Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Statistika
Goh Khang Wen
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Stock Price Prediction in Bursa Malaysia Nurfadhlina Binti Abdul Halim; Goh Khang Wen
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 7, No 1 (2007)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v7i1.949

Abstract

Investment in stock is a highly risky investment, it is because the existence of randomness in thestock price. In lecture, usually we used Binomial model to price the stock. But, in real world, how dowe price the stock? Because the stock price is random, the volatility and drift is a crucial items tobehold. The main questions is how to calculate this volatility and drift, and the answer to thequestion is the sample variance and the sample mean. At any time, the stock price will be either up ordown from the previous price. This is where we need a method or model to calculate parameters forup-state and down-state for the stock price. And it will cover the volatility and the drift in anembrace. The method we used in this paper is the Hull-White algorithm. Hull-White algorithm is tofind the parameters value of u and d for prediction to stock price. Using SPSS, we will run the data toget the sample variance and sample mean. Then, using Maple 10, we calculate the u and d beforeenter the value of u and d into programming C++.
A Co-joint Deterministic Search Direction Sampling Procedure with Probabilistic Soft Approach Ismail bin Mohd.; GOH KHANG Wen; TAN EE LING
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 9, No 2 (2009)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v9i2.999

Abstract

Since hard continuous optimization models contain more than one solution and even continuumsolution, it is impossible to seek all the solution by using the existent optimization methods.Therefore, in this paper we introduce a co-joint deterministic and probabilistic approach whichmodifies a soft approach for solving hard continuous optimization models. An algorithm of co-jointapproach and several numerical experiments have been presented in this paper. The specialnumerical test results have shown that the co-joint approach is more effective than soft approachalgorithm. Fortunately, we have found that the co-joint algorithm can be used to determine whetherthe optimization model is hard continuous optimization models or not.
Co-Authors Ismail bin Mohd. Nurfadhlina Binti Abdul Halim Tan Ee Ling