Kenneth N. Levy

Bio: Kenneth N. Levy is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Portfolio & Portfolio optimization. The author has an hindex of 17, co-authored 47 publications receiving 943 citations. Previous affiliations of Kenneth N. Levy include University of Pennsylvania.

Disentangling Equity Return Regularities: New Insights and Investment Opportunities

Abstract: (1988). Disentangling Equity Return Regularities: New Insights and Investment Opportunities. Financial Analysts Journal: Vol. 44, No. 3, pp. 18-43.

Calendar Anomalies: Abnormal Returns at Calendar Turning Points

Abstract: (1988). Calendar Anomalies: Abnormal Returns at Calendar Turning Points. Financial Analysts Journal: Vol. 44, No. 6, pp. 28-39.

Portfolio Optimization with Factors, Scenarios, and Realistic Short Positions

Abstract: This paper presents fast algorithms for calculating mean-variance efficient frontiers when the investor can sell securities short as well as buy long, and when a factor and/or scenario model of covariance is assumed. Currently, fast algorithms for factor, scenario, or mixed (factor and scenario) models exist, but (except for a special case of the results reported here) apply only to portfolios of long positions. Factor and scenario models are used widely in applied portfolio analysis, and short sales have been used increasingly as part of large institutional portfolios. Generally, the critical line algorithm (CLA) traces out mean-variance efficient sets when the investor's choice is subject to any system of linear equality or inequality constraints. Versions of CLA that take advantage of factor and/or scenario models of covariance gain speed by greatly simplifying the equations for segments of the efficient set. These same algorithms can be used, unchanged, for the long-short portfolio selection problem provided a certain condition on the constraint set holds. This condition usually holds in practice.

Residual Risk: How Much is Too Much?

Abstract: Excess returns are deviations between actual returns and benchmark returns, and residual risk is the volatility of these returns. The authors use a framework based on the information ratio, defined as the excess returns to residual risk, to examine the effects of using arbitrary constraints to control residual risk. They find that these constraints may lead to suboptimal behavior by investors and portfolio managers.

Long-Short Portfolio Management: An Integrated Approach

Abstract: With the freedom to sell short, an investor can benefit from stocks with negative expected returns as well as from those with positive expected returns The authors explain that the benefits of combining short positions with long positions in a portfolio context, however, depend critically on the way the portfolio is constructed Only an integrated optimization that considers the expected returns, risks, and correlations of all securities simultaneously can maximize the investor9s ability to trade off risk and return for the best possible performance This holds true whether or not the long–short portfolio is managed relative to an underlying asset class benchmark Despite the incremental costs associated with shorting, the authors argue that a long–short portfolio, with its enhanced flexibility, can be expected to perform better than a long–only portfolio based on the same set of insights

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Linear systems

Abstract: Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge: