IShares ETFs from 1-1-2017 to 9-12-2017

The following chart shows the 2017 performance charts of 

              "EWJ", #iShares Japan

             "EWG", #iShares Germany

             "EWU", #iShares UK

             "EWC", #iShares Canada

             "EWY", #iShares South Korea

             "EWA", #iShares Australia

             "EWH", #iShares Hong Kong

             "EWS", #iShares Singapore

          

             "EWT", #iShares Taiwan

             "EWZ", #iShares Brazil

             "EFA", #iShares EAFE

             "ERUS", #iShares Russia

             "EZA", #iShares South Africa

             "EPP" #iShares Pacific Ex Japan

Performances of ETFs  as of 9-12-207 (from 1-1-2017) Rank ETFs Annualized Returns Annualized STD Annualized Sharpe Ratio 1 EWH 46.0% 9.8% 4.6017 2 EWS 37.6% 10.1% 3.6736 3 EFA 30.2% 8.3% 3.5502 4 EWT 42.6% 11.9% 3.5114 5 EPP 32.5% 10.5% 3.0343 6 EWG 35.1% 11.8% 2.9228 7 EWY 43.2% 15.7% 2.7018 8 EWU 22.8% 8.9% 2.4874 9 EWJ 20.5% 8.0% 2.4797 10 EWA 25.2% 13.0% 1.8952 11 EWZ 42.8% 32.1% 1.3088 12 EZA 28.6% 22.1% 1.2609 13 EWC 14.5% 11.5% 1.2091 14 ERUS -0.7% 19.5% -0.0595

The following chart shows how annualized returns and risks have been moving during 2017.

Analyzing iShares ETFs from January 2003

iShares ETFs

 
             "EWJ", #iShares Japan
             "EWG", #iShares Germany
             "EWU", #iShares UK
             "EWC", #iShares Canada
             "EWY", #iShares South Korea
             "EWA", #iShares Australia
             "EWH", #iShares Hong Kong
             "EWS", #iShares Singapore
             "EWT", #iShares Taiwan
             "EWZ", #iShares Brazil
             "EFA", #iShares EAFE
             "EPP" #iShares Pacific Ex Japan
           

		EWJ	EWG	EWU	EWC	EWY	EWA	EWH	EWS	EWT	EWZ	EFA	EPP
Annualized	Return	5.9%	10.1%	6.1%	9.4%	10.2%	11.3%	11.5%	11.2%	8.2%	15.0%	7.6%	11.1%
Annualized	Std	21.9%	26.2%	23.5%	22.6%	32.4%	27.9%	25.4%	25.4%	27.7%	37.8%	22.2%	25.3%
Annualized	Sharpe	0.2431	0.3646	0.2375	0.3933	0.2976	0.383	0.431	0.4197	0.2756	0.3807	0.3197	0.4177

Currencies from January 1995 to September 2017

The following chart shows the performances of currencies from January 1995 to September 2017

The Korean Won has been depreciating at 1.6% annually against dollar.

Korea Malaysia Singapore    Taiwan China Japan Thailand    Brazil Mexico India USDOther USDBroad Annualized Return 1.60% 2.30% -0.30% 0.60% -1.10% 0.40% 1.20% 6.00% 5.30% 3.20% 2.40% 1.00% Annualized Std 14.10% 8.50% 5.90% 5.40% 1.70% 11.10% 10.40% 16.00% 13.20% 7.00% 4.30% 4.80%

The following chart shows the diagram of correlation matrix among currencies.

SPDR Sector ETFs Analysis

There are eight SPDR sector ETFs :        

             "XLB", #SPDR Materials sector
             "XLE", #SPDR Energy sector
             "XLF", #SPDR Financial sector
             "XLP", #SPDR Consumer staples sector
             "XLI", #SPDR Industrial sector
             "XLU", #SPDR Utilities sector
             "XLV", #SPDR Healthcare sector
             "XLK", #SPDR Tech sector
             "XLY", #SPDR Consumer discretionary sector

the following chart shows the performances of SPDR sector ETFs from January 2003 and: XLU (Utilities sector) and XLK (Tech sector) have the highest cumulative returns.
     

Consumer staples sector (XLP) seems to be the best investment. (Annualized return:9.65% with Sharpe ratio is 0.7015)

           

	XLB	XLE	XLF	XLP	XLI	XLU	XLV	XLK	XLY
Annualized Return	0.0942	0.0935	0.0598	0.0965	0.104	0.1117	0.0948	0.1095	0.1089
Annualized Std Dev	0.2352	0.2767	0.3182	0.1298	0.2009	0.1724	0.1604	0.2	0.2048
Annualized Sharpe (Rf=0.5%)	0.3772	0.3181	0.1712	0.7015	0.4905	0.6158	0.5567	0.5197	0.5045

Correlation Matrix Chart

             "XLB", #SPDR Materials sector
             "XLE", #SPDR Energy sector
             "XLF", #SPDR Financial sector
             "XLP", #SPDR Consumer staples sector
             "XLI", #SPDR Industrial sector




       

Tree Chart

             "XLB", #SPDR Materials sector
             "XLE", #SPDR Energy sector
             "XLF", #SPDR Financial sector
             "XLP", #SPDR Consumer staples sector
             "XLI", #SPDR Industrial sector
             "XLU", #SPDR Utilities sector
             "XLV", #SPDR Healthcare sector
             "XLK", #SPDR Tech sector
             "XLY", #SPDR Consumer discretionary sector
           

XLF (Finance sector) is connected to XLU (Utilities sector) and XLE (Energy sector) 

       

    

S&P/Case-Shiller MSA Home Price Index (Seasonal Adjusted) (1987/01/01=1.00) as of 6/2017

 The following charts display the S&P/Case-Shiller MSA Home price Index (1987/01/01 =1.00)  of several cities in the United States as of June, 2017. The home prices in many cities recover from the bottom caused by the financial crisis in 2007.

If you have purchased a home with $100K in the following cities in January 1987, then the value of house would be as of June 2017

San Fransco (506 K) > Los Angeles (439K ) > San Diego (438 K)  > Denver (392K) > DC (341K) > Miami (327K)> Boston (284K) > Chicago (255K) > New York(254K) > Las Vegas (243K)

Source: http://us.spindices.com/index-family/real-estate/sp-corelogic-case-shiller

Ratios of U.S. corporate profits by industry

Correlation Matrix

The following chart shows the correlation matrix of ratios of profits by industry in total domestic profit. The finance industry is positively correlated with retail industry and is negatively correlated with other industries. The manufacturing industry is positively correlated with transportation and wholesale industries and is negatively correlated with retail. The transportation industry is positively correlated with the manufacturing industry. The information industry is positively correlated with the transportation industry.

The following chart shows the historical ratios of profits by industry in total U.S. corporate profits.

The following chart shows the ratio of finance industry (excluding the Fed) in all industry (excluding the profits from rest of world)

The following chart shows the ratio of manufacturing industry in all industry (excluding the profits from rest of world)

Corporate profits with inventory valuation and capital consumption adjustments: Rest of the world

The following chart shows the relationship between movements of dollar and U.S. corporate profits from rest of the world. They move opposite direction. 

The profits of U.S. corporates from rest of the world had been increasing tremendously, but staying flat lately. 

However, the growth rates of U.S. corporate profits from rest of the world had been slow down. It actually decreased from 2014 to 2016 when the Fed began to tighten the monetary policy.

The following chart shows the portion of U.S. corporate profits from rest of world in total U.S. corporate profits and it is growing which indicates the profits of U.S. corporates depend on more other countries.

Source: https://fred.stlouisfed.org/series/B394RC1Q027SBEA