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Research

The liquidity and credit crisis of 2007 and 2008 left the global financial community concerned over liquidity risk. Suddenly, multi-national banking groups realised they had to broaden their understanding and measurement of risk beyond market, credit, and operational risk. They needed to be able to anticipate liquidity strains in the markets – and strategise contingent funding.

 

While Basel II guidelines had not adequately addressed liquidity risk, Basel III proposals aimed to address this, with what are viewed by many as onerous liquidity ratios that were introduced in 2015. The ratios present a conundrum for banks; they need to comply with them, while still maintaining a competitive funding structure. But it’s not the ratios themselves that bring value, it’s the data accuracy and precision they demand.

 

The validation required for liquidity risk models will actually mean banks can get both an holistic and granular view of their risks. For example, the proposals imply that banks will need to distinguish between different behavioural aspects of diverse customers within a particular product. That means enhanced risk management capability, as well as pricing and customer selection.

 

If banks want to achieve the ambitions of the Basel regulations – and create a single, integrated platform for liquidity risk management, pricing, capital management, and strategic customer selection, they’ll need to implement a data-centric, market-factor driven, liquidity risk management framework. A framework that integrates credit, market, interest rate, and liquidity risk into a consistent set of metrics. And, in order to get that integrated risk view, and look precisely at the contribution of different risks to the liquidity risk solution, banks need granular data differentiation.

 

For Monocle, that means a comprehensive measurement and manage­ment approach for deeper understanding of liquidity risk and its potential interaction with other risks. It’s an integrated way to treat Liquidity-at-Risk (LaR). The process enables stress and scenario testing under market crises, leading to quantification of levels of contingent funding. It also helps to find more optimal loan-to-deposit ratios by investigating reliance on the wholesale funding markets.

 

LaR is a framework that includes simulation of a large number of future cash flow profiles, by replicating the entire cash flow process under each circumstance of contractual cash flows, behavioural cash flows, growth in asset and liability sizes, and interest rate re-pricing of each position. Each simulation offers a picture of how the balance sheet and, ultimately, the cash inflows and outflows, may evolve under different scenarios. Ultimately, it assists banks in anticipating strains, and managing liquidity risk.

 

Liquidity Risk

When we talk Liquidity Risk, we talk, simply, about “the ability to fund increases in assets and meet obligations as they come due, without incurring unacceptable losses”. When we drill down, liquidity risk encompasses both market liquidity risk; the risk that a position cannot be offset or eliminated without economic loss – and funding liquidity risk; the risk that cash flow and collateral needs cannot be met in the normal course of business.

 

Conventionally, liquidity risk has been managed and measured within the Asset and Liability Management (ALM) function. But then the strains in the wholesale funding markets in August 2007 and September 2008 highlighted the interrelationships between funding and market liquidity risk, funding liquidity risk and credit risks, funding concentration and liquidity risk, and the effects of reputation on liquidity risk. We now know that liquidity risk is consequential – and cannot be viewed in isolation. So, today, banks veer towards an integrated risk management approach, in which all risk types are measured inclusively.

 

Monocle’s LaR model quantifies credit, market, liquidity, and interest rate risk using a single set of underlying risk factors, allowing a bank to view various ‘future states of the world’ from an integrated risk perspective. It also allows the risk management function in a bank to isolate the impact of these risk types on the liquidity shortfall for a particular tenor, with a particular confidence level. The goal? A stable, robust metric for the measurement of a liquidity gap, which includes the impact of credit, market, and interest rate risk.

 

Why LaR Measurement is Crucial

When wholesale funding markets seized up, existing liquidity models failed. Why? Because they were predominantly based on point estimates, rather than LaR distributions. Point estimates cannot accommodate the scenario tests and stress testing necessary to assess a bank’s liquidity position under market extremes, except in an overly simplified manner. The result was that many organisations didn’t fully understand the speed and severity with which their liquidity position would deteriorate in these extreme markets. And banks were not prepared for deleveraging debt markets, in which there were more sellers of debt securities than buyers. The desperate need for liquidity forced many banks to issue debt at previously unthinkable spreads, and liquidate assets at previously unthinkable prices. But if they’d been able to anticipate or predict liquidity strains in the markets – and the contingent funding requirements – that desperation could have been halted.

 

A contingent funding requirement measure needs to look at the inter-relatedness between different risk types, and give a response on funding requirements with different levels of confidence. Of course, banks are not only funded via wholesale markets, but also by deposits; reflected in the bank’s overall loan-to-deposit ratio.

 

LaR is a more distribution-based approach to the measurement of liquidity risk, under a range of different market rate scenarios. It looks at the effect that these scenarios may have, not only on the wholesale funding markets, but also on the behavioural aspects of the overnight core to non-core deposit base, and gives banks substantially greater insight.

 

Who will particularly benefit from this approach? Banks that, in the past, relied too heavily on wholesale market funding, rather than retail and commercial deposit funding. Rapid liquidity deterioration was particularly severe at these organisations, with high loan-to-deposit ratios.

 

These banks will be able to focus on creating deposit products that have longer periods of limited redemptions, by analysing the differential behaviour of different client types. This will reduce reliance on the wholesale market, identify more ‘ideal’ loan-to-deposit ratios, and may well provide cheaper funding for the bank, affording them the ability to improve pricing.

 

All that is needed is a commitment to carefully monitoring the metrics, to enhance their understanding of funding volatility and the specific circumstances that could result in a sudden funding requirement. The bank will also be able to continuously monitor access to money markets as a function of evolving macroeconomic conditions.

 

This increased awareness will provide the breathing space needed to strategise alternative funding mechanisms – before any seizing up of liquidity markets. Ideally, it can also offer real-time identification of the order in which assets should be liquidated and made available for liquidation, depending on the expected duration and severity of liquidity strains.

 

So how does it work under each circumstance of contractual cash flows, behavioural cash flows, growth in asset and liability sizes, and interest rate re-pricing of each position?

