Alpha (to use the language of Wall Street) is becoming more difficult to find in the markets.
It’s a little like gold during the California gold rush of the 19th century. When news that gold was discovered, more and more people headed west towards San Francisco to make their fortunes. And while there was plenty of gold, more and more people started to know the best spots to pan for it and eventually what little gold was left had to be divided among more and more panhandlers (not the current day type of panhandlers).
So it has been with markets. In the booming 80s and 90s, the lack of the internet meant that information was less transparent, traders could still protect their proprietary trading strategies for longer periods of time, in order to clock profit advantages over the rest of the market. But those days, ticker tape and all, are well and truly behind us. With computerized models, that calculate risk and reward in nanoseconds, the markets have become so homogenized in terms of strategies and execution that active managers are having a hard time searching out for alpha. Today, literally millions of bots scour the markets, ready to detect, devour and execute on even the tiniest of market inefficiencies and mis-pricing, with millions of other algorithms designed to detect such activity and follow suit (also known as copy trades). And because information is far more transparent than it once was, which is a good thing, the market now more closely resembles the economist’s theoretical model of a “perfectly competitive” market than ever before. But the algorithmically-driven quantitative trading models used by high-frequency traders also have another drawback — a tendency to exacerbate and exaggerate market moves by adding to them. We’ve seen this tendency in the numerous “flash crashes” which although irregular, have been occurring with increasing frequency. And with volatility in 2019 set to rise again, we can certainly expect that algorithmic trading, warts and all will add to that volatility.
Take for instance last month’s precipitous fall in U.S. equities in the absence of any fundamental economic news. To be certain, the U.S.-China trade war was still on the cards, but traders had already priced-in for those concerns and there was growing evidence that Beijing and Washington were moving closer to a compromise on the issue. The U.S. jobs report met expectation and although Apple slashed forecasts, exceeding analysts’ estimates, the decline is due more to the availability of Chinese alternatives, rather than global economic malaise. Which is why the same algos that contributed to December’s decline also contributed to the soaring rise shortly thereafter as they hoovered up the self-created market feedback that the market had overreacted and become oversold. It’s a bit like scaring yourself and consoling yourself all in the same breath — and it doesn’t get more schizophrenic than that.
And we can expect that volatility (self-created or otherwise) to prevail as the theme for the coming decades. Quantitative trading strategies, ranging from cheap, plain vanilla ones packaged into passive, low-fee funds to pricey, complex statistical hedge fund-types, according to Morgan Stanley, manage no less than US$1.5 trillion worth of assets. And while other markets, such as commodities markets, still have a more human touch, these too, slowly but surely, are also being transformed. Because algorithms are simply lines of emotionless code, they act according to pre-defined parameters, without the benefit of human hesitation, the high-frequency trading based on quantitative strategies have made markets simultaneously more fickle and fragile. And while quantitative traders (otherwise known as quants) deny their influence on the overall markets, almost a decade ago, their influence on the markets became instantly clear and immediately undeniable.
At 2.32 pm on May 6, 2010, the S&P500 inexplicably dived over 8% and just 36 minutes later, shot back up just as much. At the time, it was dubbed the “flash crash” and shed light on the rise of small, hyperspeed algorithmic trading firms that were starting to eat into the alpha of investment banks. Suddenly, code became more important than intuition. But that was not the end of flash crashes. In the fall of 2015, markets again turned schizophrenic. Concern over China’s economic slowdown (a concern which three years later remains on the cards) caused the S&P500 to crash when it opened on August 24, 2015 — triggering circuit breakers implemented in the wake of the 2010 flash crash to pause extreme market gyrations. The circuit breakers kicked in almost 1,300 times in the course of a single trading day. The effect of the algo trading was even felt through exchange-traded funds (ETFs) where for periods of time, the value of the ETFs were no longer correlated with their underlying assets — another avenue for algo trading funds to exploit because it was another inefficiency. In other words, the very volatility caused by automated trading created the very inefficiencies exploited by another group of algorithmic trading programs. A quant’s profit-minting vortex if you will. And algorithmic trading is not as unique as one would imagine. There may be some “black boxes” but for the most part, everyone has access to the same black boxes — targeting specific level of volatility is common among “risk parity” strategies for instance and hedge funds have strategies known as “trend following” which follow the trend set off by risk-parity trading, snowballing volatility.
So while most quant funds will argue that their impact on the market is relatively minuscule — trend following strategies by some estimates only amount for US$300 billion — it is the combined effect of algorithmically-driven, automated, high-frequency trading that is having an outsize effect. Consider that an avalanche doesn’t need to be caused by the Abominable Snowman, a snowball will work just as well. To be certain, markets have always been vulnerable to dramatic plunges founded in the depths of panic and to be sure, there were no automated trading algorithms during the great stock market crash of 1929 — that was purely human-driven. But the rise of automated trading means that these peaks and troughs can become far more pronounced, in a far more condensed period than ever before and are wont to behave and cause consequences which have yet to be foreseen.
While there is no doubt that HFTs are far more efficient market-makers than human pit traders doing open cry, the entire sector is estimated by researchers at Goldman Sachs to be less than one of the major banks. Liquidity provision is a key component to any functioning market — it allows traders to enter and exit trading positions seamlessly and smoothly. But automated market-makers have a tendency to adjust bids and asks far more aggressively (quickly) when pandemonium breaks out in the market. In times of market turmoil, even a small amount of selling could have out-sized impact. And because quant strategies are programmatic, without decision-making lag and automated, they can start to sell down assets so quickly in a thin market that disconnects are created between buy and sell orders leading to huge gains or falls — with significant real-world consequences.
