Inside the Black Box

A Simple Guide to Quantitative and High Frequency Trading

Rishi K. Narang

• Publisher: Wiley
• Year: 2013
• ISBN:
  9781118416990
• Edition: 2
• Topic: Business
• Subtopic: Investments & Securities
• Index: Business

New edition of book that demystifies quant and algo trading

In this updated edition of his bestselling book, Rishi K Narang offers in a straightforward, nontechnical style—supplemented by real-world examples and informative anecdotes—a reliable resource takes you on a detailed tour through the black box. He skillfully sheds light upon the work that quants do, lifting the veil of mystery around quantitative trading and allowing anyone interested in doing so to understand quants and their strategies. This new edition includes information on High Frequency Trading.

• Offers an update on the bestselling book for explaining in non-mathematical terms what quant and algo trading are and how they work
• Provides key information for investors to evaluate the best hedge fund investments
• Explains how quant strategies fit into a portfolio, why they are valuable, and how to evaluate a quant manager

This new edition of Inside the Black Box explains quant investing without the jargon and goes a long way toward educating investment professionals.

PART One

The Quant Universe

CHAPTER 1

Why Does Quant Trading Matter?

Look into their minds, at what wise men do and don't.

—Marcus Aurelius, Meditations

John is a quant trader running a midsized hedge fund. He completed an undergraduate degree in mathematics and computer science at a top school in the early 1990s. John immediately started working on Wall Street trading desks, eager to capitalize on his quantitative background. After seven years on the Street in various quant-oriented roles, John decided to start his own hedge fund. With partners handling business and operations, John was able to create a quant strategy that recently was trading over $1.5 billion per day in equity volume. More relevant to his investors, the strategy made money on 60 percent of days and 85 percent of months—a rather impressive accomplishment.

Despite trading billions of dollars of stock every day, there is no shouting at John's hedge fund, no orders being given over the phone, and no drama in the air; in fact, the only sign that there is any trading going on at all is the large flat-screen television in John's office that shows the strategy's performance throughout the day and its trading volume. John can't give you a fantastically interesting story about why his strategy is long this stock or short that one. While he is monitoring his universe of thousands of stocks for events that might require intervention, for the most part he lets the automated trading strategy do the hard work. What John monitors quite carefully, however, is the health of his strategy and the market environment's impact on it. He is aggressive about conducting research on an ongoing basis to adjust his models for changes in the market that would impact him.

Across from John sits Mark, a recently hired partner of the fund who is researching high-frequency trading. Unlike the firm's first strategy, which only makes money on 6 out of 10 days, the high-frequency efforts Mark and John are working on target a much more ambitious task: looking for smaller opportunities that can make money every day. Mark's first attempt at high-frequency strategies already makes money nearly 95 percent of the time. In fact, their target for this high-frequency business is even loftier: They want to replicate the success of those firms whose trading strategies make money every hour, maybe even every minute, of every day. Such high-frequency strategies can't accommodate large investments, because the opportunities they find are small, fleeting. The technology required to support such an endeavor is also incredibly expensive, not just to build, but to maintain. Nonetheless, they are highly attractive for whatever capital they can accommodate. Within their high-frequency trading business, John and Mark expect their strategy to generate returns of about 200 percent a year, possibly much more.

There are many relatively small quant trading boutiques that go about their business quietly, as John and Mark's firm does, but that have demonstrated top-notch results over reasonably long periods. For example, Quantitative Investment Management of Charlottesville, Virginia, averaged over 20 percent per year for the 2002–2008 period—a track record that many discretionary managers would envy.1

On the opposite end of the spectrum from these small quant shops are the giants of quant investing, with which many investors are already quite familiar. Of the many impressive and successful quantitative firms in this category, the one widely regarded as the best is Renaissance Technologies. Renaissance, the most famous of all quant funds, is famed for its 35 percent average yearly returns (after exceptionally high fees), with extremely low risk, since 1990. In 2008, a year in which many hedge funds struggled mightily, Renaissance's flagship Medallion Fund gained approximately 80 percent.2 I am personally familiar with the fund's track record, and it's actually gotten better as time has passed—despite the increased competition and potential for models to stop working.

