Pretty much everyone has heard of Fintech by now, but a more focused approach to applying new IT technologies to banking is now the nerdier Regtech. Regtech aims at applying Fintech for regulatory and compliance purposes, simplifying a process that has caused headaches to bankers due to the exponential growth of the rulebook they had to follow, and which has also been a pain on the cost side given the number of extra compliance officers they had to hire in an era of lower revenues.
Indeed, the FT reports that:
Citigroup estimates that the biggest banks, including JPMorgan and HSBC, have doubled the number of people they employ to handle compliance and regulation. This now costs the banking industry $270bn a year and accounts for 10 per cent of operating costs. […]
Spanish bank BBVA recently estimated that, on average, financial institutions have 10 to 15 per cent of their staff dedicated to this area. This heavy investment has been necessary in response to the crackdown by regulators that followed the financial crisis. European and US banks have paid more than $150bn in litigation and conduct charges since 2011, Citi estimated.
What’s the solution? ‘Regulatory technology’:
New technologies mean that banks could make vast savings in compliance, according to Richard Lumb, head of financial services at Accenture, who estimated that “thousands of roles” in the banks’ internal policing could be replaced by automated systems.
Many of recent Regtech developments involve the use of artificial intelligence to simplify compliance issues that are very burdensome from a staff (and cost) perspective. As Deloitte outlines here (and see an interview on the Financial Revolutionist about applying machine and deep learning to investment strategies here):
The Institute of International Finance (IIF) highlights AI, among others, as it has a range of applications in regulatory compliance and reporting. It can be used in analysing complex trading relationships, trading schemes, patterns and communications between banks, exchanges and other market participants. AI can also be employed to monitor internal conduct and communication to clients, comparing it to quantitative metrics such as supervisory input. As AI relies on computer-based modelling, scenario analysis and forecasting, it can also help banks in stress testing and risk management.
But what I find particularly interesting is this bit:
Another field for AI in financial regulations is to simplify the regulations themselves: there are a multitude of different jurisdictions, products, institutional differences and enforcement mechanisms and it is hoped that AI systems are better in collecting and categorizing them according to rules.
Similar points in an Economist article published a few months ago about Watson, IBM’s AI product:
The next area is to provide clarity about rules. They are sorted by jurisdictions, institutional divisions, products and so forth, and then further broken down between rules and guidance. Watson is getting better at categorising the various regulations and matching them with the appropriate enforcement mechanisms. Its conclusions are vetted, giving it an education that should improve its effectiveness in the future. Promontory’s experts are expected to help Watson learn. A dozen rules are now being assimilated weekly. Thousands are still to go but it is hoped the process will speed up as the system evolves. Ultimately, IBM hopes speeches by influential figures, court verdicts and other such sources will be automatically uploaded into Watson’s cloud-based brain. They can play a role in determining what regulations matter, and how they will be enforced.
Below is a useful chart showing all current Regtech areas and start-ups (you can also find it here):
While the industry has not explicitly said it this way (and probably never will), it seems to me that we’re on our way to AI-driven regulatory arbitrage. Once those systems are ready, AI will be able to navigate through the thousands of regulatory pages and extract the most effective ‘regulatory optimisation strategy’ within and across borders.
If all AI systems used by financial institutions reach the same conclusion, this could lead to a build-up of imbalances and systemic risks that could eventually trigger a crisis, following a process similar to that which contributed to the latest financial crisis: Basel rules facilitated the accumulation of imbalances in the credit market towards real estate lending.
It of course remains to be determined whether AI systems reach the same conclusions in the end. But this is likely to happen, for the following reasons: 1. banks whose systems are less effective will progressively attempt to catch up with the competition, leading to harmonisation in the design of those systems and 2. if AI solutions are provided by third-party firms, harmonisation will occur from the start.
A glimpse of hope remains in that the optimal regulatory arbitrage strategy may be different for financial institutions with different business models (mortgage banks vs. universal banks for instance). But let’s not hold our breath: even in this case, imbalances would still occur and universal banks still account for most of the world’s banking assets by far.
For now, explicit regulatory ‘optimisation’ does not seem to be included in the chart above (although the ‘Government/Legislation’ category could well evolve into a more arbitrage-oriented segment). But how long before it does?
As the Trump administration is considering scrapping parts of the enormous Dodd-Frank act, a number of media and economists look alarmed: Dodd-Frank made the American banking system safer, the argument goes, and getting rid of it would lead to another financial crisis.
While long-time readers of this blog know that Dodd-Frank, and the Basel 3 international accords it is based on, merely continue the mistakes of three decades of regulatory overreach that have brought about the largest financial crisis in decades, I thought it was necessary to clarify a couple of points regarding capital requirements.
In this week’s Economist, two articles seem to admit that, while the act indeed represented an unclear regulatory monster of thousands of pages that mostly penalised smaller financial institutions, it also made the system safer by reinforcing banks’ capitalisation.
In an editorial, the newspaper asserts that:
Onerous though it is, however, the act also achieved a lot. Measures to beef up banks’ equity funding have made America’s financial system more secure. The six largest bank-holding companies in America had equity funding of less than 8% in 2007; since 2010 that figure has stood at 12-14%.
In another article, it adds:
Thanks in part to Dodd-Frank, America’s banks are far safer than they were: the ratio of the six largest banks’ tier-1 capital (chiefly equity) to risk-weighted assets, the main gauge of their strength, was a threadbare 8-9% before the crisis; since 2010 it has been 12-14%.