 

Behavioural Modelling of Product Cash Flows

The LaR Model is derived from a distribution of simulated economic factors, which drive simulations of the bank’s balance sheet and results in simulated net cumulative cash flow (NCCF) profiles over the funding horizon. Distributions of the bank’s NCCF at different times are generated as the outcome of factors impacting the behaviouralisation of product cash flows over defined intervals.

 

The framework simulates many versions (say, 100 000) of future cash flow profiles by replicating the entire cash flow process under each circumstance of contractual cash flows, behavioural cash flows, growth in asset / liability sizes, and interest rate re-pricing of each position. Asset Liability Management (ALM) is generally responsible for predicting the value and timing of daily cash inflows and outflows – both on a contractual and a behavioural basis.

 

Contractually, the cash flows of assets, liabilities, and off-balance sheet items are known with relative certainty. Term loans, which follow standard amortising schedules, stipulate a monthly payment from the term loan customer. These cash flows can be disaggregated into interest and principal components for simulations of Net Interest Income (NII) and other measures of bank profitability.

 

There is, however, no world-wide standard on the contractual terms or conditions which may be imposed on a bank’s products. Cash flow functions must be customised to cater for the unique product types and payment structures a bank offers its customers.

 

The risk, across many product types, is that the cash flows expected under the contractual profile are different from actual, experienced cash flows. This is because of the options customers have to deviate from the initial terms and conditions of their account; Pre-Payment Risk (the risk that a customer will repay an asset before the contractual maturity date); Early-Redemption Risk (the risk that a client will withdraw a deposit before the contractual maturity date); or Rollover Risk (the risk that a liability will reach a maturity date, and a higher funding cost is demanded by the depositor to roll it over). But it could also be because of behavioural market impacts, such as credit risk, resulting in the cessation of a particular set of cash flows.

 

Behavioural modelling, using econometric techniques and statistical methods, can estimate future client behaviour, and transform the results of these models into predicted cash flows. It also includes statistical techniques used to predict the behaviouralised cash flows of portfolios of fluctuating products, such as savings and current accounts. Simply, it adjusts contractual cash flow calculations to reflect the most likely client behaviour in the future.

 

As a rule, behavioural models used by banks have typically been long-run averages of past behaviour, so they’re not sensitive to the prevailing economic environment, or potential future economic developments. But it is possible to directly relate the level of each behavioural risk to an economic factor. Let’s look at pre-payments, for example. We can create a statistical model which relates levels of pre-payments in housing loans to selected interest rates or interest rate changes. Logically, as interest rates decrease, banks and their competitors are able to reduce the level of interest charged to products such as housing loans. And, of course, offers of lower rates entice customers to refinance.

 

So we can estimate the level of prepayment on sub-portfolios, given the prevailing level and changes in key interest rates. Behavioural cash flow models address the full range of possible behavioural adjustments for each and every product, for diverse client types.

 

There are also other behavioural aspects that impact the assets and liabilities side of the balance sheet. In particular, banks tend to lend on an internal or ‘prime’ rate, which is adjusted non-linearly to prevailing market rates. And, much liquidity risk modelling has excluded the impact of business growth and budgeting and forecasting targets. What we need to do, then, is include interest rate sensitivity models and asset/liability portfolio growth models with a holistic consideration of liquidity risk behavioural models.

 

Interest Rate Sensitivity

Cash flow amounts are usually dependent on the amount of interest charged on/accrued to the asset/liability. One of the challenges faced in modelling future cash flows, is the fact that bank rates are often deter­mined by the bank itself, rather than by the market. This is problematic when predicting the bank’s response to changes in market interest rates.

 

Our methods would estimate the relationship between a change in market rates, and the resulting change in internal lending and deposit rates. This is then input to a simulation to determine the level of interest charged on individual and corporate accounts, and therefore the value of projected cash flows.

 

For products with a reference rate based on an internal lending or deposit rate, rather than a market rate, Monocle has a methodology to predict future levels of internal lending and deposit rates, given the levels and changes in market rates.

 

Historically, there’s been a strong relationship between internal rates and market rates, but this may not always be the case. As market rates decrease, banks generally apply these ‘savings’ to their customers by reducing internal lending rates, to remain competitive. Similarly, as market rates rise, the banks pass these costs onto their customers by raising internal lending rates.

 

By looking at 5 to 10 years of market rates of all tenors, we can estimate the relationship between these rates and internal bank rates over the same period. This relationship can then be embedded within a statistical model which translates movements in market rates into a probability of change for internal rates. When the probability of change reaches a key value, the reference or prime rate is assumed to increase or decrease, depending on recent movements in market rates.

 

Since market rates are easier to simulate and project into the future, the model allows the bank to understand how each market rate scenario in a large simulation (typically 100 000 iterations) translates into changes in internal rates, resulting in a prediction of internal rates into the future. This can then be used for Net Interest Income (NII) and other profitability scenario analyses.

 

Asset/Liability Portfolio Growth Models

Typically a liquidity risk model is premised on a run-off basis, where assets and liabilities are not replaced as they reach maturity, or on a business-as-usual basis, where assets and liabilities are replaced as they mature. However, to achieve true business value, we need to understand at a product level, and at a client-type level, what the growth in a particular asset or liability will be. LaR is an ‘adjusted business-as-usual’ approach, in which a projected growth in each asset and liability portfolio, as a result of changes in economic factors, is taken into account.

 

As part of the simulation process, there is a series of techniques to predict the future growth in asset and liability values on a bank’s balance sheet. The models rely on the historical relationship between growth rates and interest rates, which can be combined in a multi-variable regression. Of course, we know that relationships between interest rates and growth levels have not always remained intact – particularly in the recent financial crisis. Market interest rates (interbank rates) have been near all-time lows for some time, which, under normal circumstances, would be a leading indicator of higher growth rates. But financial institutions have decreased their appetite for risk, and for extending credit, drastically reducing growth levels below what would normally be expected.

 

Because of this, growth models incorporate a “desirability” factor, which is a numerical indication of the bank’s appetite for extending credit, or for growing a particular product type. This “desirability” factor adjusts the output of the growth models, which are purely linked to interest rates.

