In the three decades since David Shaw first tapped on his former Columbia University computer science professor for advice on his Wall Street career, the world of quantitative hedge funds has seen numerous ups and downs but has always trended upwards.
From the time that quantitative hedge fund D.E. Shaw received its first US$30 million seed capital, through the collapse of Long Term Capital Management (insufficient data gathering), the industry, despite its fits and starts has always tended towards growth.
All that changed in 2018.
To be sure, 2018 started off on a positive note for quantitative hedge funds. As the fastest growing segment in the investment industry, computer-driven hedge funds were on track to manage US$1 trillion in assets for the first time in their history.
But a quarter does not a year make and by the end of 2018, quantitative hedge funds were licking their wounds, battered from two bouts of extreme turbulence in financial markets.
During that time, investors have lost patience with a world they do not completely understand and in systems which they were promised would deliver absolute returns regardless of market conditions.
Questioning the Quants
And it’s not just investors who are beginning to question the quantitative quotient. Anthony Lawler, speaking to the Finacial Times admits,
“When a year like last year happens, it certainly drives internal questions.”
But 2018 was no ordinary year. For the first time in three decades, since quantitative hedge funds first surfaced, both equity and bond markets hit the skids.
And other quant strategies — like those that rely on cheaper stocks outperforming and rising assets continuing their trajectory, suffered as well.
But it’s not just flaky markets throwing systematic models out of whack, what is more likely is a case of overcrowding — too many algorithms are trained to exploit increasingly fewer market anomalies.
Quantitative hedge funds thrive on the ability to process large volumes of data to eke out tiny advantages or inefficiencies, such as the mis-pricing of securities, for profit.
Highly sophisticated algorithms hoover up market data and then employ high frequency programs to execute millions of trades making small amounts of profit on each trade to deliver profits.
Although margins are thin, the sheer volumes allow substantial profit generation, or so the theory goes.
Anything You Can Do, I Can Do Better, I Can Do Anything Better Than You
But the rise of copy- — the ability of other shops to mimic or piggyback off the larger funds’ strategies as well as the rise of copy bots, programs that detect when other strategies have detected inefficiencies and to copy them, have eroded many of the early gains held by quants.
Essentially, even when anomalies are found, these anomalies are now more short-lived and the ability to profit from them even more challenging than ever before.
And it’s shown on the sector’s results.
As a whole, quantitative hedge funds have fallen on average by 3.5% — the last time they were hit that hard was the financial crisis, where they lost a an average of 3.9% for clients.
Over the decade, quant funds produced an average annual returns of just over 2%, 80 basis points below the hedge fund industry average and well below the 9.06% delivered by the S&P500.
A Question of Faith
More alarmingly, investors may be realizing that all is not well in the state of quantitative hedge funds — with funds suffering net outflows for the first time since 2009 — and investors pulling some US$8 billion from the industry.
But it wasn’t supposed to be this way.
Over the last decade, as investors abandoned stockpickers and other fund strategies, faith in computer models soared and money flowed into quant funds.
Between 2009 and 2017, assets in quant funds more than doubled to US$962 billion, with investors betting that improved technology would give algorithms a further edge.
But two strong waves of volatility in February and October last year swept many quant funds off their feet causing them to shed 2.9% and 2.3% respectively.
One of the main selling points of quant funds is their ability to navigate choppy waters with a steady, even keel. But choppy markets are likely to be increasingly de rigeur as central banks reign in stimulus and geopolitical tensions continue to feed volatility.
Even the leading light of the quant fund industry, AQR, has not been spared the onslaught.
With over US$200 billion of assets under management, several of AQR’s most popular funds suffered big hits in 2018, including its US$7 billion Managed Futures Fund which slipped 8.9% , its US$3 billion Style Premia Alternative Fund which dropped 11.9% and its US$2 billion Long-Short Equity Fund which fell 16.3%.
And the body blows that AQR has suffered haven’t just affected the firm, which is shedding jobs, it’s also undermining confidence at other shops as well. According to one quant,
“If they (AQR) can’t get it right, what are our chances?”
“You look at the people in your industry and at least if there are some standouts, you think you could work (towards) that. But when even the best are struggling, you start to question if you’re even in the right business anymore.”
But not all quants are throwing in the towel. Hal Reynolds, a three-decade veteran of quant funds at US$25.5 billion Los Angeles Capital says,
“Each year is a learning opportunity.”
“Quant managers are always learning. If you believe that rules-based strategies that were fit for the past are fit for the future, you are destined for failure.”
But perhaps it’s not the strategies that are letting quants down — it’s the assets.
In All The Right Assets, I’m Feeling So Good
Given the size and transparency of information available in the financial markets, it has become increasingly difficult for computerized models to identify and preserve alpha as compared to the early days when quant strategies were not as prevalent nor as readily available.
In economic theory, financial markets are moving ever closer to idealized “perfectly competitive” markets, where any advantage is soon competed away thanks to near-perfect information.
Not so in the markets.
For starters, even something as simple as the price of a is not a forgone conclusion. Even the price of bellwether such as and depend on the exchange they are listed on, volumes, volatility and liquidity, all factors which even the simplest quantitative models can cater for.
Next, because markets never , a large portion of is automated — not to be confused with fake volume-generating bots — but automated by firms or individual traders.
And because such automated programs have a tendency to follow certain defined and predictable patterns, it’s not difficult to model or map price behavior within relatively small tolerances.
Finally and perhaps the biggest factor why quants should consider looking at markets is that the size of the market is so small now that nobody else is looking at it.
Even with the recent run-up in prices, the total market cap of all hardly 3% of the daily volume of in financial derivatives.
For many quants, the space is just simply not worth their time. Granted, the volumes (for now) are low, but the margins are extremely high. Even the most pedestrian quant strategies can deliver returns upwards of 30% annually, albeit on quantums far smaller than in financial markets.
There is a Chinese saying which perfectly encapsulates this idea,
“ 麻雀虽小，五脏俱全.”
which literally translated means,
“The sparrow may be small, but all its vital organs are there.”
Small is sometimes beautiful.
So while quant funds continue to suffer drawdowns and redemptions, perhaps it’s time not to re-examine asset allocations, but the very assets that these allocations are going towards.
Published at Mon, 08 Apr 2019 15:13:13 +0000
