Artificial Intelligence in Finance & Investing: State-of-the-art Technologies for Securities Selection and Portfolio Management
In Artificial Intelligence in Finance and Investing, authors Robert Trippi and Jae Lee explain this fascinating new technology in terms that portfolio managers, institutional investors, investment analysis, and information systems professionals can understand. Using real-life examples and a practical approach, this rare and readable volume discusses the entire field of artificial intelligence of relevance to investing, so that readers can realize the benefits and evaluate the features of existing or proposed systems, and ultimately construct their own systems. Topics include using Expert Systems for Asset Allocation, Timing Decisions, Pattern Recognition, and Risk Assessment; overview of Popular Knowledge-Based Systems; construction of Synergistic Rule Bases for Securities Selection; incorporating the Markowitz Portfolio Optimization Model into Knowledge-Based Systems; Bayesian Theory and Fuzzy Logic System Components; Machine Learning in Portfolio Selection and Investment Timing, including Pattern-Based Learning and Fenetic Algorithms; and Neural Network-Based Systems. To illustrate the concepts presented in the book, the authors conclude with a valuable practice session and analysis of a typical knowledge-based system for investment management, K-FOLIO. For those who want to stay on the cutting edge of the "application" revolution, Artificial Intelligence in Finance and Investing offers a pragmatic introduction to the use of knowledge-based systems in securities selection and portfolio management.
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Nature of the Security Investment Domain
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analysis Annual Sales Growth applications Artificial Intelligence assets assumptions attributes average-up beta case-based reasoning Chapter Chart composite grade constraints credibility criteria Debt Ratio developed dialogue direct input discussed efficient frontier Electronics evidence example expected return expert knowledge Expert Systems factors function fundamental analysis fuzzy logic genetic algorithm goal programming high-growth stock human experts Individual Stock inductive learning industry inference integration investment decisions Investor Preference Journal K-FOLIO knowledge acquisition knowledge base knowledge representation knowledge-based systems machine learning Markowitz model meta-knowledge moving averages Net2 Net6 neural networks object-oriented database output P/E Ratio pattern percent performance Portfolio Management portfolio selection Prediction price and trading problem quadratic programming reasoning relational database relevant represented risk rule base RULE Rule rule set Sales Growth Rate shown in Figure Stock Market stock price strategy theory trading volume trend Trippi unsystematic risk volume data