Knowledge that Transforms

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.

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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.

The findings, based on price movements alone, are that trading on the assumption that a large (small) proportion of increases in stocks' short ratios is bullish (bearish) produced significantly better than expected results at all Alphas tested above .02. This test-by-predictiveness strongly supports the validity of the assumptions.A test of the components of the short ratio produced no evidence that the success of the ratio as a stock market predictor can be attributed to either of its components singly, i.e., to either changes in short interest or changes in trading volume.A second test of the assumptions incorporated in the SR Expectations Model consisted of a comparison of model results against buy-and-hold results. In all cases except at Alpha .01 model results exceeded buy-and-hold results–and greatly so at Alphas above .02, thus strongly supporting the validity of the assumptions.The test against the buy-and-hold “control” standard was then extended to incorporate dividend and commission considerations. These considerations sharply reduced the model's performance. Therefore, an alternate strategy was tested which markedly reduced commissions, offset the opportunity cost of dividends missed when the portfolio was not long stocks, and avoided the explicit dividend drains caused by short positions. This strategy consisted of substituting Treasury Bill holdings for short positions in the basic model. This policy, consisting of switches between Treasury Bills and SSP “stocks” in accordance with Alpha .05 signals produced a terminal portfolio value greater than the buy-and-hold policy even after the introduction of a 30 percent income-tax consideration. Moreover, this higher return was generated with less risk than that inherent in the buy-and-hold policy.With respect to the optimum filter, it was found that Alpha .11 was best for price predictive purposes, with Alpha .05 a close second, but that once commission considerations are allowed for, Alpha .05 was the optimum filter.In conclusion, the hypothesis of this study is that when speculative expectations become extremely one-sided, a high probability exists that stock prices will reverse towards the unanticipated direction. This view is consistent with the theory that the stock market is generally efficient, but not perfectly so. A test of a model using changes in short ratios as a measure of shifts in investor expectations is consistent with the hypothesis: returns generated by the model significantly outperform random expectations. The test indicates a systematic tendency for investors to over-discount events when an overwhelming majority share the same optimism (pessimism) about future stock prices.