 

Economic variables other than interest rates can also be taken into account, particularly those variables which are shown to be leading indi­cators, such as economic variables; inventories, sentiment indices, and money supply growth.

 

Combining Predictive Models in the LaR

LaR typically requires generating 10 000 to 100 000 simulations of underlying market factors over a one-year horizon. At a minimum, LaR will use simulated market interest rates as a driving economic factor, because interest rates usually show the strongest predictive power in behavioural modelling. The interest rate simulation produces 10 000 to 100 000 observations of possible future interest rate paths, along with paths for other market factors.

 

These interest rate paths, and other market factor paths, are used to drive the contractual cash flow models, the behavioural cash flow models, growth models, and interest rate re-pricing models. These models interact to provide a picture of how the balance sheet ‘evolves’ over time. New ‘synthetic’ accounts are created to compensate for predicted growth value, and cash flows are, in turn, calculated for these new accounts. The replication of the balance sheet is an image of how the future balance sheet may look.

 

One method we use is to hold interbank activity conducted by the bank constant over the funding horizon, to measure the bank’s reliance on the interbank market in times of heightened demand for liquidity. With the foregoing assumptions and processes in place, the simulation is run. At each month during the horizon, it’s possible to measure the liquidity gap on a point-in-time basis. Simply, the cash inflows and outflows are considered in isolation, to assess whether that month’s liquidity gap is positive or negative.

 

At the end of the one year horizon, for example, banks can calculate a distribution of possible one year Net Cumulative Cash Flows (NCCF). This is the sum of all twelve point-in-time liquidity gaps over the year, representing an accumulation of cash flow shortages, or excesses, from the beginning to the end of the year. At the 99.97 percent confidence level for example (consistent with a AA rating), LaR will be the 31st worst observation in this distribution.

 

LaR simulates the progression of a portfolio’s value through time – how cash flows should behave, how balance sheet values should change, and ultimately the cash inflows and outflows that result from these changes. It is known as a dynamic portfolio approach as the methodology assumes that the portfolio is constantly changing as loans mature and new loans are created. The goal of LaR is to produce 10 000 to 100 000 values for the NCCF at the desired horizon, which could be overnight (1 day), 7 days or monthly, from 1 month to 12 months.

 

What that does is make it possible to assess the maximum possible liquidity shortfall over a one year period, as well as at different points through the one year period, given that interbank activity has been held constant. It may even serve to mitigate against the probability of a severe funding shortfall arising in the first place.

 

The LaR framework, essentially, is about bespoke models, methods, and techniques that assist banks in managing their liquidity risk. So that we may be able to predict, anticipate and prepare for the next time we’re in an extreme market crisis; instead of fumbling around desperate for liquidity, wondering why we didn’t see it coming.

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Opinion Pieces

Entering the job market with a university degree and no knowledge of hard skills is like being a superhero without special powers. By hard skills I mean the ability to use a range of computer programming software to manipulate, complete, validate, and ultimately to convert data into information.

 

Here’s a hard truth for today’s graduates who are looking to enter the job market: the assumption that a formal education is enough to land – and keep – you in a dream position is holding you back.

 

While you may have left university armed with theories about your industry’s intricate inner workings, chances are you’re lacking in practical, hard skills – and those are what you’re going to need in the fast-paced working world. I meet many graduates with an Honours degree, but they’ve never put together a professional presentation, compiled a computer program, or pulled information from a database. These days, you have to be able to demonstrate you can do your job faster – and better – than anyone else, and hard skills will help you do that.

 

Skills such as advanced Excel and Visual Basic for Applications, basic database logic skills or programming – for example SQL – will distinguish “potentially decent” employees from exceptional ones. Without them you’re like Batman without his tool belt of cool gadgets. You have a ‘degree cape’ and a ‘distinction mask’, but you are limited in your ability to add value. If you want to up your hard skill ability, keep some of the following tips in mind.

 

Firstly, be flexible. Flexibility and versatility in the workplace are highly sought after attributes. In a competitive working environment, employees with a range of varied hard skills are likely to be the most useful to their employers.

 

Think about the backline of a rugby team, like the All Blacks, in which great players are able to switch positions quickly, and efficiently, if needed. A player could be a fly-half in one game and act as wing in the next. That’s an efficient team, one which is flexible enough to beat all competitors under the right circumstances.

 

That ability to move around and work on various tasks is gold in the workplace, especially when resources are scarce and the competition is fierce. When learning hard skills, don’t feel pressured to focus on just one. Learn as many as you can – at least on a basic level.

 

Secondly, even if you don’t have hard skills currently, you need to understand just how important they are. When I’m interviewing candidates, and I mention a skill, I don’t want to be told, “I can’t do that”. I want to hear, “I’d love to learn to do that!”. We’re really testing people’s aversion to hard skills, and if they meet my question about coding by saying they “don’t want to be an IT person”, then I know they don’t want to grow, or keep up with the times. Those people will always have to depend on someone else.

 

Thirdly, the broader your knowledge is, the more likely you are to find new and original ways to apply that knowledge to your work. Those who think outside of the box will stand out in any corporate environment.

 

For example, Monocle once worked on a project that involved sourcing customer data on behalf of a large banking group, in order to compile reports required by the South African Revenue Service. Much to our dismay, we realised that much of the required information was on documents that were not in digital form and had to be manually scanned. One of our employees, of his own volition, took the initiative to teach himself to write code, in Python, that would convert scanned documents into characters in a database. This saved us and the bank countless hours and ensured successful delivery of the project.

 

Finally, as employers, we expect graduates to arrive with a certain degree of inexperience. We recommend strongly communicating with your employer that you want to learn. After all, a great company will know that investing in you, will ultimately lead to efficiencies and benefits to the company itself.

 

If you’re not a graduate – if you are one or two years into your career – and you still don’t have hard skills, you are probably already behind the curve. At Monocle we highly recommend getting as broadly skilled as possible, as fast possible, to help accelerate your career.