What is ironic is that many of the criticisms leveled against the cryptocurrency markets, in particular, cryptocurrency exchanges, were born and bred in the millions of lines of code written on Wall Street.
As Wall Streeters first started trickling into the cryptosphere, they brought with them their trading strategies and their algorithmic execution. Only to realize that cryptocurrency exchanges had several limits on the tricks of the trade they could bring over and ironically creating a marketplace far more in tune with competition and democracy than Wall Street had ever intended.
While HFTs may rule the day on Wall Street, such strategies have limited leverage on cryptocurrency exchanges. Almost all cryptocurrency exchanges are rate-limited. Meaning there’s a limit to how many trades can be executed within a specified time frame. And while automated trading is very much possible (most exchanges have APIs), high-frequency trading, in the Wall Street sense, is not. What this does mean is that traders (human or otherwise) who do identify an inefficiency, are also able to profit from that inefficiency for a longer period of time, locking in alpha and enjoying that alpha for that much longer. And that inefficiency can be identified by pure grit and diligence — as opposed to programmatically.
The other advantage of cryptocurrency markets is fees. For many of the HFT strategies to work in the traditional markets, the minimum exchange fees charged ensure that traders must be of sufficient size in order to execute such strategies. This makes it difficult for upstart entrants to gain access to these markets. In cryptocurrency markets, exchange fees tend to be percentage-based, meaning that it’s possible for even a small trader to use frequent, automated trades to churn a profit from trading.
It’s an open-secret that many cryptocurrency exchanges, in particular, the smaller ones (although some of the larger ones are just as guilty) post fake volumes. With little to no regulation, cryptocurrency exchanges run automated market-making bots to ensure sufficient bid-ask depth on both sides of the trade, using these bots not just to provide liquidity, but to actually trade on the market itself, giving the illusion of volume — something that would be unthinkable in a heavily-regulated market like the New York Stock Exchange. Yet the increasingly illiquid nature of some derivative products on Wall Street, for instance S&P500 futures, means that even a relatively smallish hedge fund could punch far over its weight by buying up or selling down such futures, with an outsize impact on the overall market as traders settle in before market opening to see the futures performance — a way for a trader to essentially manipulate the market and take up positions on the opposite side of that trade (e.g. purchasing or selling the S&P500 itself) to profit from that trade.
But on those cryptocurrency exchanges where the trading volumes are genuine (there are a few), what has emerged is that all market participants, regardless of size have similar access to the tools of trading — a decentralization and democratization of trading counterparties if you will — and even more importantly, at the end of the other trade is a genuine counterparty (automated or otherwise).
Unlike the traditional markets, cryptocurrencies are nascent digital assets, with the ever-present potential for any particular digital asset to become worthless for a variety of reasons. And because most digital assets are still in their relative infancy, the data points accessible and available for exploitation are few and far between. Most digital assets, in particular those based on the Ethereum blockchain have simply not existed long enough for a pure statistical basis of assessment.
Against this backdrop, traders with sufficient expertise in the underlying technology of a blockchain project, an ability to analyze the project’s technological validity and viability may therefore be put in a far more advantageous position than a trader assessing the trading space purely on the basis of price and volume action.
As more and more markets are becoming automated, human traders and algorithmic traders looking for alpha have increasingly had to look elsewhere. And while cryptocurrency exchanges are far from perfect, they are the perfect ground for traders to hone their skills and test their trading strategies. If nothing else, the breadth of access to the full functionality of cryptocurrency exchanges helps to democratize access to the market in a manner which current capital markets fail to do (for instance there are a limited number of approved market makers for most stock exchanges). Today, an upstart trader in the cryptocurrency market has access to the full suite of resources and functionality on a cryptocurrency exchange that would require assets under management in the millions on Wall Street.
And as alpha proves more and more elusive, it is only a matter of time (and this is highly speculative) before traders start looking at cryptocurrencies — a market still very much in its infancy — but precisely because it is, still provides sufficient opportunities for alpha.
Patrick is an innovative entrepreneur and a lawyer passionate about cryptocurrencies and the business world. He is the CEO of Novum Global Technologies, a cryptocurrency quantitative trading firm. He understands the business concerns of founders and business people helping them to utilise the legal framework to structure their companies to take advantage of emerging technologies such as the blockchain in order to reach greater heights. His passion for travel, marketing and brand building has led him across careers and continents. He read law at the National University of Singapore and graduated with Honors in the Upper Division and joined one of Singapore’s top law firms, Allen & Gledhill where he was called to the Singapore Bar as an Advocate & Solicitor in 2005. He created Purer Skin, a skincare and inner beauty company which melds the traditional wisdom of ancient Asian ingredients such as Bird's Nest with modern technology. In 2010, his partner and himself successfully raised $589,000 from the National Research Foundation of Singapore under the Prime Minister’s Office. He has played a key role in the growth of Purer Skin from 11 retail points in Singapore to over 755 retail points in Singapore and 2 overseas in less than a year. He taught himself graphic design, coding, website design and video editing to create the Purer Skin brand and finished his training at a leading Digital Media Company.