Showing posts with label JP Morgan. Show all posts
Showing posts with label JP Morgan. Show all posts

Saturday, August 1, 2015

Corporations’ exodus from traditional banking

In any discussion about corporate banking, it is important to remember that from the perspective of corporations, banks are a means and not an end. For example, what became apparent during the financial crisis that began in 2008 is that corporations tend to use banks as one of their funding options in times of duress. Evidence of this can be seen during the same period, when despite a dramatic fall in the quantity of new loans to large corporations of over 40%, banks’ balance sheets still experienced a spike in commerical and industrial loans (Sharfstein and Ivashina, 2010.)

At a time when credit was severely restricted, banks were able to leverage their relationships with banks to draw down existing credit lines and presumably achieve funding terms at better rates than had a relationship not been in place. Corporations may need to maintain these relationships if current figures are anything to go by. In the period after the financial crisis, a low growth environment combined with five years of near zero interest rates have incentivized corporations to increase their debts. As of the second quarter in 2015, corporate debt is at historically high levels: Net leverage for US companies, calculated as debt less cash as a multiple of annual income, was 1.88 at the end of 2014 – the same indicator was 1.63 on the eve of the financial crisis[1].

One of the consequences of the financial crisis for banks was the introduction of the Basel III framework, requiring banks to increase the level of low-risk high-quality capital on their balance sheets. One of the best means for banks to achieve this is through retaining corporate clients and their bulging cash reserves. Despite four hundred years of corporate banks existing, companies have yet to replace the deposit reserve function that those banks offer with anything better. This paper will outline several of the ways in which corporations are slowly moving away from banking insitutions and forwards some suggestions as to how banks can address this movement.

Bankers in the good old days.


Banks on the run
The recent and ongoing scandal involving the tax returns of US corporate giants was remarkable for several reasons. When a company such as Google pays tax of £11.2 million on receipts of £4.9 billion[2], plenty of comment will inevitably be generated. However, one of the most comment worthy facets of the story almost went unmentioned: the diminished role of banks in the process; aside from providing a medium (a bank account) where funds could be transferred from one jurisdiction to another, banks did nothing. The procedures were instead devised by these firms’ considerable financial teams and ‘big four’ accounting firms.

This is indicative of one of the many issues that banks currently face: corporations are not a captive audience; the services that they look for (M&A advisory, foreign exchange, tax consulting, etc.) are provided by a growing market of players at increasingly competitive prices. Corporations – aware of the large fees commanded by corporate banks and others for these services – have even began to develop teams within their walls which carry out the same functions. For example, in 2011, GE established a Latin America acquisition team whose role was to find and acquire attractive targets in Latin America[3].

The lines between banking and commerce have also blurred significantly. The Glass Steagall Act of 1933 banned the mixing of commerce and banking. In the United States, over time various parts of the act were either circumvented (as in the case of GE Finance) or repealed (as was the case in 1999[4]). As one Economist article[5] notes, ‘the case for a split is clear. Managers are even worse at dealing with financial risk than bankers are.’ Sceptics of companies taking over too many of the financial duties argue that managers of firms are better at dealing with issues like firm strategy and sales.

