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.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

From stochastic dominance to mean-risk models: Semideviations as risk measures

Mean-variance portfolio analysis provided the first quantitative treatment of the tradeoff between profit and risk. We describe in detail the interplay between objective and constraints in a number of single-period variants, including semivariance models. Particular emphasis is laid on avoiding the penalization of overperformance. The results are then used as building blocks in the development and theoretical analysis of multiperiod models based on scenario trees. A key property is the possibility of removing surplus money in future decisions, yielding approximate downside risk minimization.

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Markowitz Revisited: Mean-Variance Models in Financial Portfolio Analysis

This paper provides a brief introduction to forecasting in financial markets with emphasis on commodity futures and foreign exchange. We describe the basic approaches to forecasting, and discuss the noisy nature of financial data. Using neural networks as a learning paradigm, we describe different techniques for choosing the inputs, outputs, and error function. We also describe the learning from hints technique that augments the standard learning from examples method. We demonstrate the use of hints in foreign-exchange trading of the U.S. Dollar versus the British Pound, the German Mark, the Japanese Yen, and the Swiss Franc, over a period of 32 months. The paper does not assume a background in financial markets.

Introduction to financial forecasting

We propose a method for optimal portfolio selection using a Bayesian decision theoretic framework that addresses two major shortcomings of the Markowitz approach: the ability to handle higher moments and estimation error. We employ the skew normal distribution which has many attractive features for modeling multivariate returns. Our results suggest that it is important to incorporate higher order moments in portfolio selection. Further, our comparison to other methods where parameter uncertainty is either ignored or accommodated in an ad hoc way, shows that our approach leads to higher expected utility than the resampling methods that are common in the practice of finance.

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Portfolio Selection with Higher Moments

The general mean-semivariance portfolio optimization problem seeks to determine the efficient frontier by solving a parametric non-quadratic programming problem. In this paper it is shown how to transform this problem into a general mean-variance optimization problem, hence the Critical Line Algorithm is applicable. This paper also discusses how to implement the critical line algorithm to save storage and reduce execution time.

Computation of mean-semivariance efficient sets by the Critical Line Algorithm

In this study, we show that earnings forecasting creates an index-tracking portfolio that dominates the historical model trade-off curve. We find that using Toyo Keizai earnings forecasts improves geometric means by over 300 basis points compared to the historical model. Weighted latent root regression is used in this study to create portfolios that have outperformed the Japanese market in backtest and in real-time performance.

Yuji Yamane

Papers

Computation of mean-semivariance efficient sets by the Critical Line Algorithm

The development of efficient portfolios in Japan with particular emphasis on sales and earnings forecasting