Economic and Financial Knowledge-Based Processing

This book gives a survey of applications of artificial intelligence in banking, financial services and economics. It presents the tools, methods and application possibilities and includes an extensive survey of real projects.

A reader’s guide.- 0 Introduction.- 0.1 Introduction.- 0.2 The strategic challenge to banks and insurance.- 0.3 The strategic challenge to financial services.- 0.4 The strategic challenge to economic analysis and decision making.- 0.5 The strategic challenge for business management.- 0.6 Conclusion.- 1 Basic concepts.- 1.1 Introduction.- 1.2 Survey of AI applications in finance and economics.- 1.3 Case studies and examples.- 1.4 The mortgage loan credit granting case study.- 1.4.1 Problem statement.- 1.4.2 Knowledge base.- 1.4.3 Unification.- 1.4.4 Probability.- 1.4.5 Inference.- 1.4.6 Explanations.- 1.4.7 Knowledge acquisition.- 1.4.8 Expert system architecture.- 1.4.9 Risk analysis inference control structure.- 1.5 AI and Decision support.- 2 Applications of Artificial Intelligence in banking, financial services and economics.- 2.1 The motivations for the use of AI.- 2.2 Survey of development projects.- 2.3 Development and delivery environments.- 2.4 Generic domain utilities.- 2.5 Inference control and conflict resolution strategies.- 2.6 Table of projects.- 2.7 Project references.- 3 Knowledge Representation.- 3.1 Introduction.- 3.1.1 Motivation.- 3.1.2 Explicit vs. implicit knowledge.- 3.1.3 The knowledge representation problem.- 3.1.4 Knowledge for economic/financial reasoning.- 3.1.5 Sources of economic/financial knowledge.- 3.1.6 Knowledge representation languages and formal languages.- 3.1.7 Segmentation of knowledge types for problem-solving.- 3.1.8 Fundamental knowledge representation formalisms.- 3.1.9 Classification criteria for knowledge representation languages.- 3.1.10 Adequacy of knowledge representation formalisms.- 3.2 Case study: a tax adviser.- 3.2.1 Structure of a tax form.- 3.2.2 Representation guidelines.- 3.2.3 Knowledge representation formalisms.- 3.3 The graph and tree data structures.- 3.3.1 Motivation.- 3.3.2 Graphs.- 3.3.3 Trees.- 3.4 Semantic networks.- 3.4.1 Motivation.- 3.4.2 Causality networks.- 3.4.3 Application: a simple economic model.- 3.4.4 Dependency graphs.- 3.5 Logic.- 3.5.1 Motivation.- 3.5.2 An introduction to predicate calculus.- 3.5.2.1 Logic connectives.- 3.5.2.2 Quantifiers.- 3.5.2.3 Model theory.- 3.5.3 Guidelines for logic-based knowledge representation.- 3.5.4 Logic inference.- 3.5.5 Clausal logic and resolution.- 3.5.6 Logic and semantic networks.- 3.5.7 Application: representing part of the Italian fiscal regulation.- 3.5.8 Pros and cons of logic.- 3.6 Rules.- 3.6.1 Motivation.- 3.6.2 Facts.- 3.6.3 Rules.- 3.6.4 Rules as a knowledge representation formalism.- 3.6.5 Applications of rule-based representation.- 3.6.6 Reasoning with rules.- 3.6.7 The inference engine.- 3.6.8 Metarules.- 3.6.9 Rules vs. procedural programming.- 3.6.10 Rules vs. logic.- 3.6.11 Concluding remarks.- 3.7 Frames.- 3.7.1 Motivation.- 3.7.2 Frames, slots and facets.- 3.7.3 Procedural attachment.- 3.7.4 Interpretations of frames.- 3.7.5 Taxonomies.- 3.7.6 Hierarchical networks.- 3.7.7 Other relations among frames.- 3.7.8 Comparative descriptions.- 3.7.9 Inheritance.- 3.7.10 Inheritance mechanisms.- 3.7.11 Frames and semantic networks.- 3.7.12 Frames vs. logic.- 3.8 Temporal reasoning.- 3.8.1 Introduction.- 3.8.2 Temporal logic.- 3.8.3 Time-interval reasoning.- 3.8.4 Temporal constraints.- 3.8.5 Feature extraction in the time domain.- 3.8.6 Temporal inference.- 4 Artificial Intelligence Programming Languages.- 4.1 Introduction.- 4.1.1 Syntax and semantics.- 4.1.2 AI programming languages.- 4.1.3 Symbols and symbolic expressions.- 4.1.4 Interactivity and language interpreters.- 4.1.5 A classification of programming languages.- 4.2 Language syntax and parsing.- 4.2.1 Language syntax.- 4.2.2 Language parsing.- 4.2.3 Context-free and context-sensitive languages.- 4.3 LISP.- 4.3.1 A first look.- 4.3.2 Atoms and lists.- 4.3.3 Evaluation rules for atomic expressions.- 4.3.4 LISP functions.- 4.3.5 Functional composition and abstraction.- 4.3.6 Application: computing elasticities in economics.- 4.3.7 Functional vs. procedural programming.- 4.3.8 Boolean functions, IF and COND.- 4.3.9 Symbolic data structures.- 4.3.10 Assignment and evaluation of the data.- 4.3.11 Properties and association lists.- 4.3.12 Dynamic data typing.- 4.3.13 Identity of programs and data.- 4.3.14 Application: computing compound interests by recursion.- 4.4 Prolog.