Saiful Hafizah Jaaman

Fund allocation using capacitated vehicle routing problem

In investment fund allocation, it is unwise for an investor to distribute his fund into several assets simultaneously due to economic reasons. One solution is to allocate the fund into a particular asset at a time in a sequence that will either maximize returns or minimize risks depending on the investor’s objective. The vehicle routing problem (VRP) provides an avenue to this issue. VRP answers the question on how to efficiently use the available fleet of vehicles to meet a given service demand, subjected to a set of operational requirements. This paper proposes an idea of using capacitated vehicle routing problem (CVRP) to optimize investment fund allocation by employing data of selected stocks in the FTSE Bursa Malaysia. Results suggest that CRVP can be applied to solve the issue of investment fund allocation and increase the investor’s profit.

Vehicle routing problem in investment fund allocation

Since its introduction by Dantzig and Ramser in 1959, vehicle routing problem keeps evolving in theories, applications and variability. The evolution in computing and technology are also important contributors to research in solving vehicle routing problem. The main sectors of interests among researchers and practitioners for vehicle routing problem are transportation, distribution and logistics. However, literature found that concept and benefits of vehicle routing problem are not taken advantages of by researchers in the field of investment. Other methods found used in investment include multi-objective programming, linear programming, goal programming and integer programming. Yet the application of vehicle routing problem is not fully explored. A proposal on a framework of the fund allocation optimization using vehicle routing problem is presented here. Preliminary results using FTSE Bursa Malaysia data testing the framework are also given.

Enhanced index tracking modelling in portfolio optimization

Enhanced index tracking is a popular form of passive fund management in stock market. It is a dual-objective optimization problem, a trade-off between maximizing the mean return and minimizing the risk. Enhanced index tracking aims to generate excess return over the return achieved by the index without purchasing all of the stocks that make up the index by establishing an optimal portfolio. The objective of this study is to determine the optimal portfolio composition and performance by using weighted model in enhanced index tracking. Weighted model focuses on the trade-off between the excess return and the risk. The results of this study show that the optimal portfolio for the weighted model is able to outperform the Malaysia market index which is Kuala Lumpur Composite Index because of higher mean return and lower risk without purchasing all the stocks in the market index.

Optimizing investment fund allocation using vehicle routing problem framework

The objective of investment is to maximize total returns or minimize total risks. To determine the optimum order of investment, vehicle routing problem method is used. The method which is widely used in the field of resource distribution shares almost similar characteristics with the problem of investment fund allocation. In this paper we describe and elucidate the concept of using vehicle routing problem framework in optimizing the allocation of investment fund. To better illustrate these similarities, sectorial data from FTSE Bursa Malaysia is used. Results show that different values of utility for risk-averse investors generate the same investment routes.

A new higher moment portfolio optimisation model with conditional value at risk

Variance is a common risk measure used for constructing portfolios. However, variance strictly depends on the assumptions that the returns of assets are not normally distributed or investor’s function is quadratic. Moreover, variance not only penalises the downside deviations below the mean return but also the upside deviation, variance, thus, does not match investor’s desire to maximise the upside deviation and minimise the downside deviation. The objective of this paper is to propose a new four moment mean-conditional-value-at-risk-skewness-kurtosis model and empirically test the model. In this proposed model, variance is replaced with conditional value at risk as the risk measure. The polynomial goal programming method is used in this study as it is flexible to incorporate different degree of investor’s preference on mean, skewness and kurtosis. Results of this study demonstrate that the mean-CVaR-skewness-kurtosis model gives higher mean return and skewness and provides better performance than the mean-variance-skewness-kurtosis model for all combinations of degree of preferences. This implies that CVaR is a better risk measure than variance in portfolio optimisation.

Insolvency prediction model using artificial neural network for Malaysian general insurers

Insolvency of insurance companies has been a concern to the community due to the need to protect the general public from the aftermath of insurer insolvency and to try to minimize the costs associated to this difficulty such as the insurance guaranty funds. The artificial neural network is utilized in this study to create an insolvency predictive model that could predict any future failure of general insurance company in Malaysia. The neural networks results show high predictability, suggesting the usefulness of this method for predicting future insurer insolvency in Malaysia.

Covariance versus correlation in capacitated vehicle routing problem-investment fund allocation problem

Capacitated Vehicle Routing Problem-Investment Fund Allocation Problem (CVRP-IFAP) provides investors with a sequence of assets to allocate their funds into. To minimize total risks of investment in CVRP-IFAP covariance values measure the risks between two assets. Another measure of risks are correlation values between returns. The correlation values can be used to diversify the risk of investment loss in order to optimize expected return against a certain level of risk. This study compares the total risk obtained from CVRP-IFAP when using covariance values and correlation values. Results show that CVRP-IFAP with covariance values provides lesser total risks and a significantly better measure of risk.

R-squared measurement in multifactor pricing model

The importance of the adjusted R-squared (R2) in multiple regression is to measure how well a model explains the response variable from independent variables. R2 sometimes induces some mistaken ideas and peculiar claims. Statistically, the larger the R2 is, the better explanatory power the model has. However, large R2 does not occur commonly in empirical study, for one should consider the practical significance of the explanatory variables, not just the statistics. This study examines the usefulness of R2 in multifactor pricing model of Shanghai Stock Exchange (SHSE). The results of this study show that the ability of R2 in information interpretation is not convinced in empirical study.

Portfolio optimization in enhanced index tracking with goal programming approach

Enhanced index tracking is a popular form of passive fund management in stock market. Enhanced index tracking aims to generate excess return over the return achieved by the market index without purchasing all of the stocks that make up the index. This can be done by establishing an optimal portfolio to maximize the mean return and minimize the risk. The objective of this paper is to determine the portfolio composition and performance using goal programming approach in enhanced index tracking and comparing it to the market index. Goal programming is a branch of multi-objective optimization which can handle decision problems that involve two different goals in enhanced index tracking, a trade-off between maximizing the mean return and minimizing the risk. The results of this study show that the optimal portfolio with goal programming approach is able to outperform the Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index because of higher mean return and lower risk without purchasi...

Price returns efficiency of the Shanghai A-Shares

Beta measured from the capital asset pricing model (CAPM) is the most widely used risk to estimate expected return. In this paper factors that influence Shanghai A-share stock return based on CAPM are explored and investigated. Price data of 312 companies listed on Shanghai Stock Exchange (SSE) from the year 2000 to 2011 are investigated. This study employed the Fama-MacBeth cross-sectional method to avoid weakness of traditional CAPM. In addition, this study improves the model by adjusting missing data. Findings of this study justifies that systematic risk can explain the portfolios’ returns of China SSE stock market.