 

 

 

 

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Research

It is generally agreed amongst economists, bankers, politicians and the man on the street that the 2007/8 financial crisis was the worst economic crisis to befall western economies since the Great Depression of the 1930s. Some call it the Great Recession and some call it the Financial Crisis – its full impact has not yet been fully understood.

 

During the Great Depression, policy makers took far too long to adequately respond to the impact of the severe stock market decline of 1929. Instead, policy makers in western economies, G8 countries and across the world, in the case of the 2007/8 crisis, reacted extremely quickly, through extreme monetary interventions, as well as through some – most likely insufficient – fiscal interventions. This was of course entirely necessary given that confidence, not only in the markets, but also in the very notion of capitalism itself was under attack. Several commentators had even questioned the long term viability of capitalism as a political economic system.

 

artoffailurev-smallIn order to save the world from itself the central banks punted enormous amounts of liquidity into the market, wrote thousands of pages of legislation that would require banks to hold more capital and more liquid assets and to meet far more exacting standards than previously. To a large degree, these alterations to a previously laissez-faire economic playground have been successful. For one thing, the more extreme predictions that immediately followed the initial crisis in 2008 have not come to bear. Europe, although growing at a very slow pace is still growing. Asian economies have not yet collapsed and the US is actually growing reasonably well.

 

What is masked, however, by these extreme acts of government intervention are the basic statistical realities of what fundamentally changed within the global banking system. By this is meant that some very simple statistical realities are not as transparent as one would like them to be from an analytical perspective. For example, if one were to ask the question as to how many of the world’s top 1000 banks failed in the period during and after the 2007/8 crisis, one would find what appears to be a relative absence of information in respect of this. The main reason is that – following the repercussions of allowing Lehman to fail – policy makers used a variety of interventions to prevent further explicit failure. From these interventions emerged the phrase ‘Too Big to Fail’. Lehman’s failure, and the extreme market impact that followed, virtually eroded market confidence completely, leading policy makers to react with measures never before contemplated. From a monetary perspective, particularly, the extent of the interventions clouded over the breadth and depth of banking as well as corporate failure – and in fact to some extent continues to do so, not only within particular individual banks but across the banking fraternity itself.

 

Monocle Solutions in its continued research efforts became particularly interested in whether the new legislation for banking, written and codified by the Basel Committee for Banking Supervision (BCBS) post-crisis – in what was known as Basel 2.5 and then Basel III – will effectively address the main reasons for the crisis in the first place. As an example, we have noticed that there would appear to be far more regulation imposed upon the banks than on the periphery corporations that helped to manufacture the crisis to the extent of severity that it reached. The credit agencies, for example S&P and Moody’s – those agencies that are paid by banks to issue credit ratings for the issuance of their own debt and debt instruments – seem vastly less affected by regulation than the banks themselves. Yet they issued many thousands of triple A-rated stamps of approval on collateralized debt obligation (CDO) structures that later exploded.

 

To further this point using a recent example: The US Justice Department fine of $14bn that is to be levied against Deutsche Bank, for their negligence in selling toxic mortgage-backed securities (MBS), could potentially be putting Deutsche into a severe undercapitalised position and may even lead to their failure. This will impact not only the jobs of those people who work at Deutsche Bank, but will have a potentially severe systemic impact. Note that these fines target Deutsche Bank specifically but do not target the credit agencies that rated these securities.

 

In essence, if one is to boil down the BCBS regulations, ex-post their imposition on the banking system, they effectively achieve three things. Firstly, they almost double the amount of core tier-1 equity capital that needs to be held against the risk-weighted asset loan book of a bank. Secondly they increase the standard of that capital to be limited to only the purest of capital, i.e. unencumbered equity, excluding instruments that are pseudo-capital in nature. Thirdly the regulations introduce liquidity ratios with which banks must now comply. Specifically, the two critical ratios are the Liquidity Coverage Ratio (LCR) and the Net Stable Funding Ratio (NSFR) which have been constructed by the BCBS to address the ultimate cause of failure in the majority of banks that did fail.

 

To be clear, whilst most banks during the crisis experienced severe pressure on their loan books and the value of their assets, leading them to substantially increase provisions to absorb the forthcoming losses, their failure as institutions was primarily owing to an inability to meet immediate liability demands.

 

In fact, it is technically incorrect to say that banks failed from a credit crisis since most of these credit losses were experienced by these banks as revaluations of their asset book rather than actual experienced losses. The effect of the credit crisis, and the extreme devaluation of MBSs and CDOs, lead market participants in the interbank market, i.e. banks themselves, to cull from their own. We witnessed during the crisis the severe effects of banks hoarding cash, and an unwillingness of these same banks to take collateral from counterparty banks of anything that was of less than the highest quality. This meant that all MBS and CDO paper, even if it was triple A-rated, was not accepted by banks as collateral in repo-style transactions. This lead particular banks to experience severe short term liquidity shortfalls, which lead in some cases to extreme government intervention and in other cases to failure.

 

It was, at the outset of this study, our supposition that banks that were more reliant in their liability structure on the interbank market prior to the crisis, would have been those banks that would have been more likely to have failed. Several problems present themselves in attempting to perform such an analysis. The first is that there are significant problems with understanding and creating a stable definition of default owing to the extreme interventions performed by governments in different manners across the world. It is very difficult to say, for example, that Royal Bank of Scotland (RBS) failed or did not fail. It was certainly bailed out. Certain banks definitely failed though, for example Landsbanki in Iceland.

 

Goldman Sachs, as a further example – which was not in fact a bank at the time but was forced post-crisis to become a bank holding group – was, according to Lloyd Blankfein its CEO, forced to take bailout money from the Troubled Asset Relief Program (TARP). This $750bn bailout fund was set up by Hank Paulson, Goldman Sachs’ previous CEO.