- 4.4.1 Beliefs in Prolog.- 4.4.2 Facts.- 4.4.3 Rules.- 4.4.4 Goals.- 4.4.5 Structured objects: tuples, lists and trees.- 4.4.6 Parse trees of Prolog expressions.- 4.4.7 Pattern-matching and unification.- 4.4.8 Infinite trees.- 4.4.9 Recursion.- 4.4.10 The inference engine.- 4.4.11 Controlling backtracking:!.- 4.4.12 Identity of data and programs.- 4.4.13 Application: a Prolog knowledge-based tax adviser.- 4.5 Object-oriented programming.- 4.5.1 Introduction.- 4.5.2 Object-oriented programming concepts.- 5 Search and causal analysis.- 5.1 Motivation.- 5.2 State-based representation of problems.- 5.3 Problem graphs.- 5.3.1 Implicit representation of graphs and trees.- 5.4 Search and knowledge.- 5.5 Search procedures.- 5.5.1 A generic search procedure.- 5.5.2 Classification of search methods.- 5.5.3 Application-specific search and mixed procedures.- 5.5.4 Optimization criteria.- 5.6 Application: a simple economic model in graph form.- 5.7 Simple propagation.- 5.8 Propagation with alternatives: depth-first.- 5.8.1 Representation of directed graphs.- 5.8.2 A description of the depth-first algorithm.- 5.8.3 An implementation of depth-first algorithm.- 5.8.4 Examples.- 5.9 Introducing side effects: breadth-first.- 5.9.1 A description of the breadth-first algorithm.- 5.9.2 An implementation of breadth-first algorithm.- 5.9.3 Examples.- 5.10 Case study: causal analysis in linear economic models.- 5.10.1 Causality representation in economic models.- 5.10.2 Causal analysis of a simple economic model.- 5.10.3 Causal ordering.- 5.10.4 Algorithm for causal assignment.- 5.10.5 An algorithm for causal analysis and consistency.- 5.11 Heuristic search methods.- 5.11.1 Hill-climbing algorithm.- 5.11.2 Beam-search algorithm.- 5.11.3 Best-first algorithm.- 5.11.4 A* algorithm.- 6 Neural processing and inductive learnings.- 6.1 Introduction.- 6.2 Neural processing for learning and classification.- 6.2.1 Neural models.- 6.2.2 Neural learning.- 6.2.3 Neural learning algorithms.- 6.2.4 Consultation.- 6.2.5 Performance evaluation.- 6.3 Inductive learning.- 6.3.1 Introduction.- 6.3.2 Concept learning.- 6.3.3 Induction algorithms.- 6.3.4 ID3 induction of decision trees.- 6.3.5 Examples.- 6.4 Extensions to neural processing.- 6.4.1 Neural decision logic.- 6.4.2 Learning how to forecast.- 6.4.3 Other applications.- 7 Technical analysis for securities trading.- 7.1 Introduction.- 7.2 Curve generation by a syntactic grammar.- 7.3 Curve segmentation.- 7.4 Segmentation of noisy curves.- 7.5 Analysis evaluation rules.- 7.6 Technical analysis on several curves and software implementation.- 7.7 Time series analysis.- 7.8 Examples of concurrent trading rules.- 7.9 Off-line analysis for learning.- 7.10 Forecasting.- 7.11 Trade generation.- 8 Intelligent information screens.- 8.1 Introduction.- 8.2 Selective object-oriented data acquisition.- 8.3 Knowledge-based information screens.- 8.4 Knowledge-based filters for financial information screens.- 8.5 Information retrieval aspects.- 8.6 Data fusion.- 8.7 Correlation.- 9 Natural language front-ends to economic models.- 9.1 Introduction.- 9.2 Prolog parser for NL front-ends.- 9.3 Definite clause grammar in Prolog.- 9.4 Translation of DCG grammar rules into Prolog clauses.- 9.5 DCG parser.- 9.6 Reasoning from NL analysis.- 9.6.1 Form input.- 9.6.2 Validation of input.- 9.6.3 Modeling from NL analysis.- 9.6.4 Generation of temporal reasoning from NL analysis.- 10 Trade selection with uncertain reasoning on technical indicators.- 10.1 Introduction.- 10.2 The theory of Dempster-Shafer.- 10.2.1 Basic probability assignment.- 10.2.2 Gedibility belief and plausibility.- 10.3 Pooling evidence.- 10.4 Application: pooling evidence about trading.- 11 Currency risk management.- 11.1 Introduction: risk planning over time.- 11.2 Single period model.- 11.3 Multi-period model.- 11.4 Knowledge-based risk management.- 11.5 Risk allocation procedure.- 12 Reasoning procedures in knowledge-based systems for economics and management.- 12.1 Introduction.- 12.2 Objects in decision analysis.- 12.3 Classification of decision methods.- 12.3.1 Perception criterion.- 12.3.2 Rationality criterion.- 12.3.3 Action criterion.- 12.4 Logics and constraints.- 12.5 Truth maintenance as rational decision-making.- 12.6 Search over time and disequilibrium.- 12.7 Conflict resolution.- 12.8 Search over AND/OR graphs.- 12.9 Power relations and gaming for the selection of solutions.- 12.9.1 Game theory and AI.- 12.9.2 Definitions.- 12.9.3 Relations to search strategies.- Appendix 1 Software Codes.- Appendix 2 Predefined LISP and Prolog expressions 333.