Expert System (ES)

An ES is a type of computer software that mimics the behaviour and knowledge of human domain experts in specific areas, in order to provide a higher quality of decision- making and advice for users. ES has often been used to advise non-experts in situations when human domain experts are unavailable. ES operate through asking the user questions about the given problem, through which they will then supply the best answer possible to support decision making (Anjaneyulu, 1998; Butuza & Hauer, 2010).

The structure of an ES is designed in three parts: user interface, inference engine and knowledge base. A user interface allows an operator to query and receive advice from the ES. The knowledge base of ES is designed based on compiling human domain experts’ knowledge, and is transformed into rule conditions that are then used by ES. The structure of inference engine in ES is designed to produce reasoning based on the rules within the ES. The inference engine of ES will detect the contradictions within the rules and refine the problem, and it will provide the user with information, which is similar to the way of human expert’s thinking. Figure 2.8 shows the basic structure of an ES.

36 | P a g e Based on the research which conducted by Kim and Park (1996), they found that rule induction algorithm is a good solution to generate the rules in ES compared to former ES approach. They consulted domain experts for data features selection, and then categorised the data collection by using Iterative Dichotomiser 3 (ID3) approach to discover the decision tree. The casual model of ES shows the effect value based on the induced rules that had been fired, and then provides the explanation of the ES conclusion and information about the stock prediction for investors whether to accept or reject. The author found that ES does not always generate a successful explanation of the classification rules.

According to Lee and Jo’s (1999) research, they applied candlestick pattern to develop an ES to predict the timing to buy or sell stock. During the development of their ES, they had interviewed investment domain experts to create the rule-based and used candlestick pattern to visualise the stock price movement for predicting stock price. The

User I n ter fac e Inference Engine Knowledge Base Non- Expert User Domain Expert

37 | P a g e ES based on the candlestick patterns and the rule-based to predict the movement of stock price which was constructed from various patterns and suggests the information and decision of stock price to investors. They proved that candlestick pattern was very useful as the feature, and concluded that the limitation of their system was lack of automated learning.

Along with Chang and Liu’s (2008) study, they utilised stepwise regression to choose suitable selected features from six technical indexes and used k-mean method to cluster into different groups. To prove the proposed method, they setup the fuzzy rules- based with simulated annealing method to search the optimal value for rule parameters of fuzzy rules-based. They used the training data for experiment as well as to map with the most similar rules from fuzzy rules-based to predict the stock trend.

Table 2.6 summarises the research outcomes done by few authors. The limitation of ES is that the inability to offer automated learning. Reason could be that the implementation of rules is not directly applicable to constantly changing scenarios. To solve these issues, ES has to generate new rules to adapt and to solve different scenarios.

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Table 2.6 - Review of Financial Time Series Prediction Model using ES

Authors Year Features and Methods Research Outcome

Kim and Park

1996  Used historical transactions data.

 Interviewed with domain experts for

data collection.

 Used induction algorithm (ID3) to

create decision tree.

 Generated the inducted rules for explanation of the result with casual model.

The author found that ES does not always generate a successful explanation of the classification

rules. Furthermore, the

decisions for certain cases are inconsistent with the prediction of classification rules.

Lee and Jo 1999  Analysed stock data in candlestick

pattern.

 Represented trading rules from

financial experts.

 Collected real invested result which was used to fine tune the

knowledge.

 Adjusted the knowledge after

analysed market trends from case base.

 Utilised the candlestick chart as key information to develop the ES.

The experiment conducted by the authors had an average hit ratio 72% as prediction result.

Chang and Liu

2008  Utilised linear regression as selected

features.

 Used stepwise regression to identify the suitable selected features, then used k-mean method to cluster the parameters into few groups.  Setup the fuzzy rules-based.  Used the training dataset to attempt

and map with the most similar rules to predict the stock trend.

The experiment proved that the combination of linear regression with ES has greatly improved the ability of prediction.

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