In our annual review of global stock markets, we noticed that a relatively large number of countries put in what could be a bear market bottom in 2016. If this is true, investors should expect that global markets will continue a bull market run in 2017 rather than forming a top that leads to a bear market.
Global Financial Data has data on bull and bear markets on 100 countries beginning in 1693. With this data, we have been able to track bull and bear markets during the past three centuries. We define a bear market as a decline of 20% or more in a country’s primary stock market average and a bull market as an increase in the market of 50% or more. Although a 20% decline is a commonly accepted decline to indicate a bear market, there are no clear rules on what constitutes a bull market. The reason we settled on a 50% increase is that if a market declines by 20% (from 1000 to 800), it would have to increase by at least 50% (from 800 to 1200) to rise 20% above the previous market top. Any changes of a smaller magnitude are treated as either a bear market rally or a bull market contraction.
Have Emerging Markets Ended a Four-year Bear Market?
One country actually suffered two bear markets in 2016, Venezuela, which ended a 30% bear market in July, rallied 228% into December, then suffered a 26% decline over a period of three weeks. These fluctuations occurred in the country with the highest inflation in the world, and adjusting for inflation, the results were quite different. During 2016, the Venezuela Bolivar Fuerte (sic) fell against the US Dollar by about 75%. Measured in US Dollars, the Caracas Stock Exchange Index declined by 73% between June 2015 and December 2016. The highest denomination banknote in Venezuela, the 100 Bolivar, is worth about three cents. I think I can safely say that Venezuela has not hit a bottom yet.Country | Bull Top | Change | Bear Bottom | Change | 2016 Recovery |
---|---|---|---|---|---|
Abu Dhabi | 9/18/2014 | 144.93 | 1/21/2016 | -28.59 | 21.66 |
Belgium | 4/13/2015 | 104.31 | 2/11/2016 | -21.25 | 16.30 |
Brazil | 11/4/2010 | 133.58 | 1/20/2016 | -48.43 | 59.99 |
China | 6/12/2015 | 165.15 | 2/29/2016 | -48.02 | 15.53 |
Colombia | 11/5/2010 | 151.96 | 1/18/2016 | -51.78 | 28.75 |
Croatia | 2/11/2011 | 84.84 | 1/18/2016 | -32.55 | 26.54 |
Czech Republic | 4/15/2010 | 107.51 | 6/27/2016 | -39.42 | 16.65 |
Egypt | 9/7/2014 | 147.67 | 1/21/2016 | -48.26 | 147.74 |
France | 4/27/2015 | 92.87 | 2/11/2016 | -25.28 | 24.47 |
Germany | 4/10/2015 | 120.19 | 2/11/2016 | -29.00 | 26.40 |
Greece | 3/1/2014 | 187.51 | 2/11/2016 | -67.81 | 45.99 |
Hong Kong | 4/28/2015 | 75.03 | 2/12/2016 | -35.59 | 20.09 |
India | 1/29/2015 | 95.60 | 2/11/2016 | -22.67 | 16.01 |
Iraq | 10/4/2011 | 63.50 | 6/16/2016 | -73.18 | 28.75 |
Italy | 4/15/2015 | 91.29 | 6/27/2016 | -30.74 | 22.61 |
Japan | 8/10/2015 | 143.17 | 2/12/2016 | -29.27 | 26.91 |
Kazakhstan | 9/3/2014 | 55.26 | 1/21/2016 | -39.12 | 69.89 |
Kuwait | 6/24/2008 | 70.82 | 1/26/2016 | -68.47 | 16.46 |
Luxembourg | 4/14/2015 | 73.02 | 2/11/2016 | -34.48 | 41.39 |
Mongolia | 2/25/2011 | 626.20 | 5/5/2016 | -68.18 | 17.09 |
Namibia | 5/5/2015 | 199.62 | 1/20/2016 | -36.31 | 38.52 |
Netherlands | 4/13/2015 | 85.92 | 2/11/2016 | -24.28 | 24.42 |
Nigeria | 7/9/2014 | 118.19 | 1/19/2016 | -47.82 | 19.68 |
Norway | 4/15/2015 | 72.83 | 1/20/2016 | -26.35 | 29.50 |
Oman | 1/16/2011 | 66.38 | 1/21/2016 | -30.74 | 18.81 |
Peru | 4/2/2012 | 298.29 | 1/20/2016 | -63.09 | 75.34 |
Philippines | 4/10/2015 | 376.85 | 1/21/2016 | -25.14 | 12.43 |
Poland | 4/28/2011 | 120.89 | 1/20/2016 | -42.90 | 16.32 |
Qatar | 9/18/2014 | 239.24 | 1/18/2016 | -40.65 | 22.54 |
Romania | 8/10/2015 | 83.09 | 1/18/2016 | -21.21 | 17.71 |
Saudi Arabia | 9/9/2014 | 169.96 | 10/3/2016 | -51.42 | 33.12 |
Singapore | 5/22/2013 | 143.72 | 1/21/2016 | -26.50 | 12.75 |
Spain | 4/13/2015 | 99.78 | 2/11/2016 | -34.95 | 20.49 |
Sweden | 4/27/2015 | 109.56 | 2/11/2016 | -22.84 | 22.79 |
Ukraine | 8/1/2014 | 65.89 | 5/24/2016 | -54.63 | 23.17 |
Venezuela | 6/13/2015 | 100.40 | 12/10/2016 | -73.34 | 14.07 |
