Former MIT Sloan Finance Track student, Isa Watson, MBA ’13, founded the mission-driven startup company, Envested in her hometown after a family tragedy. Envested is a social network giving platform for nonprofit organizations to use to create local fundraising challenges and be able to show donors the specific impact of dollars given.
Category: General Finance
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.
High rates of debt growth by local governments are a cause for concern in any country. In China, where recent turmoil in the equity and foreign-exchange markets has put a spotlight on that country’s economy and growth prospects, increasing levels of borrowing by provincial and other lower levels of government has resulted in local indebtedness rising nearly fourfold since 2008, reaching about 40 percent of GDP.
Debt growth of that magnitude raises concerns about fiscal sustainability, debt affordability, transparency and accountability. Cautionary tales abound. From New York City in the ‘70s, emerging market countries in the ‘80s, Russia in the ‘90s, and Detroit, Greece and Puerto Rico more recently, there is a long list of governments that have experienced the painful economic repercussions of taking on debt they could not afford.
A new policy brief from the MIT Center for Finance and Policy by Xun Wu, a visiting scholar at the Center, suggests that while the massive debt buildup in China presents challenges, the situation is not as dire as a full-blown debt crisis. In fact, Chinese policymakers appear to be taking steps to mitigate the risks, including shutting down some of the more opaque financing channels and operations that facilitated the explosion of local debt in recent years.
Moreover, the central government of China is taking measures to restructure local debt—pointing to the possibility, and perhaps likelihood, of a larger bailout should debt levels become unmanageable. While further measures may be necessary, local debt levels also may stabilize if the pace of public infrastructure investment slows as China’s voracious appetite for public works is finally satiated.
However, a structural imbalance between local government spending and access to tax revenues remains a fundamental tension that has yet to be addressed. The central government ultimately has political and financial control over the entire public sector, and policies and regulations emanating from Beijing dominate borrowing and budgetary decisions. Currently local governments receive about 50 percent of taxes collected but are responsible for about 80 percent of expenditures. The resulting gap continues to be filled from other sources, primarily through borrowing and land sales.
As for differential impacts across the country, China’s more affluent eastern provinces have the greatest levels of debt in absolute terms, but as a share of local GDP their burden is manageable compared with the poorer western provinces.
China’s situation is complex as the country attempts to turn its government-dominated economic growth model into a market-oriented one. From that perspective, how it manages its local fiscal imbalances will be telling about the commitment to and speed of those larger changes.
The policy brief, which can be found here, is the first in a series of CFP Policy Briefs that will highlight innovative research conducted on issues residing at the intersection of finance and policy. The aim is to provide accessible, objective, quantitative, and non-partisan analyses that further public policy discourse and help inform decision-making in the public and private sectors.