What Investors Should Know About Fintech, High-Frequency Trading and Flash Crashes

#Trump, #Fintech and the #FiduciaryRule

By Irene Aldridge

President Trump has announced sweeping deregulation and the financial services industry rejoiced.  While I am personally a strong proponent of free, deregulated markets, one of the proposed deregulation measures may simply be outdated, even before the underlying regulatory measure is still to be enacted.

Of course, I am talking about the Department of Labor’s (DOL) Fiduciary Rule.  The rule, scheduled to go into effect beginning April 10, 2017, will require all investment professionals who work with individuals’ retirements accounts to legally uphold the retiree’s interests ahead of their own.  It may come as a surprise to some readers, but at present, financial advisors large and small can wheel and deal around retirements accounts, those held in their care by individuals, and those housed in large pension funds, such as State Retirement Plans. The rule specifically mandates transparent pricing of individual securities, commissions, etc., as opposed to common opaque bundling of fees that pension funds are presented with.

This is not a joke and a lot of money is at stake.  As a senior top-tier bank employee confided to me recently, if that bank were to introduce transparent pricing for its pension fund clients, their average fees for investments would fall from current 0.41% per annum to just about 0.05% per annum.  With some $300 billion of pension money under that bank’s tutelage, this will mean that the involved pension funds’ fees to the bank will go down from the current $1.2 billion (U.S. dollars) per year to $150 million per year – saving pension funds and their retiree clients a billion U.S. dollars per year!  Of course, on the flip side, the said bank’s revenues are bound to shrink by the same amount.  Oh horror, how will they afford their Hamptons summer?

The issue is not limited to banks.  Across the financial services industry, pension funds are considered to be “the holy grail” – a major source of fees for the investment managers.  Take the hedge fund Bridgewater, for example.  Last year, it generated its pension fund clients $4.9 billion dollars in gains, but at what cost?  $2 billion dollars of management fees alone plus performance fees of nearly $1 billion, resulting in respective pension funds’ net gain of just $2 billion per year or just 1% on over $200 billion of assets under Bridgewater’s management (  In comparison, a passive buy-and-hold investment in to the S&P500 ETF returned 17% over 2016, with negligible fees.  As the financial mathematics Professor Marco Avellaneda of NYU points out, today the fee structure in financial services is such that “to be as rich as your client, all you need to do is stick around for 10 years” – the huge fees compound that quickly in good times and bad.

However, is the legislation necessary to curb the excess?  How about summoning innovation instead?  Indeed, the proposed legislation may be largely outdated.  Ample Fintech resources exist to help pension funds make lower-cost decisions with the same or much better outcomes, as they pertain to allocations such as Bridgewater.  For instance, new data sources from companies like AbleMarkets enable pension funds, endowments and other investors to avoid adverse impact from, say, high-frequency trading, flash crashes and runaway algorithms, while substantially increasing returns in a structural manner.  In fact, that’s what many successful hedge funds are doing: acquiring a range of data sources that incrementally improve their performance by a few basis points at a time (each basis point = 0.01%).  Why bother regulating when the adoption of the same model by pension funds is only a click away?

Irene Aldridge is a co-author of “Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading and Flash Crashes” (Wiley, 2017).  She is President and Head of Research at AbleMarkets, a big data for capital markets company.  She can be seen at the upcoming Big Data Finance Conference 2017, on May 19, 2017, at the NYU Center for Data Science.  For more information, please visit


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