Tag Archives: MIT Sloan

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.

How much do natural disasters really cost corporate America?

See the original article posted on Fortune here>>

Sales growth of supplier firms directly hit by a natural disaster drops by around five percentage points, according to a study.

As spring begins in New England after record-setting snowfall this winter, the economic consequences of natural disaster are a common topic of discussion. We know it will have a big impact on New England, but will it affect other parts of the country? If so, who will be affected and how much?

We hear these types of questions a lot following any type of disaster whether it is weather related or not. For instance, the fear of contagion was at the root of the decision of the U.S. government to bailout Chrysler and GM in 2008. Surprisingly, Ford’s CEO Allan Mullaly himself advocated the bailout of his two competitors in front of a U.S. Senate committee, as he recognized that “the collapse of one or both of our domestic competitors would threaten Ford because we have 80% overlap in supplier networks and nearly 25% of Ford’s top dealers also own GM and Chrysler franchises.”

So the key question is: When a shock — like a natural disaster or financial crisis — hits a supplier, what really happens to the firms in that network? Is there a spillover effects? To address this issues, we studied the transmission of shock caused by natural disasters in the past 30 years in the U.S. within the supply chain of publicly traded firms. We analyzed a sample of 2000 large corporations and 4000 of their suppliers.

You’d think that at a firm level, shocks could easily be absorbed in production networks. Even when they face disruptions, firms are supposedly flexible enough to change their production mix or switch to other suppliers. However, our study showed that shocks cause significant effects in production networks.

First, we found that the sales growth of supplier firms directly hit by a natural disaster drops by around five percentage points. The customers of these suppliers are also disrupted, as their sales growth drops on average by two percentage points when one of their suppliers is hit by a natural disaster. This is a strikingly large effect. We also found evidence that customers with lower inventories are the most exposed to disruption affecting their suppliers.

Then we investigated whether the drop in firms’ sales caused by supply disruptions translates into value losses. Our study shows that supply disruptions caused a 1% drop in customer firms’ equity value. This effect is almost twice as large when the disrupted supplier is a specific supplier, meaning a supplier producing differentiated goods, generating high R&D expenses, or holding patents.

Finally, we looked at whether the shock originating from one supplier propagates to other suppliers of the same firm, which were not directly affected by the natural disaster. You might expect that firms would continue to buy from other suppliers outside of the natural disaster zone, or that the other suppliers would find alternative buyers. However, our research shows large negative spillovers of the initial shock to other suppliers. We found that other suppliers of a main customer see a drop in sales growth by roughly three percentage points.

These findings highlight the presence of strong interdependencies in production networks. In other words, production networks matter. When one of your suppliers or customers is experiencing a negative event, there will be important implications for you.

This research likely applies to contexts beyond natural disasters, such as strikes and financial recessions. More generally, shocks that originate in one part of the economy can be amplified because of the strong interconnections between firms.

As for the economic impact of the weather in New England this winter, there is good reason to think that the effects will be propagated to other parts of the economy through relationships that Massachusetts firms have with customers all over the country. But who will be affected and how much? We’ll have to wait and see.

Jean-Noel Barrot is assistant professor of finance at the MIT Sloan School of Management. Julien Sauvagnat is a postdoctoral researcher at ENSAE-CREST and is expected to join Bocconi University in September 2015.

Why the Internet did not kill RadioShack

See the original article posted on Fortune Insider here>>

Although the electronics retailer is the latest victim in the rise of e-commerce, several other missteps led to its demise.

We’ve seen the downfall of many bricks and mortar stores over the last decade, including Borders, Circuit City, and most recently, RadioShack — to name just a few. As e-commerce continues to rise, it’s seemingly becoming more difficult for traditional stores to stay in business.

It’s true that online shopping has significantly grown over the last 10 years. Even in the last year, we’ve seen a noticeable uptick. According to the U.S. Census, total e-commerce sales for 2014 in the U.S. were estimated at $304.9 billion, which is a 15.4% increase from 2013. However, plenty of bricks and mortar stores are still healthy. Is it fair to blame e-commerce for every store closing and bankruptcy?

As a U.S. bankruptcy judge on Tuesday said he would approve a plan by the electronics retailer to sell 1,740 of its stores to the Standard General hedge fund and exit bankruptcy, it’s worth taking a closer look at why RadioShack failed. E-commerce wasn’t the only culprit. One big mistake involved poor strategic decisions over its financials. Feeling undervalued, the retailer bought back $400 million in stock in 2010 when its net profit was $206 million. It did something similar in 2011 when its net profit had declined to $72 million and it did another buy back for $113 million. In the end, it spent more than $500 million trying to push up the stock price.

However, the company didn’t make enough money to finance the buy back and had to borrow money, which increased its debt-to- value ratio and left RadioShack vulnerable to a declining profits. Rather than buying back so much of the stock and taking on debt, it should have accepted the valuation, closed a few inefficient stores and avoided bankruptcy.

Another significant mistake was its decision to change its product market strategy. In prior years, RadioShack was known as the place to go for hard-to-find parts and components needed to build things. It also had knowledgeable staff who could help customers with high-level customer service. Customers were willing to pay higher prices because of this additional value. After all, there is a difference between getting helpful information in person and trying to explain an issue via the phone, an online chat, or a Google search.

When RadioShack changed its business model to sell finished products like laptops and phones, it lost that competitive edge. Customers could get those finished products from many other retailers and e-commerce sites at lower prices. And a higher-level of customer service wasn’t needed for those products.

While it’s too late for RadioShack, its demise offers some important takeaways for other bricks and mortar stores:

Find a competitive edge. If you offer a unique product, like RadioShack did with selling specialty components, don’t disregard that strength.

Be aware of price sensitivities. A big challenge for bricks and mortar stores is that they have to pay for overhead whereas online retailers don’t, allowing them to charge lower prices. Customers are more likely to price shop for larger and more expensive items, especially ones readily available online like phones and tablets. They tend to be less price sensitive about small and specific goods like the components previously sold by RadioShack.

Focus on customer experience. A big draw for RadioShack was its knowledgeable staff. When it moved to selling finished products, the need for that staff — and consumer willingness to pay higher prices — disappeared. Many people still value talking to a real person.

Looking ahead, it won’t be smooth sailing for traditional stores. But it won’t be all doom and gloom either if they can learn from the mistakes of retailers like RadioShack.

Andrey Malenko is the Jon D. Gruber Career Development Assistant Professor of Finance at the MIT Sloan School of Management.