Not all quants are successful, however. It seems that once every decade or so, quant traders cause—or at least are perceived to cause—markets to move dramatically because of their failures. The most famous case by far is, of course, Long Term Capital Management (LTCM), which nearly (but for the intervention of Federal Reserve banking officials and a consortium of Wall Street banks) brought the financial world to its knees. Although the world markets survived, LTCM itself was not as lucky. The firm, which averaged 30 percent returns after fees for four years, lost nearly 100 percent of its capital in the debacle of August–October 1998 and left many investors both skeptical and afraid of quant traders. Never mind that it is debatable whether this was a quant trading failure or a failure of human judgment in risk management, nor that it's questionable whether LTCM was even a quant trading firm at all. It was staffed by PhDs and Nobel Prize–winning economists, and that was enough to cast it as a quant trading outfit, and to make all quants guilty by association.

Not only have quants been widely panned because of LTCM, but they have also been blamed (probably unfairly) for the crash of 1987 and (quite fairly) for the eponymous quant liquidation of 2007, the latter having severely impacted many quant shops. Even some of the largest names in quant trading suffered through the quant liquidation of August 2007. For instance, Goldman Sachs' largely quantitative Global Alpha Fund was down an estimated 40 percent in 2007 after posting a 6 percent loss in 2006.3 In less than a week during August 2007, many quant traders lost between 10 and 40 percent in a few days, though some of them rebounded strongly for the remainder of the month.

In a recent best-selling nonfiction book, a former Wall Street Journal reporter even attempted to blame quant trading for the massive financial crisis that came to a head in 2008. There were gaps in his logic large enough to drive an 18-wheeler through, but the popular perception of quants has never been positive. And this is all before high-frequency trading (HFT) came into the public consciousness in 2010, after the Flash Crash on May 10 of that year. Ever since then, various corners of the investment and trading world have tried very hard to assert that quants (this time, in the form of HFTs) are responsible for increased market volatility, instability in the capital markets, market manipulation, front-running, and many other evils. We will look into HFT and the claims leveled against it in greater detail in Chapter 16, but any quick search of the Internet will confirm that quant trading and HFT have left the near-total obscurity they enjoyed for decades and entered the mainstream's thoughts on a regular basis.

Leaving aside the spectacular successes and failures of quant trading, and all of the ills for which quant trading is blamed by some, there is no doubt that quants cast an enormous shadow on the capital markets virtually every trading day. Across U.S. equity markets, a significant, and rapidly growing, proportion of all trading is done through algorithmic execution, one footprint of quant strategies. (Algorithmic execution is the use of computer software to manage and work an investor's buy and sell orders in electronic markets.) Although this automated execution technology is not the exclusive domain of quant strategies—any trade that needs to be done, whether by an index fund or a discretionary macro trader, can be worked using execution algorithms—certainly a substantial portion of all algorithmic trades are done by quants. Furthermore, quants were both the inventors and primary innovators of algorithmic trading engines. A mere five such quant traders account for about 1 billion shares of volume per day, in aggregate, in the United States alone. It is worth noting that not one of these is well known to the broader investing public, even now, after all the press surrounding high-frequency trading. The TABB Group, a research and advisory firm focused exclusively on the capital markets, estimates that, in 2008, approximately 58 percent of all buy-side orders were algorithmically traded. TABB also estimates that this figure has grown some 37 percent per year, compounded, since 2005. More directly, the Aite Group published a study in early 2009 indicating that more than 60 percent of all U.S. equity transactions are attributable to short-term quant traders.4 These statistics hold true in non-U.S. markets as well. Black-box trading accounted for 45 percent of the volume on the European Xetra electronic order-matching system in the first quarter of 2008, which is 36 percent more than it represented a year earlier.5

