Tag Archives: high-frequency trading

This is your fund manager’s secret weapon to fight high-frequency traders

See the original post on MarketWatch here>>

Mutual-fund and other asset managers trying to get the best price on a stock purchase or sale face a formidable challenge from fast-moving high-frequency traders — but managers are not defenseless.

To be sure, it’s difficult to execute large trades when HFTs deploy sophisticated pattern-recognition software in search of order-flow information that they can use to their advantage. When an asset manager unintentionally leaves footprints that tip its hand to these HFTs, the price is often impacted to the detriment of the asset manager.

So what can an asset manager do to prevent this from happening? By answering this question, we can help institutional investors improve their execution, reduce transaction costs, and ultimately deliver better investment returns.

In a recent study, my colleague and I looked into this issue. Our goal was to provide a realistic analysis of the strategic interaction between investors trading for fundamental reasons, such as pension funds, mutual funds, and hedge funds, and traders seeking to exploit leaked order-flow information, such as certain types of HFTs.

We find that asset managers have a powerful weapon against HFTs that exploit order flow information: Randomness.

Here is a typical scenario to show how this works: An asset manager legally discovers better information of a stock than the market does and trades to exploit that information. After the order is filled, a “back-runner” legally observes the institution’s filled order. The back-runner could be an HFT that uses sophisticated pattern recognition software to determine the existence of a large investor trying to buy or sell. The back-runner then competes with the institution by using that order-flow information to its advantage.

In this situation, the HFT has no crystal ball; it cannot see an order before it reaches the market. Instead, the HFT is watching the market all the time looking for patterns that indicate the intention of a large investor, as that suggests that a particular stock is over or undervalued. When the HFT sniffs out a large investor, it can become a competitor with its own transactions, driving the stock price up or down.

The best response of institutional investors is to introduce some “noise,” or the appearance of randomness, to cover their tracks. For example, if the real goal is to buy 100,000 shares, then the investor could include some sells in the mix of transactions to essentially play hide-and-seek with the HFT and mask its true intent. It also could change its buying pattern so that the number of shares per trade and the timing of the trades appear random. This use of randomization makes it riskier for the back-running HFT, as it can’t be certain of the institutional investor’s real plans or even its presence.

The takeaway from our research is that asset managers can outfox “back-running” HFTs by making their trades appear random to avoid detection. Although it may seem to be an inefficient way to complete a large trade, randomization will benefit investors in the long run by limiting the back-running behavior that increases investors’ price impacts. Reduced transaction costs not only increase investment returns, but also incentivize asset managers to invest in more fundamental price discovery.

Haoxiang Zhu is an assistant professor of finance at the MIT Sloan School of Management. He is the coauthor of “Back-Running: Seeking and Hiding Fundamental Information in Order Flows,” with Liyan Yang of the University of Toronto.

What is the optimal trading frequency in financial markets?

Trading speeds in financial markets have increased dramatically over the last decade. In markets for equities, futures and foreign exchange, transactions take place in milliseconds to microseconds (or even nanoseconds). Markets for fixed-income securities like corporate bonds and over-the-counter derivatives like interest rate swaps and CDS are also catching up quickly by adopting electronic trading.

The dramatic speed-up of financial transactions can perhaps only be matched by the intensity of the events and debates surrounding it, especially in the context of high-frequency trading. To many, the Flash Crash of May 2010 was a wakeup call for reevaluating market structure. A series of technology glitches proved to be highly costly for some brokers, proprietary firms and marketplaces in terms of profits and reputation. The SEC launched investigations into HFT firms and their strategies. The French regulators introduced financial transaction tax. Michael Lewis wrote “Flash Boys.” The list goes on.

With these events and controversy come important economic questions: What are the costs and benefits to investors for speeding up trading? Is there an “optimal” trading frequency at which the financial market should operate? And does a faster market affect one group of investors more than another?

In a recent research paper, Welfare and Optimal Trading Frequency in Dynamic Double Auctions, my coauthor Prof. Songzi Du (Simon Fraser University) and I attempt to answer these questions.

Our starting point is very simple. Trading frequency can be measured by how often investors transact through market. A higher-frequency market allows investors to access the market more often per unit of clock time. When investors meet each other in the market, they trade. Trading can be motivated by new information about future asset value and idiosyncratic trading incentives such as tax or inventory considerations.

A fundamental function of financial market is to reallocate assets from investors who value them less to investors who value the assets more, at the right price. The better this function is fulfilled, the more efficient the market is in reallocating the asset. We say that the market “improve welfare”—that is, make all investors better off—if it makes the reallocation of assets more efficient.

The bright side and dark side of a higher-frequency market

A higher trading frequency is double-edged sword. The optimal trading frequency depends on how the benefit and cost balance each other.

On the bright side, a higher-frequency market is more responsive to new information. Investors benefit from being able to react immediately to news. For example, following earnings announcements or merger-acquisition news, an investor may find his previous allocation on a stock no longer desirable. The sooner investors react to this information by trading, the better off they are. This effect favors a high-frequency market.

On the dark side, a higher-frequency market reduces the aggressiveness of investors’ trades. Investors are said to be more aggressive if they are willing to tolerate a greater market impact to achieve their target asset position. For example, aggressive execution means trading larger quantities more quickly. By contrast, unaggressive execution means splitting a large order into many small pieces and trading them gradually over time. The more frequently the market allows investors to transact, the stronger is their incentive to split orders over time to avoid price impact; hence, it takes longer to reach desired asset positions, and this is inefficient. If, however, a market opens infrequently, it encourages investors to trade aggressively now—failing to trade now means waiting for longer for the next opportunity to trade; this in turn leads to a faster convergence to efficient allocations. In this sense, somewhat counter intuitively, a lower-frequency market enhances allocation efficiency.

