Since the beta systematic risk coefficient and the standard deviation are both important statistics in the received capital market theory [22] and the received option theory [1], considerabe effort has been expended on obtaining empirical estimates of these statistics [30]. The ordinary least squares (OLS) technique is typically utilized to estimate beta as the regression coefficient of a simple linear regression. However, the OLS betas for common stocks were found to be disconcertingly unstable over time [5, 6, 13, 15, 25]. But, whether the OLS beta or an adjusted beta were used, the regression statistics could still only explain less than half of the variability of most New York Stock Exchange (NYSE) stocks' returns (more specifically, R2 < .5).

This paper is concerned with costs of capital in mathematical programming formulations of the problem of capital budgeting under capital rationing. It shows that there is a serious error in the method outlined by previous authors for converting the shadow prices from the solution of the dual into measures of the firm's marginal internal opportunity rates. In addition to demonstrating that the traditional approach leads to erroneous and nonsensical results, this paper presents a correct procedure for determining these rates.

In a recent article [1], Beedles suggests that the valuation process for cash outflows (or negative benefits using his terminology) is, in some sense, different from the valuation process for cash inflows. This result, however, is not consistent with the assumption of perfect capital markets. Any cash outflow from one firm represents a cash inflow to some other firm(s) or investor(s). Consequently, any difference in the valuation processes for cash outflows and cash inflows will create profitable arbitrage possibilities.

The properties and characteristics of financial ratios have received considerable attention in recent years with interest primarily focused on determining the predictive ability of financial ratios and related financial data. Principal areas of investigation have included the prediction of corporate bond ratings [13, 20, 23, 34], and the anticipation of financial impairment [1, 2, 3, 5, 6, 7, 18, 19, 29, 32, 33, 35]. Related studies have examined the characteristics of merged firms [25, 28], the differencesin financial ratio averages among industries [9, 10], whether firms seek to adjust their financial ratios toward industry averages [15], the relationship between accounting-determined and market-determined risk measures [4, 8, 24], and the influence of financial ratios on analysts' judgments about impending bankruptcy [14, 17]. The general conclusion to emerge from these various research efforts is that a number of financial ratios have predictive and descriptive utility when properly employed.

The existence of seasonality in security rates of return has implications for both the study of market efficiency and tests involving return models. The existence of seasonal asset returns may be an indicator of market inefficiencies. In an efficient market, investor arbitrage should remove any excess seasonal return an asset receives over a comparable asset of equal risk. The presence of seasonal returns, however, does not necessitate market inefficiency. For example, an expected seasonal return may exist in an efficient market simply because of anticipated seasonal patterns embedded in its underlying determinants. Tax regulations, government monetary policy, seasonal information lags, or risk adjustments have all been advanced as determinants of seasonal movements in return. No matter what the basis for return seasonality or the extent of market efficiency, if seasonality in asset returns exists, then these returns do not follow a strict stationary process within the year. Statistical models analyzing asset returns may use this information to improve model specification. For instance, Kinney and Rozeff [16] have shown that large efficiency gains in estimating portfolio betas can be achieved using time stratified estimates which explicitly incorporate seasonality in 4 stock returns.