The analysis has shown that insurer investment performance parallels that of other investors; greater returns are associated with greater variability. However, with the acquisition of higher levels of investment risk insurers generally reduce the level of underwriting risk which is undertaken. Thus, insurer management apparently attempts to keep ruin probabilities within some undefinable but clearly present limits. In the process of trading off between investment and underwriting risk, a higher rate of return to net worth is sacrificed.

The sacrifice of potentially higher rates of return to equity, however, does not place the insurer at a disadvantage relative to the capital market or make attractive the alternative of operating as an investment trust. Under reasonable conditions governing the risk and return associated with underwriting activities, the insurer return to net worth is in a more efficient position as the result of underwriting activities than that offered by the capital market alone. For a given risk position, the return to the insurer exceeds that available from the capital market alone. Thus, so long as marginal returns to underwriting are positive, the leveraging afforded by the expansion of premium volume produces a superior return over the traditional leveraging which might be employed by an investor in the capital market.

The purpose of this study was to test empirically the risk and return relationships for a mean-variance (E-V) and a mean-semivariance (E-S) capital asset pricing model (CAPM). To date, virtually all empirical work has focused on the Sharpe-Lintner [28,17] E-V model. In the E-V model, the risk of an efficient portfolio is measured by the standard deviation of return, σp. For individual securities, the appropriate measure of risk is the covariance of return on the security and the market portfolio. The E-V model states that the expected return of any security or portfolio equals the risk-free rate of return plus a risk premium that is η times the difference between the expected return on the market portfolio and the risk-free rate of return, i.e.,

where the tildes denote random variables, and

= expected rate of return on security i,

Rf = risk-free rate of return,

= expected rate of return on the market portfolio, and

.

Investment decision making under conditions of uncertainty, and in particular portfolio selection, is carried out mainly in the Mean-Variance framework which has been developed by Markowitz [29], [30] and Tobin [42]. By assuming the lending and borrowing of money at a given riskless interest rate, Sharpe [39], [40], Lintner [27], [28], Mossin [34], and others derived and extended the Capital Asset Pricing Model, under which an equilibrium price of each risky asset is determined. However, though the mean variance rule is quite convenient to apply, its limitations are well known, i.e., one must assume either normal probability distributions with risk aversion or quadratic utility functions.

In recent years a number of papers have been concerned with the determination of necessary and sufficient conditions for portfolio separation and for myopia. As a result of these earlier investigations, it is known that a necessary and sufficient condition both for portfolio separation and for myopia is that the investor's utility function exhibit risk tolerance, that is a linear function of wealth. What is lacking in the existing literature is a clear demonstration of the economic relevance of linear risk tolerance for portfolio separation and myopia. It is hoped that this paper will help to fill the gap by an analysis of separation and myopia using the standard tools of price theory: indifference curves, budget lines, and Engel curves. Viewed in this perspective, a substantial part of the analysis can be amplified and clarified in terms of the geometry of the situation.

Whenever the firm must borrow funds, it must also decide maturity of the new debt. Yet, the decision models which have dealt with the debt maturity decision have done so almost incidentally, as an extension of the decision to exercise the call provision on outstanding bonds ([6], [10], [23]). There has been little direct examination of the corporate debt maturity decision. In an attempt to fill this gap, this paper is an exploration of the debt maturity decision for a firm which is concerned with minimizing the present value of the expected costs of borrowing. This paper develops a discrete dynamic programming model of the debt maturity decision, in a world where interest rates follow a finite Markov process, and where the yield curve is formed from expectations regarding the future course of interest rates. With this optimization model, the influence on the debt maturity strategy of variables such as flotation costs and liquidity premiums will be explored. There will be no consideration of the risks associated with alternative borrowing strategies.

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.

1. This paper presents and tests a model of dealer inventory response. The estimated inventory responsiveness coefficient is statistically significant and its magnitude is consistent with reasonable values of underlying variables which, it is hypothesized, determine the coefficient.