But it is far from clear that Basel requirements are behind banks’ post-crisis thicker capital buffers. See Basel 3 minimum capital requirements below:
Minimum Tier-1 capital requirements are 7.875% (Tier 1 + capital conservation buffer). This is around 2008 level for large US banks. Hardly an improvement at first glance then.
However, let’s also add the recent SIFI capital surcharge, published by the FSB last November: only two institutions qualified for a 2.5% surcharge (only one of them US-based), but let’s add it this figure to our minimum above. We get to a SIFI minimum Tier 1 requirement of 10.375%. This is still an almost 2 to 4% gap with the 12% to 14% average referred to by The Economist above.
Therefore the only conclusion is that there are other parameters and considerations influencing the level of capital ratios upward. One of those parameters is indeed regulatory-related, but is discretionary at bank-level: it is bankers’ own view about the capital buffer they believe they need above the regulatory minimum in order to avoid breaching it in case of sudden large losses. This shows some of the perverse side-effects of strict minima, and I described some time ago that the ‘effective’ capital ratio was actually the differential between the level maintained by the bank and the regulatory minimum. And this ‘effective’ buffer tends to narrow rather than thicken as minima are raised.
The second is exogenous to bank’s decision making-process: the financial crisis has taught a number of investors not to get fooled by headline regulatory capital ratios. Consequently, investors now ask for higher levels of capital in order to compensate for the lack of clarity regarding the quality of capital*. Given that risk-weights (another regulatory construct) have a considerable influence on the level of capital ratios, investors also ask for extra capital buffers to compensate for the distortions they inevitably introduce in the headline figures.
Consequently, had minimal requirements stayed the same, investors would have been highly likely to demand extra protection against the uncertainty introduced by…..those same regulatory requirements.
In the end, the assumption that banks are much better capitalised and that regulation/Dodd-Frank is responsible for this is questionable.
*While Basel 3 and Dodd-Frank have indeed also touched upon the issue of capital quality, it remains unclear how a number of so-called hybrid, or ‘complementary Tier 1’, instruments will perform under stress and legal challenges.
PS: this blog post could have entered into a lot more details about the parameters driving the thickness of capital buffers, but it would then have to be split into 3 or 4 different posts. At least. So please read some of my other posts on the topic to get the bigger picture as this is a complex issue.
For real. After almost six months without writing anything, I finally got all the green lights I needed to resume both blogging and external contributions. I still won’t have the time for more than a single blog post per week or so, but my pipeline of potential posts has grown over the past few months and should keep me busy for the foreseeable future.
To be fully honest, I really hesitated to write this post. I simply didn’t know what to say. Times are now different from when I started writing back in 2013. There is less media focus on banking crises and unconventional monetary policies. Media’s attention has shifted towards topics such as Trump, Brexit and political correctness and, following years of never ending banking reforms, booming financial markets and declining unemployment, there now seems to be less urgency to understand what went wrong with our global financial system.
Yet there is still plenty of work to do: most people, journalists and academics still believe that a deregulated financial system is inherently unstable and caused the global financial crisis that started almost a decade ago. Much like economic myths such as ‘FDR’s New Deal and WW2 saved the economy from the Great Depression’ have been blindly followed by generations of academics, allowing the ‘banking is inherently unstable’ story to settle into mainstream econ textbooks would build the foundations of the next major financial crisis.
Nevertheless, it does look like the new Trump administration will bring banking and financial regulation back in the spotlight as there is talk of repealing some of the Dodd-Frank act measures. It will be interesting to follow the evolution of this idea, for the following reasons: 1. there has never been any real deregulation of the banking system on aggregate, 2. it will trigger some international regulatory competition as other jurisdictions, such as the EU, seem unlikely to leave their own banks at a competitive disadvantage, 3. we could find out how flexible the regulatory framework of a single country is within an internationally agreed agreement such as Basel 3 (which is the basis of the Dodd-Frank act), and 4. we’ll also potentially find out the extent of regulatory capture in the American economy: let’s see how eager large banks are to evolve in a less regulated environment.
It is possible that the Trump administration will only repeal the least damaging and distortive measures of Dodd-Frank. However, if they do surprisingly succeed in repealing major reforms such as Basel 3 capital and liquidity requirements, the implications for international agreements could be huge: Basel could effectively be dead in the water.
How likely is this scenario to happen? Hard to say. My fear remains that repealing parts of the Basel 3 agreements on a unilateral basis would further complexify and opacify the current financial system and bring about massive distortions through international regulatory arbitrage.
Anyway, it’s too early to speculate so I’ll keep monitoring.
Well, sort of. As some of you already know, I was in transition between two jobs this summer. I took some time off and travelled for several weeks. I drove close to 2,000 miles and saw some great mountainous, green, sunny, empty and desert landscapes as well as monuments, in Europe and America; I disconnected from the financial world and relaxed – which is something I really needed. Now I’m back and started my new job. I’m pretty excited but this also will have impacts on my other activities.
It is unclear what this is going to mean for this blog, as well as for my contributions to the Cato Institute’s Alt-m website. It’s going to take another couple of months before things get clearer. Hopefully I will be able to resume my Alt-m contributions.
What I believe is that, due to the nature of my new job, I will have less time to work on blog posts on weekdays. Therefore I am unlikely to publish more than a single blog post a week from now on; perhaps fewer. I have a few in the pipeline; so keep posted.
Oh and thank you again for your support over those past three years!
George Selgin’s latest monetary policy primer was a very good explanation of the money multiplier in fractional reserve banking systems. He also suggested that a number of factors may be affecting the current surprisingly low level of the multiplier; a fact that prompted a number of endogenous money theorists to (wrongly) assert that the multiplier was ‘dead’.
