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Can Predictive Data Revolutionize Capital Markets?

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Can Predictive Data Revolutionize Capital Markets?

Can Predictive Data Revolutionize Capital Markets?

For investors around the world, the information age presents a double-edged sword.

On one hand, all this data can be harnessed to make intelligent and timely investment decisions. However, with data growing at exponential rates, this also means that there is a lot of noise – and finding the right signal can be like looking for a needle in a haystack.

Today’s infographic from Mergalim shows how the power of predictive data analytics is growing, and using tools like AI and Bayesian inference to anticipate the outcome of events before they happen is more feasible and applicable than ever.

The Big Data Landscape

Before we get into predictive analytics, let’s look at what investors are up against in the first place.

Volume: The rate of data creation is accelerating so fast, that in 2017 there will be more data created than the previous 5,000 years combined. To put this in perspective: in the U.S. alone, approximately 2,657,000 GB of data is created every minute.

Variety: Data is not uniform, and there are many types of data. With data coming from many sources simultaneously, useful analysis can be very difficult.

Velocity: Especially in the markets, data needs to be monitored in real-time to be useful. Getting information too late could mean zero liquidity for a portfolio in some sort of crisis.

In other words, one missed data signal can cause irreparable harm to a portfolio in a situation where things go awry. Therefore, along with having a smart allocation of assets, it can be advantageous to also be one step ahead of the game to know what’s coming.

The Power of Predictive Data

Predictive analytics is defined as:

“The branch of advanced analytics used to make predictions about unknown future events. It uses techniques from data mining, statistics, modelling, machine learning, and artificial intelligence to make predictions about the future.”

This kind of predictive power is already widely used by companies like Amazon, which uses algorithms to sort through billions of data points, your buying history, and current trends to recommend to you the items that you are most likely to buy. In fact, experts estimate that 35% of Amazon’s revenue comes from this practice of anticipating exactly what you want.

Not surprisingly, Wall Street has jumped on this bandwagon too.

  • Goldman Sachs famously employs more engineers than Facebook, Twitter, or LinkedIn.
  • Citadel, a secretive hedge fund, calculates the outcomes of more than 500 “doomsday scenarios” per day to assess potential risk for the firm from geopolitical and other potential crises
  • Quantitative traders use streams of data and complex algorithms to create models of the market to find predictable patterns, and create machine-derived forecasts

But there is one possible limitation with these approaches. Markets are complex systems and need to be analyzed as such. After all, human decision makers can be irrational, events can be “triggered” by seemingly random factors, and traditional mathematical models can fall apart when markets get volatile.

A Multi-Disciplinary Approach?

One solution to this limitation may be to borrow ideas from the intelligence industry, which must anticipate irrational or “random” human actions before they happen.

As an example of this, intelligence agencies like the CIA have already been working to apply other disciplines to the techniques already widely used in predictive data:

Bayesian Inference: A formula in which the probability for the hypothesis is updated based on new data.

Behavioral Psychology: The science behind how responses to environmental stimuli shape people’s actions.

Complexity Theory: Born in the 1960s, the science around how complex systems work is now well established.

Fuzzy Cognitive Maps: A way of representing social scientific knowledge and modelling decision making in systems.

Historical Perspective: Applying knowledge of past events and subject matter experts to these other disciplinary fields.

Artificial Intelligence: Today’s deep learning now allows AI to instantly recognize and process all types of previously impenetrable data.

If predictive data analytics becomes the norm and its potential is fully realized, having data only in real-time may not be enough for many active market participants. In turn, this could set up a very different landscape than exists today.

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Central Banks

The History of Interest Rates Over 670 Years

Interest rates sit near generational lows — is this the new normal, or has it been the trend all along? We show a history of interest rates in this graphic.

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The History of Interest Rates Over 670 Years

Today, we live in a low-interest-rate environment, where the cost of borrowing for governments and institutions is lower than the historical average. It is easy to see that interest rates are at generational lows, but did you know that they are also at 670-year lows?

This week’s chart outlines the interest rates attached to loans dating back to the 1350s. Take a look at the diminishing history of the cost of debt—money has never been cheaper for governments to borrow than it is today.

The Birth of an Investing Class

Trade brought many good ideas to Europe, while helping spur the Renaissance and the development of the money economy.

Key European ports and trading nations, such as the Republic of Genoa or the Netherlands during the Renaissance period, help provide a good indication of the cost of borrowing in the early history of interest rates.

The Republic of Genoa: 4-5 year Lending Rate

Genoa became a junior associate of the Spanish Empire, with Genovese bankers financing many of the Spanish crown’s foreign endeavors.

Genovese bankers provided the Spanish royal family with credit and regular income. The Spanish crown also converted unreliable shipments of New World silver into capital for further ventures through bankers in Genoa.

