Stock price forecasting using computational intelligence approaches

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Stock price forecasting using computational intelligence approaches

Ritsumeikan UniversityThe Graduate School of Science and EngineeringStock Price Forecasting Using Computational Intelligence ApproachesStudent: Nguyen

Stock price forecasting using computational intelligence approaches n Lu Dang Khoa (ID: 6124050299-1)Academic Supervisors: Professor Nishikawa IkukoA ThesisIn Partial Fulfilment of the Requirements forThe Degree of Mas

ter of Engineering39264AbstractThis research predicts the US stock index s&p 500 one month in the future using computational intelligence based approa Stock price forecasting using computational intelligence approaches

ches. The research simulation uses three different monthly data sets with different market characteristics in more than 20 years. Multi-layer feedforw

Stock price forecasting using computational intelligence approaches

ard neural networks trained by back propagation with early stopping technique are used as a framework for the prediction. Besides, other techniques ar

Ritsumeikan UniversityThe Graduate School of Science and EngineeringStock Price Forecasting Using Computational Intelligence ApproachesStudent: Nguyen

Stock price forecasting using computational intelligence approaches or functions in back propagation learning with time and profit adjustment factors, the use of recurrent neural networks, and the use of neural network

ensembles.The study shows the predictability of the stock price with the neural network based approach. Neural networks are able to capture the non-l Stock price forecasting using computational intelligence approaches

inear and chaotic characteristics in the Slock price data. The integrations of other techniques into neural networks do improve the prediction ability

Stock price forecasting using computational intelligence approaches

of neural networks in the stock price forecasting problem.The best profit gained by a prediction model using genetic algorithms to select inputs for

Ritsumeikan UniversityThe Graduate School of Science and EngineeringStock Price Forecasting Using Computational Intelligence ApproachesStudent: Nguyen

Stock price forecasting using computational intelligence approaches l using an adjustment factor of both time and profit information in the error function is approximately 1.25 times higher than the profit gained by an

ordinary least square training. Moreover, the research shows an effectiveness of a neural network ensemble over a single network when individual netw Stock price forecasting using computational intelligence approaches

orks in the ensemble have different forecasting abilities. It also suggests that in unstable periods of the market, average based methods should be us

Stock price forecasting using computational intelligence approaches

ed. In more stable ones, majority vote based methods are a better choice. The best ensemble predictions using weighted average and majority vote have

Ritsumeikan UniversityThe Graduate School of Science and EngineeringStock Price Forecasting Using Computational Intelligence ApproachesStudent: Nguyen

Stock price forecasting using computational intelligence approaches the ‘time capture' capabilities have better prediction results than feedforward neural networks. The best profit gained by recurrent neural networks

is approximately 1.41 times higher than the profit gained by feedforward neural networks..Acknow ledgementsI would like to express my sincere gratitud Stock price forecasting using computational intelligence approaches

e to my supervisors Professor Nishikawa Ikuko and Professor Kamci Kalsuan lor then kmd guidances, encouragements, advices, and for sharing their valua

Stock price forecasting using computational intelligence approaches

ble time, knowledge, and experiences throughout the research. I am grateful to the Japan International Cooperation Center (JICE) ibr financial grant a

Ritsumeikan UniversityThe Graduate School of Science and EngineeringStock Price Forecasting Using Computational Intelligence ApproachesStudent: Nguyen

Stock price forecasting using computational intelligence approaches ort in the research period. My sincere thanks are also for Noshiki Manabu. Iritani Takeshi. Saidamin Usmanov, Nouno Ikuc. Hasegawa Hiroc, and Sakamoto

Hirotaka for their comments and suggestions in my research.I am grateful to all friends and colleagues in Computational Intelligence Laboratory, Rits Stock price forecasting using computational intelligence approaches

umeikan University and Faculty of Computer Science and Engineering. Ho Chi Minh City University of Technology for their help and support. Finally, I t

Stock price forecasting using computational intelligence approaches

hank my parents, my sisters for their support and encouragement during more than two years in Japan.liTable of ContentsAbstract.......................

Ritsumeikan UniversityThe Graduate School of Science and EngineeringStock Price Forecasting Using Computational Intelligence ApproachesStudent: Nguyen

Stock price forecasting using computational intelligence approaches ...........................................................illList of Tables.................................................................vlList of

Figures...............................................................vllChapter One: Introduction................................................... Stock price forecasting using computational intelligence approaches

....1Chapter Two: Stock Price Forecasting Problem....................................42.1Introduction to the Financial Market.........................

Stock price forecasting using computational intelligence approaches

............42.2Market Efficiency........................................................52.3Literature Review and Problem Analysis...................

Ritsumeikan UniversityThe Graduate School of Science and EngineeringStock Price Forecasting Using Computational Intelligence ApproachesStudent: Nguyen

Stock price forecasting using computational intelligence approaches ..............62.3.3Inputs selection for neural networks................................72.3.4Neural network ensembles................................

............8Chapter Three: Artificial Neural Networks.......................................93.1Structure of Neural Networks......................... Stock price forecasting using computational intelligence approaches

....................93.2Back Propagation Learning...............................................10

Ritsumeikan UniversityThe Graduate School of Science and EngineeringStock Price Forecasting Using Computational Intelligence ApproachesStudent: Nguyen