The challenge of leadership is to look beyond the current expansion to consider the long–term outlook for the U. S. economy. My good friend Paul W. McCracken once described this process as looking across the valley to see what is on the other side. His message was: “What will be different on the other side of the valley is far more relevant to business planning than the valley itself.” Such advice is particularly meaningful at this time because of the basic need for more stability in our economic policies.

M. W. Jones-Lee, A Note on a Property of the Inverse of a Bordered Matrix and Its Implication for the Theory of Portfolio Selection, The Journal of Financial and Quantitative Analysis, Vol. 9, No. 6 (Dec., 1974), pp. 1081-1087

Lawrence B. Smith, Comment: Brueggeman-Peiser and Noland Papers, The Journal of Financial and Quantitative Analysis, Vol. 14, No. 4, Proceedings of 14th Annual Conference of the Western Finance Association, June 21-23, 1979 (Nov., 1979), pp. 801-803

We use a database of recent high tech IPOs to estimate opportunity cost of capital for venture capital investors and entrepreneurs. Entrepreneurs face the risk-return tradeoff of the CAPM as the opportunity cost of holding a portfolio that necessarily is underdiversified. For early stage firms, we estimate the effects of underdiversification, industry, and financial maturity on opportunity cost. Assuming a one-year holding period, the entrepreneur's opportunity cost generally is two to four times as high as that of a well-diversified investor. With a 4.0% risk-free rate and 6.0% market risk premium, for the sample average, we estimate the cost of capital of a well-diversified investor to be 11.4%, which equates to 16.7% before the management fees and carried interest of a typical venture capital fund. For an entrepreneur with 25% of total wealth invested in the venture, our corresponding estimate of cost of capital is 40.0%.

Using survey data, we analyze institutional investors’ expectations about the future performance of fund managers and the impact of those expectations on asset allocation decisions. We find that institutional investors allocate funds mainly on the basis of fund managers’ past performance and of investment consultants’ recommendations, but not because they extrapolate their expectations from these. This suggests that institutional investors base their investment decisions on the most defensible variables at their disposal and supports the existence of agency considerations in their decision making.

Empirical studies of dual class shares indicate that superior voting shares (SVS) sell at a premium relative to their counterpart restricted shares (RVS). This paper uses Toronto Stock Exchange data to show that SVS price premium over RVS reflects the expected takeover premium paid to shareholders outside the control block. Thus, marginal shareholders pay a higher SVS price in anticipation of receiving a differential takeover bid as suggested by the extra merger hypothesis. Further analysis indicates that voting power increases the price premium while ownership, size, and the higher trading liquidity of RVS are inversely related to the premium.

This paper addresses the problem of testing financial models in the presence of market microstructure effects. The moment restrictions implied by the financial and market microstructure models are jointly tested using Hansen’s (1982) GMM approach. To illustrate the methodology, I consider the random walk model in combination with the bid-ask price effect model of Blume and Stambaugh (1983). Within this sufficiently simple framework, I obtain closed-form expressions for the estimators, standard errors of the estimators, and the test statistic, which affords an opportunity to examine the precision of the estimators and the power of the test as the return interval increases. I show that apparent rejections of the random walk model cannot be sustained when tests of the model are adjusted for market microstructure effects, and I discuss other applications of the methodology.

Robert H. Litzenberger, Charles P. Jones, Adjusting for Risk in the Capital Budget of a Growth-Oriented Company: Comment, The Journal of Financial and Quantitative Analysis, Vol. 4, No. 3 (Sep., 1969), pp. 301-304

Empirical research has played an important role in recent theoretical developments in the theory of finance, particularly in the formulation and testing of various theories of capital asset pricing. A common procedure in much of that empirical research is to use historical price and dividend data to estimate the parameters of a characteristic line which relates the return on an asset or portfolio to the return on the market. While several possible limitations of such procedures have been explored, one recurring question is the appropriate length of each interval used in the estimation. The purpose of this study is to investigate intervaling in greater detail so as to better understand its impact on the results of empirical research and hence of further developments in the field of finance. This is accomplished by examining the effect of different intervals on the return distributions and estimated characteristic lines of 200 common stocks over the two decades 1950–1969. Section II reviews the relevant literature and attempts to place the intervaling effect in perspective. Research design for the investigation is described in Section III, and findings are presented in Section IV. A brief conclusion appears as Section V.

There appears to be growing interest in the development and estimation of simultaneous equation models for finance. Simkowitz and Jones [11] stimulated much of this concern in their observations on the need for these structures. Moreover, Simkowitz's application to the modeling of security returns with Logue [12] provides some support for these suggestions. Recently Lloyd [6] has argued that there may be significant problems in using two-stage least squares (hereafter 2SLS) with such models as a result of the potential for contemporaneous correlation in the structural errors across equations. The purpose of this note is to question several of Lloyd's conclusions and to provide some evidence that his findings may not be representative for the broad array of simultaneous models applicable to financial problems.