However, if the low-growth environment that corporations in the US and elsewhere currently find themselves in persists, it’s hard to see any way other than a gradual diminishing of the responsibilities assigned to corporate banks by their clients. Where there is an imperative from shareholders to ‘produce’ growth, firms can have the option of opting for M&A (risky), innovation (difficult to predict outcomes) or financial engineering, which more of them are turning to in greater numbers.
In this global climate, non-financial firms own $9 trillion of currency derivatives[6]. In China, low interest rates offered by corporate banks has led firms into shadow banking,[7] which offers higher returns but much less regulation than traditional corporate banking channels. In Brazil, corporate banks have effectively been crowded out by the national development bank, BNDES[8], which subsidizes financing for corporations at the expense of taxpayers. In 2014 alone, it borrowed $190m to corporations – 60% of them large multinationals – at an average rate of 5.5%[9] As of July 29th 2015, the interest rate set by the Central Bank of Brazil is 14.25%; it’s not easy for corporate banks to compete with such policy.
In India and elsewhere in Asia, companies are turning away from banks for funding and opting instead for commercial paper.[10] Bonds issued by first-time buyers have grown by close to 20% CAGR over the past five years. Presumably, this can have more dramatic consequences in places like India than in Anglo-Saxon countries, as close to half its 1.2 billion population don’t have regular bank accounts – amounting to far less cash on the balance sheets of banks to issue credit to corporations and others.
Even the largest company in the world by market cap has largely turned away from corporate banking; when the question arose ‘what is Apple going to do with its massive cash pile,’ the answer given by most analysts was to return the cash to stockholders through a buyback. Apple did that with some of the cash but most of it found its way to Braemore Capital in Nevada – a hedge fund which is a wholly-owned subsidiary of Apple Inc. and now probably the biggest hedge fund in the world by assets under management[11].
The aforementioned discussion highlights many of the difficulties faced by banks and explains why the number of commercial banks startups has virtually dried up in the past decade. Since 1990, an average of nearly 200 banks were established per year in the United States. This number inevitably fell during the financial crisis and by 2011, no banks at all were founded[12] – the first time this happened since 1934 and the introduction of the Glass-Steagall Act. Compare this to the number of financial startups on private investor platform Angel List – at time of writing, the number of these startups was in excess of 4,500 with an average valuation of over $4 million – and you might begin to wonder if we’re witnessing the beginning of the end for corporate banking.
Corporate Banks: Fighting Back
What is notable about most corporate banks is their longevity. In a comparison between the average age of the oldest banks and the oldest non-financial companies, banks come out on top and by some distance. Well known multinational banks such as Bank of Scotland and Barclays were founded in the 17th century, while Bank of New York, Caja Madrid and JP Morgan Chase were all founded in the 18th century. By contrast, Cigna is the only publicly-listed firm in the United States which has survived in its original form from the 18th century.
Longevity matters for several reasons. Firstly, it shows the systemic importance of corporate banks, even if, as the figures in the introduction suggest, this importance is falling for the moment at least. Secondly, it shows that banks have always had accesss to large amounts of cash, which is one of the secrets of longevity, allowing them to see out downturns. Thirdly, and closely related to the second point, is that the tenets of corporate banking have not changed to the extent that most industries have in the past three centuries (although more on this later). Finally, to give some credit to modern corporate banks – it shows that they are above all, resourceful.
In terms of being resourceful, there are examples everywhere. For example, the average startup figures in the previous section can be a little misleading when it comes to corporate banks. One of the reason for the fall, not just in the number of startups but the number of existing banks, has been the huge consolidation that has occurred in the US banking industry since the 1980s. The current number of over 5,000 is around 40% of the total number of banks that existed in the United States since the 1980s. The United States isn’t alone. In countries as diverse as Sweden, Australia, China, Japan and Spain, the domestic banking sector is dominated by four banks.
Also, few industries have been shown to be as shrewd with legislation as corporate banks. Every financial crash is defined as much by the new raft of legislation as much as the aftermath of the crash itself. Corporate banks have learned to adapt to these changes quickly. Most of the major banks have capitalized their balance sheets well in advance of the deadline set by the Basel III regulations. This has also taught banks to develop lobbying. In April 2015, several large corporate banks announced they were increasing their lobbying spend in the EU; among them, JP Morgan Chase raised its costs from €50,000 in 2013 to €1,499,999 in 2014, while Goldman Sachs’ rose from €50,000 to €799,999 and UBS rose from €200,000 to €1.7  million[13].  
This lobbying doesn’t just give sway to banks – it can also lend influence to their largest clients. There’s a real unspoken element here of how it’s better for corporations to be onside with banks and to piggybank on some of their lobbying. Leaders of the large banks effectively become spokesmen for heads of industry – and not just the banking industry – at the government table. There is probably little that financial innovation can do to change this, providing another reason why most banks will still be around long after many of today’s largest corporations.