In this post, I wish to elaborate on the reasons behind the low multiplier. And those reasons are, in my view, related to banking mechanics and regulatory dynamics.
Let’s first start with a little bit of history to put things in perspective. Some time ago, and following one of my blog posts on the topic, Levi Russel from the Farmer Hayek blog – who is much better than I am at manipulating FRED data – kindly sent me the following chart representing the M2 multiplier (‘MM’) since 1920:
As you can see, the MM also experienced a huge fall during the Great Depression. It then took about forty years for the MM to progressively get back to its pre-Depression level.
Independently of regulatory frameworks, there is a simple underlying reason behind this long recovery time: banking mechanics. As corporations, banks are subject to operating constraints that limit the short run supply of credit. Banks employ a number of bankers, analysts, risk experts and so forth that are in limited numbers and already working full time to extend loans to creditworthy customers in adequacy with the bank’s risk appetite. The client onboarding process, the analysis of his risk, as well as the negotiations of legal agreements, aren’t instantaneous. The funding process itself isn’t either: despite what endogenous money experts assert, extending new loans still require looking for additional non- central bank funding before or shortly after putting the credit line in place.
At any point in time, it is likely that banks are close to the microeconomic equilibrium ideal of having marginal revenues equal to the marginal economic costs of employing staff and retaining adequate levels of capital and liquidity, and that its managers decided not to extend credit further on purpose: additional revenues were not attractive enough to justify the costs of acquiring them.
The implication of a fall in the MM is that liquidity (under the form of bank reserves/high-powered money) is now abundant in the system relative to the amount of bank money in circulation. Liquidity cost not being an issue anymore, banks nevertheless remain subject to operational and credit risk constraints, implying that they cannot put this liquidity to work rapidly.
Indeed, this situation is amplified during a crisis, as the number of creditworthy borrowers falls and banks lay off some of their employees to offset the fall in revenues and rising loan losses. Moreover, liquidity costs also rise and banks decide to hold on to higher liquidity buffers than they used to, mechanically lowering the MM. Consequently, there is no way the MM can rapidly rise. It takes time.
And this was the mistake made by a number of economists who wrongly predicted that hyperinflation would strike in the years following the implementation of quantitative easing policies. Credit cannot mechanistically and instantaneously grow. The financial system is a source of sticky constraints and rigidities. Of course we did see periods of above average MM growth (like just before the Depression or between 1980 and 1987*). But even if those particular growth rates were applied to today’s world, it would take more than twenty years for the MM to get back to its pre-crisis level.
Some could reply that banks don’t need extra resources to invest their liquidity into government bonds. While this is true some constraints remain in place: 1. the supply of government bonds is limited, and buying large quantities of them would become uneconomical for banks’ margin as bonds yield fall towards zero; 2. only a handful of governments have top credit ratings, and this rating fall as they issue more debt; 3. banks want to diversify their portfolio and certainly do not wish to only be exposed to sovereign risk.
The description above effectively applies to banking systems free of exogenous regulations. But regulatory dynamics can dramatically hinder the money creation process and hence the return of the MM to more normal levels.
Following the 2008/9 crisis, the Western world has been quick at altering regulatory requirements despite the weak economic recovery. In the decade following the crash, Basel 3 (implemented in the US under Dodd-Frank and in the EU under CRD4) built on previous versions of the Basel framework to progressively tighten operating restrictions – thereby reducing banks’ ability to generate marginal revenues – as well as capital, liquidity and funding requirements.
This regulatory package made it even more complex for bank to engage in lending. These are some of the steps that bankers now typically have to take in order to set up a new committed credit line:
- Client onboarding/Know-Your-Customer, which is getting increasingly tightened by authorities due to international sanctions, tax evasion and terrorism
- Credit analysis/risk assessment facility type/comparison with risk appetite and internal risk management guidance
- Estimate what the regulatory liquidity (LCR) and funding (NSFR) requirements are going to be for this specific credit facility.
- Estimate the cost of getting hold of the specific liquid assets and funding instruments (which both are in limited supply on the market and hence costly to acquire) that rules require
- Estimate the amount of regulatory capital (also in limited supply) required for such a facility
- Estimate total risk-adjusted revenues of the new credit facility (plus any other revenues from this customer), deduct total costs, and compare with required regulatory capital
- If return on capital too low vs. management policy, decide whether or not to extend credit based on relationship
- Negotiate loan agreement/covenants
Those steps require human resources in relationship management, risk management, legal and treasury. As the process has been lengthened and complexified by Basel 3 in the post-crisis years, it is unsurprising that banks, already facing declining revenues and costs-cutting (i.e. staff), haven’t been able to grow their balance sheet as rapidly as bank reserves were flowing into the system. Moreover, faced with harsher capital regulations and unending litigation costs in a world of low or negative interest rates, banks found it extremely hard to find remunerative lending opportunities. Consequently, many banks have now entirely exited a number of lending products whose marginal costs have been pushed up by regulation above their marginal revenues. They have deleveraged in order to be compliant with capitalisation rules rather than raise capital to avoid diluting shareholders already suffering from zero return (therefore at risk of exiting their investment altogether). I guess I don’t have to explain that a deleveraging banking system is antithetical with a rising MM.
Finally, I shall include monetary policy in the ‘regulatory dynamics’ category, and more particularly the decision by a number of central banks to pay interests on excess reserves. It is not the purpose of this post to focus on this rather strange monetary tool; George Selgin wrote plenty of excellent posts deconstructing its rationale.
