Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/59210
Title: Computational intelligence for cash flow planning
Authors: Su Yadana Zaw
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2014
Abstract: Computational Intelligence for Cash Flow Planning is a computational tool for decision making support for choosing financial investments using a famous evolutionary algorithm called genetic algorithm. Genetic algorithm (GA) is applied for selecting of high quality stocks with investment value. The Genetic Algorithm will identify stocks that have excess return and the potential to outperform the market using the fundamental financial and price information of stocks trading. The investors and analysts use different financial indicators to identify a good quality stock. In this project, the program accepts three important financial indicators as the input parameters namely “Return on Equity (ROE)”, “Price-to-Earnings Ratio (P/E)” and “Dividend Yield”. The initial population (chromosomes) of the genetic program is attained by encoding the input variables to 3-bits binary numbers. The chromosomes are then assessed by the fitness function which is the actual rank of the participating stocks based on the annual price return. The subsequent selection stage then chooses the fittest chromosomes by mean of roulette wheel. After going through one-point crossover and mutation processes, the resultant chromosomes are evaluated whether they fulfil the certain termination condition. If yes, the final population is achieved or else, the program will iterate. The output of the program is the optimized stocks ranking based on their quality.
URI: http://hdl.handle.net/10356/59210
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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