Search

Purpose - This study aims to use gray models to predict abnormal stock returns. Design/methodology/approach - Data are collected from listed companies in the Tehran Stock Exchange during 2005-2015. The analyses portray three models, namely, the gray model, the nonlinear gray Bernoulli model and the Nash nonlinear gray Bernoulli model. Findings - Results show that the Nash nonlinear gray Bernoulli model can predict abnormal stock returns that are defined by conditions other than gray models which predict increases, and then after checking regression models, the Bernoulli regression model is defined, which gives higher accuracy and fewer errors than the other two models. Originality/value - The stock market is one of the most important markets, which is influenced by several factors. Thus, accurate and reliable techniques are necessary to help investors and consumers find detailed and exact ways to predict the stock market. [ABSTRACT FROM AUTHOR]

We investigate an iterative, data-driven approximation to a problem where the investor seeks to maximize the expected return of her portfolio subject to a quantile constraint, given historical realizations of the stock returns. The approach, which was developed independently from Calafiore (SIAM J Optim 20:3427-3464 ) but uses a similar idea, involves solving a series of linear programming problems and thus can be solved quickly for problems of large scale. We compare its performance to that of methods commonly used in the finance literature, such as fitting a Gaussian distribution to the returns (Keisler, Decision Anal 1:177-189 ; Rachev et al. Advanced stochastic models, risk assessment and portfolio optimization: the ideal risk, uncertainty and performance measures, Wiley, New York ). We also analyze the resulting efficient frontier and extend our approach to the case where portfolio risk is measured by the inter-quartile range of its return. Our main contribution is in the detail of the implementation, i.e., the choice of the constraints to be generated in the master problem, as well as the numerical simulations and empirical tests, and the application to the inter-quartile range as a risk measure. [ABSTRACT FROM AUTHOR]

A pure investment is defined here as any transaction where the invested capital is nonnegative at all times during the life of the transaction. This article proves that any transaction whose first non-zero term is negative can be uniquely partitioned into pure investments with strictly decreasing internal rates of return (IRRs). The IRRs of the partitioning transactions put bounds on the IRRs of the original transaction. The partitioning theorem gives a simple characterisation of Arrow/Levhari's results on the optimal truncation of a transaction. The partitioning theorem also has applications to the problem of the optimal time for an investor to sell the remaining terms in a transaction. [ABSTRACT FROM AUTHOR]