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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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Markets

Animation: The 20 Largest State Economies by GDP in the Last 50 Years

This animation shows how the largest state economies by GDP have changed over the last five decades of time, and what such a ranking looks like today.

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Animation: The 20 Largest State Economies by GDP

When it comes to understanding the size and scope of the $18 trillion U.S. economy, it’s sometimes easier to consider that it’s the sum of many parts.

Many states already have economies that are comparable to some of the world’s largest countries, giving you a sense of what they might be combined.

And while every state plays a role in the bigger picture, some states such as New York and California have an outsized impact on fueling the country’s overall economic engine.

The State of State Economies

Today’s animation comes to us from SavingSpot, and it covers the size of state economies by GDP going back all the way to 1963.

The video uses inflation-adjusted data from the U.S. Bureau of Economic Analysis, showing how the ranking of top state economies has changed over time as different states have taken advantage of economic booms.

Let’s dive into the data to see how things have changed.

Going Back in Time

The earliest data in the animation comes from 1963, when New York led the pack with a $70.6 billion economy in inflation-adjusted terms.

State Economies by GDP, Inflation-Adjusted Chained $USD (1963)

RankState EconomyGDP, Billions of USD (1963)Share of U.S. Economy
🇺🇸 United States (Total)$607.0100.0%
#1New York$70.611.6%
#2California$67.811.2%
#3Illinois$39.56.5%
#4Pennsylvania$34.55.7%
#5Ohio$33.35.5%
#6Michigan$30.55.0%
#7Texas$29.34.8%
#8New Jersey$23.43.9%
#9Massachusetts$17.42.9%
#10Indiana$15.62.6%
#11Florida$14.72.4%
#12Missouri$13.62.2%
#13Wisconsin$12.72.1%
#14North Carolina$12.62.1%
#15Virginia$11.71.9%
#16Washington$11.21.8%
#17Minnesota$10.71.8%
#18Georgia$10.31.7%
#19Maryland$10.31.7%
#20Connecticut$9.91.6%
#21Louisiana$9.71.6%
#22Tennessee$9.11.5%
#23Kentucky$8.41.4%
#24Iowa$7.91.3%
#25Alabama$7.31.2%
#26Oklahoma$6.21.0%
#27Kansas$6.11.0%
#28Colorado$5.91.0%
#29Oregon$5.70.9%
#30District of Columbia$5.10.8%
#31South Carolina$5.10.8%
#32West Virginia$4.60.8%
#33Arizona$4.50.7%
#34Mississippi$4.40.7%
#35Nebraska$4.30.7%
#36Arkansas$3.80.6%
#37New Mexico$3.00.5%
#38Utah$3.00.5%
#39Rhode Island$2.70.4%
#40Maine$2.40.4%
#41Hawaii$2.40.4%
#42Montana$2.00.3%
#43Delaware$1.90.3%
#44Idaho$1.80.3%
#45Nevada$1.80.3%
#46New Hampshire$1.70.3%
#47North Dakota$1.60.3%
#48South Dakota$1.60.3%
#49Wyoming$1.40.2%
#50Alaska$1.10.2%
#51Vermont$1.00.2%

California ($67.8 billion), Illinois ($39.5 billion), Pennsylvania ($34.5 billion) and Ohio ($33.3 billion) round out the top five, and together they added up to 40.5% of the national GDP.

The Largest State Economies by GDP Today

Looking at the most recent data from 2017, you can see the ranking changes significantly:

State Economies by GDP, Inflation-Adjusted Chained $USD (2017)

RankState EconomyGDP, Billions of USD (2017)Share of U.S. Economy
🇺🇸 United States (Total)$18,051100%
#1California$2,57614.3%
#2Texas$1,6169.0%
#3New York$1,4147.8%
#4Florida$8834.9%
#5Illinois$7454.1%
#6Pennsylvania$7013.9%
#7Ohio$5913.3%
#8New Jersey$5473.0%
#9Georgia$5112.8%
#10Michigan$4592.5%
#11North Carolina$4842.7%
#12Virginia$4642.6%
#13Massachusetts$4902.7%
#14Washington$4812.7%
#15Maryland$3632.0%
#16Indiana$3211.8%
#17Arizona$2971.6%
#18Minnesota$3221.8%
#19Tennessee$3151.7%
#20Wisconsin$2921.6%
#21Colorado$3231.8%
#22Missouri$2761.5%
#23Connecticut$2391.3%
#24Louisiana$2271.3%
#25Alabama$1931.1%
#26South Carolina$1991.1%
#27Kentucky$1851.0%
#28Oregon$2081.2%
#29Oklahoma$1911.1%
#30Iowa$1690.9%
#31Nevada$1430.8%
#32Kansas$1480.8%
#33Utah$1500.8%
#34Arkansas$1140.6%
#35District of Columbia$1220.7%
#36Mississippi$1000.6%
#37Nebraska$1110.6%
#38New Mexico$910.5%
#39Hawaii$790.4%
#40West Virginia$710.4%
#41New Hampshire$740.4%
#42Delaware$640.4%
#43Idaho$670.4%
#44Maine$560.3%
#45Rhode Island$530.3%
#46Alaska$520.3%
#47Montana$440.2%
#48Wyoming$390.2%
#49South Dakota$450.3%
#50North Dakota$510.3%
#51Vermont$300.2%

California is the largest economy today – it has a state GDP of $2.6 trillion, which is comparable to the United Kingdom.

