An empirical assessment of different risk measures for stocks traded on the ase

This paper examines three different measures of risk, the standard deviation, the correlation coefficient and the beta coefficient. The measures are compared with each other in relation to the way with which they estimate the risk, using the Spearman’s rank correlation coefficient. The results show that the risk is valued differently in every case. The same rank correlation coefficient is being used to form portfolios with relative stable beta coefficients in order to minimize the variation of the associated risk. The results showed that a relationship exists to beta coefficients between stocks that can give useful information about the portfolio diversification, as it is possible for portfolios with relative constant coefficients and higher returns in relation to risk they undertake to be formed

Heuristic Optimisation in Financial Modelling

There is a large number of optimisation problems in theoretical and applied finance that are difficult to solve as they exhibit multiple local optima or are not ‘well- behaved’ in other ways (eg, discontinuities in the objective function). One way to deal with such problems is to adjust and to simplify them, for instance by dropping constraints, until they can be solved with standard numerical methods. This paper argues that an alternative approach is the application of optimisation heuristics like Simulated Annealing or Genetic Algorithms. These methods have been shown to be capable to handle non-convex optimisation problems with all kinds of constraints. To motivate the use of such techniques in finance, the paper presents several actual problems where classical methods fail. Next, several well-known heuristic techniques that may be deployed in such cases are described. Since such presentations are quite general, the paper describes in some detail how a particular problem, portfolio selection, can be tackled by a particular heuristic method, Threshold Accepting. Finally, the stochastics of the solutions obtained from heuristics are discussed. It is shown, again for the example from portfolio selection, how this random character of the solutions can be exploited to inform the distribution of computations.Optimisation heuristics, Financial Optimisation, Portfolio Optimisation

Systematic risk factors in stock pricing modeling: A new theoretical conceptualization.

Stock pricing modeling in both modern- and behavioral finance paradigms are remain divided, still incomplete and have been criticized for some philosophical, theoretical and model limitations. These cause the identification of risk factors in stock pricing modeling to remain puzzling. This analytical conceptual paper aims to address these issues with a new theoretical conceptualization of the risk factors in stock pricing modeling

Heuristic optimisation in financial modelling

There is a large number of optimisation problems in theoretical and applied finance that are difficult to solve as they exhibit multiple local optima or are not ‘well-behaved' in other ways (e.g., discontinuities in the objective function). One way to deal with such problems is to adjust and to simplify them, for instance by dropping constraints, until they can be solved with standard numerical methods. We argue that an alternative approach is the application of optimisation heuristics like Simulated Annealing or Genetic Algorithms. These methods have been shown to be capable of handling non-convex optimisation problems with all kinds of constraints. To motivate the use of such techniques in finance, we present several actual problems where classical methods fail. Next, several well-known heuristic techniques that may be deployed in such cases are described. Since such presentations are quite general, we then describe in some detail how a particular problem, portfolio selection, can be tackled by a particular heuristic method, Threshold Accepting. Finally, the stochastics of the solutions obtained from heuristics are discussed. We show, again for the example from portfolio selection, how this random character of the solutions can be exploited to inform the distribution of computation

Estimation of the underlying structure of systematic risk with the use of principal component analysis and factor analysis

AbstractWe present an improved methodology to estimate the underlying structure of systematic risk in the Mexican Stock Exchange with the use of Principal Component Analysis and Factor Analysis. We consider the estimation of risk factors in an Arbitrage Pricing Theory (APT) framework under a statistical approach, where the systematic risk factors are extracted directly from the observed returns on equities, and there are two differentiated stages, namely, the risk extraction and the risk attribution processes. Our empirical study focuses only on the former; it includes the testing of our models in two versions: returns and returns in excess of the riskless interest rate for weekly and daily databases, and a two-stage methodology for the econometric contrast. First, we extract the underlying systematic risk factors by way of both, the standard linear version of the Principal Component Analysis and the Maximum Likelihood Factor Analysis estimation. Then, we estimate simultaneously, for all the system of equations, the sensitivities to the systematic risk factors (betas) by weighted least squares. Finally, we test the pricing model with the use of an average cross-section methodology via ordinary least squares, corrected by heteroskedasticity and autocorrelation consistent covariances estimation. Our results show that although APT is very sensitive to the extraction technique utilized and to the number of components or factors retained, the evidence found partially supports the APT according to the methodology presented and the sample studied

Arbitrage Pricing Model; Determining the Number of Factors and Their Consistency Across Markets

Purpose - The discovery of a true financial equilibrium model that could explain the prices of stocks has long been a sought after challenge and a vital area of research in modern financial theory. The concept is based on the fact that the price of the stock is affected by the present value of the future cash flows from the stock, and anything that will affect the discount rate of these future cash flows. Many brokerage firms, financial institutions and financial consulting firms use multi-index models to aid in the investment process Thus the APT model is becoming increasingly popular and has been a subject of several empirical studies. These models have been tested on both developed and developing markets. The purpose of this research is to analyze the Arbitrage Pricing Theory (APT) introduced by Ross (1976), which is a more simplified, multifactor model, with fewer relative assumptions to other models, across different representative markets, giving particular attention to the number of factors. Design/methodology/approach – The research is quantitative in nature and principal component analysis will be used to determine the ideal number of factors that should be included in the model, as well as the identity of these factors. Findings - Results indicate that the ideal number of factors vary from four to five factors across markets, with their identity differing across markets. Findings provide valuable insights for professionals in the market as well as academics who want to gain further knowledge on the number of factors. Research limitations/implications –The application of Principal Component Analysis (PCA) is based only on a sample of stocks and not on the whole population in the stock market, and thus there remains a question of how accurate these approximations actually are. Practical implications –The APT is a popular multi-index model that should be used by financial analysts to allow risk to be more tightly controlled and allow investors to protect against specific type of risk to which he or she is particularly sensitive or to make specific bets on certain types of risks. Originality/value – No research has yet been carried out across different markets for the same time period as will be carried out in this research, and thus the empirical study in this research aims to add knowledge on whether the number of factors will be consistent across borders or will change from market to market. Keywords Arbitrage Pricing Theory, Number of factors, Emerging markets Paper type Research Pape

The impact of corporate hedging on stock price performance

<p.This study explores the extent and benefit of corporate hedging in South Africa by examining the disclosure of financial derivative instruments in the annual reports of non-financial companies listed on the JSE. The conflicting academic theory on hedging and the shortage of empirical evidence to support corporate hedging provide decision-makers, especially in South Africa, with poor information on the impact of hedging on the market value of their companies and, therefore, the total return provided to their shareholders. A database of derivative usage was constructed from the annual reports of all non-financial JSE-listed companies. The data was used to quantify the extent of derivative usage in South African and to construct the portfolios necessary to calculate the risk factors for the regression model. The Fama and French four-factor model was used as the basis for the regression analysis necessary to show whether or not hedging has a positive impact on annual stock price performance. The results show that hedging is prevalent in South Africa. However, the results provide evidence that corporate hedging through the use of derivative instruments is only a value-adding strategy for firms that exclusively use currency derivatives. The use of commodity or interest rate derivatives is not a value-adding strategy, nor is the use of currency derivatives in conjunction with commodity or interest rate derivatives.Dissertation (MBA)--University of Pretoria, 2010.Gordon Institute of Business Science (GIBS)unrestricte

Conference Proceedings of Social Sciences Postgraduate International Seminar (SSPIS) 2014

The objectives of this seminar are: To provide an avenue for postgraduate students to present their research findings, impart knowledge and get feedback; To promote interactions among participants; To enhance networking among researchers; and To assist postgraduate students with publication opportunities