Knowledge that Transforms

Richard C. Burgess, Keith H. Johnson, The Effects of Sampling Fluctuations on the Required Inputs of Security Analysis, The Journal of Financial and Quantitative Analysis, Vol. 11, No. 5 (Dec., 1976), pp. 847-854

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.

A temporary trading suspension in a listed security represents a temporal discontinuity in a continuous auction market. Although the SEC occasionally suspends trading in specific securities, the NYSE itself administratively halts trading in individual NYSE issues. The latter occur quite frequently (almost three per day on average), and typically last about two hours. NYSE-initiated suspensions are the focus of the present paper.

Michael E. Echols, Jan Walter Elliott, A Quantitative Yield Curve Model for Estimating the Term Structure of Interest Rates, The Journal of Financial and Quantitative Analysis, Vol. 11, No. 1 (Mar., 1976), pp. 87-114

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.

The weighted average cost of capital (Ko) is presented in virtually all textbooks in financial management and capital budgeting as a practical concept fundamental to the actual selection of optimal financial and investment alternatives. As often employed Ko can be defined aswhereKo = the weighted average cost of capital, Ks = the cost of equity capital, Kb = the cost of debt capital, S = the market value of the firm's equity, B = the market value of the firm's debt, andV = S + B, the total market value of the firm.

Models of return generation for securities are potentially important for a number of reasons, including their possible utility in normative portfolio construction. Multi-index models of the process are frequently suggested as an alternative to the familiar single-index models, but, while the multi-index models are intuitively appealing, their empirical superiority remains largely undemonstrated. This paper examines the extent to which three multi-index models succeed in eliminating dependence in the return residuals for a portfolio of common stocks. The relevance of this research lies in the promise that, while obviously requiring additional inputs to determine the efficient set of portfolios, multi-index models may succeed in identifying a more accurate set of efficient portfolios.

Empirical research has cast so much doubt on chart readers that most capital theorists have about as much faith in charts as astronomers have in astrology. Certainly there is overwhelming evidence that attempting to predict future price changes on the basis of past price behavior is unproductive. There is, however, another aspect of technical analysis which has received much less attention from academicians. In its narrow form technical analysis seeks to forecast the direction of price movements of individual securities from past price and volume data. A second and somewhat broader type of technical analysis concentrates on the prediction of general market movements and trends relying on a broader set of information. Various market indicators are said to offer signals useful in forecasting future prices. One type seeks to measure investor sentiment through what might be called mood variables. A second type of indicator is more closely related to fundamental factors affecting future supply and demand for securities. Both types of indicators, however, are designed to be used in predicting future market movements rather than the movements of individual stock prices. This is to be contrasted with fundamental analysis which is concerned with predicting future prices of individual securities by analyzing the underlying factors related to the firm's future profitability. Most of the prior work with market indicators takes one or another proposed market indicator and examines the historical relation, between the indicator and some market index such as the Dow Jones Industrial Average.

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.