Category: Research

Finance, policy, and global warming; A Q&A with Dr. Robert Litterman

Dr. Robert Litterman, an expert in risk management and quantitative investment strategies, returns to MIT Sloan Feb. 28 to deliver the second of three lectures he will give as the inaugural recipient of the S. Donald Sussman Award.

Robert Litterman

In advance of his talk, Litterman discussed the need for appropriate emissions pricing, the asset allocation model that bears his name, and the role of academia in the development of financial policy.

The first of your three lectures at MIT Sloan was a strong argument that pricing of carbon emissions worldwide must incorporate the cost of the risk emissions pose to society. Have you seen any indication that governments are moving toward appropriate pricing of emissions?

The most important recent development has been the announcement just last week by the Chinese Ministry of Finance that local tax authorities in China will soon berequired to institute a carbon tax. We don’t have much information yet about the level of the tax, or how robust it will be, but this positive development, coupled with the announcement of a carbon tax to take effect in South Africa in 2015, means that carbon taxes will not exist in Europe, Australia, California, China, South Africa, and South Korea.

However, the most important current front on which to focus in carbon pricing is the negotiation to institute a uniform global tax on carbon emissions in aviation. These negotiations are taking place in the International Civil Aviation Organization, the global body that governs civil aviation. Such a tax would be a strong signal that there is a global recognition that appropriate incentives are required to avoid wasting the remaining capacity of the earth’s atmosphere to safely absorb emissions.

The United States position in these negotiations remains unclear. Despite the current administration’s rhetoric about supporting market-based solutions in climate policy and the strong environmental record of Secretary of State John Kerry, there has been no clear signal from the U.S. government that it will support the creation of a market-based-mechanism to institute an appropriate price for emissions in commercial aviation, a policy that the aviation sector committed to more than a decade ago. Without appropriate incentives both airlines and the public will be led to continue their current inappropriate behavior, which creates excessive emissions. Sadly, if this waste of the atmosphere’s capacity to safely absorb emissions leads to much higher than necessary emissions prices in the future both the aviation sector and those who wish to fly will pay the price. In fact, even though aviation is currently a small part of the climate problem, because of its steep demand curve for future emissions capacity, it is in aviation’s best interest to lead the effort to immediately price emissions globally at an appropriate level in all sectors in order to efficiently allocate this remaining scarce resource across time.

Your second lecture at MIT Sloan will discuss the Black-Litterman model for asset allocation, which you developed at Goldman Sachs in 1990 with former MIT Sloan professor Fischer Black. Two decades later, what is your assessment of the model?

The Black-Litterman model has proven to be a very useful tool for building portfolios. As I will discuss in my second lecture, the incorporation of a prior that centers asset expected returns on equilibrium values provides a framework that allows investors to more flexibly express their views.

The model has been usefully employed in asset allocation contexts as well as portfolio construction of actively managed portfolios. The performance of Black-Litterman optimized portfolios, however, depends not so much on the Black-Litterman framework as the accuracy of the views that are supplied by the investor.

Thus, the benefit of Black-Litterman is to allow investors to efficiently allocate risk to take advantage of their forecasts. This is particularly valuable in contexts where there are constraints, transactions costs, or other trade-offs, for example, the use of leverage or balance sheet constraints.

In recent years many other academics and practitioners have extended Black-Litterman in ways not imagined early on. See, for example, Attilio Meucci’s paper, “The Black-Litterman Approach: Original Model and Extensions.”

As the inaugural recipient of the S. Donald Sussman Award, what has been the highlight of your experience visiting MIT Sloan and meeting with finance faculty and students?

Even though I was an assistant professor at MIT over 30 years ago, coming back to give a public lecture was a unique opportunity for me to participate in this incredible center of intellectual excellence. Obviously, coming back in the context of the Sussman Award, and having the opportunity to invite my family and friends to listen to a talk on a topic that I feel very passionate about was a personal highlight. Beyond that, the substantive discussions that took place with both students and faculty over lunch and during the afternoon of the day of the first lecture were very useful. And finally, I remember the dinner in the MIT Museum was lots of fun in an incredibly nice venue.

MIT Sloan this year is debuting the MIT Center for Finance and Policy, which will connect academics and policymakers in the public and private sectors to develop better, more informed financial policy and decision-making. Considering the recent financial crisis, what role do you see for the academic world in the development and support of financial policy?

Academia provides an incredibly important opportunity for impartial and informed debate about many of the most important issues in finance. This academic debate provides the backdrop for the development of financial policy and practice. I have had a rather unique opportunity in my career to participate in all three venues: in academia for two years at MIT, in government policy development for five years within the Federal Reserve system, and then for a 25 year career on Wall Street in the private sector.

All three venues—academia, the public sector, and the private sector—are important, but the incentives in each are different, and those incentives matter. Only in academia are participants rewarded for developing knowledge for its own sake. This independence and high ideal is a precious aspect of the academic environment, although for those who choose this venue it is unfortunately necessarily coupled with a frustrating amount of subjectivity in the recognition of valuable contributions.