 

From an analytical perspective, therefore, it was essential to settle upon a clear definition of default that we could use in our study of what the frequency of default of banks was post-crisis, as well as which ratios might have been indicative of failure pre-crisis. The second problem that presents itself, from an analytical perspective, is that the rules created by the BCBS for LCR and NSFR were based on new underlying information that would form either part of the numerator or the denominator of those ratios. That information prior to the Financial Crisis was not publically available and is not publically available since the crisis – in all the forms it would need to be in order to reconstruct those ratios for banks – unless one had insider information.

 

As such, it was decided that we would address these two issues for the study in the following manner. Firstly, through literature research, we came across a study by McKinsey – Working Papers on Risk, Number 15 (2009): Capital Ratios and Financial Distress Lessons from the Crisis. This study had been conducted on the same basis that we wished to conduct our study, i.e. to examine whether financial ratio analysis would have been a good indicator of financial distress post-crisis. This study examined a sample of 115 banks, their financial distress outcome post-crisis, and the ability for their ratios to predict this outcome. The criteria for the sample banks was that the banks needed to have a minimum asset size of $30 billion representing $62.2 trillion in total assets – about 85 percent of developed-market banking assets, and 65 percent of total banking assets worldwide. Broker-dealers specifically were excluded from their analysis as data on risk-weighted assets for such institutions in December 2007 was unavailable. Performance between 1 January 2008 and 1 November 2009 was used to identify banks that became distressed.

 

McKinsey defined banks as being financially distressed if any one of the following four criteria were met: where banks had declared bankruptcy, been placed into government receivership, been acquired under duress, or received bailout capital in excess of 30% of their tier-1 equity.

 

A range of financial and risk ratios were then calculated as of 31 December 2007 to determine their ability to predict financial distress. McKinsey found that the tangible common equity (TCE) to risk-weighted assets (RWA) ratio was the best predictor of future distress.

 

Whilst we believe this was a good starting point, we were concerned that they had not established a clear default frequency for the top 1000 banks. In fact, we could find no study that had been conducted across the worlds’ top 1000 banks in terms of a frequency of default post the largest single financial crisis since the Great Depression. We therefore conducted initial analysis work to simply calculate what the default frequency of banks was during and post crisis based on the McKinsey definition. We also decided to use a longer observation period, from 2008 through to 2010, and across the top 1000 banks.

 

For each of the 1000 banks on The Banker List (2007), an investigation was conducted into the status of each bank from 1 January 2008 through to 30 June 2010, identifying whether any of the McKinsey criteria for financial distress were met by the specific bank in the defined time period. Banks were identified as being either financially distressed (FD) or non-financially distressed (NFD). The results are surprising – out of the top 1000 banks investigated, 106 banks conform to the definition of Financial Distress. This result implies that the effective default frequency for banks during the Financial Crisis 2007/8 was in excess of a staggering 10%. To put this number into perspective, recall that a poor performing mortgage book in a classic retail banking environment would be of the order of 2-3% during the height of the crisis. The default frequencies that caused the ripple effect into the MBS market and the CDO market was of the order of 10-15% in the worst performing areas of the United States, such as Las Vegas. Banks themselves, therefore, experienced previously unheard of default frequencies comparable only to the defaults that occurred post 1929.

 

The second area of interest was the ratios themselves. It was decided by the Monocle Research Team to turn towards the fundamental analysis concept from the beginning. In 1966, William H Beaver used financial ratios with a univariate technique to predict financial distress. He classified financial distress as bankruptcy, insolvency, liquidation for the benefit of a creditor, firms which defaulted on loan obligations, or firms that missed preferred dividend payments.

 

Beaver’s technique accurately classified 78 percent of the sample “distressed” banks up to five years prior to failure. His research concluded that the cash flow to debt ratio was the single best indicator of bankruptcy. To overcome many of the inconsistencies found in Beaver’s research, in 1968, Edward I Altman improved on Beaver’s univariate model by introducing a multiple discriminant approach. His results found that five financial ratios were significant predictors in the financial distress prediction model. These ratios are: working capital to total assets, retained earnings to total assets, earnings before interest and taxes to total assets, market value equity to par value of debt and sales to total assets.

 

Banks and financial institutions in particular are generally more highly leveraged than the industrial institutions Beaver and Altman studied. We could not therefore make use of the same ratios or the same coefficients of such ratios to create anything like the Altman z-score. In order to create our own database and to conduct the necessary analysis, the income statements and balance sheets of a sample of banks were extracted, constructed and normalised into a single common format. The sample of banks consisted of 20 financially distressed banks selected from the top 1000 banks according to size and demographics and 20 non-financially distressed banks randomly chosen from the top 60 non-financially distressed banks on the Banker List. Is it essential to note that the selection process was not perfectly scientific and was based on our desire to observe outcomes in different demographic regions of the world. Our sample size of a total of 40 banks was also small owing to the time constraints involved in normalizing banks’ income statements and balance sheets into a single common format. Further research is currently underway within our research team and these should be noted to be preliminary results.

 

We found that of all the ratios we examined – in excess of 9 ratios – by far the strongest was customer loans to deposits. This, of course, makes perfect sense because the ratio effectively demonstrates the concept of reliance on the interbank market. Recall that a bank in its liability structure to meet its asset demand will depend on either deposits made by corporate or individual customers or on the interbank market. We can therefore deduce that the higher the percentage of total loans to customer deposits the more reliant banks are in their liability structure on the interbank market.

 

Based on the limited sample size, we are able to conclude that had the extent to which banks were funded by interbank loans versus customer deposits been known pre-crisis it would have been a powerful indicator of future financial distress with an accuracy level of 80% on 31 December 2007.

 

The ultimate finding of the study is that 10.6% of banks failed. This is an extremely high proportion and one that we have not found any primary research for. Secondly we found that customer loans to deposits is significantly indicative of risk and would have been a very useful indicator prior to the Financial Crisis.

 

It goes without saying that the constructions of the LCR and NSFR, the latter in particular, are extremely difficult for banks to comply with. To a large degree these ratios conflict with the original notion underlining the business of banking – that is to use an upward sloping convex yield curve to arbitrage on tenor to achieve interest rate differentials to make profit.

