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

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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.