Erik Lie, Heidi J. Lie, The Role of Personal Taxes in Corporate Decisions: An Empirical Analysis of Share Repurchases and Dividends, The Journal of Financial and Quantitative Analysis, Vol. 34, No. 4 (Dec., 1999), pp. 533-552

Charles J. Corrado, John Schatzberg, A Nonparametric Distribution-Free Test for Serial Independence in Stock Returns: A Correction, The Journal of Financial and Quantitative Analysis, Vol. 25, No. 3 (Sep., 1990), pp. 411-415

The purpose of this paper is to provide a link between the various multivariate tests of asset pricing and a performance measure for asset sets. The paper includes a unified summary of various F tests for mean-variance efficiency, intersection, and spanning for sets and subsets of financial assets. Both the risk-free asset and no risk-free asset environments are discussed. These tests are then related to the concept of potential performance for asset sets. The potential performance measure can be viewed as an extension of the Sharpe performance measure for single portfolios. The economic intuition behind the tests is that the multivariate tests of portfolio efficiency, intersection, and spanning are tests of zero potential performance at particular margins between the asset or portfolio subset and the full asset set.

J. D. Jobson, Bob Korkie, Statistical Inference in Two-Parameter Portfolio Theory with Multiple Regression Software, The Journal of Financial and Quantitative Analysis, Vol. 18, No. 2 (Jun., 1983), pp. 189-197

Edward Miller [5], expanding on the work of Williams [8], Smith [6], and Lintner [4], has proposed a direct relationship between a stock's “risk” and its “divergence of opinion.” Under conditions of uncertainty, potential investors in a stock arrive at different assessments of expected return. Thisvariation in expectations is characterized as the stock's divergence of opinion. Miller argues persuasively that at a point in time a stock's price does not reflect the expectations of all potential investors, but rather the expectations of only the most optimistic minority who are trading the issue. As long as this minority can absorb the entire supply of stock, an increase (decrease) in divergence of opinion-leaving the average expectation unchanged-will increase (decrease) the market clearing price.

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.

Most research in modern portfolio theory and capital market theory is based on investor selection of portfolios that are efficient in the sense that they are not dominated by other portfolios in terms of their risk-expected return characteristics. The most widely used measure of portfolio risk is the variance about the mean of the exante distribution of portfolio returns. The theoretical framework from which this measure of risk is usually derived was initially suggested by Markowitz [12], and is by now well known. Although variance has the attention of most researchers, another measure, semivariance, had some early support from Markowitz himself, and from Quirk and Saposnik [17], Mao [10], and others. Semivariance as a measure of risk can be derived from the same theoretical framework as is variance; it requires only a slightly different utility function. The semivariance of returns of portfolio p below some point h is defined aswhere fp (R) represents the probability density function of returns for portfolio p. Semivariance portfolio theory is enjoying something of a revival in the works of Porter [15, 16], Hogan and Warren [6] and Klemkosky [8], and semivariance capital market models have been developed by Hogan and Warren [7] and Greene [5].

Sherman J. Maisel, Robert Jacobson, Interest Rate Changes and Commercial Bank Revenues and Costs, The Journal of Financial and Quantitative Analysis, Vol. 13, No. 4, Proceedings of Thirteenth Annual Conference of the Western Finance Association, June 20-26, 1978 (Nov., 1978), pp. 687-700

Bradford Cornell, J. Kimball Dietrich, Mean-Absolute-Deviation Versus Least-Squares Regression Estimation of Beta Coefficients, The Journal of Financial and Quantitative Analysis, Vol. 13, No. 1 (Mar., 1978), pp. 123-131

In this paper we show that selecting mutual funds using alpha computed from a fund’s holdings and security betas produces better future alphas than selecting funds using alpha computed from a time-series regression on fund returns. This is true whether future alphas are computed using holdings and security betas or a time-series regression on fund returns. Furthermore, we show that the more frequently the holdings data are available, the greater the benefit. This has major implications for the Securities and Exchange Commission’s recent ruling on the frequency of holdings disclosure and the information plan sponsors should collect from portfolio managers. We also explore the effect of conditioning betas on macroeconomic variables as suggested by Ferson and Schadt (1996) to identify superior-performing mutual funds as well as the alternative way of employing holdings data proposed by Grinblatt and Titman (1993).