Multi agent and artificial neural networks prediction framework development for stock investment strategy
In personal wealth management, it is necessary to have a plan before making investment in order to ensure a profitable return for the investors. The process of , generating an investment portfolio with good investment options is complex as it needs to consider a lot of parameters such as the trac...
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Online Access: | https://eprints.ums.edu.my/id/eprint/18762/1/Multi%20agent%20and%20artificial.pdf |
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| Summary: | In personal wealth management, it is necessary to have a plan before making
investment in order to ensure a profitable return for the investors. The process of ,
generating an investment portfolio with good investment options is complex as it
needs to consider a lot of parameters such as the track record of the companies, the
company's revenue projection, the risk assessment, the political conditions and the
nature of business. In this case, a multi-agent framework can be applied to solve the
problem. This thesis focuses on the development of a multi-agent framework for
wealth management particularly on stock market investment. The core objective is
to develop an Intelligent Investment Planner which utilizes multiple agents that work
together to plan, predict, assemble and generate a profitable investment portfolio for
its investor. Kuala Lumpur Stock Exchange (KLSE) was selected as the targeted
stock market. Four types of agents were developed, including the Web Mining Agent
(WMA), the Wealth Forecasting Agent (WFA), the Strategy Agent (SA), and the
Wealth Planning Agent (WPA). WMA comprises of an algorithm for web mining which
enables it to mine and extract semi-structured information and create new
structured information using ontology. The ontology developed is not limited to just
a knowledge base to store data in structured format but it plays an important role as
an inference sources in the decision making of the buying and selling of stock by
performing fundamental analysis. WFA consists of a forecasting model to predict the
stock price. This work involves the investigation of the performance of different
classifiers (established/combinations/new prediction methods) that are used in stock
market prediction. Artificial Neural Network (ANN) was chosen as the target
candidates for the forecasting model in this work because of its ability to solve
complex problems such as the stock price prediction. Feed Forward Neural Network
(FFNN), Elman Recurrent Neural Network (ERNN), Jordan Recurrent Neural Network
(JRNN) and Ensemble Neural Network (ENN) were tested in the experiments. Based
on the results, ENN outperformed the other ANNs and so it was used in the stock
market prediction. SA is responsible to generate the buy-sell signal based on the
predicted stock prices. WPA generates the investment portfolio based on the buy-sell
signal and the fundamental analysis of stock. It selects potential stocks based on
investor's preferences and passes these potential stock candidates to WFA for stock
price prediction. In turn, WPA decides on a suitable trading strategy that gives the
most profitable Investment returns and presents the investment portfolio to the
investor. Several experiments were conducted to investigate the performance of the
Intelligent Investment Planner in different environments using two trading strategies
and the results obtained showed that the proposed planner was able to generate a
profitable investment portfolio. |
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