Zimbabwe | 8/1/2013 | 133.18 | 6/17/2016 | -59.95 | 54.76 |
MSCI EAFE | 7/3/2014 | 52.30 | 2/12/2016 | -25.21 | 12.84 |
MSCI Emerging | 5/2/2011 | 153.96 | 1/21/2016 | -42.93 | 5.24 |
MSCI Europe | 5/21/2015 | 53.45 | 6/27/2016 | -25.53 | 12.89 |
Global Financial Data has introduced commodity indices which have the longest histories available anywhere as part of its family of GFD Indices. These commodity indices begin in the year 1000 and are current through 2016. The data are monthly and are updated on an ongoing basis.
The GFD Commodity Indices include a composite index, three commodity indices and eight commodity sub-indices. The Composite Index is broken down into three categories, energy, agriculturals and industrials. There are no sub-indices for energy, but there are five sub-indices for agriculturals (beverages, soft foods, grains, livestock and oils and meal) and three sub-indices for industrials (non-food agriculturals, base metals and precious metals).
The energy index begins in 1252, beverages in 1287, softs in 1209, grains in 1000, livestock in 1209, oils and meal in 1306, non-food agriculturals in 1248, base metals in 1268 and precious metals in 1000. Every index has at least 700 years of history.
For the agricultural indices, beverages include beer, cocoa, coffee, milk and tea; the soft foods include butter, cheese, eggs, potatoes and sugar; the grains include barley, corn, hay, oats, rice and wheat; livestock includes, hogs, cattle and lambs; the oils and meal include corn oil, cottonseed oil, cottonseed meal, flaxseed, lard, oatmeal, palm oil, soybean meal, soybean oil, soybeans and tallow. For the industrial indices, base metals include aluminum, copper, iron, lead, nickel, steel, tin and zinc; the non-food agriculturals include cotton, rubber, tobacco, wood and wool; and the precious metals include gold, palladium, platinum and silver. The energy index includes coal, coal gas, firewood, natural gas and oil (which includes lamp oil, whale oil and petroleum).
Data are taken from the United States and England. Annual data from England use historical series calculated by Gregory Clark in “The Price History of English Agriculture, 1209-1914.” British data is used until the 1700s when data from the United States becomes available. Most U.S. data before 1861 comes from Arthur H. Cole, Commodity Prices in the United States, 1700-1861, Cambridge: Harvard University Press, 1938. Series from the 1900s and 2000s use government and traded spot prices data.
The underlying commodities have for each series have changed over time. Whenever a new commodity source was introduced, we chain-linked different series to provide a continuous set of data for the underlying commodity. Composites were created for iron (beginning in 1268), steel (1720), coffee (1708), milk (1287), tea (1673), coal (1259), oil (1272), butter (1261), potatoes (1724), sugar (1265), barley (1209), corn (1756), hay (1258), oats (1209), rice (1000), wheat (1209), hogs (1732), cattle (1209), lambs (1874), cotton (1749), tobacco (1618), wood (1443), wool (1248) and lard (1797).
Commodities are weighted based upon the amount of trade and consumption of each component. No exact data are available for the amount traded and consumed before the 1900s, so the weights have been estimated.
The GFD Commodity Indices are available to subscribers to the GFD Indices, which include hundreds of proprietary series calculated for commodities, stocks and bonds. To get more information on these indices, or if you would like a list of the indices and the companies that have been added, call today to speak to one of our sales representatives at 877-DATA-999 or 949-542-4200.
Standard and Poor’s and MSCI recently revised their GICS sector indices, introducing Real Estate as a separate sector, removing it as an industry within Finance. S&P/MSCI also revised several of its sub-indices, both adding and subtracting several of them. The GICS Codes now include 11 Sectors, 24 Industry Groups, 68 Industries and 157 Sub-Industries. Because of these changes, GFD has decided to reorganize and simplify the tickers for its S&P Sector Indices.
