Testing Stochastic Dominance Of Manufacturing Stocks At The NSE Market

Abstract

Stochastic dominance relationships between two or more variables is crucial in the field of actuarial science, econometrics and in studying reliability. This project applies the Stochastic Dominance (SD) portfolio optimization methods to test the stocks that would do better during Kenyan electioneering periods. More and more scholars have shifted their attention to studying stochastic dominance relationships in decision theory. Some evidences presented by scholars in the area for many years have revealed that the methodology dominates many other solutions. Many methodologies assume contemporaneous as well as serial independence (assumption of no independence between samples and within a sample) which cannot be met by most observations in application since financial data features time series properties and positive correlation among observations from various samples. SD uses a distribution-free assumption framework which makes it suitable in checking dominance relationships between agricultural and manufacturing stocks. Besides, the SD relationships are based on empirical distribution differences. The methodology requires non-parametric statistical estimation as well as inference methods. SD is quite appealing to asset classes as well as investment strategies that exhibit asymmetric risk profiles. For example, smallcap stocks and momentum strategies where variance would not adequately measure investment risk since it makes no distinction between bad risk and good risk. The assumptions of no arbitrage and tendency of investors to dislike risk are largely supported by capital market equilibrium models. The project focuses on first degree SD, second degree SD and third-degree SD tests to check for the stocks that would dominate the other in the two sectors of the economy during hard economic times brought about by electioneering periods.