Volume 6, No. 2, Second Quarter 2008

Practitioner’s Digest • Vol. 6, No. 2

The “Practitioners Digest” emphasizes the practical significance of manuscripts featured in the “Insights” and “Articles” sections of the journal. Readers who are interested in extracting the practical value of an article, or who are simply looking for a summary, may look to this section.

Are Analysts All Alike? Identifying Earnings Forecasting Ability

by: Louis K. C. Chan, David Ikenberry, Josef Lakonishok and Sangwoo Lee

Investors and the financial media apparently believe that some Wall Street equity analysts research is superior to others. We examine whether such quality differentials exist, in terms of analysts ability to forecast earnings accurately, and whether these differentials are identifiable on an ex ante basis. The results suggest that there is some persistence in analysts forecast accuracy. In particular, forecast accuracy is associated with analyst experience, breadth of coverage, timeliness, and brokerage firm size. Analysts selected for All- Star status by industry publications also tend to have higher forecast accuracy. However, the differences in forecast accuracy do not produce material differences in the dollar magnitudes of forecast errors.

Optimal Static Allocation Decisions in the Presence of Portfolio Insurance

by: Felix Goltz, Lionel Martellini and Koray D. Simsek

The focus of this paper is to determine what fraction a myopic risk-averse investor should allocate to investment strategies with convex exposure to stock market returns in a general economy with stochastically time-varying interest rates and equity risk premium. Our conclusion is that typical investors should optimally allocate a sizable fraction of their portfolio to such portfolio insurance strategies, and the associated utility gains are significant. While the fact that static investors would benefit from accessing dynamic investment strategies is in essence not surprising, we have found the size of the rational investment in such structures to be rather remarkable. This strong result is robust with respect to various parametric assumptions, as well as the presence of realistic levels of market frictions and heterogeneous expectations on volatility.

First Come First Disserved

by: Joseph Cerniglia and Joshua Livnat

This study investigates whether stock market reactions to earnings information of firms that release their earnings close to quarter-end (Early) are systematically different from their industry peers which report later during the quarter (Late). Unexpectedly, we find that immediate market reactions to early reporters are weaker than those to late or middle reporters. We also find that stock market returns subsequent to the earnings announcements are stronger for positive earnings surprises of early reporters than late reporters, indicating that the market systematically under reacts to the positive surprises of early reporters. These results have implications for investors who can use this systematic under reaction in their trading strategies and academics who can understand better how market participants gather and process earnings information.

How Does Investor Sentiment Affect the Cross-Section of Stock Returns?

by: Malcolm Baker, Johnathan Wang and Jeffrey Wurgler

Broad waves of investor sentiment should have larger impacts on securities that are more difficult to value and to arbitrage. Consistent with this intuition, we find that when an index of investor sentiment takes low values, small, young, high volatility, unprofitable, non-dividend paying, extreme growth, and distressed stocks earn relatively higher subsequent returns. When sentiment is high, the aforementioned categories of stocks earn relatively lower subsequent returns.

Beyond Value at Risk: Forecasting Portfolio Loss at Multiple Horizons

by: Lisa R. Goldberg, Guy Miller and Jared Weinstein

We develop a portfolio risk model that uses high-frequency data to forecast the loss surface, which is the set of loss distributions at future time horizons. Our model uses a fully automated, semi-parametric fitting procedure that has its basis in extreme value statistics. We take account of distributional asymmetry, heavy tails, heteroscedasticity and serial correlation. Loss distributions are time aggregated by taking products of characteristic functions. We test loss-surface-implied forecasts of value at risk and expected shortfall out of sample on a diverse set of portfolios and we compare our forecasts to industry-standard risk forecasts that are based on asset and factor covariance matrices. The empirical results make a compelling case for the application and further development of our approach.

Best Seller Productions

by: Jack L. Treynor

“Case Studies” presents a case pertinent to contemporary issues and events in investment management. Insightful and provocative questions are posed at the end of each case to challenge the reader. Each case is an invitation to the critical thinking and pragmatic problem solving that are so fundamental to the practice of investment management.

Capital Ideas Evolving

by: Peter L. Bernstein • reviewed by: Frank J. Jones

“Book Reviews” identifies important, and often popular, new books from a wide range of investment topics. Beyond providing a summary and review of the content and style of the books, “Book Reviews” seeks to contribute to a conscious, critical, and informed approach to investment literature.