Until now, the S&P Sector index tickers used abbreviations to name a particular sector. For example, RAIL was used for railroads and AIRL was used for Airlines. The problem was that it was impossible to know which alphabetic abbreviation was used for each sector. Going forward, GFD will use the actual GICS numerical codes in the tickers to make it easier for our subscribers to find the sector index they want. Once they have the GICS Code for any sector or industry, they can easily put together the ticker for the index using a simple formula. An Excel file of the current GICS codes can be found at
https://www.msci.com/documents/10199/4547797/GICS+Structure+effective+Sep+1%2C+2016.xls/d8600f87-cc12-4070-912f-08590232441d
All S&P tickers used in the GFDatabase begin with an underscore (_), followed by a one or two-digit number indicating whether the index is from the S&P 500 Large Cap (5), S&P 400 Midcap (4), S&P 600 Small Cap (6) or S&P 1500 Supercomposite (15) index. The number is then followed by “SP”, and then the GICS Code.
For example, the code for the S&P 500 Energy Sector is _5SP10 using 5SP for the S&P 500 and 10 for the Energy Sector. The code for the S&P 400 Transports Industry Group is _4SP2030 using 4SP for the S&P 500 and 2030 for the Transportation Industry Group. The code for the S&P 600 Health Care Providers and Services Industry is _6SP351020 using 6SP for the S&P 600 and 351020 for the Health Care Providers and Services Industry. The code for the S&P 1500 Electric Utilities Sub-Industry is _15SP55101010 using 15SP for the S&P 1500 and 55101010 for the Electric Utilities Sub-Industry. This methodology eliminates the need to determine the letter suffix for each sector and industry.
Because the GICS Sectors and Industries are global codes, some industries may not be available for the United States S&P Indices, but GFD Includes all the indices that are available for the S&P 500 Large Cap Indices, S&P 400 Midcap Indices, the S&P 600 Small Cap Indices and the S&P 1500 Supercomposite Indices.
To get more information on these indices, or if you would like a list of the indices, call today to speak to one of our sales representatives at 877-DATA-999 or 949-542-4200.
Fear is powerful. Especially when it comes to the economic forces in the world and tied to our quality of life. This basic concept is pertinent to not only those in first world countries, but maybe even more so to people in developing nations. When the crash of 2008 happened on Wall Street, people reacted with fear. Fear of the crash happening again, and that sentiment drove politicians to implement changes so that they could try to contain the next market downturn.
Developing nations are no different. Their economies fluctuate through peaks and lows, and their citizenry and political elite operate similarly with varying degrees and spit out the same patented responses to the threat of instability. So much so that when the price of oil dropped in the 1980s, plummeting Saudi Arabia’s GDP over 20%, the kingdom began taking measures to ensure their over-reliance on oil couldn’t drain their economy again.
On January 23, 2015, King Abdullah died and Salman bin Abdulaziz Al Saud ascended the throne of Saudi Arabia. King Salman appointed his son from his third spouse, 31-year-old Deputy Crown Prince Mohammed bin Salman, as minister of defense. Since then, Prince Salman’s power and influence has grown and, as such, the King appointed him chair of the newly established Council for Economic and Development Affairs, which replaced and disbanded Supreme Economic Commission.
Led by the Deputy Crown Prince, the kingdom began bold reforms for economic diversification to secure themselves against the next break in oil prices. Unfortunately, markets don’t wait for legislation and government studies. After a thirty-year hiatus, oil prices have fallen to the bottom again and, reminiscent to the 1980s, has a stranglehold on Saudi Arabia’s GDP.
Not a moment too soon, too, because in 2016, the kingdom released its National Transformation Program 2020, created by Vision 2030, where they have an unemployment rate of 11.6% (and have a target of 9% by the year 2020) – compared to the United States 4.9%, the cost of housing is ten times that of an ordinary Saudi’s annual income, home ownership rates are at 47%, whereas the U.S. is 62%, and private sector generated jobs are at 650; meager compared to the international benchmark of 4,000. Oil amounts to nearly 80% of budget revenues and almost half of Saudi Arabia’s total GDP. With the drop in oil prices, its impact on Saudi Arabia’s GDP has been catastrophic, resulting in a fifteen percent change in its GDP.
- Create more than 450,000 jobs in the non-governmental sector by the year 2020;
- Open financing and subsidy opportunities for home ownership with a target of 52% of the Saudi population being home owners by 2020;
- Increase non-oil related revenues from 163 Billion SR to 530 Billion SR;
- Overhaul its tax system;
- Enhance the importing and exporting operation & process; and
- Reform and restructuring of primary health care systems.