2. The sign of the inventory responsiveness coefficient indicates that dealers tend to be passive and acquire shares when prices fall and sell shares when prices rise. This type of behavior is sometimes termed “stabilizing.”

3. Dealer inventories tend to increase on days prior to price declines and tend to decrease on days prior to price increases; that is, inventory changes tend to be “destabilizing” with respect to future price changes. This implies that a fraction of the public trades on superior information and that dealers tend to lose money to such information traders.

4. There is a strong tendency for dealer inventory levels to return to normal, presumably zero. The implied typical inventory holding period is about 8 to 10 trading days.

5. Comparison of NASDAQ dealers and NYSE specialists shows that the pattern of inventory responsiveness is very much the same for the two. This suggests that both act in accordance with the underlying economic model and that differential regulation has little effect on typical inventory responsiveness.

6. This finding does not obviate the possibility that individual dealers or specialists behave in atypical or undersirable ways, and that the extent of such atypical behavior might depend on the degree of public regulation of dealer activities. An exhaustive comparative study of deviations from normal behavior was not possible. However, it was possible to compare the frequency of nonstabilizing transactions in which price change and inventory change on a given day are in the same, rather than opposite, direction. One could not conclude that NASDAQ dealers had more nonstabilizing activity than NYSE specialists.

In this paper, the Markowitz and Sharpe portfolio selection approaches are viewed as alternative analytic processes for portfolio selection. By “analytic process,” we mean a process that begins with data collection and culminates when a decision is made. The properties of these analytic processes are examined in the same sense that one studies the properties of a statistical estimator, except that a global view of the analytic process is taken. The properties of decisions that result from applications of these processes are studied experimentally, and are reported in terms of the objectives of the portfolio manager.

The opponents and proponents of competitive brokerage commission rates for the New York Stock Exchange have, for nearly a decade, been dueling in the hearing rooms of Congress and the Securities and Exchange Commission (SEC). The contest developed because financial institutions, in attempting to skirt the New York Stock Exchange (NYSE) and its fixed commission rates, had used a variety of trading practices that were sharply criticized by the government overseers of the securities markets. The securities industry, the government overseers, and scholars have debated what would be the most effective regulatory approach to improving the social performance of the securities marketplace. Would it be through initiating even more stringent federal regulation of exchange behavior? Or, would it be through selective deregulation to increase competition, particularly in the determination of commissions? Competitive forces might constrain and direct that behavior. The policy that has been developing would deregulate and restructure the marketplace to create a “central market system.” Competition would replace regulation to whatever extent may be possible, in determining both commission rates and the quality of marketplace services provided [6]. But, the contest has been long and often heated. From the thrusts and parries, there can be identified some fundamental issues concerning the economics of the stock exchange as a form of marketplace organization.

Jensen [6] employed the instantaneous systematic risk concept to eliminate the problem associated with time horizon. Based upon the effective rate of return argument, Cheng and Deets [3] claimed that Jensen instantaneous risk is not independent of the time horizon used in the investment analysis. They have also proposed a so-called Cheng-Deets instantaneous risk to substitute for the Jensen instantaneous risk.

Following the log normal distribution assumption, this paper has shown that Cheng-Deets instantaneous risk is identical to Jensen instantaneous risk. The relationship between finite systematic risk and instantaneous risk is also identified. The roles played by the effective and the nominal rate-of-return concepts in the capital asset pricing process are also clarified. It is shown that both Jensen and CD instantaneous risks are biased unless the investment horizon is instantaneous. A testable generalized CAPM is derived to test the instantaneous investment horizon assumption. Finally, 30 securities of the Dow- Jones industrial average were used to test the generalized CAPM derived in this paper.

A capital asset-pricing model which relates risk and return under conditions of changing price levels has been developed in this paper. The resulting model implies that price-level changes do not affect the expected real returns on individual assets except through their impact on the return of the market portfolio. If real market returns are independent of price-level movements, the model is very much like the standard capital asset-pricing model expressed in real returns. This version of the capital asset-pricing model does not, however, resolve all the difficulties associated with changing price levels, since we have assumed that the nominal default-free rate is determined outside the model and that relative prices do not change. These limitations, however, also apply to all other single-period capital asset-pricing models.

