How Market Complexity Could Trigger the Next Crash
Complex systems are all around us.
By one definition, a complex system is any system that features a large number of interacting components (agents, processes, etc.) whose aggregate activity is nonlinear (not derivable from the summations of the activity of individual components) and typically exhibits hierarchical self-organization under selective pressures.
In today’s infographic from Meraglim we use accumulating snow and an impending avalanche as an example of a complex system – but really, such systems can be found everywhere. Weather is another complex system, and ebb and flow of populations is another example.
Markets are Complex Systems
Just like in the avalanche example, where various factors at the top of a mountain (accumulating volumes of snow, weather, temperature, geology, gravity, etc.) make up a complex system that is difficult to predict, markets are similarly complex.
In fact, markets meet all the properties of complex systems, as outlined by scientists:
System actors have different points of view. (i.e. bullish, bearish, long, short, leveraged, non-leveraged, etc.)
Capital markets are over-connected, and information spreads fast. (i.e. chat rooms, phone calls, emails, Thomson Reuters, Dow Jones, Bloomberg, trading systems, order entry systems, etc.)
Trillions of dollars of securities are exchanged in transactions every day (i.e. stocks, bonds, currencies, derivatives, etc.)
4. Adaptive Behavior
Actors change their behavior based on the signals they are getting (i.e. making or losing money, etc.)
And like the avalanche example, where a single snowflake can trigger a much bigger event, there are increasing signs that the complexity behind the stock market has also reached a critical state.
Markets in a Critical State
Here are just some examples that show how the market has entered into an increasingly critical state:
The VIX, an index that aims to measure the volatility of the market, hit all-time lows this summer.
Bull Market Length
Meanwhile, the current bull market (2009-present) is the second-longest bull market in modern history at 3,109 days. The only bull market that was longer went from the 1987 crash to the Dot-com bust.
Valuations at Highs
Stock valuations, based on Robert Schiller’s CAPE ratio (which looks at cyclically-adjusted price-to-earnings), are approaching all-time highs as well. Right now, it sits 83.3% higher than the historical mean of 16.8. It was only higher in 1929 and 2000, right before big crashes occurred.
Market Goes Up
Investor overconfidence leads investors to believe the market only goes up, and never goes down. Indeed, in this bull market, markets have gone up 67 of the months (an average gain of 3.3%), and have gone down only 34 months (average drop of -2.6%).
Here are some additional signs of systemic risk that make complex markets less stable:
- A densely connected network of bank obligations and liabilities
- Over $70 trillion in debt added since Financial Crisis
- Over $1 quadrillion in notional value of derivatives
- Non-bank shadow finance through hedge funds and securitization make risk impossible to measure
- Increased leverage of banks in some markets
- Greater concentration of financial assets in fewer companies
In other words, there are legitimate reasons to be concerned about “snow” accumulation – and any such “snowflake” could trigger the avalanche.
In complex dynamic systems that reach the critical state, the most catastrophic event that can occur is an exponential function of scale. This means that if you double the system, you do not double the risk; you increase it by a factor of five or 10
– Jim Rickards, author of Road to Ruin
The Next Snowflake
What could trigger the next avalanche? It could be anything, including the failure of a major bank, a natural disaster, war, a cyber-financial attack, or any other significant event.
Such “snowflakes” come around every few years:
1987: Black Monday
The Dow fell 508 points (-22.6%) in one day.
1994-95: The Mexican peso crisis
Systemic collapse narrowly avoided when the U.S. government bailed out Mexico using the controversial $20 billion “Exchange Stabilization Fund”.
1997: Asian financial crisis
East Asian currencies fell in value by as much as -38%, and international stocks by as much as -60%.
1998: Long Term Capital Management
Hedge fund LTCM was in extreme distress, and within hours of shutting down every market in the world.
2000: The Dotcom crash
Nasdaq fell -78% in 30 months after early Dotcom companies crashed and burned.
2008: Lehman Brothers bankruptcy
Morgan Stanley, Goldman Sachs, Bank of America, and J.P. Morgan were days away from same fate until government stepped in.
Shelter from the Avalanche
The Fed and mainstream economists use equilibrium theory, regressions, and correlations to quantify the markets. And while they pay lip-service to black swans, they don’t have a good way of forecasting them or predicting them.
Markets are complex – and only complexity theory and predictive analytics can help to shed light on their next move.
Alternatively, investors can seek shelter from the storm by investing in assets that cannot be digitally frozen (bank accounts, brokerage accounts, etc.) or have their value inflated away (cash, fixed-income). Such assets include land, precious metals, fine art, and private equity.
Visualizing the Importance of Trust to the Banking Industry
In the digital age, the issue of trust is emerging as the game-changing factor in how consumers choose financial services brands.
Visualizing the Importance of Trust to the Banking Industry
In the digital age, money is becoming less tangible.
