By Christian L. Dunis, Peter W. Middleton, Andreas Karathanasopolous, Konstantinos Theofilatos
As expertise development has elevated, as a way to have computational purposes for forecasting, modelling and buying and selling monetary markets and data, and practitioners are discovering ever extra complicated ideas to monetary demanding situations. Neural networking is a powerful, trainable algorithmic method which emulates convinced points of human mind services, and is used widely in monetary forecasting taking into account quickly funding determination making.
This e-book provides the main state of the art man made intelligence (AI)/neural networking functions for markets, resources and different parts of finance. break up into 4 sections, the ebook first explores time sequence research for forecasting and buying and selling throughout various resources, together with derivatives, alternate traded cash, debt and fairness tools. This part will concentrate on development acceptance, marketplace timing types, forecasting and buying and selling of economic time sequence. part II presents insights into macro and microeconomics and the way AI options will be used to higher comprehend and are expecting monetary variables. part III makes a speciality of company finance and credits research offering an perception into company buildings and credits, and setting up a courting among financial plan research and the impression of assorted monetary eventualities. part IV specializes in portfolio administration, exploring purposes for portfolio thought, asset allocation and optimization.
This ebook additionally presents many of the most up-to-date study within the box of man-made intelligence and finance, and offers in-depth research and hugely appropriate instruments and strategies 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
They combined rule induction and fuzzy logic and observed that their system performed better. Keles et al.  developed a model for forecasting domestic debt (MFDD). They applied ANFIS to few microeconomic variables of Turkish economy. They observed that the MFDD performed better in terms of forecasting. Huang et al.  combined an average autoregressive exogenous (ARX) model for prediction with grey system theory and a rough set to forecast the stock market automatically of the Taiwan stock exchange.
Vol. 6. IEEE). B. (1998). Neural network as a simulation metamodel in economic analysis of risky projects. European Journal of Operational Research, 105(1), 130–142. , & Grothmann, R. (2001). Active portfolio- management based on error correction neural networks, Advances in neural information processing systems, NIPS. Cambridge: The MIT Press. , & Willson, P. (2005). Can a neural network property portfolio selection process outperform the property market? Journal of Real Estate Portfolio Management, 11(2), 105–121.
Results from their computer simulations and experiments on stock data reveal that kernel functions in SVMs are unable to predict accurately the cluster feature of volatility. Miazhynskaia et al.  attempt to forecast volatility with numerous models. Their conclusion shows that statistical models account for non-normality and explain most of the fat tails in the conditional distribution. As a result, they believe that there is less of a need for complex non-linear models. In their empirical analysis, the return series of the Dow Jones Industrial Average index, FTSE100 and NIKKEI 225 indices over a period of 16 years are studied.