[1] http://www.wsj.com/articles/u-s-firms-shoulder-rising-debt-1430689085
[2] http://www.ft.com/intl/cms/s/0/c6ff0ebc-29c4-11e3-bbb8-00144feab7de.html#axzz3h3liUxx1
[3] http://www.bloomberg.com/news/articles/2011-04-29/general-electric-starts-m-a-team-in-latin-america-as-regional-orders-surge
[4] http://www.usnews.com/opinion/blogs/economic-intelligence/2012/08/27/repeal-of-glass-steagall-caused-the-financial-crisis
[7] http://www.businessspectator.com.au/article/2015/3/31/china/are-chinas-shadow-banks-going-bring-economy-down
[8] Banco Nacional de Desenvolvimento
[11] http://qz.com/393093/the-mysterious-fund-in-the-desert-that-manages-apples-cash/
[13] https://euobserver.com/institutional/128520

Sunday, July 20, 2014

The Perils of Attempting to Predict Financial Risk

Can one predict risk in financial markets?
“We believe that the boat is unsinkable,” is the famous piece of hubris attributed to Phillip Franklin, when speaking of the Titanic in 1912. Franklin was  Vice-President of the White Star Line, a shipping company which hadn’t lost one ship at sea since 1893. In that time, the company had undertaken tens of thousands of voyages. None of the White Star Lines’ ships up until that point had been as statistically safe as the RMS Titanic and many had travelled on the same path that it would take. It isn’t impossible to see how Franklin could be led to make such a remark, even with the benefit of hindsight.
The Titanic: Unsinkable
But hindsight can be dangerous and misleading when making forecasts about the future. One of the fundamental weaknesses of experts, commentators, risk models and quantitative teams at financial institutions among others, is that they are drawing from past data. Data in their nature cannot be taken from the future. If a country with an average annual rainfall of 750mm per year reaches a cumulative 300mm by the last day of a given year, it doesn’t mean that the last day will have 450mm of rainfall. A quantitative model might suggest a range around 450mm, illustrating the danger of relying on such models.

Similarly, the financial system is set up in such a way that encourages a lack of transparency. Despite the best attempts of governments to uncover the workings of the shadow banking industry and establish increased controls on tax havens, the financial industry remains murky. It’s unlikely to become discernibly clearer any time soon. This lack of transparency in turn makes important data unavailable for forecasting models. As a result, we’re then unable to assess the interconnectedness of financial institutions and players (which came back to haunt us in 2008, when the liquidity crisis struck).

Despite these inherent drawbacks, the desire to measure risk is still commendable. To not do so would be reckless at best. Even when making small investments, the standard practise is to use the Capital Asset Pricing Model (CAPM), a model developed over 50 years ago, which tells us how much return we should expect for a given level of risk. If anything, this model is a good place to show that risk isn’t a bad thing in itself: more risk can mean more returns. Therefore, we reach an important distinction, which is often overlooked: the management of risk versus the measurement of risk.

Risk Management
The key word here is “exposure.” It comes down to an investor not exposing themselves to losing more than they can afford to. Warren Buffett uses the analogy of somebody betting their house on a sure thing. Why would anyone expose themselves to that kind of risk even if it was a sure thing? Hence, the need to legislate for minimum capital requirements at financial institutions – the idea that no matter what a bank invests in, they have the cushion of a certain amount of capital (which is usually not enough, but here is not the place to discuss that).

Risk Management is also now de rigeur in all annual reports. Its scope is quite huge. From currency transactions where the currency is hedged (McDonalds lose $100 million for every 1 cent fall in the euro against the dollar) to purchasing oil several months in advance to insure against hikes in price which could adversely affect revenues (airlines couldn’t survive without this form of energy trading – most don’t survive even with it). There are many examples. In fact, the financial risk is measurable because it has been managed. Measuring financial risk outside the parameters of those that are fully-managed and thus predictable, is quite a different story.

