Chong Mun Ho

An empirical analysis of calendar anomalies in the Malaysian stock market

This study investigates the ‘day of the week’ effect and the ‘twist of the Monday’ effect for Kuala Lumpur Composite Index for the period May 2000 to June 2006. Our empirical results find support for the Monday effect in that Monday exhibits a negative mean return (−0.09%) and represents the lowest stock returns in a week. The returns on Wednesday are the highest in a week (0.07%), followed by returns on Friday (0.04%). Monday returns were partitioned into positive and negative returns; we found that the Monday effect is clearly visible in a ‘bad news’ environment, but it failed to appear in ‘good news’ environment. This study also found evidence on ‘twist of the Monday’ effect, where returns on Mondays are influenced by previous weeks returns and previous Fridays returns. The median return on a Monday following a previous week and a previous Friday with declining returns was −0.21% and −0.26%, respectively. The median return on a Monday following a previous week and a previous Friday with rising returns was 0.02% and 0.13%, respectively. The evidence of negative Monday returns in this period is consistent with the relevant empirical literature.

Impact of exchange rate volatility on import flows: the case of Malaysia and the United States

This article investigates empirically both linear and nonlinear relationships between exchange rate volatility and import flows for the United States and Malaysia. Previous empirical work has neglected nonlinear relationships, focusing instead on linear causal relationships between exchange rate volatility and import flows, which may have generated misleading conclusions. Using annual American and Malaysian data for the periods 1975/2009 and 1980/2009, this article differs from earlier studies by adding a Brock–Dechert–Scheinkman (BDS) test to investigate the independent and identically distributed (i.i.d.) residual and then employing nonlinear causality tests to investigate the existence of nonlinear causal relationships. Two major findings emerge. First, the BDS test shows the residual of the linear model is not i.i.d. Second, the nonlinear causality test shows both Malaysia and the US have nonlinear causal relationships between exchange rate volatility and import flows.

Agent for Predicting Online Auction Closing Price in a Simulated Auction Environment

Auction markets provide centralized procedures for the exposure of purchase and sale orders to all market participants simultaneously. Online auctions have effectively created a large marketplace for participants to bid and sell products and services over the Internet. eBay pioneered the online auction in 1995. As the number of demand for online auction increases, the process of monitoring multiple auction houses, picking which auction to participate in, and making the right bid become a challenging task for the consumers. Hence, knowing the closing price of a given auction would be an advantage since this information will be useful and can be used to ensure a win in a given auction. However, predicting a closing price for an auction is not easy since it is dependent on many factors. This paper reports on a predictor agent that utilises the Grey System Theory to predict the closing price for a given auction. The performance of this predictor agent is compared with another well known technique which is the Artificial Neural Network. The effectiveness of these models is evaluated in a simulated auction environment.

Predictor agent for online auctions

In the last few years online auctions have become a popular method in purchasing and selling goods over the Internet. Bidding in an online auction is a challenging task since we do not know the outcome of our bid until the auction is closed. It is difficult to predict the winning bid of any particular auction. Hence, many investors have been trying to find a better way to predict auction closing price accurately. Knowing the closing price of a given auction would be an advantage since this information will be useful and can be used to ensure a win in a given auction. This information is beneficial to bidders since the outcome of the auction is dependent on several factors such as the number of auctions selling the same item, the number of bidders participating in that auction as well as the behaviour of every individual bidder. If the closing price of an auction is known, then bidder could decide which auction to participate and at what price. This paper reports on the development of a predictor agent that attempts to predict the online auction closing price. The performance of this predictor agent is compared with two well known techniques which are the Simple Exponential Function and the Time Series in a simulated auction environment.

Predict the online auction's closing price using Grey System Theory

The introduction of online auction has resulted in a rich collection of problems and issues especially in the bidding process such as the process of monitoring multiple auction houses, picking which auction to participate in, and making the right bid. If bidders are able to predict the closing price for each auction, they are able to make a better decision on the time, place and the amount they can bid for an item. However, predicting closing price for an auction is not easy since it is dependent on many factors such as the behaviour of each bidder, the number of the bidders participating in that auction as well as each bidders reservation price. This paper reports on the development of a predictor agent that utilizes Grey System Theory GM (1, 1) to predict the online auction closing price in order to maximize the bidders profit. The performance of this agent is compared with an Artificial Neural Network Predictor Agent (using Feedforward Backpropagation Prediction Model). The effectiveness of these two agents is evaluated using real eBay auctions data (Apple IPhone 8GB).

