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" Artificial intelligence in financial markets : "
Christian L. Dunis, Peter W. Middleton, Konstantinos Theofilatos, Andreas Karathanasopoulos, editors
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BL
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| Record Number
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662999
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| Doc. No
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dltt
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| Title & Author
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Artificial intelligence in financial markets : : cutting edge applications for risk management, portfolio optimization and economics /\ Christian L. Dunis, Peter W. Middleton, Konstantinos Theofilatos, Andreas Karathanasopoulos, editors
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| Series Statement
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New developments in quantitative trading and investment
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| Page. NO
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1 online resource.
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| ISBN
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9781137488800
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: 1137488808
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9781137488794
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1137488794
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Includes bibliographical references and index
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| Contents
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Preface; Contents; The Editors; Acknowledgements; Final Words; References; Contents; Notes on Contributors; Part I: Introduction to Artificial Intelligence; 1: A Review of Artificially Intelligent Applications in the Financial Domain; 1 Introduction; Applications of ANN in Finance; Portfolio Management; Stock Market Prediction; Risk Management; 2 Application of Expert Systems in Finance; Portfolio Management; Stock Market Prediction; Risk Management; 3 Applications of Hybrid Intelligence in Finance; Portfolio Management; Stock Market Prediction; Risk Management; 4 Conclusion
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5 Appendix 1 Regression Analysis [7]; Classification [7]; Clustering [7]; Fuzzy c-means clustering [7]; Back propagation Algorithm Code in MATLAB [111]; Sample Code of NN Using MATLAB for Finance Management; Required functions [6]; Load Historic DAX Prices; Plotting Financial Data [6]; CAPM [6]; Stock Price Prediction Based on Curve Fitting [6]; References; Part II: Financial Forecasting and Trading; 2: Trading the FTSE100 Index: 'Adaptive' Modelling and Optimization Techniques; 1 Introduction; 2 Literature Review; 3 Related Financial Data; 4 Proposed Method
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5 Empirical Results Benchmark Models; Trading Performance; 6 Conclusions and Future Work; References; 3: Modelling, Forecasting and Trading the Crack: A Sliding Window Approach to Training Neural Networks; 1 Introduction; 2 Literature Review; Modelling the Crack; Training of Neural Networks; 3 Descriptive Statistics; 4 Methodology; The MLP Model; The PSO Radial Basis Function Model; 5 Empirical Results; Statistical Accuracy; Trading Performance; 6 Concluding Remarks and Research Limitations; 7 Appendix; Performance Measures; Supplementary Information
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ARMA Equations and Estimations GARCH Equations and Estimations; PSO Parameters; Best Weights over the Training Windows; References; 4: GEPTrader: A New Standalone Tool for Constructing Trading Strategies with Gene Expression Programming; 1 Introduction; 2 Literature Review; Genetic Programming and Its Applications to Financial Forecasting; Gene Expression Programming and Previous Applications; 3 Dataset; 4 GEPTrader; Proposed Algorithm; GEPTrader Graphical User Interface; 5 Empirical Results; Benchmark Models; Statistical Performance; Trading Performance; 6 Conclusions
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| Abstract
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As technology advancement has increased, so to have computational applications for forecasting, modelling and trading financial markets and information, and practitioners are finding ever more complex solutions to financial challenges. Neural networking is a highly effective, trainable algorithmic approach which emulates certain aspects of human brain functions, and is used extensively in financial forecasting allowing for quick investment decision making. This book presents the most cutting-edge artificial intelligence (AI)/neural networking applications for markets, assets and other areas of finance. Split into four sections, the book first explores time series analysis for forecasting and trading across a range of assets, including derivatives, exchange traded funds, debt and equity instruments. This section will focus on pattern recognition, market timing models, forecasting and trading of financial time series. Section II provides insights into macro and microeconomics and how AI techniques could be used to better understand and predict economic variables. Section III focuses on corporate finance and credit analysis providing an insight into corporate structures and credit, and establishing a relationship between financial statement analysis and the influence of various financial scenarios. Section IV focuses on portfolio management, exploring applications for portfolio theory, asset allocation and optimization. This book also provides some of the latest research in the field of artificial intelligence and finance, and provides in-depth analysis and highly applicable tools and techniques for practitioners and researchers in this field
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| Subject
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Finance-- Computer programs.
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Finance.
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Corporate Finance.
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Investments and Securities.
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Banking.
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Risk Management.
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Quantitative Finance.
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Artificial Intelligence (incl. Robotics)
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| Dewey Classification
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332
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| LC Classification
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HG173.A78 2016
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Dunis, Christian.
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Middleton, Peter W. Middleton.
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Theofilatos, Konstantinos.
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Karathanasopoulos, Andreas.
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Ohio Library and Information Network.
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