A last note however. While we’ve mostly been describing the factors influencing the supply of credit, let’s not forget to factor in the other side of the equation: demand for credit. During or following a credit crisis, borrowers often attempt to repair their balance sheets by deleveraging, affecting the demand for new loans.
In the end, it looks unsurprising to see the money multiplier remaining so low and taking decades to recover following a rapid fall. As history shows, this is a recurring fact, dictated by the day to day operating rigidities of the business of banking, and with consequences for the bank lending channel of monetary policy. Our dear multiplier isn’t dead; it is just sleeping and merely unlikely to reach pre-crisis levels for another few decades.
*Such rapid growth rate in the 1980s is probably linked to banks trying to add more remunerative lending to their portfolio as rapidly as possible. This is because, as both nominal interest rates and inflation were shooting up, banks’ margins were becoming rapidly compressed due to legacy lending extended in earlier periods of lower nominal rates.
This post was re-published on Alt-M.
Following my latest post on the regulatory regime uncertainty caused by Brexit, evidence of the damages has started to emerge.
Unsurprisingly, and in line with the studies mentioned in my previous post, uncertainty is affecting both the demand and supply sides of credit.
On the demand side, the FT reports that
like other small British companies … longer term prospects have been altered by the EU referendum. Last month’s vote has dramatically increased uncertainty on issues ranging from regulatory standards to supply chains. […]
Other local companies have reported laying off staff, raising prices, or scaling back on investment plans, among a range of responses that also include seeking to take advantage of the weaker pound, in a survey compiled by Business West, a lobby group in the south-west of England.
This is evidently not conducive to borrowing and investments, and City AM reports that the number of M&A deals in Britain indeed significantly dropped in the first half of the year. Furthermore, the FT also reports that large British banks “expect demand for credit from businesses and households to fall as a result of post-Brexit economic uncertainty, according to a Bank of England review.”
The same article seems to show that, on the supply side, banks are for the time being only tightening commercial real estate lending given their pessimistic view of the sector (which also was a major contributor to UK banks’ losses during the financial crisis).
Finally, another FT article shows that
An index tracking sentiment in the European banking sector has reached an all-time low, even surpassing levels seen in 2012 when Mario Draghi promised the European Central Bank was prepared to do “whatever it takes” to stabilise the bloc and protect the euro.
This is likely to affect the supply of credit in medium-term across the whole of Europe as Brexit uncertainty exacerbates already-existing European banking issues. The shorter this lasts the better.
Sadly, this situation could take up to six years, according to the UK foreign minister…
The British voted in favour of leaving the European Union at the end of last week. Whether the UK effectively leaves the EU and what sort of arrangement emerges is yet to be determined. What is certain is that the business world now finds itself in the most uncertain of political environments: no one knows what kind of ruleset is actually going to be put in place and how long this state of limbo will last.
The whole situation is likely to prove damaging for the UK economy as businesses freeze hiring and investments until they have a better understanding about the rules they’re going to have to comply with. Brexit is in effect a typical case of what Robert Higgs named ‘regime uncertainty’.
Higgs described how regulatory regime uncertainty considerably hampered private investments in the US in the 1930s, which in turn affected economic recovery. This period saw Hoover and then FDR’s New Deal make considerable changes to the US legal and regulatory frameworks. According to Higgs:
given the unparalleled outpouring of business-threatening laws, regulations, and court decisions, the oft-stated hostility of President Roosevelt and his lieutenants toward investors as a class, and the character of the antibusiness zealots who composed the strategists and administrators of the New Deal from 1935 to 1941, the political climate could hardly have failed to discourage some investors from making fresh long-term commitments … there exists a great deal of direct evidence that investors did feel extraordinarily uncertain about the future of the property-rights regime between 1935 and 1941. Historians have recorded countless statements by contemporaries to that effect; and the poll data presented earlier confirm that in the years just before the war most business executives expected substantial attenuations of private property rights ranging up to “complete economic dictatorship.
Seen in light of modern expectations framework, Higgs’ theory does make sense: businesses are unlikely to engage into activities whose legal treatment is uncertain. In one of my first ever posts I wrote that, given the uncertainty inherent to a productive process that takes time, a stable ruleset and predictable property rights treatments were fundamental features of intertemporal coordination between savers/investors and borrowers/entrepreneurs.
Stable rules provide a clear guide to entrepreneurs: constraints are known in advance allowing them to anticipate future demand and plan accordingly with the understanding that their investments are protected as long as they remain within legal boundaries. The Rule of Law – what Hayek described as a ‘meta-legal’ framework that spontaneously and progressively emerged, and which is mostly incarnated today by the Common Law – represents the most effective instance of stable rules. But even the less stable Civil Law – which is mainly comprised of what Hayek called ‘legislation’ – can represent a relatively effective legal framework as long as rules aren’t changed on a regular basis. Surprisingly however, the academic litterature on regime uncertainty remains rather thin.
Coincidentally, a paper published earlier this year by Bordo, Duca and Koch (Economic Policy Uncertainty and the Credit Channel: Aggregate and Bank Level U.S. Evidence Over Severa Decades, also available on NBER here) adds extra empirical evidence to Higgs’ original findings. Basing their research on a recently published ‘economic policy uncertainty index’ (EPU thereafter), and controlling for other macroeconomic indicators, they look at how regime uncertainty affects bank lending in the US between 1961 and 2014. Overall, they find that
policy uncertainty significantly slows U.S. bank credit growth, consistent with it having an effect on broad loan supply and demand. We find that lagged changes in the EPU index are negatively and significantly linked to the growth rate of bank lending both at the aggregate and cross-sectional levels.