 

Given the current environment of very low interest rates in advanced economies, we believe that this study indicates that a more simplistic ratio could have been used by policy makers as early as 2008/9 instead of LCR and NSFR. The use of such a ratio might have had a less deleterious effect on funding structures for banks. Ultimately a combination of the NSFR ratio as well as the radical levels of monetary intervention – which have led to very low interest rates – have had extreme effects on the profitability of banks. If these conditions perpetuate for another 5 to 10 years, it is questionable whether banks are going to be able to attract sufficient equity to remain attractive to investors. A simplistic ratio such as customer loans to customer deposits could be a potential solution to avoid banking ultimately becoming a utility.

 

We wish to acknowledge that the primary research was conducted by a Masters student of North-West University’s Business Mathematics and Informatics (BMI) faculty during a 6 month period while working closely in conjunction with Monocle Solutions. We are grateful to all the support and assistance from the University professors.

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Opinion Pieces

If one were to conjure up a perennial image of apartheid South Africa, it would have to be one of the grainy photographs taken in Soweto on 16 June 1976. There are the shots of the South African Defence Force armoured vehicles in a stand-off with the baying scholars who had formed a roughshod group on the outskirts of Soweto, and there are shots of the crowd in panic after the first bullets had been fired. And then there are the photos of Hector Pieterson.

 

tobaccobeerv1-0smallThese are the images that are conjured, that have been nurtured by the process of history into permanence, that are now indistinguishable from the concept of apartheid itself.

 

I grew up in apartheid South Africa, and these were not the images that I had in my head at the time. The images I had were of large, loosely-fenced properties, of dull brown and grey landscapes in winter, of single-story houses, and of pastel-coloured cars, of Saturday barbecues with neighbours’ children, and of swimming, endlessly, throughout summer.

 

There were of course some small anxieties of things to come. There was the moment when I was told that we should not go to school because it was ‘kill-a-white day’. It would only be many years later that I would come to realise that this day was the anniversary of the Soweto massacre.

 

But they were only ripples. Like any totalitarian regime, there was a bubble that was carefully maintained around us. We had little real notion of the world in which we lived. Our main convictions of ourselves were that we were somewhat politically behind, that the international community was repulsed by us, that we would spend two years in the army, and that we were pretty good at rugby.

 

On Saturday afternoons, having been excluded from international sport, we would watch the Currie Cup games. My father and his friends would drink South African Breweries-made beer, and smoke British American Tobacco-made cigarettes. They would watch the rugby whilst keeping an eye on us in the pool.

 

The extent to which things have changed since the 1970s is dramatic. This country has had, for some time now, the most liberal forward-thinking constitution ever written – in any country, anywhere. Our freedoms are guaranteed. Two decades of self-realisation through the implementation of democratic principles, and our inclusion into the international fold, have made South Africa and its people change enormously.

 

Nevertheless, in spite of these freedoms, and in spite of tremendous structural change, the economic reality for many in this country has altered very little. Frequently, and particularly more recently, stagnant growth and continued stifling inequality is blamed either on externalities or on the idiosyncrasies of leadership.

 

These externalities are often the same kind of externalities that any emerging economy with a heavy reliance on commodities would cite: the pull-back in Chinese demand, the falling copper price, the oversupply of steel. These are the structural arguments; the medium- to long-term reasons given for lacklustre performance.

 

In the short-term, one recognises clearly that the enormous gyrations in the value of the currency, and the immediate impact this has on the ‘markets’ – meaning bond prices and the value of JSE tickers – are primarily driven by the notion that the utterances of leadership itself are events, rather than utterances only. These are not structural impingements, but the fear is that they could become institutionalised.

 

What is not spoken of however, what is analytically absent, is the extent to which the financial markets – in spite of all of the remarkable changes we have experienced as a society – have not changed very much. This is not because the economy has not changed. The economy today is radically different to what it looked like in the 1970s. It is the markets that look distressingly similar to apartheid South Africa. The markets have become substantially, audaciously, out of sync with our economy.

 

Just a few facts elicit this. Looking at the JSE holistically and then drilling down, one notices that SAB Miller and Anheuser-Busch – two enormous beer-making firms that are set to merge after international competition commissions approve their R1.5 trillion deal – constitute 27% of the total value of the JSE. That is remarkable: the brewing of beer is valued by investors at over a quarter of the total stock market. Now add British American Tobacco, another ‘sin-tax’ firm, and we have three JSE firms that make up roughly 39% of the total stock market value of the JSE.

 

Even more absurdly, if one then adds Naspers, the next largest company by market capitalisation on the JSE, that fraction goes up to 46%. Naspers, in and of itself, is worth interrogating. Of its R1.0 trillion market capitalisation, more than 80% of that value is made up of its investment in Tencent, a Chinese messaging app.

 

To put that into perspective consider this: The largest four banks in South Africa employ about 190 000 people, whereas Naspers employs only 24 000 people. The Big Four banks are all considered blue-chip shares, yet their total market value, all together, is worth less than Naspers alone.

 

The party line amongst local and international economists reads something like this: The South African economy is likely to grow sluggishly in 2016 and 2017, afflicted by severe electricity constraints and the downturn in the global commodity cycle. Policy uncertainty, labour unrest and resultant investor uncertainty have also undermined SA’s potential growth trajectory in recent years, although South Africa is actively working to ease electricity supply constraints in the longer-term.

 

What the headline should read is this: Of the R15 trillion value in the South African equity markets, almost one-half is invested in a beer company, a cigarette company, and in a Chinese messaging app. This is because institutional investors are divesting themselves of South African economic exposure and SAB, BAT and Naspers will provide the investor approximately three quarters diversified offshore exposure.

 

As ironic as it is plain: the image in my head of a childhood spent growing up during apartheid in South Africa – men drinking beer, smoking cigarettes and watching rugby on TV – was accurately prescient of the constituents of the JSE more than thirty-five years later. My childhood image however has very little relevance to the broader economic reality that exists within South Africa today: a picture of tepid growth, violence, xenophobia, extreme unemployment, cronyism, and a proliferation of mediocrity in all echelons of utilities.

