Fundamental Investment Analysis

This 2005 roundtable addressed stock market valuation and its implications for a number of important corporate financial management functions, including internal performance evaluation and capital budgeting. Panelists included Tom Copeland of MIT, Bennett Stewart of Stern Stewart, Trevor Harris of Morgan Stanley, Stephen O'Byrne of Shareholder Value Advisors, Justin Pettit of UBS, David Wessels of University of Pennsylvania, and Don Chew of Morgan Stanley. John Martin of Baylor University and Sheridan Titman of University of Texas at Austin moderated.

This 2005 roundtable aimed to present corporate managers and academics with a more accurate picture of how influential and sophisticated investors really think and make decisions. Panelists included Andrew Alford of Goldman Sachs Asset Management, Michael Corasaniti of Pequot Capital, Steve Galbraith of Maverick Capital, Mitch Julis of Canyon Capital, Andrew Lacey of Lazard Asset Management, Michael Mauboussin of Legg Mason, Henry McVey of Morgan Stanley, and Stephen Penman of Columbia University. Trevor Harris of Morgan Stanley moderated the discussion.

Valuation models are useful tools, but they need to be handled with care. When taking the form of mathematical formulas, they can easily be made to convey a false sense of precision. In particular, selective choice of long-term growth rates and discount rates can be used to justify almost any desired valuation.

Focusing on asset returns governed by a factor structure, the APT is a one-period model, in which preclusion of arbitrage over static portfolios of these assets leads to a linear relation between the expected return and its covariance with the factors. The APT, however, does not preclude arbitrage over dynamic portfolios. Consequently, applying the model to evaluate managed portfolios is contradictory to the no-arbitrage spirit of the model.

Dispersion in investor beliefs and short-selling constraints can lead to stock market bubbles. This paper argues that firms, unlike investors, can exploit such bubbles by issuing new shares at inflated prices. This lowers the cost of capital and increases real investment. Perhaps surprisingly, large bubbles are not eliminated in equilibrium nor do large bubbles necessarily imply large distortions. Using the variance of analysts?

We develop a general equilibrium model of a production economy which has a risky production technology as well as a growth option to expand the scale of the productive sector of the economy. We show that when confronted with growth options, the representative consumer may sharply alter consumption rates to improve the likelihood of investment. This reduction in consumption is accompanied by an erosion of the option value of waiting to invest, leading to investment near the zero NPV threshold.

This paper outlines a financial statement analysis for use in equity valuation. Standard profitability analysis is incorporated, and extended, and is complemented with an analysis of growth. The perspective is one of forecasting payoffs to equities. So financial statement analysis is presented first as a matter of pro forma analysis of the future, with forecasted ratios viewed as building blocks of forecasts of payoffs. The analysis of current financial statements is then seen as a matter of identifying current ratios as predictors of the future ratios that drive equity payoffs.

This paper provides a model of investment timing by managers in a decentralized firm in the presence of agency conflicts and information asymmetries. When investment decisions are delegated to managers, contracts must be designed to provide incentives for managers to both extend effort and truthfully reveal private information. Using a real options approach, we show that an underlying option to invest can be decomposed into two components: a manager's option and an owner's option. The implied investment behavior differs significantly from that of the first-best no-agency solution.

Bayesian learning implies decreasing weights on prior beliefs and increasing weights on the accuracy of the analyst?s past forecast record, as the number of forecast errors comprising her forecast record (its length) increases. Consistent with this model of investor learning, empirical tests show that investors? reactions to forecast news are increasing in the product of the accuracy and length of analysts? forecast records. Moreover, the Bayesian learning predicted by our model is more descriptive of investor reactions than is a static model which predicts that investors?