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[PDF] Top 20 ARTIFICIAL NEURAL NETWORKS - AN APPLICATION TO STOCK MARKET VOLATILITY

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ARTIFICIAL NEURAL NETWORKS - AN APPLICATION TO STOCK MARKET VOLATILITY

ARTIFICIAL NEURAL NETWORKS - AN APPLICATION TO STOCK MARKET VOLATILITY

... that volatility in a rising market is less than the volatility in a falling ...attributed to leverage effect, whereby a fall in the value of a firm’s stock causes the debt to ... See full document

10

Hybrid artificial intelligence algorithms for short-term load and price forecasting in competitive electric markets

Hybrid artificial intelligence algorithms for short-term load and price forecasting in competitive electric markets

... “regularity” to the load data series, an important precondition for the successful application of neural ...proposed to improve neural network learning ...capacity to ... See full document

111

Spatial predictive mapping using artificial neural networks

Spatial predictive mapping using artificial neural networks

... Artificial neural networks (ANN) allow a multivariate data analysis of complex ...ability to learn from given examples without an explicit programming of problem solution allows a ... See full document

8

Entropy: a new measure of stock market volatility?

Entropy: a new measure of stock market volatility?

... is to compare two different approaches: one based on the statistical measure of the standard deviation or variance and the other one centered on the concept of ...attributed to the Information Theory, and ... See full document

7

Flood routing modelling with Artificial Neural Networks

Flood routing modelling with Artificial Neural Networks

... used to generate a database to train multilayer feedforward ...networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate res- ... See full document

6

Application of neural networks to predict volume in eucalyptus

Application of neural networks to predict volume in eucalyptus

... Due to the large number of trees from which DBH and height data are collected, many errors may be made at field level, harming forestry measurement, and consequently the selection of superior genotypes useful for ... See full document

7

Estimating soybean yields with artificial neural networks

Estimating soybean yields with artificial neural networks

... used to estimate the productivity of many crops, including soybeans, restricts the use of this practice, but an alternative is the use of artificial neural networks ...aimed to ... See full document

9

Application of multivariable control using artificial neural networks in a debutanizer distillation column

Application of multivariable control using artificial neural networks in a debutanizer distillation column

... objective to develop a control strategy based on neural identification of a mutivariable input- mutivariable output (MIMO) ...plant to control was simulated in software HYSYS as a classic debutanizer ... See full document

1

APPLICATION OF ARTIFICIAL NEURAL NETWORKS AS AN ALTERNATIVE TO VOLUMETRIC WATER BALANCE IN DRIP IRRIGATION MANAGEMENT IN WATERMELON CROP

APPLICATION OF ARTIFICIAL NEURAL NETWORKS AS AN ALTERNATIVE TO VOLUMETRIC WATER BALANCE IN DRIP IRRIGATION MANAGEMENT IN WATERMELON CROP

... RESUMO: A irrigação de precisão busca estabelecer estratégias que alcancem uma relação eficiente entre o volume de água utilizado (redução do insumo) e a produtividade obtida (aumento da produção). Há diversos trabalhos ... See full document

14

Application of Artificial Neural Networks for Predicting Generated Wind Power

Application of Artificial Neural Networks for Predicting Generated Wind Power

... proportional to wind speed in ...comparison to wind ...proportional to wind speed during all the seasons of a year ...speed to start the turbine for generating useful power, rated wind speed ... See full document

4

An Artificial Neural Network Application for Estimation of Natural Frequencies of Beams

An Artificial Neural Network Application for Estimation of Natural Frequencies of Beams

... using artificial neural networks stabilizing large flexible space structures, in which the neural controller learns the dynamics of the structure to be controlled and constructs control ... See full document

9

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

... used to estimate the risks of drought in the Tagus river ...applied, an essential phase is the generation of multiple future hydrological scenarios spanning several months in the future (between 24 and 60, ... See full document

14

RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks.

... gene networks based on an ensemble of multilayer ...larger networks and on biological data consisting of submodules of Escherichia ...data to the accuracy of the inference ...ability to ... See full document

19

Forecasting the Portuguese stock market time series by using artificial neural networks

Forecasting the Portuguese stock market time series by using artificial neural networks

... that neural networks can be used to uncover the non-linearity that exists in the financial ...Portuguese stock exchange index and some typical features are ...test to investigate the ... See full document

14

An Artificial Neural Network for Data Forecasting Purposes

An Artificial Neural Network for Data Forecasting Purposes

... the stock market prediction is one of the most difficult tasks of time series ...aims to investigate the potential of artificial neural networks (ANN) in solving the forecast ... See full document

12

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

... devoted to dynamic and statistical downscaling for analysis of both climate variabil- ity and climate ...introduces an application of artificial neural networks (ANNs) and ... See full document

8

Can model-based forecasts predict stock market volatility using range-based and implied volatility as proxies?

Can model-based forecasts predict stock market volatility using range-based and implied volatility as proxies?

... implied volatility index of KOSPI (South Korea) against RiskMetrics, Black-Scholes’ implied volatility and GJR-GARCH ...option market of KOSPI ranks highest in terms of trading volume and investor’s ... See full document

55

Evaluation of deciduous broadleaf forests mountain using satellite data using neural network method near Caspian Sea in North of Iran

Evaluation of deciduous broadleaf forests mountain using satellite data using neural network method near Caspian Sea in North of Iran

... Landsat satellites program, and the seventh satellite which has successfully reached to the orbit. This satellite which is originally called Continuity Mission (LDCM) Landsat Data, is a product of collaboration ... See full document

6

Fuzzy nonlinear regression using artificial neural networks

Fuzzy nonlinear regression using artificial neural networks

... Abstract: Fuzzy linear regression analysis with symmetric triangular fuzzy number coefficient has been introduced by Tanaka et al. In this work we propose to approximate the fuzzy nonlinear regression using ... See full document

11

Estimation of flood warning runoff thresholds in ungauged   basins with asymmetric error functions

Estimation of flood warning runoff thresholds in ungauged basins with asymmetric error functions

... (having an height similar to the ANN-Symm one for low α values) become less compact (on the side of the positive errors) for α equal to ...close to the zero-error line and, correctly, their ... See full document

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