On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
A new class of fuzzy implications, axioms of fuzzy implication revisited
Fuzzy Sets and Systems
Combining belief functions when evidence conflicts
Decision Support Systems
Current Approaches to Handling Imperfect Information in Data and Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
Investment using technical analysis and fuzzy logic
Fuzzy Sets and Systems - Special issue: Optimization and decision support systems
Adequacy of training data for evolutionary mining of trading rules
Decision Support Systems - Special issue: Data mining for financial decision making
Applying rough sets to market timing decisions
Decision Support Systems - Special issue: Data mining for financial decision making
Analyzing the combination of conflicting belief functions
Information Fusion
Information Sciences: an International Journal
Mathematics and Computers in Simulation
A new approach to the rule-base evidential reasoning: Stock trading expert system application
Expert Systems with Applications: An International Journal
A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment
Expert Systems with Applications: An International Journal
Combining uncertainty and imprecision in models of medical diagnosis
Information Sciences: an International Journal
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Belief rule-base inference methodology using the evidential reasoning Approach-RIMER
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Optimization Models for Training Belief-Rule-Based Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Generally, stock trading expert systems (STES) called also ''mechanical trading systems'' are based on the technical analysis, i.e., on methods for evaluating securities by analyzing statistics generated by the market activity, such as past prices and volumes (number of transactions during a unit of a timeframe). In other words, such STES are based on the Level 1 information. Nevertheless, currently the Level 2 information is available for the most of traders and can be successfully used to develop trading strategies especially for the day trading when a significant amount of transactions are made during one trading session. The Level 2 tools show in-depth information on a particular stock. Traders can see not only the ''best'' bid (buying) and ask (selling) orders, but the whole spectrum of buy and sell orders at different volumes and different prices. In this paper, we propose some new technical analysis indices bases on the Level 2 and Level 1 information which are used to develop a stock trading expert system. For this purpose we adapt a new method for the rule-base evidential reasoning which was presented and used in our recent paper for building the stock trading expert system based the Level 1 information. The advantages of the proposed approach are demonstrated using the developed expert system optimized and tested on the real data from the Warsaw Stock Exchange.