In addition, the model was converted into nominal returns by assuming that price-level changes and the real market returns are uncorrelated. The resulting equation illustrates the difficulty involved in using nominal returns to test a model expressed in real returns. The same equation also provides a possible explanation for the noted discrepancies between the empirical' evidence found by Black, Jensen, and Scholes [3] and the prediction of the traditional capital asset-pricing model.

The Securities and Exchange Commission (SEC) and the New York Stock Exchange are concerned with the full disclosure of information insiders normally would be expected to possess about their company, including any facts that would materially affect the market's valuation of the firm's worth if they were publicly known. At present, the regulatory agencies have limited their activities to the collection and dissemination of historical information and facts. The motives of insiders, based in large part, presumably, on their knowledge regarding future operating results are hidden from the public eye. The SEC in compiling the Official Summary of Stock Transactions does not require insider to reveal his motivation for trading.

In this paper, we present a new model for the measurement of yield curve relationships that is derived from interest rate theory, utilizes an objective procedure, and provides measures of the accuracy of the results obtained. In empirical tests of the model, the structure postulated is found to consistently provide a high level of explained variation in observed market yields on U.S. Treasury bonds. In a comparison with a yield curve model previously offered by Cohen et al., the present model is superior in terms of both goodness of fit and other associated statistical criteria. Clear evidence exists that the impact of coupon level upon yield is statistically significant, consistently positive in direction, substantial in magnitude, and variable over time. These results indicate that correction for coupon differences in the calculation of forward rates for use in empirical tests of interest rate theory is necessary in order to obtain reliable results. Finally, the yield curve model is used to calculate estimates of the risk-free pure discount rate.

There is still a great deal of doubt whether we can avoid a capital shortage as economic recovery proceeds. In the near term, one sign of an impending capital shortage will be the appearance of bottlenecks in the industrial sector of our economy. Presently the data on capacity and its utilization are seriously defective.

The Federal Reserve Board, in order to remedy the deficiency of the data, is improving its series on utilization rates. The new series in general will show that we have substantially less unused capacity than indicated by the old series.

My preliminary reading of the improved data, ne ertheless, is that we need not be greatly worried about major bottlenecks well into 1977.

Thereafter, the pace of recovery will be a critical factor. If the economy expands very rapidly, we may not have time to put in place enough capacity to avoid shortages. A moderately paced recovery will give us more time to produce the plant and equipment.

In 1952, Harry M. Markowitz [6] described a theory on the selection of assets in forming a portfolio. Assuming asset returns are stochastic, his theory postulated that rational investors should select a portfolio from the set of all portfolios which offered minimum risk (measured by variance) for varying levels of expected return. This set was named the efficient set by Markowitz.

The shareholder equities of most construction lending Real Estate Investment Trusts (REITs) have been rapidly eroded by massive losses. Some trusts, however, have maintained a reasonable degree of stability. Our objective was to statistically analyze variations in the stability of shareholder equity to obtain insights into the operation of the REITs and to suggest possible causal relationships for their lower overall performance.

Investor behavior was measured on a firm-by-firm basis by the volume of transactions in the stock of a firm. While data on an individual investor by individual investor basis would be desirable, it is not as readily available as stock volume data. Volume represents a simple summation of individual actions and can be considered at least a partial disaggregation of stock-market activity.

The reasons for individual investor action were considered to be (1) a change in trade-off between risk and return, (2) the unfolding of time-dependen consumption plans, and (3) perception of information that changes expectations. It was argued that, in general, the first reason can be ignored, occurring at discrete and probably lonq intervals, and that the second reason is unlikely to have an effect over a few years on the total volume of transactions in the stock of a given firm. Thus, fluctuations in such volume were considered to reflect perception of information about the given firm by investors in that firm.