They also find that this effect is more pronounced for larger, less well-capitalised banks, as well as banks holding smaller amounts of cash reserves, and that the effect is likely amplified in Europe*:
The results have several important implications. First, statistical evidence suggests that economic policy uncertainty has affected bank lending in the U.S., which other studies have found to have important effects on economic activity and which we also find. This could have implications for Europe, where the Baker-Bloom-Davis (BBD) index of economic policy uncertainty rose more than in the U.S. during the post-crisis slump and the economies are more bank dependent. More recently, the EPU index in Europe has not recovered as quickly as in the U.S., where the subsequent recovery in bank-lending growth has been stronger as has been the overall recovery in GDP growth.
Given that London represents a substantial share of Europe’s financial activity and is the main Euro clearing centre (a situation that the ECB has fought for years), the implications for a post-Brexit Europe are clear. Domestically, the demand and supply of loans in the UK are likely to remain subdued as long as the legal framework that will apply to British banks and corporations in the future is unknown. The uncertainty is also going to hurt foreign banks, which have large operations in London thanks to the UK’s ‘passporting’ rights (which allow financial firms based in the UK to offer services throughout the EU under single market rules). Many of those institutions are unsure whether to move operations to other EU jurisdictions as nobody knows if the UK will be able to retain single market access and Euro clearing. This paralyses business-making in a period of already heightened regulatory uncertainty.
Legal uncertainty affecting the financial sector is of the worst kind given its repercussions on economic activity. It is therefore unsurprising that, following the Brexit vote, stockmarkets in the EU have fallen more than those in the UK. The announcement even triggered the worst fall in EU banks’ share price in history.
Brexit will have repercussions on lending and investments both in the UK and in the EU as long as this state of uncertainty lasts. And it can even end up being more damaging for other EU countries, already suffering from low economic growth and constantly-changing banking regulation. Of course, politicians seem unaware of this issue: some of the top ‘Leave’ campaign leaders mentioned triggering the article 50 of the Lisbon Treaty (which brings about the departure of a member of the EU) only in… 2020.
Given that it takes two years of negotiation following the trigger for a country to be formally out of the union, and that undoing EU laws while negotiating new trade deals can last many more years, it is clear that those politicians are at best – some would say unsurprisingly – completely ignorant of the damages they are making. It took Greenland, which withdrew from the pre-EU in 1985, three years to negotiate the terms of its exit with the union at a time when EU laws were not as invasive as they are now. Good luck to Europeans.
*They also question the timing of the implementation of new harsh banking regulations (i.e. Basel 3) which may have delayed the post-crisis economic recovery in their view (a point I have made in a number of posts over the years).
PS: Bordo, Duca and Koch also provide further evidence of the 1990s deregulation myth:
The term ‘subprime crisis’ is still widely used to describe the recent financial crisis. Yet it gives the impression that the crisis emanated from a very narrow asset class (i.e. subprime mortgage lending) that somehow managed to spread throughout the US economy and the wider world and knock down most Western economies. The narrative is that financial innovation and engineering fuelled this process by creating products like RMBS and CDOs.
On closer examination, this story cannot hold. Real estate markets boomed and fell simultaneously around the world despite no subprime lending going on in these other markets. Mortgage lenders in countries such as Spain, Ireland and the UK suffered or even collapsed when falling house prices forced them to provision huge amounts, damaging their balance sheet and ability to lend.
Even in the US, the crisis wasn’t triggered by subprime but by an unsustainable allocation of resources towards the entire real estate sector. New research adds further evidence to this view (Loan Origination and Defaults in the Mortgage Crisis: The Role of the Middle Class). In this paper, Adelino, Schoar and Severino demonstrate that “mortgage originations increased for borrowers across all income levels and FICO scores” and that “middle-income, high-income, and prime borrowers all sharply increased their share of delinquencies in the crisis.” This conclusion is in stark contrast with that of Mian and Sufi’s famous 2009 article (which they reformulated in their book House of Debt), which had fuelled the theory of a subprime origin to the crisis (I had already voiced doubts about their theory here).
More precisely, Adelina et al find that credit flowed towards all sorts of borrowers in the years preceding the crisis, and not just disproportionately towards subprime ones:
between 2002 and 2006 mortgage origination increased for borrowers across the whole income distribution, not just for low-income or subprime borrowers. In line with previous years, the majority of new mortgages by value were originated to middle-class and high-income segments of the population even at the peak of the boom. Similarly, the share of originations to subprime borrowers (those with a credit score below 660) relative to high credit score borrowers remained stable across the pre-crisis period. Although the pace of origination rose in low-income ZIP codes, this increase did not translate into significant changes in the overall distribution of credit, given that it started from a low base (borrowers in low-income and subprime ZIP codes obtain fewer and significantly smaller mortgages on average).
They also find that non-performing loans rose across the board, implying that losses were triggered by all sorts of real estate loans:
We show that the share of mortgage dollars in delinquency stemming from the lowest income groups decreased during the financial crisis. In contrast, middle- and high-income borrowers constituted a larger share of mortgage dollars in delinquency than in any prior year. The magnitudes are large: for the 2003 mortgage cohort, the top quintile of the income distribution constituted only 13% of mortgage dollars in delinquency three years later, whereas for the 2006 cohort, the top income quintile made up 23% of the delinquencies three years out. In contrast, over the same period, the contribution to delinquencies from the ZIP codes in the lowest 20% of the income distribution fell from 22% to only 11%.
