Download Artificial Intelligence in Financial Markets: Cutting Edge by Christian L. Dunis, Peter W. Middleton, Andreas PDF
By Christian L. Dunis, Peter W. Middleton, Andreas Karathanasopolous, Konstantinos Theofilatos
As know-how development has elevated, so that you can have computational functions for forecasting, modelling and buying and selling monetary markets and data, and practitioners are discovering ever extra complicated suggestions to monetary demanding situations. Neural networking is a powerful, trainable algorithmic process which emulates yes facets of human mind capabilities, and is used widely in monetary forecasting bearing in mind quickly funding determination making.
This ebook provides the main state of the art man made intelligence (AI)/neural networking functions for markets, resources and different components of finance. break up into 4 sections, the e-book first explores time sequence research for forecasting and buying and selling throughout a number of resources, together with derivatives, alternate traded cash, debt and fairness tools. This part will specialise in trend attractiveness, marketplace timing types, forecasting and buying and selling of economic time sequence. part II offers insights into macro and microeconomics and the way AI options can be used to raised comprehend and are expecting fiscal variables. part III makes a speciality of company finance and credits research delivering an perception into company constructions and credits, and constructing a dating among financial plan research and the impression of varied monetary situations. part IV specializes in portfolio administration, exploring purposes for portfolio concept, asset allocation and optimization.
This e-book additionally offers a number of the most recent study within the box of man-made intelligence and finance, and gives in-depth research and hugely acceptable instruments and strategies for practitioners and researchers during this box.
Read or Download Artificial Intelligence in Financial Markets: Cutting Edge Applications for Risk Management, Portfolio Optimization and Economics PDF
Similar banking books
The appliance of information Mining (DM) applied sciences has proven an explosive development in progressively more various components of commercial, executive and technology. of an important enterprise components are finance, particularly in banks and insurance firms, and e-business, equivalent to net portals, e-commerce and advert administration prone.
Each one new bankruptcy of the second one version covers a facet of the fastened source of revenue marketplace that has turn into suitable to traders yet isn't coated at a sophisticated point in current textbooks. this is often fabric that's pertinent to the funding judgements yet isn't freely to be had to these now not originating the goods.
Extra resources for Artificial Intelligence in Financial Markets: Cutting Edge Applications for Risk Management, Portfolio Optimization and Economics
The problem of ES is that they do not learn through experience and are unable to handle non-linear data. To overcome these problems hybrid intelligent systems, which are able to handle linear and non-linear data, could be implemented. HIS can combine the capabilities of various systems to overcome the limitations of individual techniques. It is observed that limited literature is available on ES and HIS in finance domain. Computational finance is a blending of computational power and machine- learning techniques to cope with problems of practical interest in the financial domain.
Lean et al. concluded that the proposed model performed better. Hyunchul et al.  focused on the important issue of corporate bankruptcy prediction. Various data driven approaches are applied to enhance prediction performance using statistical and AI techniques. Case based reasoning (CBR) is the most widely used data-driven approach. The model is developed by combining CBR with a genetic algorithm (Gas). It was observed that the model generates accurate results along with reasonable explanations.
2002). AI Techniques for Game Programming. Cincinnati: Premier Press. 3. org/wiki/Artificial_neural_network. Lam, M. (2004). Neural network techniques for financial performance prediction, integrating fundamental and technical analysis. Decision Support Systems, 37, 567–581. Rich, E. , (1991). , pp. 4–6), New York, NY: McGraw Hill. Christian Groll. (2011, August). Working with financial data: Regression analysis and curve fitting. MATLAB Academy. (2015). , & Hajmeer, M. (2000). Artificial neural networks: Fundamentals, computing, design, and application.