of the changes in index prices is relevant, and there is no need to convert index prices into a single currency. The analysis demonstrates that most indexes followed similar patterns during some time spans. For example, a general decline in the indexes was observed in Summer and Autumn 2015. The decline in prices of some indexes, such as RTSI, CSI300, Kospi and HIS started in May 2015. However, other indexes, namely S&P500 and Nikkei did not show any decline movements over this sub-period. This suggests that while correlations between the indexes are observed for longer terms, some discrepancies in the movements of index prices can be captured over shorter periods. Nevertheless, the decline of such indexes as Nikkei and S&P500 started in August 2015, which may indicate at a possibility of cointegration between the indexes (Cavenaile et al., 2014). The declines in index prices in 2015-2016 can be attributed to a substantial decline in assets around the globe. The fall in assets was observed overnight and could be attributed to different economic and political factors. One of such factors could be a slowdown of Chinese economic growth. Other factors could be renewed uncertainty in terms of Greece and the Eurozone, increasing strength of the US dollar, and the possibility of interest rates growth (Ro and Udland, 2015). 4
The major general trend captured for all indexes is noted for the period after March 2016. In April 2016 all indexes demonstrated a growth pattern that has been consistent until April 2017, while further data is not yet available. A more detailed presentation of the changes in index prices is provided in Figure 2. Figure 2 Group A Indexes Correlation Graph
The analysis shows a considerably higher volatility of the RTSI index compared to other indexes, which can be explained by oil price volatility and the dependence of Russian economics and markets on oil exports (Amin et al., 2016). Volatility of oil prices led to the fluctuation of Russian stock market index volatility. However, this effect does not influence the long-term correlation between RTIS and other indexes. On the one hand, higher volatility is associated with higher risks (Khan et al., 2015). On the other hand, the research reveals that RTSI index achieved the highest growth over the entire period. The index demonstrated the most significant fall over May 2015 – May 2016, but then demonstrated an impressive growth to the level that was 45% higher than its initial January 2015 price. Despite this peculiarity of RTSI, the same pattern of development is observed for other indexes as well. The indexes’ changes are characterised by a lower volatility, but the changes in the direction of index price movements are similar.
5
Further analysis of the indexes’ development is presented in Figure 3, that reflects the changes in Sensex, Nikkei225, HIS, CSI300, and NZX50. Figure 3 Group B Indexes Correlation Graph
The analysis reveals a significant correlation between some of the indexes, but the correlation can be negative. Specifically, the study shows the co-movement of Sensex, Nikkei225, HIS, and CSI300 indexes. An exception is the growth of CSI300 in February – June 2015, while only Nikkei225 also showed some growth over the period. Meanwhile, a negative correlation between NZX50 and some other indexes is captured. For instance, the study finds that while all indexes declined substantially over December 2015 – March 2016, NZX50 index demonstrated growth over the same period. This observation of negative correlation may be indicative of the opportunities available for investors. Negative correlation between indexes may provide openings for international portfolio diversification (Li, 2007). The growth of the indexes after mid-2016 can be explained by global events and factors. Oil prices increased in August 2016, forcing energy stocks up. The decision of the Fed not to raise interest rates could also contribute to stock price development, while government bond rates were at historically low and sometimes negative levels across the globe (Walden, 2016).
6
Figure 4 illustrates average index monthly returns over the period explored. Figure 4 Average Monthly Returns
The findings show that the highest returns were obtained by RTSI, but the study also revealed its significant volatility. The second highest returns were achieved by NZX50, and the research showed that the volatility of this index was not as significant. CSI300 is the only index that had negative returns over the period, while HIS gained the lowest average monthly return, which amounted to 0.1%. The growth of nine out of ten indexes over the period implies a common trend in the index price development, which further confirms the likeliness of correlation between them.
7
References Khan, F., Anuar, M. A., Choo, L. G., & Tahir, M. (2015). Trading Effect Emerging Stock Markets Risks-Return Volatility Dynamics and Enterprises Economic Exposure. Journal Transition Studies Review, 21(1), pp.91-112. Cavenaile, L., Gengenbach, C., & Palm, F. (2014). Stock markets, banks and long run economic growth: a panel cointegration-based analysis. De Economist, 162(1), pp.19-40. Vermeulen, R. (2013). International diversification during the financial crisis: A blessing for equity investors?. Journal of International Money and Finance, 35, 104-123. Amin, M. Y., Amin, Z. Y., & Zadeh, H. R. (2016). Oil Price Shocks, Asymmetric Effect and Volatility Spillover; Case of Stock Market in Oil Exporting Countries. Journal of Insurance and Financial Management, 1(2). Leon Li, M. Y. (2007). Volatility states and international diversification of international stock markets. Applied Economics, 39(14), 1867-1876. Eom, C., & Park, J. W. (2017). Effects of common factors on stock correlation networks and portfolio diversification. International Review of Financial Analysis, pp.49, 1-11. Ro, S. and Udland, M. (2015). Market Mayhem. Business Insider, [online], available at: http://www.businessinsider.com/us-markets-sell-off-aug-24-2015-2015-8, [accessed 25 April 2017]. Walden, G. (2016). August 2016 Market Recap: The Markets Take a Summer Vacation. [online], available
at:
https://www.thriventfunds.com/insights/market-update/monthly-recap-aug-
2016.html, [accessed 25 April 2017].
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