Not Edgar Too! Do High Frequency Traders Have an Advantage?

Bloomberg reports that a study highlights another way that high frequency traders appear to be taking advantage of slower market players.  The SEC's EDGAR system receives companies' required  filings electronically.  There are some participants that pay to receive this service directly while most can access it for free online.

The study indicates that the documents are received between 0 seconds and up to one minute earlier by those who pay compared to when the documents are made available online to all others, 10 seconds earlier on average. The study also shows that in cases where the filing availability was made earlier to paying market participants, abnormal volume and price moves began on average 30 seconds before availability to the general public.  The study does not tie the early availability to these moves, stating that the cause is unknown.

While there are many reasons that some are concerned about high frequency traders making money at the expense of slower moving investors, this has not been heard of before by us.  While the article states that this is most likely unintentional, as high frequency traders did not exist when the system was initiated in the 1990s, it does seem to highlight another way that certain market players keep ahead of the regulators and the rest of the market.  Ironically the system replaced a much longer availability time discrepancy when reports were not available electronically at all.  The SEC has been reviewing the situation at least since June.

We will need to await the SEC's review to assess market impacts and the potential for a new set of "market rigging" lawsuits.

Estimate of Regulator Fines on Banks for FX Fix Misconduct - $41 Billion

Bloomberg reported that Citibank analysts' estimate of fines relating to the FX fix could total over $40 billion between US, UK and European regulators, spread among money center banks.  The analysis excludes fine reductions or waivers for those banks cooperating with investigations.  As many banks are cooperating (some required to do so by their agreements with regulators in the LIBOR scandal) this might substantially reduce the actual fines.

As most banks have been reserving for these investigations, which started in the middle of last year, there may not be a big hit to the earnings of the banks as a whole from the eventual fines.  As to reputational hits, the fact that so many banks will be included may, in effect,  protect all of them.

Number26: the future of banking? Or just banking the way it ought to be?

From TechCrunch is a piece about the new European online bank Number26. I had no idea how difficult it is to open a bank account in Europe. Americans complain about our banking system but wow, needing to go to the post office to mail copies of your passport to prove your identity, that's something else.  I like the sound of Number26, which is currently in private Beta testing. Those readers in Europe, you should apply now. 

Outline of FX Fix Reforms is Clearer

The recommendations of the Financial Stability Board last week regarding changes to the WM Reuters fix will be presented at the G20 meeting in November.  These include extending the fix window (they support a move from from 1 minute to 5, but want the WM Reuters company to set the period), making prices transparent and appropriate for the risk borne (meaning that banks should be paid for fix trades, unlike past practice) and codes of conduct and internal guidelines should be more explicit.

While fully agreeing with all of the above, we see the two remaining major recommendation as problematic.  First is the recommendation that "banks establish ... separate processes for handling such orders".  Handling fix trades separately from other fx trades will be costly, probably causing some smaller players to eliminate their participation in the fix.  An article from FX Week (subscription or free trial) refers to banks considering the possibility of creating sealed trading rooms, away from other fx traders and order flow.   While it is unclear how seriously this is being looked at, such a possibility appears a bit absurd to us, as enforcing more explicit codes of conduct and internal guidelines as already suggested, should improve the outcome without the costs or need for quarantined traders.

The other problematic recommendation of the FSB is "the development of industry-led initiatives to create independent netting and execution facilities for transacting fix orders". While a longer term recommendation, this continues on the path of much higher cost and uncertain outcome.  Just to mention one issue, commonsense dictates that the largest incentive for manipulating rates is when there is a large discrepancy between buy and sell orders for a currency pair.  At such times, the only way to clear these separate trades is to trade with the rest of the market, the same traders that this recommendation is trying to avoid.

All in all, we approve of the recommendations as promoting that which Financial PESTs stands for - ethics, simplicity and transparency, albeit with the two exceptions discussed which require additional scrutiny before any implementation.

Peer-to-peer enters its growth phase

Peer-to-peer (P2P) lending is the latest business sector that has captured the attention of both the Internet economy (tech startups, disruption, transformation) and the old economy (Wall Street, flow of capital).

SoFi, a P2P student loan lender, has recently closed two securitizations that were heavily oversubscribed.

Lending Club, one of the pioneers of P2P lending, has filed to go public.

At the recent ABS East Conference dedicated to securitization held by IMN, a separate sub-conference focusing specifically on P2P was standing room only. According to attendees, a majority of the booths at the ABS conference were P2P businesses looking for capital.  Mind you, all this interest on P2P securitizations is based off of only a handful of rated deals.

ABS East was not the only conference to address P2P, as the Lend Academy provides a list for those interested.

Everyone from Nasdaq to institutional investors are voicing their belief and support for the P2P model. Charles Moldow, a partner of Foundation Capital and an investor in a number of P2P business has some interesting takes in Tech Crunch on the P2P sector and how he sees its growth. Another interesting aspect of P2P businesses is the attraction that institutional investors have to the business model and how they have provided both debt and equity capital to P2P originators.

