Modeling Gold Price via Artificial Neural Network

Mombeini, Hossein; Yazdani–Chamzini, Abdolreza

doi:10.7763/joebm.2015.v3.269

Cited by 64 publications

(44 citation statements)

References 18 publications

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“…An incomplete list includes Harr, Meyer family, Daubechies family, Coiflet family, and Symmlet family [28]. Prior studies [29,30] show that gold has nonlinear multiresolution characteristic in different time scales. Daubechies wavelets are selected in this paper due to their outstanding performance in detecting waveform discontinuities for evaluating the memorial breakpoint [31].…”

Section: Reconstructionmentioning

confidence: 99%

Memories of the Gold Foreign Exchange Market Based on a Moving V-Statistic and Wavelet-Based Multiresolution Analysis

Zheng

Liu

Zhou

2018

Discrete Dynamics in Nature and Society

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Memory in finance is the foundation of a well-established forecasting model, and new financial theory research shows that the stochastic memory model depends on different time windows. To accurately identify the multivariate long memory model in the financial market, this paper proposes the concept of a moving -statistic on the basis of a modified / method to determine whether the time series has a long-range dependence and subsequently to apply wavelet-based multiresolution analysis to study the multifractality of the financial time series to determine the initial data windows. Finally, we check the moving -statistic estimation in wavelet analysis in the same condition; the paper selects the volatilities of the gold foreign exchange rates to evaluate the moving -statistic. According to the results, the method of testing memory established in this paper can identify the breakpoint of the memories effectively. Furthermore, this method can provide support for forecasting returns in the financial market.

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Section: Reconstructionmentioning

confidence: 99%

Memories of the Gold Foreign Exchange Market Based on a Moving V-Statistic and Wavelet-Based Multiresolution Analysis

Zheng

Liu

Zhou

2018

Discrete Dynamics in Nature and Society

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“…La adición del precio del petróleo como entrada mejoró el comportamiento de la RNA, sin embargo, los menores errores se obtuvieron al incluir el índice DXY y el índice S&P500, donde los errores de predicción obtenidos son menores a los reportados por otros trabajos que utilizan los mismos índices de error (Ismail et al, 2009;Mombeini y Yazdani-Chamzini, 2015). Las figuras 4 y 5 muestran una mejor predicción de la RNA con estos últimos índices como entradas.…”

Section: Redes Neuronales Artificiales Aplicadas a La Predicción Del unclassified

“…La principal ventaja de estas redes de encontrar relaciones no lineales entre las variables de entrada y salida, ha fomentado su aplicación en diferentes ramas de la ingeniería y de los mercados financieros; estos últimos caracterizados por su alta volatilidad (García et al, 2008;Martínez, 2014;Villada et al, 2014;Agudelo et al, 2015). Otras referencias han pronosticado el precio del oro, mostrando un mejor rendimiento de las RNA en comparación con series econométricas y modelos lineales (Parisi et al, 2008;Hussein et al, 2011;Mombeini y Yazdani-Chamzini, 2015). En el último de estos trabajos se estudia el comportamiento mensual del precio del metal precioso mediante modelos ARIMA y RNA, encontrando menores errores en la predicción al utilizar el modelo neuronal.…”

Section: Introductionunclassified

Redes Neuronales Artificiales aplicadas a la Predicción del Precio del Oro

2016

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ResumenEn este trabajo se predice el comportamiento del precio del oro mediante un modelo basado en redes neuronales artificiales (RNA). El objetivo del modelo es predecir los precios de cierre diarios del mercado de Londres, los cuales son tomados como referencia por el Banco central de Colombia. Se estudian varias configuraciones de RNA tipo propagación hacia adelante tomando como variables de entrada las series diarias del índice del dólar estadounidense DXY, el índice SP500, los precios del petróleo y los precios del oro. Se entrenan diferentes estructuras de RNA utilizando la serie histórica de datos, donde una parte de los mismos se utiliza para entrenamiento y la restante para la predicción. Los resultados obtenidos muestran un buen desempeño del modelo tanto en el periodo histórico analizado como en la predicción, encontrando como mejor estructura aquella que utiliza las series de precios diarias del oro, el índice DXY y el índice SP500. Palabras clave: mercados financieros; mercado del oro; redes neuronales; predicción de precios Artificial Neural Networks applied to the Prediction of the Gold Price AbstractGold price prediction using an artificial neural network model (ANN) is proposed in this work. The objective of the model is to predict the daily closing prices in the London market, which are taken as reference prices for the Central Bank of Colombia. Different configurations of type feed-forward ANN are considered using the dollar index DXY, the SP500 index, the daily oil price series, and the daily gold price series, as inputs to the ANN model. A set of ANN structures are trained using the historical series of data, where one portion is used for training and the other portion is used for testing (prediction). The results show good performance of the model both in the analyzed historical period and the predictions, where the best structure includes the daily price series of gold, the DXY index and the SP500 index.

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“…Domain of Gold price prediction is already rich with various time series models, computational intelligence models and hybrid models. In last few years Artificial neural networks have been appeared as most promising predictor models compared to traditional time series models such as GARCH, EGARCH, GJR-GARCH, ARI-MA and soon [1][2][3]. A prediction model designed with back propagation neural network with an improved EMD online learning is proposed in [4] for gold price forecasting.…”

Section: Introductionmentioning

confidence: 99%

Gold price prediction using an evolutionary pi-sigma neural network

Dash¹,

Routray²,

Rautray³

et al. 2018

IJET

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Gold Price Prediction has always been a fascinating area of study for researchers and decision makers who wish to determine its future value efficiently and accurately. In this study a predictor model is designed using a Pi-Sigma Neural Network (PSNN) for prediction of future gold price. Two evolutionary estimation paradigms such as Particle Swarm Optimization (PSO) and Differential Evolution (DE) are suggested during training step of the Pi-Sigma Neural Network to optimize the tunable weights of the network. The model is evaluated based on certain performance evaluation criteria such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) over two dataset such as Gold/INR and Gold/AED accumulated within the same period of time. The result analysis illustrates the better prediction

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Modeling Gold Price via Artificial Neural Network

Cited by 64 publications

References 18 publications

Memories of the Gold Foreign Exchange Market Based on a Moving V-Statistic and Wavelet-Based Multiresolution Analysis

Memories of the Gold Foreign Exchange Market Based on a Moving V-Statistic and Wavelet-Based Multiresolution Analysis

Redes Neuronales Artificiales aplicadas a la Predicción del Precio del Oro

Gold price prediction using an evolutionary pi-sigma neural network

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