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2. BACKGROUNDThere are several reasons why the area of finance has become such a popular a domain for expert system applications. Finance deals, to a large extent, with uncertain data, but most problems in finance can be decomposed into quantitative parts and qualitative parts and, to some extent, formulated as rules and facts, cases, or as frames, or semantic networks. The finance domain can, therefore, clearly benefit from the application of expert system technology. Hayes-Roth and Jacobstein (1994, p .31) list the following benefits for building expert systems, all of which hold for finance applications, some examples of which are in italics:
As a specific subdomain example, credit granting assessment systems lend themselves very well to the adoption of expert system technology, decision-making problems in the area often being too complex for handling with conventional methods. The judgment procedures are often inherently nondeterministic, and assessment also involves making judgments based on uncertain data. Moreover, the reasoning often takes place on several levels and has therefore to be decomposed. The process then leads to separate judgments for each level. For the final assessment, the subassessments have to be weighted according to some defined heuristic or rules. 3. APPLICATIONS3.1. STOCK OPTIONS PRICING -- AESOPThe AESOP system is an attempt to apply expert system methodology to the domain of stock options pricing to be used at the American Stock Exchange. At the time when AESOP was developed, many of the options specialists at the American Stock Exchange Market (AMEX) used a classical model for valuing options developed by Black and Scholes. The Black-Scholes model arrives at a theoretical options price based on a number of assumptions (Clifford et al., 1992):
AESOP integrates the Black-Scholes mathematical model with a symbolic model in the form of an expert system. It provides recommendation quotations for the specialists that are closer to what they can post than the theoretical prices produced by the mathematical model alone. AESOP is an example of an expert system that has to operate close to "real-time" as the market changes. 3.1.1. The Symbolic Model The symbolic part of AESOP represents the pricing strategy of a specialist on the AMEX, a domain that may not be receptive to mathematical modeling. The symbolic model always considers the specialist's desired spreads (the difference between bid and ask price) and always applies the specialist's rounding rules. It takes the output of the mathematical Black-Scholes model and adjusts it to incorporate the pricing strategies, with the goal of recommending bid and ask prices for each put and call for particular option series assigned to the specialist. If the specialist's position in any series exceeds a threshold level, the symbolic model adjusts the price of that option to encourage (specialist is long) or discourage (specialist is short) trading. The symbolic model also looks for limit orders and adjusts the bid/ask prices based on the presence of these orders. Limit order adjustments are the most complicated and, potentially, the most valuable feature of the symbolic model. The symbolic model always checks the AMEX rules to be sure that exchange regulations are not violated, and also scans for arbitrage possibilities. In almost all cases, arbitrage arises because bid/ask prices have been adjusted away from the theoretical price for some reason, most often because of the presence of a limit order. 3.1.2. Knowledge Representation AESOP uses rules to represent the knowledge of a senior specialist at the AMEX. The developers considered this a natural approach to knowledge representation given that the American Stock Exchange has a series of rules that apply to option prices. They also observed that the heuristics used by the expert specialist seemed to follow an if-then structure, for example, "if I am long on contracts, then reduce the asking price by one increment." 3.1.3. Rules The symbolic model of AESOP is represented using over 3000 lines of Prolog with nearly 200 rules (predicates) consisting of 400 clauses and nearly 1500 terms. Some of these predicates capture the pricing rules used by the specialist and constitute the knowledge base of AESOP, while other predicates are used to control the overall execution of AESOP, in particular the priority of various rules in the overall pricing strategy. The rules comprise Limit Order Rules, AMEX Rules, and Arbitrage Rules.
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