The fractal feature and price trend in the gold future market at the Shanghai Futures Exchange (SFE)

Wu, Binghui; Duan, Tingting

doi:10.1016/j.physa.2016.12.048

Cited by 16 publications

(7 citation statements)

References 10 publications

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“…Another data set used for a robustness check in this paper is futures gold prices quoted at the Shanghai Futures Exchange (the SHFE) in the period from 9 January 2008 to 6 May 2016. The considered period for futures prices only starts on 9 January 2008 because the SHFE only started gold futures trading on that date (Ruan et al ., ; Wu and Duan, ; Jena et al ., ; Lin et al ., 2018a). We follow Beckmann and Czudaj (2014), Lin et al .…”

Section: Methodsmentioning

confidence: 99%

The seasonality of gold prices in China does the risk‐aversion level matter?

Hoang

Zhu

Xiao

et al. 2018

Accounting & Finance

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This article aims to investigate the seasonality of gold prices at the Shanghai Gold Exchange over the 2002-2016 period. Our contributions rely in the distinction between risk-averse and risk-seeking investors regarding their investment strategies. The results show the existence of positive Monday and January effects. However, the Monday effect is more suitable to risk-seeking investors while the January effect is more suitable to risk-averse investors in bearish periods only. A robustness check shows that the Monday effect does not hold on gold futures prices. These results indicate the importance to consider the risk-aversion level of investors in seasonal investment strategies.

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Section: Methodsmentioning

confidence: 99%

The seasonality of gold prices in China does the risk‐aversion level matter?

Hoang

Zhu

Xiao

et al. 2018

Accounting & Finance

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“…Zheng [9] used an Elman NN to forecast opening prices of the Shanghai Stock Exchange. Wu and Duan applied the Elman NN in predicting stock [10] and gold future markets [11], respectively. In the area of electricity prediction, Rani and Victoire [12] integrated the decomposition method and group search optimization algorithm into the Elman NN.…”

Section: Introductionmentioning

confidence: 99%

Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market

Wang

Tang

2021

Complexity

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With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. We developed a hybrid approach which combined Elman recurrent network with factorization machine (FM) technique, i.e., the FM-Elman neural network, to predict stock market volatility. In this paper, the Standard & Poor’s 500 Composite Stock Price (S&P 500) index, the Dow Jones industrial average (DJIA) index, the Shanghai Stock Exchange Composite (SSEC) index, and the Shenzhen Securities Component Index (SZI) were used to demonstrate the validity of our proposed FM-Elman model in time-series prediction. The results were compared with predictions obtained from the other two models which are basic BP neural network and the Elman neural network. Some experiments showed that the FM-Elman model outperforms others through different accuracy measures. Furthermore, the effects of volatility degree on prediction performance from different stock indexes were investigated. An interesting phenomenon had been found through some numerical experiments on the effects of different user-specified dimensions on the proposed FM-Elman neural network.

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“…Therefore, the gold price forecasting is significant in influencing the decision making of investors and helping support the national economic policy and the national economic development plan. Because futures price has the characteristics of nonlinearity, chaos, and a long history (Wu & Duan, 2017), many previous studies have focused on comparing the accuracy of various models in price forecasting. Generally, time-series models and their combined approaches are widely used in the analysis and prediction of prices (Batten & Lucey, 2010;Xu, 2017Xu, , 2018Xu, , 2019aXu, , 2019bXu, , 2020.…”

Section: Introductionmentioning

confidence: 99%

“…ANNs make it easier to deal with incomplete, uncertain, or irregular data, which imply that the capability of function approximation is extremely effective. The effectiveness of different ANNs has been studied (Guresen et al, 2011); for example, single Elman neural networks (ENNs) have been applied in China to forecast the price of gold futures at the Shanghai Futures Exchange (SFE) and achieve better results (Wu & Duan, 2017). To achieve better performance, some scholars have also combined ANN with other methods to deal with asset price prediction problems.…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Gold Futures Prices at the Shanghai Futures Exchange Based on the MEEMD-CS-Elman Model

Wang

2021

SAGE Open

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The Gold futures market is a complex nonlinear system with the prediction of the futures prices of gold, one of the core issues faced by investors. Compared with more traditional approaches, empirical mode decomposition (EMD) and artificial neural network are the more powerful tools with which to deal with nonlinear and nonstationary price problems. By introducing mirroring extension (ME), EMD, Cuckoo Search (CS) algorithm, and Elman neural network, this article constructs the mirroring extension empirical mode decomposition (MEEMD)-CS-Elman model to forecast the price of gold futures using gold future AU0 price data from August 29, 2013, to October 18, 2018, at the Shanghai Futures Exchange (SFE) in China. Empirical results show that Elman combined with EMD is superior to single Elman in performance. Moreover, there exists an obvious endpoint effect by applying EMD to the price of AU0. By introducing the ME method, the endpoint effect can be dealt with better. Furthermore, by introducing the CS algorithm to optimize the initial weights and biases for Elman, the constructed MEEMD-CS-Elman model achieves far more accurate prediction results compared with either the EMD-Elman or the MEEMD-Elman model in terms of performance criterion: mean absolute difference (MAD), mean absolute percentage error (MAPE), root-mean-square error (RMSE), and directional symmetry (DS). In particular, the DS indicator, which reflects rising and falling prices, tends to be more attractive for investors. The value of the DS indicator in the MEEMD-CS-Elman model reaches 0.75207, meaning that the proposed model predicts the directions of increasing and falling prices quite precisely. Hence, by applying the proposed model, investors can make more scientific and accurate decisions and better reduce their investment risks.

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The fractal feature and price trend in the gold future market at the Shanghai Futures Exchange (SFE)

Cited by 16 publications

References 10 publications

The seasonality of gold prices in China does the risk‐aversion level matter?

The seasonality of gold prices in China does the risk‐aversion level matter?

Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market

The Prediction of Gold Futures Prices at the Shanghai Futures Exchange Based on the MEEMD-CS-Elman Model

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