7/5/2014: Simple vs Complex Financial Regulation under Knightian Uncertainty

Bank of England published a very interesting paper on the balance of uncertainty associated with complex vs simplified financial regulation frameworks.

Titled "Taking uncertainty seriously: simplicity versus complexity in financial regulation" the paper was written by a team of researchers and published as Financial Stability Paper No. 28 – May 2014 (link: http://www.bankofengland.co.uk/research/Documents/fspapers/fs_paper28.pdf), the study draws distinction between risk and uncertainty, referencing "the psychological literature on heuristics to consider whether and when simpler approaches may outperform more complex methods for modelling and regulating the financial system".

The authors find that:
(i) "simple methods can sometimes dominate more complex modelling approaches for calculating banks’ capital requirements, especially if limited data are available for estimating models or the underlying risks are characterised by fat-tailed distributions";
(ii) "simple indicators often outperformed more complex metrics in predicting individual bank failure during the global financial crisis"; and
(iii) "when combining information from different indicators to predict bank failure, ‘fast-and-frugal’ decision trees can perform comparably to standard, but more information-intensive, regression techniques, while being simpler and easier to communicate".

The authors key starting point is that "financial systems are better
characterised by uncertainty than by risk because they are subject to so many unpredictable factors".

As the result, "simple approaches can usefully complement more complex ones and in certain circumstances less can indeed be more."

The drawback of the simple frameworks and regulatory rules is that they "may be vulnerable to gaming, circumvention and arbitrage. While this may be true, it should be emphasised that a simple approach does not necessarily equate to a singular focus on one variable such as leverage… [in other words, simple might not be quite simplistic] Moreover, given the private rewards at stake, financial market participants are always likely to seek to game financial regulations, however complex they may be. Such arbitrage may be particularly
difficult to identify if the rules are highly complex. By contrast, simpler approaches may facilitate the identification of gaming and thus make it easier to tackle."

Note, the above clearly puts significant weight on enforcement as opposed to pro-active regulating.

"Under complex rules, significant resources are also likely to be directed towards attempts at gaming and the regulatory response to check compliance. This race towards ever greater complexity may lead to wasteful, socially unproductive activity. It also creates bad incentives, with a variety of actors profiting from complexity at the expense of the deployment of economic resources for more productive activity."

The lesson of the recent past is exactly this: "These developments [growing complexity and increased capacity to game the system] may at least partially have contributed to the seeming decline in the economic efficiency of the financial system in developed countries, with the societal costs of running it growing over the past thirty years, arguably without any clear improvement in its ability to serve its productive functions in particular in relation to the successful allocation of an economy’s scarce investment capital (Friedman (2010))."

And the final drop: clarity of simple systems and implied improvement in transparency. "Simple approaches are also likely to have wider benefits by being easier to understand and communicate to key stakeholders. Greater clarity may contribute to superior decision making. For example, if senior management and investors have a better understanding of the risks that financial institutions face, internal governance and market discipline may both improve."

Top line conclusion: "Simple rules are not a panacea, especially in the face of regulatory arbitrage and an ever-changing financial system. But in a world characterised by Knightian uncertainty, tilting the balance away from ever greater complexity and towards simplicity may lead to better outcomes for society."

13/02/2011: What a Jeopardy champ can do in the world of finance

Here is my article along with Shanker Ramamurthy that was published last Thursday in the American Banker, discussing IBM's Watson super computer system's potential applications in the financial services industry - helping to advance industry thinking on how in the era of "big data" only advanced non-linear analytics can make sense of structured and unstructured data flows to transform it into valuable insights.

VIEWPOINT: New Computer, New Modeling Possibilities
By Shanker Ramamurthy and Constantin Gurdgiev
February 10 , 2011 - p8

Next Monday a new IBM computer system called Watson will battle two quiz-show champions in a game of Jeopardy! There is more at stake here than winning a game. The potential applications of this technology to transform the operations of industries such as health care, government and finance are enormous.

In the financial services industry, integrated risk management is an everyday struggle. Financial practitioners and supervisory and regulatory authorities must make split-second decisions using information coming from all sides: the Internet to corporate and call center channels.

The challenge is to efficiently process diverse data streams and pick out relevant data insights to apply to strategic business and regulatory decisions.

In the banking industry today, data "fuzziness" abounds. Uncertainty exists about the quality of data, assumptions and models that are being used to make judgments. This, of course, clouds the true picture of risk and biases our decision-making, often in econometrically undetectable ways.
Most banks today run risk models on a discrete and disaggregated basis while relying on often subjective assumptions. High-performance computing advances, represented by Watson's capabilities, can rectify this - by providing visibility into concentrations of risks and risk-related activities, as they happen. Simultaneously, it deploys nonlinear analytics in selecting both the statistically and operationally important scenarios.

The beauty of a nonlinear computer that "learns" is that it can analyze a complex set of implied possible scenarios and give answers to the broadest set of questions. This potentially can lead to the emergence of analytical systems that not only report on probabilistically likely events but also identify latent "Black Swan" events and even sense deeper levels of uncertainty.

For example, a legislative decision altering a specific set of financial strategies can have no impact on traditional linear models because the outcomes can be weighted by an extremely low assigned or assumed probability. But in a nonlinear world, such an outcome can still be testable as part of the selection list for reporting. More importantly, it can be made recognizable by the analytic system and, therefore, objectively reportable.

A system like Watson has the potential to get answers to incredibly difficult questions about strategic decisions, risks and market changes that can otherwise be elusive.

For example, it has the ability to create an interactive risk-pricing system using a menu of models that evolve as the system learns, detecting structural breaks in data before analysts can spot them and build them into existing programs.

Of even more significance, Watson will be able to deliver scenario analysis based not just on either event probability or expected loss/gain but also on more complex company objectives.

This can involve analyzing corporate strategy inputs, including non-quantifiable questions, alongside fully quantifiable inputs. Imagine asking a computer "How do I increase my loan book profit margin by 10%?" or "What actions can I take to strengthen my capital reserves, with minimum impact to my asset base?"

At a much deeper level, the nonlinear learning capabilities that Watson pioneers can lead to the creation of systems that are able not only to handle traditional risks and their interactions but also to evolve into systems capable of transforming deep uncertainty into explicit models. Though still some years away, this could mean an artificial intelligence able to sense Donald Rumsfeld's famous "unknown unknowns," converting them into specific models suitable for risk analysis and getting meaningful, actionable responses.

The real-time, decision-making capability that is so sought after in the financial industry will be a crucial, competitive differentiator.

As risk intensifies within interconnected global markets, the complexity and exploding volumes of data will only rise.

Shanker Ramamurthy is the general manager of banking and financial markets at IBM Corp. Dr Constantin Gurdgiev is the head of macroeconomics in the Center for Economic Analysis at the IBM Institute for Business Value.