Scheduled versus stochastic news

We show that the optimal trading frequency depends on the nature of information arrivals, which determines the tradeoff between the benefit and cost of a higher trading frequency.

For scheduled arrivals of information, such as earnings announcements and macroeconomic data releases, we find that the optimal trading frequency should be equal to or lower than the frequency of information arrivals. For example, if news only arrives once per day, it is never optimal for investors to trade more than once a day. Moreover, if the market is competitive enough, the optimal trading frequency is equal to the information frequency.

For stochastic arrivals of information, such as surprise news of mergers and acquisitions, we show that the optimal trading frequency can be much higher. Moreover, if the market is competitive enough, continuous trading (the highest-frequency market) turns out to be optimal.

What does this tell us about optimal trading frequency in reality? Assets such as large-cap stocks or Treasuries that have frequent, unpredictable news shocks should be traded close to continuously. Small, illiquid stocks or bonds that have scarce news may best trade in a low-frequency market that only opens a few times a day; concentrating trading interests at specific time creates a deeper market. Therefore, there is no “one size fits all” optimal trading frequency for all securities.

For more details of this research, see “Welfare and Optimal Trading Frequency in Dynamic Double Auctions”, by Songzi Du and Haoxiang Zhu, http://ssrn.com/abstract=2040609.

Haoxiang Zhu is an Assistant Professor of Finance at MIT Sloan School of Management.

Professor Zhu’s research on this topic has also been featured on MarketWatch, click to view the article.

Early Peek Advantage?

From 2007 to June 2013, a small group of fee-paying, high-speed traders received the bi-monthly results of the Michigan Index of Consumer Sentiment (ICS) from Thomson Reuters at 9:54:58, two seconds before the broader release at 9:55:00. This arrangement was initially reported on April 5, 2013 by Financial Times regarding a complaint by a former Thomson Reuters employee for his dismissal after telling US federal agent about this arrangement of tiered distribution of information. Wider media coverage followed in June and July as well as a review by the office of New York Attorney General (see, for example, “Thomson Reuters Gives Elite Traders Early Advantage” by CNBC and “Traders Pay for an Early Peek at Key Data” by Wall Street Journal on June 12, “Seconds Out” by Economists on July 13, 2013). In July 2013, Thomson Reuters decided to suspend the program.

Despite the negative “optics” projected by this practice of tiered information release, a series of questions were raised: To what extent does it give an advantage to those with early information? Does it hurt general investors and hence damage the integrity of the financial market? How does it affect the efficiency of the price discovery process in the market? More careful analysis is needed in order to answer these questions.

In a recent research paper with Professor Xing Hu at University of Hong Kong and Professor Jun Pan at MIT, we have examined in detail the price dynamics and trading activity in E-mini S&P 500 futures around ICS releases during this episode. We focus on S&P 500 futures because ICS, reflecting consumer opinions of the overall economy, is likely to move the entire market instead of individual stocks. Being highly liquid and unaffected by short-sale constraints, E-mini S&P 500 futures is an ideal instrument to trade on both positive and negative market-wide information.

During the period when Thomson Reuters offers early peek advantage, we find abnormally high trading activity in E-mini S&P 500 futures at 9:54:58 on ICS announcement days. On average, the trading volume jumps to 1,473 contracts per second at 9:54:58, well above the sample average of 124 contracts per second, which reflects the trading volume of general investors. One second later at 9:54:59, the abnormal volume drops to 261 contracts, still well above the sample average but sharply down from the trading volume at 9:54:58. The volume pattern makes two points: First, early peek by high-frequency traders does generate high volume of trading, but mostly among themselves. (There is no reason be believe that general investors will choose to trade more at the time when they have an information disadvantage.) Second, the first second at 9:54:58 is disproportionally more meaningful to them.

More detailed study of price dynamics after the early peek reveals a clearer picture: We find that the prices are fully adjusted to the ICS news after the first 10% of the trades during 9:54:58, which lasts about 14 to 16 milliseconds. There is no evidence of further price drift after the initial price discovery. This implies that most of the transactions during 9:54:58 and all the transactions afterwards, including the public announcement at 9:55:00, are traded at the fully adjusted market prices. The scope of the early peek advantage is therefore narrowly contained and limited to high-speed traders trading amongst themselves. Outside of this narrow time window, general investors, as well as high-speed traders, trade at fully adjusted prices and are not disadvantaged by the early peek of a few.

The initiation and later suspension of the early peek program by Thomson Reuters also provides a natural experiment for us to examine how different mechanisms of information release might impact the speed of price discovery. Associated with the early peek program is highly concentrated trading amongst those fee-paying, high-speed traders over a span of two seconds. As a result of this intense and coordinated trading, we see a superfast price discovery in the order of 14 to 16 milliseconds. After the suspension of the early peek program, however, we do not see the same level of trading intensity and we find that the price discovery takes much longer. From this perspective, one might argue that, as a mechanism of information release, the tiered program provides a venue to facilitate concentrated and coordinated trading among informed high-speed traders and therefore makes price discovery more efficient.

How information actually transmits and impounds into market prices remain a central question in our understanding of how the financial market functions. Empirical investigations aimed at tackling this question are always hindered by the fact that most information is private in nature and hence unobservable to researchers, even ex post. The multi-tiered process adapted by data vendors in feeding market-moving information to their different clients, as in the case of Thomson Reuters when releasing CSI data, offers a rare instance where we know precisely what information is transmitted, when and to what subset of market participants. This situation allows us to examine with more clarity how information, private to some traders, drives their trading behavior and influences the market. It may also help us to better design and regulate the information dissemination process in the market.

For details of this research, see Grace Xing Hu, Jun Pan and Jiang Wang, “Early Peek Advantage?”