Titlebook: Artificial Intelligence in Financial Markets; Cutting Edge Applica Christian L. Dunis,Peter W. Middleton,Konstantinos Book 2016 The Editor(

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書目名稱Artificial Intelligence in Financial Markets影響因子(影響力)




書目名稱Artificial Intelligence in Financial Markets影響因子(影響力)學(xué)科排名




書目名稱Artificial Intelligence in Financial Markets網(wǎng)絡(luò)公開度




書目名稱Artificial Intelligence in Financial Markets網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Intelligence in Financial Markets被引頻次




書目名稱Artificial Intelligence in Financial Markets被引頻次學(xué)科排名




書目名稱Artificial Intelligence in Financial Markets年度引用




書目名稱Artificial Intelligence in Financial Markets年度引用學(xué)科排名




書目名稱Artificial Intelligence in Financial Markets讀者反饋




書目名稱Artificial Intelligence in Financial Markets讀者反饋學(xué)科排名

新娘

https://doi.org/10.1007/978-1-4899-3266-2rtificial intelligence techniques. It is observed that artificial intelligent methods are more accurate when compared to traditional statistical methods. This review would be helpful to the researchers planning to explore the interdisciplinary field of computational finance.

Ganglion-Cyst

PARA

languor

茁壯成長(zhǎng)

Linear Regression Versus Fuzzy Linear Regression: Does it Make a Difference in the Evaluation of themodified when we use FLR instead of OLS. These two approaches are applied on both the Treynor-Mazuy and Henriksson-Merton models over three sub-periods for a small sample of Greek equity mutual funds of different risk levels. According to our results, OLS and FLR models show great similarities, as observed by the estimated models.

Osmosis

cloture

Trading the FTSE100 Index: ‘Adaptive’ Modelling and Optimization Techniquess chapter, which include the introduction of an input selection criteria when utilizing an expansive universe of inputs, adaptive sliding window modelling, a hybrid combination of PSO and RBF algorithms, the application of a PSO algorithm to a traditional ARMA model, and finally the introduction of

ineluctable

是剝皮

GEPTrader: A New Standalone Tool for Constructing Trading Strategies with Gene Expression Programminon Programming (GEP) techniques. This tool is offered with a user-friendly interface that allows even the less sophisticated analysts, investors and academics to construct their own forecasting models. In a case study this chapter applies the GEPTrader to the task of forecasting and trading the SPDR