Judul: Optimasi alokasi portofolio saham pada pasar modal indonesia menggunakan algoritma genetik
Abstrak: The selection process of investment portfolio comprise of the selection of investment that have high returns and determine its weight allocation. Determinations of stock weight allocation of shares that consist of millions of possible combinations with manual calculations require a long time and high accuracy. Furthermore, it is needed a tools to facilitate the optimizing process for selection of stock portfolio in the Indonesian capital market in order to easier for investors to determine stock portfolio. The aim of this research was to (1) design the genetic algorithm (GA) model to optimize the allocation of stock portfolio in the Indonesian capital market, (2) develop a prototype system for stock portfolio optimization by using GA. The data used were based on data published by the Indonesia Stock Exchange from January 2004 until December 2009. In order to facilitate the selection of stocks that have good performance, it is needed to groups the stocks in the LQ45 index. In addition, the company's financial condition and its growth prospects were another factor in the selection process. Furthermore, the stock prices be used as research data were the closing price of each month. The design of GA to optimize the stock portfolio was done by determine the components of GA including encoding scheme, fitness function, parent selection, crossover, mutation, elitism and population replacement. Thus, validation testing of the model to analysis the model that has been developed. Beside that, it will be tested the weight changes of the stock allocation at certain periods, to assess the implementation of portfolio rebalancing. The results showed that GA can be used as a tool in the preparation of an optimal stock portfolio. Stock portfolio allocation using adaptive GA method to generate the highest total profit compared with the market index or same weight methods.
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Judul: Optimization of Technical Indicators Based on Genetic Algorithm in Stock Exchange.
Abstrak: Stock trading is become popular in Indonesia. The generation of profitable trading rules for stock exchange investments is a difficult but popular problem. The use of Machine Learning in this problem is to obtain objective results by using information in the past market behavior. In this research, genetic algorithm is used to automatically generate trading rules based on Technical Indicators. The method in this research used Hirabayashi (2009) model and also modification model of Hirabayashi by adding 2 additional technical indicators. The results shows that Hirabayashi Model and Modification Model generate better profit result compare with buy and hold strategy in small capitalization stock or big capitalization stocks in Indonesian Stock Exchange. By using Genetic Algorithm, some of the technical indicators could be ignored. It’s also found that modification model could adapt the price trend better than Hirabayashi Model.
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Judul: Hamiltonian formulation for linear waves motion in two layers fluid
Abstrak: The surface wave could be considered as a wave that separates two fluids, namely water and air. Based on this assumption, it is introduced the interfacial wave, a wave between two layers of fluid with different density. The formulation of interfacial waves motion begins with deriving the base equation of irrotational ideal fluid. Furthermore, according to irrotational fluid assumption, the base equation can be stated in velocity potential. In this derivation, the fluids domain is assumed to be restricted by rigid lid boundary conditions, both at the upper and lower limit. Therefore, the interfacial waves motion can be explained in a hamiltonian formulation. In the hamiltonian formulation, total energy is defined as the sum of kinetic and potential energy. The hamiltonian system is obtained from reduction of kinetic energy by using the Dirichlet Neumann Operator. The resulted kinetic energy equation is nonlinear. Therefore, this form is linearized by first part of the Taylor expansion. This linearization gives a dispersion relation of linear wave. Based on this dispersion relation, the phase speed of the linear wave depends on the density ratio of the two layers fluid.
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