We find a similar pattern when we look at credit scores: the share of mortgage defaults from borrowers with high credit scores increased during the crisis, whereas the share for subprime borrowers dropped.
Adelina et al therefore provide evidence that links the US real estate crisis with that of other countries: same roots, same consequences. The US did not experience a crisis because of a sudden and sharp increase in subprime borrowing. Subprime merely was a bystander; a symptom of deeper economic problems. Rather, the US experienced the same sort of crisis as its European counterparts: an overall debt-fuelled rise in real estate prices.
And this makes perfect sense: I keep emphasising the role played by Basel’s capital regulation in inflating the housing bubble. Low capital requirements on real estate loans provided bankers an incentive to maximise profitability within regulatory boundaries, directing the flow of credit towards housing, inflating the bubble. A purely subprime story with relatively low or no effect on other types of borrowers would not really fit this theory and would not match the experience of other countries.
Do not get me wrong though: subprime lending probably amplified the losses that some banks experienced, and did spread the crisis abroad to some extent. While the German real estate market remained roughly stable throughout the period, a number of German Landesbanks that had invested in tranches of structured products based on US subprime or near-subprime mortgages did suffer quite badly when the market price of those products radically fell.
Another very recent publication (The effect of bank shocks on firm-level and aggregate investment, by Amador and Nagengast) looks at what happened to lending and investment in the Portuguese economy following bank shocks during the 2005 to 2013 period. While I am not aware of similarly-structured studies of bank shocks in the US economy, this paper does seem to be in line with empirical results obtained by other researchers focusing on countries as diverse as Japan, Germany and emerging markets. They found that
credit supply shocks have a strong impact on firm-level investment in the Portuguese economy over and above aggregate demand conditions and firm-specific investment opportunities. In addition, we also consider how the effect of credit supply shocks on investment varies with the capital structure and size of firms. We find that firms with access to alternative financing sources are generally less vulnerable to the adverse effect of bank shocks on investment and partially manage to offset their shortfall of bank credit by increasing their financing from other sources. Larger firms also appear to be in a better position to cope with the unfavourable effects of bank shocks mainly since their banks do not curtail their credit supply as much as for small firms.
Those results look unsurprising to me and surely amplified by Basel rules that stipulate that small firms require proportionally more capital than large ones (a requirement that is hardly justified).
But combined with the empirical evidence provided above by Adelina et al, they allow me to reiterate my doubts regarding the claim that NGDP targeting would have merely led to a mild recession. This is a view of the crisis that some market monetarists accept, and which was summarised by Beckworth and Ponnuru in an NYT column earlier this year:
In retrospect, economists have concluded that a recession began in December 2007. But this recession started very mildly. Through early 2008, even as investors kept pulling money out of the shadow banks, key economic indicators such as inflation and nominal spending — the total amount of dollars being spent throughout the economy — barely budged. It looked as if the economy would be relatively unscathed, as many forecasters were saying at the time. The problem was manageable: According to Gary Gorton, an economist at Yale, roughly 6 percent of banking assets were tied to subprime mortgages in 2007.
I wrote elsewhere why I believe this view is inaccurate. But this new evidence provided by Adelina et al adds strength to my previous arguments by showing that the crisis was a full-scale real estate collapse rather than a mere and ‘manageable’ subprime-focused crisis. It should also make us think twice about the ability of NGDP targeting to cope with a situation during which banks’ balance sheets are highly damaged, leading to reduced lending and aggregate private investments throughout the economy.
That said, I do view NGDPT as a much better alternative to our current monetary arrangement. While it could potentially have alleviated the worst symptoms of the crash, I believe it is quite a stretch to think it could have led to a merely ‘mild’ recession. Please bear in mind however, that my reasoning only applies within the current institutional constraints on the implementation of monetary policy.
Were those constraints lifted, NGDP targeting could be more effective in stimulating the economy post-crash. Whether this is desirable is another issue altogether, and I tend to adhere to Salter and Cachanosky’s view that the composition of NGDP also matters*. After all, NGDP growth was roughly stable for the decade preceding the crisis, yet hid some unsustainable allocation of resources. Consequently, it seems to me that distortionary regulatory frameworks limit the effectiveness of a stable NGDP path. But would we even need NGDP targeting in a free market?
*As a side note, an interesting paper published last year seems to provide some evidence of the distortionary effect on relative prices of the usual monetary injection channel of monetary policy (i.e. Cantillon effect). This is only a lab experiment, but its conclusions are clear:
Although the theoretical model predicts, in line with mainstream economics, that the process of monetary injection is irrelevant and neutral, the experiment shows that credit expansion exerts a significant distortionary effect on resource allocation. Credit expansion also has a redistributive effect across subjects in favor of those who have a high consumption preference for the good whose production is stimulated by credit. The allocative effect of credit expansion comes from the fact that the increase in money is injected into the credit market, whereas lump-sum transfers affect all sectors evenly.
This finding is reminiscent of the insights of Cantillon (1755), who emphasised that an increase in money primarily affects relative prices rather than all prices to the same extent because money enters the economy at a certain point. This suggests that the process of monetary injection and its economic consequences should be addressed in implementing specific monetary policy measures or, more importantly, in designing the monetary system as a whole.
PS: This is my first blog post in a while. I am currently transitioning between jobs, and am pretty busy as a result.
Lately, there have been a lot of discussions in the media and in the academic sphere surrounding banks’ net interest margin in the low (or negative) interest rate environment. I have explained before how lowering interest rates below a certain threshold led to ‘margin compression’ (see here), which in turned depressed banks’ profitability and hence their internal capital generation, solidity and ability to lend.