Market behavior was analyzed in terms of general market movements and market movements specific to the firm. Market-wide effects on both price changes and volume of transactions.in the stock of a given firm were filtered out. This left price changes indicating the flow of, and volume indicating a reaction to, information unique to the given firm.

Perception of information about a given firm by investors, measured as indicated above, was then examined for association with the flow of information coming onto the market as indicated by fluctuations in the price of the stock of the firm net of market-wide effects. The percentage of volume of transactions in the stock of a given firm that was explained by fluctuations in the stock price was taken as a measure of the efficiency of investor behavior with respect to the given firm. The validity of accepting this interpretation is based on the following assumptions: (1) the probability that investors' demands for a given stock continually exactly offset each other in such a manner that volume occurs without price change is negligible, (2) specialists in securities are unable to perfectly anticipate changes in market demand in such a manner that price changes occur without volume.

In a general equilibrium model of risky assets, prices would be determined by the interaction of the supply and demand. Unpredictable events would impact one or both sides of the asset market and thereby influence the return on assets. The probability distribution of returns would thus be endogenous. Since expectations as to subsequent asset returns influence asset demand, the probability distribution of returns is a crucial element in the general equilibrium system; it is the consequence of the demand and supply of assets and, at the same time, a central determinant of expectations and, hence, of the demand for assets. Nevertheless, it is possible to undertake a partial equilibrium analysis in which the behavior of asset demand, conditional upon a postulated probability distribution of returns, is examined. In such a limited context, it is natural to postulate a “convenient” probability distribution of returns, namely, one that facilitates the analysis of demand. A leading assumption has been that returns are serially independent and obey a stationary distribution.

In this paper, a Bayesian model for forecasting future security prices under nonstationarity has been described and compared with a corresponding stationary model. In terms of the short-run behavior of the models, greater uncertainty is retained under nonstationarity than under stationarity. In terms of the limiting behavior of the models, the values of the parameters of interest cannot be ascertained with certainty under nonstationarity, even after the process has been observed for many time periods, and any given observed returns receive less weight as the length of time since the observed returns increases. These properties are not shared by the corresponding stationary model, and in general, the nonstationary model considered in this paper appears to have more realistic properties than the corresponding stationary model.

With respect to portfolio choice under linear utility, nonstationarity has no effect in the short run but may prevent the curtailment of trading in the long run that occurs under the stationary model. For a risk-averse decision maker considering one risky security and one risk-free security, nonstationarity decreases the attractiveness of the risky security. This implies that in general, a risk-averse decision maker will invest less money in a portfolio of risky securities in the nonstationary case than in the stationary case. When the two securities under consideration are both risky, the effect of nonstationarity for a risk-averse decision maker can be related to the expected returns for the two securities. With respect to traditional mean-variance analysis, nonstationarity does not affect membership in the efficient set of portfolios, but the efficient set does shift in mean-variance space due to the additional uncertainty under nonstationarity, and this causes a change in the optimal portfolio.

Various extensions of the forecasting model could be considered, and the portfolio selection and revision model could be reexamined in the light of such extensions. In view of recent empirical support for nonstationary variance terms in stock price distributions, the analysis of the effects of nonstationary variances and covariances on portfolio choice would be a logical extension of the analysis in this paper. Winkler [25] considered the case of an unknown covariance matrix, and that approach could be extended to include a nonstationary covariance matrix. Another possible extension is to consider the case in which changes in the unknown parameters occur at random intervals of time rather than at fixed intervals of time. Carter [6] considered such an extension for the univariate situation studied by Bather [3], and it appears to add considerable realism to the model. However, analytical results for that case may be difficult to obtain.

Nonstationarity has long been neglected in the study of economic decision models in general and in the study of portfolio analysis in particular. Although the results of this paper are obtained under a relatively simple model, the point is that nonstationarity can have effects on portfolio decisions and hence upon the functioning of capital markets. Further work of both an empirical and analytical nature concerning the existence of and effects of nonstationarity appears warranted.