Dutch Perpetual Bonds

A perpetual bond is a bond with no maturity date. Investors can treat this type of bond as an equity, not as debt. Issuers pay a coupon on perpetual bonds forever, and do not have to redeem the principal—much like the dividend from a blue-chip company.

By 1640, there was so much confidence in Holland’s public debt, that it made the refinancing of outstanding debt with a much lower interest rate of 5% possible.

Dutch provincial and municipal borrowers issued three types of debt:

  1. Promissory notes (Obligatiën): Short-term debt, in the form of bearer bonds, that was readily negotiable
  2. Redeemable bonds (Losrenten): Paid an annual interest to the holder, whose name appeared in a public-debt ledger until the loan was paid off
  3. Life annuities (Lijfrenten): Paid interest during the life of the buyer, where death cancels the principal

Unlike other countries where private bankers issued public debt, Holland dealt directly with prospective bondholders. They issued many bonds of small coupons that attracted small savers, like craftsmen and often women.

Rule Britannia: British Consols

In 1752, the British government converted all its outstanding debt into one bond, the Consolidated 3.5% Annuities, in order to reduce the interest rate it paid. Five years later, the annual interest rate on the stock dropped to 3%, adjusting the stock as Consolidated 3% Annuities.

The coupon rate remained at 3% until 1888, when the finance minister converted the Consolidated 3% Annuities, along with Reduced 3% Annuities (1752) and New 3% Annuities (1855), into a new bond─the 2.75% Consolidated Stock. The interest rate was further reduced to 2.5% in 1903.

Interest rates briefly went back up in 1927 when Winston Churchill issued a new government stock, the 4% Consols, as a partial refinancing of WWI war bonds.

American Ascendancy: The U.S. Treasury Notes

The United States Congress passed an act in 1870 authorizing three separate consol issues with redemption privileges after 10, 15, and 30 years. This was the beginning of what became known as Treasury Bills, the modern benchmark for interest rates.

The Great Inflation of the 1970s

In the 1970s, the global stock market was a mess. Over an 18-month period, the market lost 40% of its value. For close to a decade, few people wanted to invest in public markets. Economic growth was weak, resulting in double-digit unemployment rates.

The low interest policies of the Federal Reserve in the early ‘70s encouraged full employment, but also caused high inflation. Under new leadership, the central bank would later reverse its policies, raising interest rates to 20% in an effort to reset capitalism and encourage investment.

Looking Forward: Cheap Money

Since then, interest rates set by government debt have been rapidly declining, while the global economy has rapidly expanded. Further, financial crises have driven interest rates to just above zero in order to spur spending and investment.

It is clear that the arc of lending bends towards ever-decreasing interest rates, but how low can they go?

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Central Banks

$69 Trillion of World Debt in One Infographic

What share of government world debt does each country owe? See it all broken down in this stunning visualization.

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$69 Trillion of World Debt in One Infographic

Two decades ago, total government debt was estimated to sit at $20 trillion.

Since then, according to the latest figures by the IMF, the number has ballooned to $69.3 trillion with a debt to GDP ratio of 82% — the highest totals in human history.

Which countries owe the most money, and how do these figures compare?

The Regional Breakdown

Let’s start by looking at the continental level, to get an idea of how world debt is divided from a geographical perspective:

RegionDebt to GDPGross Debt (Millions of USD)% of Total World Debt
World81.8%$69,298100.0%
Asia and Pacific79.8%$24,12034.8%
North America100.4%$23,71034.2%
Europe74.2%$16,22523.4%
South America75.0%$2,6993.9%
Africa56.9%$1,3131.9%
Other37.1%$1,2311.8%

In absolute terms, over 90% of global debt is concentrated in North America, Asia Pacific, and Europe — meanwhile, regions like Africa, South America, and other account for less than 10%.

This is not surprising, since advanced economies hold most of the world’s debt (about 75.4%), while emerging or developing economies hold the rest.

World Debt by Country

Now let’s look at individual countries, according to data released by the IMF in October 2019.

It’s worth mentioning that the following numbers are representative of 2018 data, and that for a tiny subset of countries (i.e. Syria) we used the latest available numbers as an estimate.