Risk Measurement
Companies use a range of neat and simplistic ratios to measure their financial risk. These range from debt to equity and debt to capital to the capital expenditure ratio and times interest earned. With so many companies going bankrupt every year, something would suggest that the ratios might in fact be a little too neat and simplistic. What’s happening with companies’ risk measurement procedures that causes so many to go bankrupt?

Well, on one side, there’s human error and moral hazard; human error being incompetent CFOs who don’t see the flashing red lights on a company’s financial statements and the CEOs that hire them. John Coates, the author of The Hour Between Dog and Wolf: Risk-Taking, Gut Feelings and the Biology of Boom and Bust, says, “this is actually a biologically-driven phenomenon,” letting incompetent risk-takers everywhere off the hook. In addition to the human element, there’s moral hazard. For example, limited liability companies; setting up a limited company allows its founders to limit their losses (but not the losses of others), no doubt increasing the financial risk-taking (risky loans etc.) of the founders at the expense of everyone else and increasing financial risk in the system.

But these, for all our purposes, are known risks. On the other side of the equation are “unknown risks.” In the aftermath of the 2008 financial crisis, a book by Nicholas Taleb was published to some acclaim. In the book, Black Swans: The Impact of the Highly Improbable, a three-fold definition is given: “A Black Swan is a highly-improbable event with three principal characteristics: it is unpredictable; it carries a massive impact; and after the fact, we concoct an explanation that makes it appear less random, and more predictable, than it was;” all in all, a good description for the financial crisis which occurred the same year.

Maybe the name black swan isn’t even appropriate for the kind of financial risk that Taleb was trying to give an analogy for. An actual physical black swan, by its definition, is easy to picture in the mind´s eye; it is foreseeable in some sense. The point is that the kind of financial risk Taleb was alluding to was unforeseeable. And in 2008, it was not just financial risk – but financial risk aligned with liquidity risk – and then both combining with human nature (which we’re beginning to think may have been the missing element in risk models all along) to create a financial crisis of devastating proportions. The basis of it all is the normal curve (see below).

The normal curve has been assigned the task of telling the finance industry where financial returns (and risks occur). The curve - and those who use it - basically tells us that its logic “stands to reason until it doesn’t stand to reason.” That is, 95-99% of the time, results will fall within a certain defined range of the average result (that we believe to be the average based on past data). What happens in the space outside the 99% are known as outliers. They’re extreme results and in layman’s terms, can be defined as, “everything else.” Everything else can an extremely good financial result (in which case somebody will more than likely take the credit for it), or it can be an extremely detrimental financial result (in which case, the models will be blamed).

The drawback here of course is that it’s impossible to list everything else. To begin with, there’s tail risk. This is the not unreasonable assumption that the tails of the normal curve are fatter than they appear, thus severely increasing the risk on the downside (see below).

Now, as the curve illustrates, the chances of achieving a financial result outside 3 standard deviations to the mean have significantly enhanced. On the downside, this means more severe losses and more often. If you’re wondering what kind of events might fall under the fat left-tail, there have been a multitude in the past 20 years: the European debt crisis, the Japanese earthquake/tsunami, Enron, the Argentinian default, LCTM, the fall of the Russian rouble, the collapse of the Asian “tiger” economies and the list goes on. How many did the man on the street predict?

Therefore, one can see the problems inherent in using the standard bell-curve in attempting to measure financial risk. But this is standard practise in the financial industry. “Rank beliefs not according to their plausibility, but by the harm they may cause,” says Nicholas Taleb, which seems to fit the normal curve.[i]  Incidentally, the normal curve is used  in the industry standard model for risk measurement - Value at Risk (VaR). This is a tool which measures the probability of a risk, the size of the risk in dollar terms and the time-frame expected for the risk. So, a VaR model can tell you that there is a 5% chance that you will lose $10 million every 3 months, for example. However, VaR models failed to forecast the collapse of the US housing market and ensuing crisis, catching off guard banks that relied on them and landing them with huge, unexpected losses.[ii]

The normal curve (sometimes referred to as the “Gaussian curve” or a “bell curve”) is one of the main drawbacks behind the model. For example, in 2008, Goldman Sachs profit and loss distribution looked a lot more like an elongated letter “U” than the bell shape predicted by VaR.[iii] As one mathematician puts it in the Scientific American: “If the things that fluctuate are not correlated at all with one another, than it’s demonstrable that a Gaussian function is the correct histogram. The catch is: in a financial market, everything is correlated.”[iv] It continues: “No model can consistently predict the future. It can’t possibly be.”

