Paul J. Bolster

An industrial bond rating model based on the Analytic Hierarchy Process

Abstract Bond ratings serve as perceived indicators of the default risk associated with bond issues and issuers. A review of the descriptions of the ratings process followed by the agencies reveals that an informal judgmental system is used to rate bond issues. However, informal judgmental systems have the potential to induce serious inconsistencies in decisions. Such inconsistencies can occur with respect to the relative importance of criteria applicable to the same issues over time and also across issuers. We present a formal judgmental model of the bond ratings process that does not suffer from the weaknesses of informal judgmental systems. The model relies on the descriptions of the process published by Standard and Poors Corporation (S&P) and is based on the Analytic Hierarchy Process. Model use is illustrated by analyzing the bond ratings of Chrysler Corporation and General Motors. The model should prove useful to ratings agencies, bond issuers and institutional investors. Ratings agencies can adopt a more systematic and consistent approach towards rating bond issues. Issuers and institutional investors can examine the perceived consistency and appropriateness of the rating assigned to a firms debt by providing inferences concerning the rating criteria.

How Mad Is Mad Money? Jim Cramer as aStock Picker and Portfolio Manager

This article examines a widely popular and controversial source of investment advice: buy and sell recommendations made by Jim Cramer on his popular nightly Mad Money show on CNBC. The results show that Cramer’s recommendations impact share prices of the companies that he mentions. The effects are short-lived and reverse for buy recommendations, although they persist for sell recommendations. Returns for a portfolio of Cramer recommendations are significantly different from zero for some subperiods. Factor analysis suggests that these returns are driven by beta exposure, smaller stocks, growth-oriented stocks, and momentum effects. Overall performance is average after adjusting for factor exposures. Further analysis indicates a significant cluster of new or updated analysts’ ratings of stocks just before and just after a Cramer recommendation. The results provide insights into the performance of a popular but controversial market pundit and should be of interest to followers and detractors seeking to develop an active, or alpha-generating, investment strategy.

Matching Investors with Suitable, Optimal and Investable Portfolios

Suitability is a legal concept that refers to the propriety of the match between the individual and his or her portfolio. The authors develop a model of suitability using the Analytic Hierarchy Process (AHP) to create unique asset allocations for individual investors based on their personal attributes. They then compare the mean-variance performance of these suitable model portfolios with portfolios generated using mean-variance optimization. They find that minor alterations to the AHP model and optimization inputs minimize the distinction from a mean-variance efficient portfolio. Finally, they show that a wide range of model portfolios can be closely matched with a small selection of mutual funds.

Information Effects of Changes to Analysts’ Recommendations: Morningstar Star Ratings Changes for Stocks

We study the nature and impact of ratings changes for individual stocks provided to investors by Morningstar, Inc. Morningstar’s recommendations follow negative momentum for upgrades and positive momentum for downgrades. When ratings change, upgraded stocks experience positive abnormal returns, while downgraded stocks experience negative abnormal returns. Morningstar recommendations not only impact stock prices at announcement, but statistically significant abnormal returns occur over the following 30 trading days. Additional variables tracked by Morningstar, such as economic moat and uncertainty, explain variation in abnormal returns associated with ratings change announcements. Overall, the results suggest that Morningstar analysts provide valuable information to investors.

Some Betas Are Less Dead Than Others: Beta as a Predictor of Future Price Changes for Individual Stocks

Why is beta a more effective price forecasting tool for some securities than others? Empirical studies of the basic capital asset pricing model (CAPM) are predominantly focused on the relationship between a portfolio’s returns and its beta. The model predicts a positive correlation. These studies employ portfolios to reduce the impact of firm-specific unsystematic risk on the analysis. By contrast, in this article, the authors examine beta as a forecasting tool for individual security values. The objective is not to validate the CAPM but instead to provide insight into how and why beta-driven forecasts of future value are more accurate for some firms than for others. They develop estimation models to explain 1) how well the market model fits the data (measured by R2s) and 2) forecast errors derived from the difference between actual and beta-predicted equity prices. They find market and company-related factors that explain differences in R2 and forecast error within their sample.

An Expert System for Assigning Investment Quality Ratings Using the AHP

A vast majority of security issues in financial markets are evaluated by rating agencies and assigned quality ratings. These ratings provide investors with an indication of default risk associated with the securities. Ratings are assigned to numerous types of financial assets including commercial paper, bonds, sovereign government debt, common and preferred stock, and securities offered on euromarkets such as eurobonds and eurocommercial paper.