Stochastic Optimization for Financial Decision Making: Portfolio Selection Problem [QA402.5. K45 2008 f rb].

Tesis ini mengaplikasikan pengoptimuman berstokastik sebagai penyelesaian kepada masaalah pemilihan portfolio. Pemilihan portfolio merupakan satu bidang penting dalam pembuatan keputusan kewangan. Ciri penting bagi masaalah dalam pasaran kewangan umumnya terpisah dan tertakrif dengan jelas. In this thesis stochastic optimization was applied to solve portfolio selection problem. Portfolio selection problem is one of the important areas in financial decision making. An important distinguishing feature of problems in financial markets is that they are generally separable and well defined

Maximum Downside Semi Deviation Stochastic Programming for Portfolio Optimization Problem

Portfolio optimization is an important research field in financial decision making. The chief character within optimization problems is the uncertainty of future returns. Probabilistic methods are used alongside optimization techniques. Markowitz (1952, 1959) introduced the concept of risk into the problem and used a mean-variance model to identify risk with the volatility (variance) of the random objective. The mean-risk optimization paradigm has since been expanded extensively both theoretically and computationally. A single stage and two stage stochastic programming model with recourse are presented for risk averse investors with the objective of minimizing the maximum downside semideviation. The models employ the here-and-now approach, where a decision-maker makes a decision before observing the actual outcome for a stochastic parameter. The optimal portfolios from the two models are compared with the incorporation of the deviation measure. The models are applied to the optimal selection of stocks listed in Bursa Malaysia and the return of the optimal portfolio is compared between the two stochastic models. Results show that the two stage model outperforms the single stage model for the optimal and in-sample analysis

Mathematical model of hiring a new lecturer / Nur Idalisa Norddin, Khlipah Ibrahim and Ahmad Aziz

In the educational system, lecturers play important role in producing marketable graduates. This can be achieved by employing potentially competent lecturers. As being practiced in Universiti Teknologi MARA (UiTM), the process of selecting new lecturers involves three phases: academic qualification, mock teaching and face-to face interview. In UiTM, the candidates will be shortlisted after performing the mock teaching session. Then, the shortlisted candidates will be called for the face-to-face interview. This paper proposes a selection model based on Analytical Hierarchy Process (AHP), which uses both qualitative and quantitative decision making approaches. This model contains 5 levels of hierarchy, starting with the goal (new lecturer), 3 types of interview, 13 criteria of the interview, 8 sub-criteria, and finally the candidates

IDENTIFICATION OF OUTLIERS: A SIMULATION STUDY

ABSTRACT This paper compares two approaches in identifying outliers in multivariate datasets; Mahalanobis distance (MD) and robust distance (RD). MD has been known suffering from masking and swamping effects and RD is an approach that was developed to overcome problems that arise in MD. There are two purposes of this paper, first is to identify outliers using MD and RD and the second is to show that RD performs better than MD in identifying outliers. An observation is classified as an outlier if MD or RD is larger than a cut-off value. Outlier generating model is used to generate a set of data and MD and RD are computed from this set of data. The results showed that RD can identify outliers better than MD. However, in non-outliers data the performance for both approaches are similar. The results for RD also showed that RD can identify multivariate outliers much better when the number of dimension is large