Financial industry practitioners have recognized (some of) the imperfections of VaR but will continue to rely on similar models. New tests called stress tests and expected shortfall have arisen since the failure of VaR. Both address some of the inadequacies of the VaR but still fail to address a once-in-a-lifetime (or as it happens, a once-every-fifteen-years) event, which are now commonly referred to as “black swans.” The models still start with basic assumptions – reasonable in itself – but the assumptions don’t always hold and when they don’t, they fail spectacularly.

Conclusion: Can one predict financial risk?
Former US Secretary of Defense Donald Rumsfeld attracted the world’s mirth in February 2002 when he made a speech about, “known knowns,” “known unknowns,” and “unknown unknowns.” Aside from the clumsy nature of the speech, Rumsfeld was actually communicating something extremely important – and something which cost the world in dramatic fashion in the global financial crisis in 2008: we cannot yet predict financial risk as effectively as we would like to think.

Prediction, not narration, is the real test of our understanding of the world.[v] The financial risk models which have been developed thus far are imperfect in the extreme. What’s more, it’s difficult to see how a model will ever be developed which can predict financial risk. Think of it this way: People who talk about the roll of a dice automatically assume that the dice should have six sides. That’s a one-in-six chance! The paradigm changes when the number of sides changes. What about a dice with seven sides? Or eight? The dice is flawed in that it assumes there can only be six outcomes. Existing financial risk models are similar.

Take another example: the price of oil. Given its location, the price of oil depends on social and political events in the Middle East. Does this seem something predictable? For 2014, Goldman Sachs predicted a price of oil of $105,[vi] JP Morgan predicted a price of oil of $122.5[vii] and Morgan Stanley predicted a price of oil of $103.[viii] Who to believe? Currently, Brent crude oil is trading at around $110 and is as volatile as ever with the perpetual difficulties in the Middle East changing form on a daily basis.
Statistics are dangerous. Recall the example of the mathematician who drowned in a swimming pool with an average depth of two inches if you don’t believe it. One can predict financial risk in the same way that one can make weather forecasts: you can generally trust the forecasters to get it right. But sometimes, they get it wrong and you get wet. And sometimes, they get it really wrong and you get soaking wet.

Even an Economic Scenario Generator (ESG) such as a Monte-Carlo simulation can only begin to touch on the economic scenarios that may play out. In addition, they can provide variations which are large, ultimately telling us a lot but telling us very little. These simulations also require lots of data input, but ultimately, is it the right data? The answer, invariably, is “no.”
We can predict financial risk. It’s as inevitable as night follows day. However, as of yet, we just can’t predict it with the accuracy that we require.




[i] Fooled by Randomness (2001). Page 203
[ii] http://www.ft.com/intl/cms/s/0/67d05d30-7e88-11e1-b7e7-00144feab49a.html#axzz36ppjuSoh
[iii] http://ftalphaville.ft.com/2009/06/24/58871/on-goldmans-fat-tail-risk/
[iv] http://www.scientificamerican.com/article/can-math-beat-financial-markets/
[v] See Black Swan, page 3.
[vi] http://stream.marketwatch.com/story/markets/SS-4-4/SS-4-17298/
[vii] http://www.investorvillage.com/smbd.asp?mb=5028&mn=28108&pt=msg&mid=12453217
[viii] http://www.oilngold.com/ong-focus/insights/ibank-focus-2014-crude-oil-price-outlook-2013122826944/