By Christian L. Dunis, Peter W. Middleton, Andreas Karathanasopolous, Konstantinos Theofilatos
As know-how development has elevated, with the intention to have computational functions for forecasting, modelling and buying and selling monetary markets and data, and practitioners are discovering ever extra complicated strategies to monetary demanding situations. Neural networking is a powerful, trainable algorithmic method which emulates definite facets of human mind features, and is used commonly in monetary forecasting bearing in mind fast funding choice making.
This booklet offers the main state of the art man made intelligence (AI)/neural networking purposes for markets, resources and different parts of finance. cut up into 4 sections, the publication first explores time sequence research for forecasting and buying and selling throughout a variety of resources, together with derivatives, trade traded money, debt and fairness tools. This part will concentrate on trend acceptance, industry timing versions, forecasting and buying and selling of monetary time sequence. part II presents insights into macro and microeconomics and the way AI suggestions might be used to raised comprehend and are expecting financial variables. part III makes a speciality of company finance and credits research delivering an perception into company constructions and credits, and developing a dating among financial plan research and the impression of varied monetary eventualities. part IV makes a speciality of portfolio administration, exploring purposes for portfolio thought, asset allocation and optimization.
This ebook additionally presents a few of the most recent study within the box of synthetic intelligence and finance, and offers in-depth research and hugely appropriate instruments and methods for practitioners and researchers during this box.
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Additional info for Artificial Intelligence in Financial Markets: Cutting Edge Applications for Risk Management, Portfolio Optimization and Economics
The major difficulty with ANN is that they are trained using past data, which may not be repeated. An alternative method for this could be an ES as they generate predictions. 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.
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Artificial Intelligence in Financial Markets: Cutting Edge Applications for Risk Management, Portfolio Optimization and Economics by Christian L. Dunis, Peter W. Middleton, Andreas Karathanasopolous, Konstantinos Theofilatos