In a recent article, Modigliani and Pogue [2] raised the issue of “leverage bias” in portfolio performance measures. Specifically, they contended that the value of the Jensen's alpha (α) could be affected by borrowing or lending at the risk-free rate, while the Treynor index (TI) does not suffer from this shortcoming. They illustrated this effect through the use of a graphical example similar to the one in Exhibit I where A and B are two unlevered portfolios with the same α's but different TI's. Modigliani and Pogue argued that by leveraging, i.e., borrowing at Rf, the portfolio with the greater slope (TI), A, could attain a levered portfolio AL which clearly dominates portfolio B. In other L words, the line with the higher TI will dominate the line with a lower TI regardless of α values. This seems to imply that, in general, TI is a better measure of ex post portfolio performance, and that ranking based on TI's is consistent and invariant to the leverage effect, while ranking based on a's is not.

This study provides, as a result of comprehensive search, a better description of the intertemporal behaviors of corporate debt policy, comparable to those that exist for dividend policy. Although leverage policy may vary a great deal from firm to firm, we found that: (1) The rather simple partial adjustment model with constant payout ratio to have the best predictive performance and other superior models include the first-order markov process and the historical average leverage ratio; (2) in general, firms seem to operate with a concept of “target leverage ratio, ” e.g., target ratio computed from the partial adjustment models, or from historical or industry averages; (3) there is some weak evidence of the presence of unused debt capacity for the total sample; (4) the average speed of adjustment to close the gap between the desired and actual leverage ratio is a respectable 67 percent in the first year (due to the lumpiness of debt issue, individual firms tend to be either under or overadjusted); (5) there are some indications that firms also adjust debt behavior to anticipated future increases or decreases in assets.There are several areas for future research, for instance, the best debt model could serve as the first stage of a possible two-stage equation in the empirical verification of the MSM's assumption of the independence of the investment decision to the financing decision (e.g., [7]), on a further exploration of how firms' expectations affect debt behavior. Finally, the existence of a rational target leverage ratio should encourage research interest concerning the existence of an empirically testable optimal leverage ratio.

The purpose of this note is to present a simple computational algorithm to approximate the E, S portfolio selection model. The essential feature of the model is the utilization of the familiar linear programming framework by representing risks as a series of linear constraints. Suppose we have m states and n securities, and we assume the investor is able to specify the contingent returns for all securities in each state. Following [7], we define risk as being the downside deviation from the investor's target rate of return.

The composite measures of investment performance: the reward-to-variability index, by Sharpe ([29], [30]) and Lintner [23], and the reward-to-volatility index, by Treynor [33], were developed after Markowitz ([24], [25]) and Tobin [32] popularized the mean-variance framework of analyzing the problems of certain investments. Since these are ex ante measures they are not directly applicable to the evaluation of ex post performance. A theoretical basis for doing so has been provided by Jensen ([17], [18]) who also developed another composite performance measure, the predictability index. In practice, these composite measures have been found to have problems. Foremost, they have been observed to exhibit systematic biases. Various causes of the biases have been proposed. These are: the existence of unequal lending and borrowing rates, the failure to consider higher moments of return distributions, and the elusive “true” holding period.

How does trust affect business contracting at the firm level? We analyze the case of foreign high-tech companies investing in China, where the risk of expropriation of their intellectual property is high. We find that firms mitigate this type of risk by taking local trustworthiness into account when making investment decisions. Firms prefer to invest in regions where local partners and employees are considered more trustworthy; they are also more likely to establish joint ventures and to make greater research and development investments. We employ instrumental variable regressions and dynamic panel generalized method of moments estimators to alleviate endogeneity concerns and control for time-invariant heterogeneity.