Banks, as everyone knows, continues to retreat from certain areas of traditional lending, shadow banking has stepped in providing capital. The application of the P2P model to all areas of finance has only started. If the marriage between securitization and P2P is successful, then this will become a fast growing segment of the economy.

Bitcoin crashes already?

Yesterday's TechCruch post points out that Bitcoin is trading around the $300 mark down from its peak of $1,150 last year, with Paul Krugman, Dealbook and others questioning whether this is the crash. If this is indeed the crash then it's come awfully fast. In today's world where news headlines finish their cycle in 24 hours, perhaps it's only fitting that speculation and crashes run through ever shorter lives as well. 

How to Build DISASTROUSLY WRONG Financial Models

Here’s the secret:  begin with the wrong goal.

(Adapted from How to Build Disastrous Financial Models, a Quant Perspectives column published by the Global Association of Risk Professionals)

Perhaps the greatest weakness we quantitative financial people have is that we assume at the outset of our careers that all colleagues and competitors share the philosophy that the goal of model development is to seek truth.  That is, imagine the current model task is to estimate the value of a loan or derivative trade, or the risk of a portfolio, or the proper credit rating of a bond, or the likelihood of repayment of a residential mortgage.  Clearly, we assume, everybody would prefer that the model have good accuracy (i.e., truth) in estimating value, or risk, or credit rating, or repayment likelihood.

Unfortunately, real life is different.  Many, though not all, actors in the financial world – business heads, traders, rating analysts, executives, regulators, consultants, auditors, politicians – desire models that describe and promote their reality.  As an example, the head of a trading desk wants models for derivative pricing that permit her group to win an adequate number of trades in competition with other firms.  (The direct experience of a friend of mine is that the tranche correlation desk of a first-tier investment bank rejected the quant team’s improved pricing model because it made the desk lose trades!)  In this case, rather than accuracy, the “reality” of the trading desk is that a good model will help win trades.

Another example is the difficulty of the CEBS (Committee of European Banking Supervisors) and EBA (European Banking Authority) in implementing stress tests for European banks beginning in 2009.  Stress tests are models.  For the CEBS and then the EBA, the “reality” of the stress test model is that it must be credible to the public and build confidence that the banks are adequately capitalized.  (See Kevin Dowd’s penetrating and entertaining “Math Gone Mad,” CATO Institute 754, 1-64, September 3, 2014.)  Needless to say, the goals of credibility and confidence are not synonymous with truth and accuracy.

Yet another, albeit indirect, example of a manipulated model is the U.S. Consumer Financial Protection Bureau (CFPB) determination that bank lenders enjoy a presumption of prudent mortgage lending practices under “Ability-to-Repay and Qualified Mortgage Standards.”  This “QM” standard specifically does not require the lender to impose or consider the loan-to-value (LTV) ratio of the mortgage loan.  Yet, if the goal of mandated underwriting standards is to reduce loan defaults, which harm both lender and borrower, then omission of LTV consideration from the “model” for a qualified mortgage is a huge oversight.  (See, for example, “Housing Industry Awaits Down-Payment Rule for Mortgages,” Bloomberg News, January 18, 2013.)  Unfortunately, the “reality” for the CFPB and self-appointed advocates is wide access to mortgage loans rather than low default risk of the loans.

There are numerous further examples of both high and low public notoriety in which practitioners create or adjust models in “helpful” directions only.  Lehman Brothers in 2007-8 (see page 180 of the Examiner’s Report) and J.P. Morgan in 2012, for example, tweaked their internal models to reduce apparent risk.

The focus on reaching desired end results rather than true and accurate results is certainly a misuse of financial models, but there’s a nuance to consider.  To judge truth and accuracy, one must inspect the model results and determine somehow whether the results “seem right.”  It could well be that the loan underwriter who watches competing lenders make loans that he had rejected will legitimately question the accuracy of his own bank’s model.  But how does one distinguish legitimate questioning of the model result from abusive adjustment of the model?

There is no simple answer other than to rely on the expert judgment of the quantitative model developer and for all analysts, users, and management to adhere to a principle of good faith.  This good-faith standard is the commitment to truth and accuracy.  Senior executives of the institution must understand that models are, by nature, malleable given their numerous judgments and assumptions.  With this understanding, the executives must then set, proclaim, and maintain a culture of good-faith, unbiased model construction and use.

The best uses of quantitative models are:  (i) the learning, intuition, and judgment one develops while building the model and (ii) the testing for completeness and quality of the firm’s data that exercising the model provides.  By virtue of assumptions and insufficient information, many financial models are less useful as generators of precise numerical results (e.g., for bank capital, loan default probability, et cetera).  When it’s imperative to have such numerical model results, then the principle of good-faith model construction is critical.