The net interest margin (NIM thereafter) is roughly the difference between the average interest rate earned on assets and the average interest rate paid on funding, and is usually defined as
Net interest income / average earning assets, with NII being the difference between interest income (from loans and securities mostly) and interest expense (on deposits and other types of debt/funding instruments)
We now see conflicting articles and research pieces on the effects of low rates on banks’ NIM (see two of the most recent ones by the St Louis Fed here and other Fed researchers here). But, to my knowledge, most, if not all of those pieces make the same fundamental mistake: they do not look at risk-adjusted NIMs.
‘Risk adjustment’ is a critical concept but sadly often overlooked in the literature. I once defined the interest rate on a loan as the following:
LR = RFR + IP + CRP – C,
where LR is the loan rate, RFR is the applicable, same maturity, risk-free rate, IP the expected inflation premium, CRP the credit risk premium that applies to that particular customer and C the protection provided by the collateral (which can be zero).
As I explained elsewhere, margin compression occurs when the risk-free rate declines so much that interest rates banks pay on their funding reaches the zero lower bound while their interest income continues to decline (which led me to hypothesise that the zero-lower bound was actually a ‘2%-lower bound’ in the case of the banking/credit channel of monetary policy). This however assumes no fundamental change in the rest of the economy’s credit (or default) risk.
Indeed, in bad economic times, the CRP usually increases for most borrowers, partially offsetting the effects of the decline in the risk-free rate on new lending. Moreover, bankers can easily boost their NIM by lending relatively more to higher-risk customers or investing in higher-risk projects, even in good economic times. Consequently, it looks like the headline NIM isn’t suffering or declining that much. It can sometimes even improve, in particular when economic conditions are benign. For instance, emerging market banks often boast high NIMs, but also high default rates (and high ‘losses given default’). In such cases, margin compression seems not to be occurring. But this is just an accounting illusion.
See the example in the chart below, which represents the hypothetical evolution of the different components of a given unsecured loan rate throughout a long recession:
Once you adjust the NIM for the loan book’s underlying risk, the story is different. Banks’ interest income can rise but the risk of default on new lending, as well as that of their legacy loan portfolio, also rises. Because the CRP is often fixed at inception, legacy lending now underpays relative to its risk profile, potentially implying economic losses down the line.
Most studies don’t factor this phenomenon in. They look at unadjusted NIMs, which in many cases do not provide any useful information.
A very good and quite recent paper on banking mechanics by Claudio Borio and his team (The influence of monetary policy on bank profitability), which looks at the impact of the shape of the yield curve on margin compression and banks’ profitability, does understand that accounting plays a significant role:
The second form [of dynamic effects in the transmission of the level of interest rates to net interest income], which is more relevant, relates to accounting practices. Any interest margin on new loans also covers expected losses. But provisions in the period we examine follow the “incurred loss model”, so that, in contrast to interest rates, they are not forward-looking. As a result, extending new loans raises profitability temporarily, since losses normally materialise only a few years later at which point loans also become non-performing, eroding the interest margin. This also means that if lower market rates induce more lending, they will temporarily boost net interest margins. The strength of this effect will depend on background economic conditions. For instance, it is likely to be weak precisely when interest rates are unusually low and the demand for loans anaemic.
However, they stop short of providing a solution, or a correction, to this effect. To be fair, risk-adjusted NIMs are not directly observable and very difficult to estimate, given that disclosures about banks’ loan portfolio are very limited and that only some of their customers (i.e. large corporates) have bonds or credit default swaps traded on the secondary market. Therefore, some analysts use the following ex-post adjusted NIM ratio:
(Net interest income – loan impairment charges) / average earning assets
Default risk, expressed in the income statement by loan impairment charges (LICs – also called loan-loss provisions), is directly deducted from net interest income, making the NIM easier to compare across banks or countries. But even this version can be highly inaccurate, as LICs are backward-looking and depend on each bank’s accounting policies. In the short-run, some banks tend to over-provision, others to under-provision.
You’ve reached the end of this post perhaps wondering whether I had a solution to this problem. Unfortunately no, I don’t. But I believed that a clarification was in order. In finance, or economics in general, any decision involves risk-taking, and studies that do not take risk into account must be taken with a pinch of salt.
PS: The inflation premium is stripped out of the risk-free rate in this post, but in practice benchmark market rates such as Treasuries already factor in inflation expectations.
This post was re-published on Alt-M.
The shadow banking literature has vastly and rapidly expanded since the financial crisis, and has produced some interesting pieces, as well as some exaggerated claims, in my view. While I am not writing today to address those claims, I still wish to question a closely linked concept that has simultaneously sprung up in the literature and in particular in the post-Keynesian one: shadow money.
One of the most elaborated and comprehensive academic research papers on this particular topic is the recently published Gabor’s and Vestergaard’s Towards a theory of shadow money. It’s an interesting and recommended piece. But while I agree with some of their writings, I have to find myself in disagreement with a number of their points and examples* and in particular their central claim: that repurchase agreements (‘repos’ thereafter) are shadow money; that is, a type of monetary instrument used within the shadow banking system.
For some readers that might not know how a repo works, below is a concise definition provided by the IMF:
Repo agreements are contracts in which one party agrees to sell securities to another party and buy them back at a specified date and repurchase price.48 The transaction is effectively a collateralized loan with the difference between the repurchase and sale price representing interest. The borrower typically posts excess collateral (the “haircut”). Dealers use repos to borrow from MMFs and other cash lenders to finance their own securities holdings and to make loans to hedge funds and other clients seeking to leverage their investments. Lenders typically rehypothecate repo collateral, that is, they reuse it in other repo transactions with cash borrowers.