 

Most importantly however, this contrast between the JSE and the economy displays in the collective investment strategies of just about every investor within the borders of this country, including the State’s own investment arm, a substantial absence of faith in this country.

 

Little real equity investment has risked itself on our future. The distortion between the real economy and the markets – the structures through which the real economy is meant to be funded – is astonishing. In nominal terms, the figures are even more distressing: the value of unencumbered equity invested in the real economy in South Africa is strikingly low – something like a sixth to an eighth of the same ratio in the US.

 

Money has simply gone to what it knows best, its past successes. These are successes that have repeated themselves on a worldwide stage, but somehow only in industries that distract one from the rigours of reality.

 

 

 

 

 

 

 

 

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Opinion Pieces

For the past sixty-one years Fortune magazine has published a list of the largest 500 firms in the world. An idiosyncrasy of the list is that it is published in order by revenue rather than by profit or by total assets. It will come as no surprise to most that Apple comes out as the most profitable firm in the world, achieving an incredible profit after tax margin of 23% on revenue of USD 234bn.

 

china-olympics-smallWhat may surprise some is that the next four positions – by profit – are occupied by four companies in the same industry from the same country. Amazing as it may seem, positions two through five are occupied by Chinese banks. The total profit after tax reported by the four largest Chinese banks in the 1 August 2016 edition of Forbes magazine is in excess of USD 136bn.

 

To put this into perspective: the total profit of the largest four US banks is only USD 80bn. A modicum of legwork will divine the following: JP Morgan came in sixth with USD 24bn, Wells Fargo came in eighth with USD 23bn, Citigroup came in thirteenth with USD 17bn, and finally Bank of America came in seventeenth with USD 16bn.

 

Putting aside the niggling doubt that must infect one’s perception in comparing American firms to Chinese firms – for example one has to ignore the fact that China is an autocracy, is notoriously opaque, and is prone to excessive government intervention – it still seems a legitimate pursuit. Given that these numbers are reported as accurate income and balance sheet figures by these firms, there is no reason not to compare them, irrespective of whether what emerges is sensible. In fact, merely by reporting them in a list as Fortune magazine does, it gives these numbers a certain degree of validity.

 

It does seem more than innocuous however, and somewhat incredible, that the four largest Chinese banks made nearly double the profit of the four largest US banks. US GDP ending Q3 2016 is reported as USD 18.4trn and Chinese GDP is reported as USD 10.9trn.

 

In simple terms, this allows one a comparative ratio for financial productivity – so to speak – of the US versus China. In the case of the US, the ratio between banking profits and GDP is 0.44%, whereas in China this ratio is 1.25%. That means that Chinese banks must be 2.9 times more efficient than US banks.

 

A couple of points spring to mind. Firstly, there is no possible way in which this could be true – US banks are older, wiser, more battle-weary and generally more visible worldwide than Chinese banks. Secondly, in running a large diversified bank, one has to have a certain minimum infrastructure in place, which is costly and requires highly-educated skill-sets to administer. This would account for the relatively high cost-to-income ratios observed in US, UK and European banks. Usually one sees numbers between 50% and 65% for this metric.

 

In order for Chinese banks to be 2.9 times more efficient than their US counterparts they would either have to have substantially better interest rate margins or they would have to have far less costly infrastructure and far fewer educated staff. Both are possibilities but nowhere near the three times efficiency pick-up implied by the official numbers.

 

In fact, what is far more likely than uncanny Chinese efficiency is simply that Chinese banks have not recorded provisions for the enormous debt overhang that they are failing to account for. The next financial crisis is possibly imminent and is made poignantly obvious by a cursory analysis of Fortune magazine’s data.

 

The International Monetary Fund estimated as recently as June 2016 that potential losses for Chinese banks’ corporate loan portfolios could be equal to about 7 percent of GDP. That translates into USD 760bn in actual losses. Recall that we used the same concept to estimate financial efficiency for Chinese banks, and that the profits of the four largest Chinese banks came out as USD 136bn. In other words, it would take five and a half years of sustained Chinese banks’ profits to absorb only their corporate loan losses, never mind the consumer loan losses built into their system.

 

So, on the one hand we have the regular financial press – Fortune magazine – awarding ranking honours to Chinese banks – and on the other hand we have the losses forecasted by the IMF.

 

Even if one were to factor in the political leanings of the IMF, there still remains two unfortunate conclusions: the opacity in Chinese statistical and financial reporting, and the obvious compelling improbability of Chinese operators being three times more efficient than US operators. These two realities lead to only one derisory conclusion: that the Chinese numbers must be wrong. What irks is the near absence of mainstream financial media focus on what would appear to be the next crisis made plain in numbers.

 

The buying power of the Chinese economy has led to a wilful myopia in the western acceptance of the Chinese story. Even the most basic analysis leads one to very disturbing conclusions. It is nine years since the near collapse of the financial markets. Yet the lessons apparently learned – primarily that excessive leverage in banking and exponential growth in property prices signalled a future overhang of severe magnitude – have already been forgotten.

 

There is not a single line of analysis in Fortune magazine on the irascible truth that there is no possible way that Chinese banks could be this profitable. Nor on the consequences of this blatant myopia. Something wicked this way comes. And it will be global.