Nonetheless, both public policy and private practice are eventually constrained, as they should be, by the knowledge that flows from the free and open academic debate. The better these sectors are connected to and communicate with academia, the better off will be the functioning of financial markets. Bringing academic insights to the public and private sectors is a key aspect of both my role as a board member of the Heller-Hurwicz Economics Institute at the University of Minnesota, and as the Executive Editor of theFinancial Analysts Journal. Facilitating this cross-sector communication of knowledge is incredibly important and I look forward to the contribution of the MIT Center for Finance and Policy.

Visit MIT Sloan Finance on TechTV here to view the S. Donald Sussman Lecture videos.

Does academic research destroy stock return predictability?

 

David McLean, Visiting Associate Professor of Finance at MIT Sloan School of Management

The first published paper that I know of to document that returns are predictable across stocks was published in 1972. In that paper, the authors showed that price level predicts returns in that low priced stocks tend to have higher returns than high priced stocks. Since then, this has been an active research area with numerous academic papers showing that various strategies based on observable firm traits (e.g., size, past performance) can predict returns across stocks.

In research conducted with Prof. Jeffrey Pontiff of Boston College, we asked how well these strategies perform after the strategy has been published in an academic journal. We replicated 82 different strategies that have been shown to predict stock returns in leading finance, accounting, and economics journals. We found that on average, the return of a strategy decays by 35% after a paper has been published. In other words, investors relying on a published strategy generating a 5% abnormal return should expect to make an average of 3.75% in abnormal returns during the years following publication.

Why does this decline happen? One explanation is that the abnormal returns of these strategies reflect returns to buying and selling mispriced stocks. When scholars discover a new trading strategy and publish a paper about it, investors begin to trade on that strategy, thereby pushing the prices of the stocks within the strategy towards “correct” or fundamental values.

Consistent with this idea, the post-publication decay is greatest in strategies that consist of larger stocks that are less costly to trade. These strategies decline by more than 35%, which seems reasonable because investors are more likely to follow a published strategy when the cost of trading in it is low. In contrast, when a strategy requires trading in smaller, more volatile, and less liquid stocks, investors are less inclined to try to exploit the strategy, and we find that such strategies decline less after the paper is published. Moreover, we find that after a strategy has been published, there is an increase in trading activity among the stocks in the strategy’s portfolio.

As for whether investors should follow strategies identified in academic papers, investors should on average expect to make 35% less on a strategy as compared to what is reported in a published paper. Investors should also keep in mind that these papers are written by researchers whose first priority is to better understand how financial markets work, and not to identify money making mechanisms. As a result, most studies do not estimate the costs of implementing the strategy, which can be substantial.

A silver lining in our results is the notion that academic research makes markets work better. What our findings suggest is that market mispricing is at least partially corrected once a study has drawn attention to it.

Prof. R. David McLean is visiting MIT Sloan from the University of Alberta. He recently coauthored the paper “Does Academic Research Destroy Stock Return Predictability” with Prof. Jeffrey Pontiff of Boston College.

“Dark Pools” can improve price discovery in open exchanges

When big investors want to execute trades but fear the size of the transaction could move the market, they often go to dark pools—alternative trading systems where orders are not publicly displayed. These opaque trading venues, now accounting for about 12 percent of equity trading volume in the United States, have sparked concern among regulators and in the financial press. With so many transactions occurring out of public view, critics warn that price discovery, the accurate determination of asset prices, will become more difficult.

Professor Haoxiang Zhu

But despite their sinister sounding name, dark pools can actually improve price discovery in open exchanges, I have found in my research, “Do dark pools harm price discovery?” Open a dark pool alongside a ‘lit’ pool, such as the New York Stock Exchange or Nasdaq, where orders are visible to all, and pricing can become more accurate on the open exchange.

To understand why this happens, it helps to consider investors to be divided loosely into two groups: informed and uninformed. Informed investors examine balance sheets, study analyst reports, read company press releases, and use advanced technology to monitor the market. When informed investors decide to execute a trade, they hope to profit from the information they gathered–and quickly.

Uninformed investors trade mainly for liquidity reasons. They may need to get cash to meet an obligation, or they may have cash they need to invest. They want to make their transaction regardless of a company’s fundamentals.

A dark pool presents different execution risks to informed and uninformed investors. This risk arises because a dark pool relies on matching, rather market makers, to execute trades. For example, if a dark pool has 300 shares to buy and 200 shares to sell, then only two thirds of each buy order is executed. Failure of execution is costly. (On an open exchange, those extra 100 shares to buy would be executed by market makers or liquidity providers). Informed investors have a high execution risk in the dark pool because they tend to trade in the same direction. Uninformed traders have a lower execution risk because their orders tend to be balanced on either side of the market.

This difference in execution risk tends to drive a greater proportion of informed investors to the open exchange and a larger share of uninformed investors to the dark pool. Having more informed investors trading on an exchange improves price discovery in the exchange and yield more accurate asset prices—precisely the opposite of what critics of dark pools fear.

While having non-displayed orders can help price discovery, dark pools are opaque in other, potentially harmful, ways. For example, dark pools typically do not disclose how they operate, and investors and the public often don’t know how the venues set execution prices or process orders. Increased oversight and closer scrutiny of dark pools could well be a very good thing for financial markets. But not displaying orders in itself needs not threaten price discovery in transparent venues. Instead, it could help.

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