Volume 8, No. 4, Fourth Quarter 2010

Practitioner’s Digest • Vol. 8, No. 4

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.

A New Taxonomy of the Dynamic Term Structure Models

by: Sanjay K. Nawalkha, Natalia A. Beliaeve and Gloria Soto

This paper gives a new taxonomy of dynamic termstructure models (TSMs) that classifies all existing TSMs as either fundamental models or preference-free single-plus, double-plus, and triple-plus models.We exemplify the new taxonomy by considering preference-free versions of some well-known fundamental short rate models. Single-plus extensions of the fundamental models are shown to be both time-homogeneous and preference-free-two characteristics which do not simultaneously hold under any existing class of TSMs. Though the analytical apparatus for pricing fixed income securities is identical under fundamental models and single-plus models, the latter models are consistent with general non-linear forms of MPRs which may also depend upon an arbitrary set of state variables, leading to better estimates of risk-neutral parameters. The preference-free doubleplus and triple-plus extensions of the fundamental models are similar to the Heath et al. (1992) models, in that time-inhomogeneous drifts and volatilities are used as "smoothing variables" to fit the initial bond prices and initial term structure of volatilities, respectively.

Implementing Option Pricing Models Using Python and Cython

by: Sanjiv R. Das and Brian Granger

In this article we propose a new approach for implementing option pricing models in finance. Financial engineers typically prototype such models in an interactive language (such as Matlab) and then use a compiled language such as C/C++ for production systems. Code is therefore written twice. In this article we show that the Python programming language and the Cython compiler allows prototyping in a Matlab-like manner, followed by direct generation of optimized C code with very minor code modifications. The approach is able to call upon powerful scientific libraries, uses only open source tools, and is free of any licensing costs. We provide examples where Cython speeds up a prototype version by over 500 times. These performance gains in conjunction with vast savings in programmer time make the approach very promising.

Equally Weighted Rebalancing as the Average of all Investment Strategies

by: Masahito Shimizu

In a performance evaluation, it is important for both sponsors and portfolio managers to estimate the opportunity set of possible performances. In this regard, we investigate the average performance of all possible strategies and how performance varies across strategies. We show that the average is equal to the performance of the equally-weighted rebalancing strategy and that the standard deviation of all the performances during a period is approximately equal to that of all the investment assets' performances divided by the square root of the sum of the number of the assets and one, given certain conditions.

How Quickly Do Equity Prices Converge to Intrinsic Value?

by: Dennis R. Capozza and Ryan D. Israelsen

This research hypothesizes that in markets where information costs, transactions costs and the economic impact of information can vary widely, we should expect both significant predictability and systematic variation in the predictability. Controlling for other factors, we find that on average, 15-30% of the difference between the stock price and the estimated intrinsic value is removed in a year. We document that levels of predictability vary with firm characteristics like leverage, size and number of analysts. Momentum is stronger for larger firms with more analysts. Reversion to the intrinsic value is greater for smaller firms with more analysts.

The Rule of 72 for Lifetime Savings

by: Thomas K. Philips

Financial planners often impress upon their clients the power of compounding by quoting them the Rule of 72: With annual compounding, a dollar invested in an investment account at a constant interest rate of r% per annum grows to two dollars in approximately 72/r years. In this note I show that the Rule of 72 is easily extended to lifetime savings: If an investor invests one dollar at the start of each year over the course of her working life at a constant interest rate of r% per annum, approximately half the terminal value of her account can be attributed to the first 72/r years of contributions. The result, while simple, seems not to be well known, and has repeatedly proven useful when counseling young investors on the importance of saving for retirement from an early age, particularly when their primary retirement vehicle is a 401(k) plan.

What’s the Best Way to Trade Using the January Barometer?

by: Michael J. Cooper, John J. McConnell and Alexei V. Ovtchinnikov

According to Streetlore, as embedded in the adage "As goes January so goes the rest of the year," the market return in January provides useful information to would-be investors in that the January market return predicts the market return over the remainder of the year. This adage has become known as the January Barometer. In an earlier paper (Cooper, McConnell and Ovtchinnikov, 2006) we investigated the power of the January market return to predict returns for the next 11 months using 147 years of U.S. stock market returns. We found that, on average, the 11-month holding period return following positive Januarys was significantly higher, by a wide margin, than the 11-month holding period return following negative Januarys. In this paper we update that analysis through 2008 and address the question of how an investor can best use that information as part of an investment strategy. We find that the best way to use the January Barometer is not the obvious one of being long following positive Januarys and short following negative Januarys, but to be long following positive Januarys and invest in t-bills following negative Januarys. This strategy beats various alternatives, including a passive long-the-market-all-the-time strategy, by significant margins over the 152 years for which we have data.

Linear Causality

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.

The Little Book of Safe Money

by: Jason Zweig • reviewed by: Javier Estrada

The Little Book of Bulletproof Investing

by: Phil Demuth • reviewed by: Javier Estrada

“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.