Given repos’ (and their asset counterpart: reverse repos) properties, my view is that repos aren’t shadow money but a shadow funding instrument. While it might not sound such a big issue, I believe the distinction is important from an analytical perspective as well as to avoid confusion. Let me elaborate.
Gabor and Vestergaard define shadow money as “repo liabilities, promises backed by tradable collateral.” According to them, shadow money has four key characteristics:
a) In modern money hierarchies, repo claims are nearest to settlement money, stronger in their ‘moneyness’ than ABCPs or MMF shares.
b) Banks issue shadow money. The incentives to issue repos are incentives to economize on bank deposits and bank reserves.
c) Shadow money, like bank money, relies on sovereign structures of authority and creditworthiness. The state offers a tradable claim that constitutes the base asset supporting the issuance of shadow claims.
d) Repos create (and destroy) liquidity at lower levels in the hierarchy of credit claims.
They offer this chart of ‘modern’ money hierarchy:
I have to object to repos being classified as ‘money’.
Money, as typically defined by economists, has three characteristics: it is a medium of exchange, a unit of account and a store of value. High-powered money (the ‘outside money’ of the financial system) currently fits this definition, as a final settlement medium.
The ‘moneyness’ concept, a term now popularised by JP Koning’s excellent blog, asserts that various types of assets have various degrees of money-like properties. In this quite old but classic post, JP argues that anything from beers and cattle to deposits benefits from some degree of moneyness. In another old post, Cullen Roche provided the following good ‘money spectrum’ chart (although I’d disagree with his outside money/deposit ranking):
Therefore, most goods and assets have some monetary properties: some can be used as media of exchange or store of value. All represent a claim of some sort on money proper. As a general (but inaccurate) rule, the more their price in terms of outside money fluctuate, and hence their conversion risk raises, the further away they are on the moneyness scale. But conversion (almost) on demand also implies that, in order to have some money characteristics, a good or asset needs to be tradable.
Now let’s get back to repos as shadow money.
Repos are a liability issued by the debtor in exchange for high-powered money, of which reverse repos are the asset counterpart held by the creditor (and hence the claim on the high-powered money originally transferred, plus interest). The debtor also transfers an extra asset (i.e. the collateral) to the creditor for security purposes at a pre-agreed haircut depending on its credit quality and market risk sensitivity.
We get here to the main point of this post: repos have little money-like property due to their non-tradability and lack of on-demand convertibility.
Indeed, a repo liability is of course non-tradable, in the same way that any debt that one owes cannot be traded for another type of liability. It can only be refinanced and/or extinguished. A reverse repo (or repo claim) however, could potentially have tradable properties, allowing a creditor to exchange his claim almost instantaneously on the market. Problem is: this does not happen. Unlike bonds or other assets, and due to the very specific features of such private agreements, there is no secondary market for repo claims. Once a repo has been agreed upon, the contract is fixed between the two parties until maturity (or default). Consequently, repo claims can effectively be assumed to have no liquidity.
Seen this way, it is hard to classify repos as ‘money’, and they certainly do not deserve their third place in the moneyness hierarchy above. So what are repos? As I previously said, they are a funding instrument. Given that the shadow banking system makes use of repos on a large scale, we can potentially call them a ‘short-term secured shadow funding instrument’. And please note that repo issuance isn’t limited to banks and broker-dealers; other institutions also use them.
You’ll be tempted to reply: “what about deposits? They have no secondary market and are not tradable either.” This isn’t strictly accurate. While they are both promises to pay a certain amount of money proper at a certain date, there is a very specific difference between deposit liabilities (‘on demand’ ones especially) and repo liabilities. Banks themselves are deposits’ secondary market: deposits can be ‘traded’ within the bank’s own balance sheet and swapped for cash on demand. And when dealing with a counterparty that does not hold an account with the same bank, banks take over the responsibility of transferring the underlying funds (i.e. high-powered money).
If repos aren’t ‘money’, what else could be considered ‘shadow money’? Well, assets provided as collateral do have liquidity, tradability, and therefore some ‘moneyness’. Those assets can sometimes be used in further transactions. This is why I am wondering whether or not there isn’t some confusion with ‘shadow money’ proponents’ terminology. While their writings clearly emphasise the ‘shadow money’ nature of repos themselves (and Poszar seems to be using the same definition here), many other academic authors have instead referred to the most commonly-used types of repo collateral (high quality and highly liquid sovereign and corporate bonds) as ‘shadow money’ (which indeed makes more sense to me, although I do not fully adhere to this concept either).
There are plenty of things worth discussing regarding this theory of shadow money and the use of repos in general, but the money-like properties of repurchase agreements isn’t one of them. Let’s focus on their funding properties instead.
*I also believe that their shadow money expansion theory is subject to the same critique as other endogenous outside money theories, such as MMT’s.
PS: the fact that repos are backed by marketable collateral does not confer any specific monetary property to repo claims. Marketable collateral is used in many other types of lending transactions, in particular in private banking-type lending. Also, repos and any other collateralised lending are expected to be repaid at par, independently of the valuation fluctuations of their underlying collateral.
PPS: Baker and Murphy build on Gabor’s and Vestergaard’s piece and just published a blog post that argues for a new ‘investment state’, in a typical post-Keynesian interventionist fashion.
This post was re-published on Alt-M.