The large presence of quants is not limited to equities. In futures and foreign exchange markets, the domain of commodity trading advisors (CTAs), quants pervade the marketplace. Newedge Alternative Investment Solutions and Barclay Hedge used a combined database to estimate that almost 90 percent of the assets under management among all CTAs are managed by systematic trading firms as of August 2012. Although a great many of the largest and most established CTAs (and hedge funds generally) do not report their assets under management or performance statistics to any database, a substantial portion of these firms are actually quants also, and it is likely that the real figure is still over 75 percent. As of August 2012, Newedge estimates that the amount of quantitative futures money under management was $282.3 billion.

It is clear that the magnitude of quant trading among hedge funds is substantial. Hedge funds are private investment pools that are accessible only to sophisticated, wealthy individual or institutional clients. They can pursue virtually any investment mandate one can dream up, and they are allowed to keep a portion of the profits they generate for their clients. But this is only one of several arenas in which quant trading is widespread. Proprietary trading desks at the various banks, boutique proprietary trading firms, and various multistrategy hedge fund managers who utilize quantitative trading for a portion of their overall business each contribute to a much larger estimate of the size of the quant trading universe.

With such size and extremes of success and failure, it is not surprising that quants take their share of headlines in the financial press. And though most press coverage of quants seems to be markedly negative, this is not always the case. In fact, not only have many quant funds been praised for their steady returns (a hallmark of their disciplined implementation process), but some experts have even argued that the existence of successful quant strategies improves the marketplace for all investors, regardless of their style. For instance, Reto Francioni (chief executive of Deutsche Boerse AG, which runs the Frankfurt Stock Exchange) said in a speech that algorithmic trading “benefits all market participants through positive effects on liquidity.” Francioni went on to reference a recent academic study showing “a positive causal relationship between algo trading and liquidity.”6 Indeed, this is almost guaranteed to be true. Quant traders, using execution algorithms (hence, algo trading), typically slice their orders into many small pieces to improve both the cost and efficiency of the execution process. As mentioned before, although originally developed by quant funds, these algorithms have been adopted by the broader investment community. By placing many small orders, other investors who might have different views or needs can also get their own executions improved.

Quants typically make markets more efficient for other participants by providing liquidity when other traders' needs cause a temporary imbalance in the supply and demand for a security. These imbalances are known as inefficiencies, after the economic concept of efficient markets. True inefficiencies (such as an index's price being different from the weighted basket of the constituents of the same index) represent rare, fleeting opportunities for riskless profit. But riskless profit, or arbitrage, is not the only—or even primary—way in which quants improve efficiency. The main inefficiencies quants eliminate (and, thereby, profit from) are not absolute and unassailable, but rather are probabilistic and require risk taking.

A classic example of this is a strategy called statistical arbitrage, and a classic statistical arbitrage example is a pairs trade. Imagine two stocks with similar market capitalizations from the same industry and with similar business models and financial status. For whatever reason, Company A is included in a major market index, an index that many large index funds are tracking. Meanwhile, Company B is not included in any major index. It is likely that Company A's stock will subsequently outperform shares of Company B simply due to a greater demand for the shares of Company A from index funds, which are compelled to buy this new constituent in order to track the index. This outperformance will in turn cause a higher P/E multiple on Company A than on Company B, which is a subtle kind of inefficiency. After all, nothing in the fundamentals has changed—only the nature of supply and demand for the common shares. Statistical arbitrageurs may step in to sell shares of Company A to those who wish to buy, and buy shares of Company B from those looking to sell, thereby preventing the divergence between these two fundamentally similar companies from getting out of hand and improving efficiency in market pricing. Let us not be naïve: They improve efficiency not out of altruism, but because these strategies are set up to profit if indeed a convergence occurs between Companies A and B.

This is not to say that quants are the only players who attempt to profit by removing market inefficiencies. Indeed, it is likely that any alpha-oriented trader is seeking similar sorts of dislo...