Volume 11, No. 1, First Quarter 2013

Practitioner’s Digest • Vol. 11, No. 1

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.

Bayesian Modeling in Finance

by: Xiaojing Dong and Carrie H. Pan

The Bayesian statistical method provides an alternative approach to study some of the classical problems in finance. In the existing finance literature, research that uses Bayesian econometrics is primarily in the area of asset pricing. Bayesian applications in corporate finance have been rather limited, despite its great potential as a viable alternative to address some challenging problems in corporate finance that are difficult to solve by the traditional approach. Bayesian estimation techniques, the Markov Chain Monte Carlo (MCMC) methods in particular, are very conductive to estimating nonlinear models with high dimensional integrals in the likelihood or models with a hierarchical structure. In this paper, we outline the basic concepts of Bayesian modeling, describe most commonly used estimation techniques, and review its applications in the existing finance literature.

Deconstructing Black-Litterman: How to Get the Portfolio You Already Knew You Wanted

by: Richard O. Michaud, David N. Esch and Robert O. Michaud

The Markowitz (1952, 1959) mean-variance (MV) efficient frontier has been the theoretical standard for defining portfolio optimality for more than a half century. However, MV optimized portfolios are highly susceptible to estimation error and difficult to manage in practice (Jobson and Korkie 1980, 1981; Michaud 1989). The Black and Litterman (BL) (1992) proposal to solve MV optimization limitations produces a single maximum Sharpe ratio (MSR) optimal portfolio on the unconstrained MV efficient frontier based on an assumed MSR optimal benchmark portfolio and active views. The BL portfolio is often uninvestable in applications due to large leveraged or short allocations. BL use an input tuning process for computing acceptable sign constrained solutions. We compare constrained BL to MV and Michaud (1998) optimization for a simple data set. We show that constrained BL is identical to Markowitz and that Michaud portfolios are better diversified under identical inputs and optimality criteria. The attractiveness of the BL procedure is due to convenience rather than effective asset management and not recommendable relative to alternatives.

Investing in What You Know: The Case of Individual Investors and Local Stocks

by: Mark S. Seasholes and Ning Zhu

This paper tests the performance of individuals' equity investments. We study over 40,000 accounts and 950,000 trades from a large discount broker. Individuals invest heavily in local stocks and put 14% more into these stocks than a market-neutral portfolio would suggest. Using holdings-based calendar-time portfolios, we find the local holdings do not generate positive alphas. Using the transactions data, we find local stocks bought actually underperform local stocks sold (though the underperformance is more severe when considering remote stocks). We find no support for the folk wisdom that one should "invest in what you know."

Stock Strategies with the January Barometer and the Yield Curve

by: Licheng Sun, Chris Stivers and Ajay Kongera

The January Barometer states that the sign of the stock-markets returns in January can predict the subsequent 11-month stock-market return over February to December. Cooper et al. (2010) show that the best way to use the January Barometer is to be long following positive Januarys and invest in T-bills following negative Januarys. In this study, similar to the January Barometer, we find that the 11-month average return following upward-sloping yield curves is significantly higher than the 11-month average return following downward-sloping yield curves. Further, we find that trading strategies that combine the trading signals from the January Barometer and the yield curve comfortably outperform the best strategy that relies on the January Barometer alone. We show that the combined January barometer-yield curve strategy has lower risks and higher Sharpe ratios.

VarGamma: A Unified Measure of Portfolio Risk

by: Kent Osband

Most portfolio risk analysis implicitly assumes that risks are stable, despite copious evidence of instability. This article presents an alternative, VarGamma, that provides neat formulas for certainty equivalents (risk-adjusted returns) even with stochastic volatility and volatility-dependent drift. VarGamma measures are far more flexible and robust than standard mean-variance formulations or quantiles (VaR), with minimal extra complexity. Parameters can be readily inferred from either historical data or options prices. Compared to Sharpe ratio maximization, VarGamma encourages more diversification and caps exposure to highly volatile volatility. Compared to standard VaR, VarGamma discourages herding, pro-cyclical lending behavior, and wasteful regulatory arbitrage.

Marginal Propensity to Consume

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.

Finance and the Good Society

by: Robert J. Shiller • reviewed by: Bruce Grantier

“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.