 

 

 

 

 

 

 

 

 

 

 

 

4

Opinion Pieces

A few years ago, we were asked to assist a bank with their methodology for credit risk models. It was a relatively usual request – with one glaring difference. The bank wasn’t exactly around the corner from our slick London offices; it was in the Faroe Islands. In case you haven’t zoomed in on a map for a while, let me refresh your memory: somewhere where the North Atlantic Ocean meets the Norwegian Sea (take a left at Norway, if you hit Iceland you’ve gone too far) sits a cluster of 11 rocky islands. Not the sunny, clear-watered islands featured in alcohol adverts; the cold, wet, windy islands featured in National Geographic documentaries about deep-sea fishing. Or extreme mountain climbing. Although today, they’re an autonomous country within the Kingdom of Denmark, the Islands have a history as rocky as its mountains, ravaged by Scandinavian Vikings, and invaded by the British in 1940. In short, no, it wasn’t on my list of holiday choices. To be honest, I didn’t even know they had banks there…

 

sadalur-feroeAnd yet, there I was, on a plane, heading to my Island meeting. Essentially, landing a plane in the Faroe Islands is just slightly more dangerous than landing a plane in the actual sea. We’re getting closer and closer and in front of me, I can just see – through the thick fog that’s delaying our landing – peaks of rocky outcrops. There’s barely room to breathe, let alone fly a plane between the mountains. At the last second, the plane banks, so the wingspan can fit snugly between the jagged edges. And then suddenly we were making a bee-line to the ‘runway’. I use ‘runway’ loosely here. At the end of the runway is a lake, the final destination for unlucky touchdowns (a very reassuring sight, I’m sure you can imagine). I was clinging on for dear life, hoping for the best, as the plane’s tyres screech to a halt seconds before Lake City.

 

My colleague and I were supposed to be accommodated in one of only five hotels in Tórshavn, the capital of the Faroe Islands, but it turns out we were visiting Faroe in its five minutes of bustling holiday season, and all hotels were fully booked. Fortunately, the bank’s project manager kindly offered to have me stay in his personal home. Unfortunately, that meant sleeping in his child’s single bed, underneath a duvet covered in dinosaurs. Nevertheless we were received with the warmest hospitality and great food.

 

The next day, we headed to the bank to consult on their credit risk modelling methodology; a space in which I was far more comfortable. Considering the Islands are about as far from civilisation as you can get, the bank was incredibly sophisticated – and the meeting went well. Of course, no business trip would be complete without a little sightseeing, and soon we were huddled at the harbour, crawling into thick, weather-proof suits.

 

In a speedboat, we crashed through the waves of the freezing North Atlantic, with the rain and backsplash trying unsuccessfully to penetrate our weather suits. We stopped at an even smaller island and hiked up the rugged trail through a small village, wind at our backs (and fronts, and sides) to a local’s home. But the welcoming entertainment wasn’t a cocktail on the deck, but a show; of the local stuffing a puffin (no, that’s not a euphemism). The bird stood no chance against his skilled hands.

 

Despite the fact that a stuffing isn’t always my aperitif of choice, our next stop was back on the mainland, for a meal at one of their fanciest, finest restaurants. Nestled at the top of a hill, the place is straight out of a Tolkien novel, overlooking miles of tiny houses, complete with traditional dark-wooden panelling and insulation-friendly grassy roofs. Whilst admiring the beautiful view, sipping an ice-cold beer and feeling proud of my adventures of the day, I suddenly picked up a strange odour. Without even seeing the menu, I was presented with a plate, offering five different local delicacies; likkja (that is whale biltong to us), whale blubber, skerpikjøt (or wind-dried mutton) which smelled more like it was buried for a few days so that it had begun to rot, dried shark, and lastly potatoes (or so they said). I needed a strategy, and fast. Luckily, the locals believe that the best way to have a beer is to chase it with a strong schnapps, which meant there was ample amounts of Dutch Courage to help me through. I followed each bite with a quick shot of schnapps. But after the dried shark, I respectfully retired from the table, and headed home to crawl into my Dino bed.

 

While the world of consulting is often filled with interesting characters and outlandish last-minute presentations, my time in the Faroe Islands was by far the most fascinating few days on the job. Did I learn anything in particular? Yes. Never underestimate your clients based on where they’re located. Be prepared for a challenge. And always check the end of a runway before you land a plane.

 

 

 

 

 

 

 

 

0

Opinion Pieces

There’s no arguing that the opportunity to wear more comfortable clothing and express some small sense of personal style boosts team morale – improving productivity and mitigating some of the disillusionment employees can feel, as more than just a number at a desk. FNB demonstrates that rather effectively, by encouraging its staff to wear their rugby jerseys on rugby Fridays, a simple device for bringing its staff together under one banner – a nation in love with rugby. So, I’m not against Casual Friday, albeit with a proviso.

 

SuitsBanks and insurance companies are balanced on an incredibly intricate array of processes and carefully determined risks, making them challenging to run at the best of times. Now imagine yourself to be the CEO of one of these august organisations, all of whom must navigate potentially debilitating market fluctuations, regulatory changes and a transition away from legacy systems. Would you trust me with operational success of your bank if I were to arrive in washed denim and sandals?

 

When working in an industry faced with constant disruption, as consultants do, we are at battle with a world that fundamentally resists change. It’s our job to guide clients to a brighter future, but I’d struggle to arrest the inclination to shy away from the current ebb and flow, if I came across as someone they couldn’t depend on. I want to represent myself as capable, professional, trustworthy and a cut above the rest. That can be downright impossible in casual attire.

 

Speak and dress are crucial in the world of consulting, where we’re presenting ourselves as the brand; in consulting, we are the product. That happens a lot more effectively when my colleagues and I work closely with our clients, promoting integrity, transparency and instilling a peace of mind, for which we need to look the part. It’s essential, then, that Casual Fridays do not come at the expense of professionalism, even if it’s only perceived as such.

 

I’m certainly not against casual attire when the occasion calls for it. From the onset of every relationship, we make it clear that Monocle is not an island, having always strived for a united vision among its shareholders, employees and community. We’re passionate about comradery, because we’re a results-focused consulting firm for one of the most challenging sectors in South Africa, and we need to function as a unit to produce stellar results. Casual Fridays are one cog in the machine to achieving that, but like everything we do, we need to be smart about it.

 

To distinguish myself from both competitors and clients, I focus on my work, as well as my overall appearance, to project an aesthetic of confidence and certitude in my work. My job isn’t just to liberate companies from system inefficiencies, the risk of a liquidity shortfall or to ensure data governance, though that is certainly a part of it. My job is to assure and persuade. So, for me, casual means business casual – that I can still wear a jacket and be smart.

 

 

 

 

 

 

 

 

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