RankCountryDebt to GDPGross Debt ($B)% of World Total
#1🇺🇸 United States104.3%$21,46531.0%
#2🇯🇵 Japan237.1%$11,78817.0%
#3🇨🇳 China, People's Republic of50.6%$6,7649.8%
#4🇮🇹 Italy132.2%$2,7444.0%
#5🇫🇷 France98.4%$2,7363.9%
#6🇬🇧 United Kingdom86.8%$2,4553.5%
#7🇩🇪 Germany61.7%$2,4383.5%
#8🇮🇳 India68.1%$1,8512.7%
#9🇧🇷 Brazil87.9%$1,6422.4%
#10🇨🇦 Canada89.9%$1,5402.2%
#11🇪🇸 Spain97.1%$1,3862.0%
#12🇲🇽 Mexico53.6%$6550.9%
#13🇰🇷 Korea, Republic of37.9%$6520.9%
#14🇦🇺 Australia41.4%$5880.8%
#15🇧🇪 Belgium102.0%$5430.8%
#16Netherlands52.4%$4790.7%
#17Argentina86.1%$4470.6%
#18Singapore113.6%$4140.6%
#19Greece184.9%$4040.6%
#20Austria73.8%$3370.5%
#21Indonesia30.1%$3080.4%
#22Portugal120.1%$2890.4%
#23Poland48.9%$2860.4%
#24Switzerland40.5%$2860.4%
#25Ireland63.7%$2440.4%
#26Russian Federation14.6%$2420.3%
#27Turkey30.2%$2330.3%
#28Egypt92.7%$2310.3%
#29Pakistan71.7%$2260.3%
#30Israel60.8%$2250.3%
#31Sweden38.5%$2140.3%
#32Thailand42.1%$2130.3%
#33South Africa56.7%$2090.3%
#34Taiwan Province of China35.1%$2070.3%
#35Malaysia55.6%$1990.3%
#36Venezuela182.4%$1800.3%
#37Norway40.0%$1740.3%
#38Colombia52.2%$1730.2%
#39Finland59.3%$1630.2%
#40Saudi Arabia19.0%$1490.2%
#41Iran32.2%$1440.2%
#42Vietnam55.6%$1340.2%
#43Philippines38.9%$1290.2%
#44Denmark34.3%$1210.2%
#45Hungary70.8%$1140.2%
#46Iraq49.3%$1110.2%
#47Nigeria27.3%$1090.2%
#48Bangladesh34.0%$98.10.14%
#49Angola89.0%$94.30.14%
#50Qatar48.6%$93.00.13%
#51Romania36.7%$87.90.13%
#52Lebanon151.0%$85.10.12%
#53Czech Republic32.6%$79.90.12%
#54United Arab Emirates19.1%$79.10.11%
#55Ukraine60.2%$78.80.11%
#56Morocco65.0%$77.00.11%
#57Chile25.6%$76.30.11%
#58Sri Lanka83.3%$74.10.11%
#59Sudan212.1%$72.70.10%
#60Algeria38.3%$66.50.10%
#61New Zealand29.8%$60.50.09%
#62Peru26.1%$58.80.08%
#63Puerto Rico55.5%$56.10.08%
#64Kenya60.1%$52.80.08%
#65Slovak Republic48.9%$52.10.08%
#66Ecuador45.8%$49.60.07%
#67Ethiopia61.0%$49.00.07%
#68Croatia74.6%$45.40.07%
#69Dominican Republic50.5%$43.20.06%
#70Oman53.4%$42.30.06%
#71Jordan94.4%$39.90.06%
#72Ghana59.3%$38.90.06%
#73Slovenia70.4%$38.10.05%
#74Uruguay63.5%$37.90.05%
#75Kazakhstan21.0%$36.30.05%
#76Bahrain94.7%$35.70.05%
#77Costa Rica53.5%$32.30.05%
#78Tunisia77.0%$30.70.04%
#79Belarus47.8%$28.50.04%
#80Serbia54.5%$27.50.04%
#81Myanmar38.2%$26.20.04%
#82Panama39.5%$25.70.04%
#83Cyprus102.5%$25.10.04%
#84Côte d'Ivoire53.2%$22.90.03%
#85Bolivia53.8%$21.80.03%
#86Tanzania37.3%$21.20.03%
#87Zambia78.1%$20.90.03%
#88Kuwait14.7%$20.80.03%
#89Guatemala24.7%$19.40.03%
#90Lithuania34.2%$18.20.03%
#91Syria30.0%$18.00.03%
#92Yemen64.8%$17.90.03%
#93El Salvador67.1%$17.50.03%
#94Cameroon39.1%$15.10.02%
#95Luxembourg21.4%$14.90.02%
#96Jamaica94.4%$14.60.02%
#97Senegal61.6%$14.50.02%
#98Mozambique99.8%$14.40.02%
#99Bulgaria20.4%$13.30.02%
#100Latvia35.9%$12.50.02%
#101Turkmenistan29.1%$11.90.02%
#102Uganda41.4%$11.60.02%
#103Albania69.9%$10.50.02%
#104Uzbekistan20.6%$10.40.02%
#105Lao P.D.R.57.2%$10.40.01%
#106Gabon60.7%$10.20.01%
#107Congo, Republic of87.8%$10.20.01%
#108Trinidad and Tobago45.1%$10.20.01%
#109Iceland37.6%$9.80.01%
#110Honduras40.2%$9.60.01%
#111Mauritius66.2%$9.40.01%