Competition among intelligent agents and standard bidders with different risk behaviors in simulated English auction marketplace

Agents have been used to bid in online auctions to take over the role of human bidders. We can find bidder agents with a variety of bidding strategies that participate in online auction. However, it is not known how the presence of these agents will affect the marketplace in terms of closing price and the chances of winning. In this paper, we study the economic consequence from a simulated English auction market populated by intelligent agents and three groups of standard bidders with different risk preferences. Our study revealed that when intelligent agents compete with the standard bidders, these agents generally perform better than their counterparts. More specifically, we analyse their average winning utility, the average closing price of auctions and the number of auctions won by them. Based on our experimental results, the intelligent agents outperformed the standard bidders in all three aspects.

A test of the present value model of stock prices under rational and adaptive expectations using Bursa Malaysia data from 1983 to 2003

The rational expectations model has been the central expectations hypothesis used by economists while the adaptive expectations hypothesis has been considered by many as inefficient because expectations cannot fully exploit all available information. The aim of this study is to determine which of these two expectations formation hypotheses best explains the behaviour of investors in the Malaysian stock market. We employ the Chow (1988) methodology in which the two expectations hypotheses are applied to the present value model of stock prices for Malaysian stock market data consisting of stock prices and dividends for 13 companies over 21 years. Our results provide strong statistical support for the adaptive expectations hypothesis. This finding is in line with the empirical findings of Chow and his collaborators.

Regression analysis for yield loss of oil palm due to Ganoderma disease

Oil palm industry is well-established in Malaysia as well in Indonesia. The industry is facing a devastating crop disease which is Ganoderma Basal Stem Rot Disease (BSR) or popularly known as Ganoderma disease caused by a fungal called as Ganoderma boninense. The objective of this study is to develop a yield loss model of oil palm due to the disease. Backward elimination based regression method was used in developing the yield loss model where the independent variables also include interaction categorical variables up to the second order of interaction. Residual analysis was conducted on the best model developed and it showed that the mean value and standard deviation of the standardized residuals were zero and one respectively. The distribution of the standardized residual was also normal, homoscedastic, no presence of outlier. The value of the mean absolute percentage error (MAPE) which was used to measure the forecast performance of the best model was considered reasonable forecasting. The yield loss model developed in this study can be used in estimating the economic loss as well as the economic advantage of any Ganoderma disease control.

Homogeneous and Heterogeneous Agents in Electronic Auctions

When considering the agents mediated electronic marketplace, agents play an active role in both sellers and buyers sides. A seller agent may advertise its products in the market, placing the selling price and looking for the potential buyers in the market. On the other hand, a buyer agent would look for the desired goods or services requested by its user and it has a task to bargain about the price of the products and find the best deal (Dignum, 2001). Besides that, due to the rapid growth of Information Technology and popularities of the Internet, more trading that could be done in bricks and mortar is now available without geographical constraint by using the computer and the Internet. Therefore, sellers are now looking for a larger group of potential buyers while buyers are looking for a better offer of their desired goods in the online marketplace.

The Performance of Grey System Agent and ANN Agent in Predicting Closing Prices for Online Auctions

The introduction of online auction has resulted in a rich collection of problems and issues especially in the bidding process. During the bidding process, bidders have to monitor multiple auction houses, pick from the many auctions to participate in and make the right bid. If bidders are able to predict the closing price for each auction, then they are able to make a better decision making on the time, place and the amount they can bid for an item. However, predicting closing price for an auction is not easy since it is dependent on many factors such as the behavior of each bidder, the number of the bidders participating in that auction as well as each bidders reservation price. This paper reports on the development of a predictor agent that utilizes Grey System Theory GM 1, 1 to predict the online auction closing price in order to maximize the bidders profit. The performance of this agent is compared with an Artificial Neural Network Predictor Agent using Feed-Forward Back-Propagation Prediction Model. The effectiveness of these two agents is evaluated in a simulated auction environment as well as using real eBay auctions data.