Category: General Finance

What Your Credit-Card Offers Say About You

See the original article on WSJ Experts page>>

credit_card_finances_gettyAs more and more personal data becomes available, businesses are now able to target customers in a personalized and sophisticated way.  On the bright side, that means you can get products and services that are tailored to your needs. As a result, you are much less likely to get catalogs featuring dresses your grandmother might wear. But, according to our research, the downside is that companies can also more effectively target your behavioral weaknesses, self-control issues or lack of attention to the fine print. We find that credit-card companies tend to offer those customers who are least able to manage the complexity of credit-card contracts, the most complex features and hidden charges.

As part of our research at MIT with my colleague Hong Ru, we recently studied over one million credit-card mailing campaigns that were sent to a representative set of U.S. households from March 1999 to February 2011. We devised algorithms to classify the terms of the credit cards and also the advertising material. Studying the wide variety of offers and who received which offer was illuminating. Credit-card terms offered to more financially sophisticated consumers differ significantly from those offered to less sophisticated customers, where educational attainment served as a proxy for sophistication.

The offers differed in both substance and style.  Less-sophisticated borrowers received offers with low teaser rates, more rewards, visual distractions, and fine print at the end of the offer letter. However, these offers also had more back-loaded and hidden fees. For example, after the introductory period, these cards have higher rates, late fees and overlimit fees.

In contrast, cards that are offered to sophisticated customers rely much less on back-loaded fees and instead have higher upfront fees, such as annual fees.  These cards tend to have higher regular annual percentage rates and often carry an annual fee, but they have low late fees and over-the-limit fees and are more likely to carry airline miles as rewards.

Not surprisingly, the worse the credit terms, the more likely they are to appear either in small font or on the last pages of the offer letters.  Similarly, offer letters with back-loaded terms contain more photos and less text, perhaps to distract from the details of the offer—what we refer to as shrouded attributes.

In fact, we found that banks seem to carefully monitor how the use of such shrouded attributes might affect the likelihood that unsophisticated customers will default on their debts. Our study showed that less-educated consumers who have lower default risk are more subject to back-loaded or shrouded fees. We also found that in states where there was an increase in unemployment insurance benefits that help borrowers maintain more stable cash flows in the event of a job loss, banks issued potential borrowers within that state more offers with lower teaser rates but higher late fees and default penalties. Banks also increased the flashiness of the offer letter, with more colors and photos, but moved the information about the back-loaded features to the end of the letter.

Taken together, these results suggest that credit-card companies realize that there is an inherent trade-off in the use of back-loaded features in credit-card offers: They might induce customers to take on more (expensive) credit, but at the same time, they expose the lender to greater risk if those consumers do not anticipate the true cost of credit.

So what’s the upshot of our study? First, you are lucky if you have a good education, since it means that the set of credit cards you get to choose from is already better from the start. But independent of your educational status, consumers should know that they have the power and information to choose well. Each credit-card offer in the U.S. must by law have a text box that contains all the relevant terms of the offer in one place; this is called the Schumer box after Sen. Chuck Schumer of New York.

So the best way to choose a credit card is to literally throw away all the marketing material at the front of the offer and simply focus on the real information in the Schumer box. This is true no matter what your income or education level.

Antoinette Schoar is the Michael Koerner ’49 professor of entrepreneurial finance and chair of the finance department at the MIT Sloan School of Management.

A Mission-Driven Startup

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.

Read the full article on Fast Company here>>

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.