#112Paraguay21.5%$9.00.01%
#113Azerbaijan18.8%$8.80.01%
#114Nepal30.2%$8.80.01%
#115Papua New Guinea35.5%$8.20.01%
#116Bahamas, The63.3%$7.90.01%
#117Zimbabwe37.1%$7.80.01%
#118Georgia44.9%$7.30.01%
#119Congo, Dem. Rep. of the15.3%$7.20.01%
#120Cambodia28.6%$7.00.01%
#121Bosnia and Herzegovina34.3%$6.90.01%
#122Namibia45.8%$6.60.01%
#123Malta45.2%$6.60.01%
#124Mali37.3%$6.40.01%
#125Barbados125.7%$6.40.01%
#126Armenia51.3%$6.40.01%
#127Burkina Faso42.9%$6.10.01%
#128Equatorial Guinea43.3%$5.90.01%
#129Benin41.0%$5.90.01%
#130Madagascar45.7%$5.50.01%
#131Chad48.3%$5.30.01%
#132North Macedonia40.5%$5.10.01%
#133Niger53.8%$5.00.01%
#134Nicaragua37.2%$4.90.01%
#135Guinea38.2%$4.60.01%
#136Kyrgyz Republic56.0%$4.50.01%
#137Mauritania82.9%$4.30.01%
#138Malawi62.9%$4.30.01%
#139Togo76.2%$4.10.01%
#140Montenegro72.6%$4.00.01%
#141Rwanda40.7%$3.90.01%
#142Maldives68.0%$3.60.01%
#143Tajikistan47.9%$3.60.01%
#144Eritrea174.3%$3.50.01%
#145Moldova29.7%$3.40.00%
#146Haiti33.3%$3.20.00%
#147Bhutan102.4%$2.60.00%
#148Sierra Leone63.0%$2.60.00%
#149Estonia8.3%$2.60.00%
#150Fiji46.2%$2.60.00%
#151Suriname72.8%$2.50.00%
#152Cabo Verde124.5%$2.50.00%
#153Aruba84.5%$2.40.00%
#154Botswana12.1%$2.30.00%
#155Guyana52.9%$2.10.00%
#156Burundi58.4%$2.00.00%
#157South Sudan, Republic of42.2%$1.90.00%
#158Belize95.2%$1.80.00%
#159Eswatini35.2%$1.70.00%
#160Antigua and Barbuda89.5%$1.40.00%
#161Gambia, The86.6%$1.40.00%
#162Djibouti48.0%$1.40.00%
#163Afghanistan6.9%$1.40.00%
#164Kosovo17.0%$1.40.00%
#165Liberia39.9%$1.30.00%
#166San Marino77.9%$1.30.00%
#167Saint Lucia64.3%$1.20.00%
#168Lesotho44.5%$1.20.00%
#169Central African Republic49.9%$1.10.00%
#170Guinea-Bissau64.3%$0.90.00%
#171Seychelles56.9%$0.90.00%
#172Grenada63.5%$0.80.00%
#173Saint Vincent and the Grenadines74.5%$0.60.00%
#174Saint Kitts and Nevis60.5%$0.60.00%
#175Vanuatu51.4%$0.50.00%
#176Samoa50.3%$0.40.00%
#177Dominica74.1%$0.40.00%
#178Hong Kong SAR0.1%$0.40.00%
#179Brunei Darussalam2.6%$0.40.00%
#180São Tomé and Príncipe74.5%$0.30.00%
#181Comoros21.0%$0.20.00%
#182Timor-Leste6.1%$0.20.00%
#183Solomon Islands9.4%$0.10.00%
#184Micronesia, Fed. States of20.3%$0.10.00%
#185Nauru58.3%$0.10.00%
#186Marshall Islands25.2%$0.10.00%
#187Kiribati20.6%$0.00.00%
#188Tuvalu28.1%$0.00.00%

In absolute terms, the most indebted nation is the United States, which has a gross debt of $21.5 trillion according to the IMF as of 2018.

If you’re looking for a more precise figure for 2019, the U.S. government’s “Debt to the Penny” dataset puts the amount owing to exactly $23,015,089,744,090.63 as of November 12, 2019.

Of course, the U.S. is also the world’s largest economy in nominal terms, putting the debt to GDP ratio at 104.3%

Other stand outs from the list above include Japan, which has the highest debt to GDP ratio (237.1%), and China , which has increased government debt by almost $2 trillion in just the last two years. Meanwhile, the European economies of Italy and Belgium check the box as other large debtors with ratios topping 100% debt to GDP.

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