Meanwhile, Florida and Georgia are two states that did not crack the top 10 back in the 1960s, while Texas jumped up to become the second largest state economy. It’s actually not a coincidence that all of these states are in the southern half of the country, as air conditioning has played a surprisingly pivotal role in shaping modern America.

In fact, the share of the nation’s population living in the Sunbelt rose from 28% in 1950 to 40% in 2000, and this increase in population has coincided with economic growth in many of the states that used to be a sweaty mess.

A Final Look

Here is a final animated version of the top 10 largest states by GDP, also provided by SavingSpot:

Animation: The 20 Largest State Economies by GDP in the Last 50 Years

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Where the World’s Banks Make the Most Money

Last year, the global banking industry cashed in an impressive $1.36 trillion in profits. Here’s where they made their money, and how it breaks down.

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Where the World’s Banks Make the Most Money

Profits in banking have been steadily on the rise since the financial crisis.

Just last year, the global banking industry cashed in an impressive $1.36 trillion in after-tax profits ⁠— the highest total in the sector seen in the last 20 years.

What are the drivers behind revenue and profits in the financial services sector, and where do the biggest opportunities exist in the future?

Following the Money

Today’s infographic comes to us from McKinsey & Company, and it leverages proprietary insights from their Panorama database.

Using data stemming from more than 60 countries, we’ve broken down historical banking profits by region, while also visualizing key ratios that help demonstrate why specific countries are more profitable for the industry.

Finally, we’ve also looked at the particular geographic regions that may present the biggest opportunities in the future, and why they are relevant today.

Banking Profits, by Region

Before we look at what’s driving banking profits, let’s start with a breakdown of annual after-tax profits by region over time.

Banking Profit by Year and Region ($B)

 2009201020112012201320142015201620172018
Global ($B)$388$530$635$703$859$963$1,070$1,065$1,144$1,356
United States$19$118$176$263$268$263$291$275$270$403
China$95$135$174$225$255$278$278$270$301$333
Western Europe$78$34$21-$70$28$95$154$159$186$198
Rest of World$196$243$265$285$309$327$348$361$387$421

In 2018, the United States accounted for $403 billion of after-tax profits in the banking sector ⁠— however, China sits in a very close second place, raking in $333 billion.

What’s Under the Hood?

While there’s no doubt that financial services can be profitable in almost any corner of the globe, what is less obvious is where this profit actually comes from.

The truth is that banking can vary greatly depending on location ⁠— and what drives value for banks in one country may be completely different from what drives value in another.

Let’s look at data and ratios from four very different places to get a sense of how financial services markets can vary.

CountryRARC/GDPLoans Penetration/GDPMargins (RBRC/Total Loans)Risk Cost Margin
Global Average5.1%124%5.0%0.8%
United States5.4%121%5.0%0.4%
China6.6%147%6.0%1.4%
Singapore13.0%316%4.6%0.4%
Finland3.4%133%2.8%0.2%

1. RARC / GDP (Revenues After Risk Costs / GDP)
This ratio shows compares a country’s banking revenues to overall economic production, giving a sense of how important banking is to the economy. Using this, you can see that banking is far more important to Singapore’s economy than others in the table.

2. Loans Penetration / GDP
Loans penetration can be further broken up into retail loans and wholesale loans. The difference can be immediately seen when looking at data on China and the United States:

CountryRetail LoansWholesale LoansLoan Penetration (Total)
United States73%48%121%
China34%113%147%

In America, banks make loans primarily to the retail sector. In China, there’s a higher penetration on a wholesale basis — usually loans being made to corporations or other such entities.

3. Margins (Revenues Before Risk Costs / Total Loans)
Margins made on lending is one way for bankers to gauge the potential of a market, and as you can see above, margins in the United States and China are both at (or above) the global average. Meanwhile, for comparison, Finland has margins that are closer to half of the global average.

4. Risk Cost Margin (Risk Cost / Total Loans)
Not surprisingly, China still holds higher risk cost margins than the global average. On the flipside, established markets like Singapore, Finland, and the U.S. all have risk margins below the global average.

Future Opportunities in Banking

While this data is useful at breaking down existing markets, it can also help to give us a sense of future opportunities as well.

Here are some of the geographic markets that have the potential to grow into key financial services markets in the future:

  1. Sub-Saharan Africa
    Despite having 16x the population of South Africa, the rest of Sub-Saharan Africa still generates fewer banking profits. With lower loan penetration rates and RARC/GDP ratios, there is significant potential to be found throughout the continent.
  2. India and Indonesia
    Compared to similar economies in Asia, both India and Indonesia present an interesting banking opportunity because of their high margins and low loan penetration rates.
  3. China
    While China has a high overall loan penetration rate, the retail loan category still holds much potential given the country’s population and growing middle class.

A Changing Landscape in Banking

As banks shift focus to face new market challenges, the next chapter of banking may be even more interesting than the last.

Add in the high stakes around digital transformation, aging populations, and new service opportunities, and the distance between winners and losers could lengthen even more.

Where will the money in banking be in the future?

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