Not only is carrying physical cash more of a rarity, but we are now able to even make contactless payments for many of the products and services we use on the fly.
Our financial transactions are starting to be analyzed and optimized by artificial intelligence. Meanwhile, investments and bills are paid online, and even checks can now be deposited through our phones. Who has the time to visit a physical bank these days, anyways?
Trust in the Digital Age
The migration of financial services to the cloud is increasing access to banking solutions, while breaking down barriers of entry to the industry. It’s also creating opportunities for new service offerings that can leverage technology, data, and scale.
However, as today’s infographic from Raconteur shows, this digital migration has a crucial side effect: trust in financial services has emerged as a dominant driver of consumer activity.
This likely boils down to a couple major factors:
Financial services are becoming less grounded in physical experiences (using cash, visiting a branch, personal relationships, etc.)
- Personal Data
Consumers are rightfully concerned about how personal data gets treated in the digital age
Further, the above factors are compounded by memories of the 2008 Financial Crisis. These events not only damaged institutional reputations, but they elevated trust to become a key concern and selling point for consumers.
Trust, by the Numbers
In general, trust in banks has been slowly on the rise since hitting a low point in 2011 and 2012.
At the same time, consumers are consistently ranking trust as a more important factor in their decision of where to bank. To the modern consumer, trust even outweighs price.
Top Five Factors for Choosing a Bank:
- Ease and convenience of service (47%)
- Trust with the brand (45%)
- Price/rate (43%)
- Service resolution quality and timeliness (43%)
- Wide network coverage of ATMs (40%)
It’s important to recognize here that all five of the above factors rank quite closely in percentage terms. That said, while they are all crucial elements to a service offering, trust may be the most abstract one to try and tackle for companies in the space.
With this in mind, how can financial services leverage tech to increase the amount of trust that consumers have in them?
Tech Factors That Would Increase Consumer Trust:
- Reliable fraud protection (36%)
- Technology solves my problems (13%)
- Useful mobile application (9%)
Better fraud protection capability stands out as one major trust-builder, while designing technology that is useful and effective is another key area to consider.
Visualizing the Future of Banking Talent
Banking talent is undergoing a fundamental shift. This infographic explores how banks are adapting to rapid automation and digitization in the industry.
Visualizing the Future of Banking Talent
View the full-size version of the infographic by clicking here
Many organizations say that their greatest asset is their people. In fact, Richard Branson has famously stated that employees come first at Virgin, ranking ahead of customers and shareholders. So, how do businesses effectively manage this talent to drive success?
This question is top of mind for many bank CEOs. As processes become increasingly automated and digitized, the composition of banking talent is changing – and banks will need to become adept at hitting a moving target.
Six Ways Banks are Becoming Talent-First
Today’s infographic comes from McKinsey & Company, and it explores six ways banks are becoming talent-first organizations:
1. They understand future talent requirements.
43% of all bank working hours can be automated with current technologies.
Consequently, talent requirements are shifting from basic cognitive skills to socio-emotional and technological skills. Banks will need to analyze where they have long-term gaps and develop a plan to close them.
2. They identify critical roles and manage talent accordingly.
It is estimated that just 50 key roles drive 80% of bank business value. Banks will need to identify these roles based on data rather than traditional hierarchy. In fact, 90% of critical talent is missed when organizations only focus at the top.
Then, banks must match the best performers to these roles and actively manage their development.
3. They adopt an agile business model.
Banks will need to shift from a hierarchical structure to an agile one, where leadership enables networks of teams to achieve their missions. As opportunities come and go, teams are reallocated accordingly.
This flexible structure has many potential benefits, including fewer product defects, lower costs, shorter time-to-market, increases in customer satisfaction, and a bump in employee engagement.
4. They use data to make people decisions.
Instead of making decisions based on subjective biases or customary practices, banks will need to rely on the power of data to:
For example, company data can be used to develop a heatmap of the roles with the highest attrition rates. Leaders can then focus their retention efforts accordingly.
5. They focus on inclusion and diversity.
Gender and ethnicity diversification leads to higher financial performance, better decision making, higher employee satisfaction, and an enhanced company image.
Industry-leading banks will set measurable diversity goals, and re-evaluate all processes to expose unconscious biases. For example, one organization saw 15% more women pass resume screening when they automated the process.
6. They ensure the board is focused on talent.
Only 5% of corporate directors believe they are effective at developing talent.
To be successful, boards will need to recognize Human Resources (HR) as a strategic partner rather than as a primarily transactional function. The CEO, CFO, and CHRO (Chief Human Resources Officer) form a group of three that makes major decisions on human and financial capital allocation.
CEOs worldwide see human capital as a top challenge, and yet they rank HR as only the eighth or ninth most important function in a business. Clearly, this is a disconnect that needs to be addressed. To keep up with rapid change, banks will need to bring HR to the forefront – or risk being left behind.
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