Deep belief network for gold price forecasting

Zhang, Pinyi; Ci, Bicong

doi:10.1016/j.resourpol.2020.101806

Cited by 55 publications

(25 citation statements)

References 65 publications

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“…Although the GABP model (already mentioned in this text) had a lower mean absolute percentage error, root-mean-square error, and mean absolute error than the standard backpropagation model, it was outperformed by the DBN model. The DBN model also offered the highest value of directional statistics, revealing good alignment with the actual data (Zhang and Ci 2020).…”

Section: Metalsmentioning

confidence: 63%

“…Similarly, the DBN proposed by Zhang and Ci (2020) offers much better predictions than a genetic algorithm when it comes down to the difficulties of non-linearity and multiple factors. In the case of another method applying a genetic algorithm, the resulting improvement (dealing effectively with non-linearity and the volatility of data) seems to stem not from the genetic algorithm itself, but rather from another part of the hybrid model (i.e., the VMD) (Li et al 2019).…”

Section: Discussionmentioning

confidence: 99%

“…However, it seems that it is not always necessary to include genetic algorithms in order to yield proper forecasts. Zhang and Ci (2020) proposed the deep belief network (DBN) for the gold price forecasting task, which deals more efficiently with nonlinearity and multiple factors describing the price distribution than the benchmark models. Although the GABP model (already mentioned in this text) had a lower mean absolute percentage error, root-mean-square error, and mean absolute error than the standard backpropagation model, it was outperformed by the DBN model.…”

Section: Metalsmentioning

confidence: 99%

“…Although in specific cases, some novel methods, such as the neural network using chaotic grasshopper optimization algorithm (CGOA-NN), neural network whale optimization algorithm (NN-WOA), or deep belief network (DBN), seem to outperform them (Alameer et al 2019a;Ewees et al 2020;Zhang and Ci 2020), some improvements to genetic algorithms can be made. Section 4 draws conclusions from both the theoretical and practical parts of the article and synthetically evaluates the genetic algorithms and genetic algorithm-based hybrids in their applications for forecasting commodity prices.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Drachal

Pawłowski

2021

Economies

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This paper is focused on the concise review of the specific applications of genetic algorithms in forecasting commodity prices. Genetic algorithms seem relevant in this field for many reasons. For instance, they lack the necessity to assume a certain statistical distribution, and they are efficient in dealing with non-stationary data. Indeed, the latter case is very frequent while forecasting the commodity prices of, for example, crude oil. Moreover, growing interest in their application has been observed recently. In parallel, researchers are also interested in constructing hybrid genetic algorithms (i.e., joining them with other econometric methods). Such an approach helps to reduce each of the individual method flaws and yields promising results. In this article, three groups of commodities are discussed: energy commodities, metals, and agricultural products. The advantages and disadvantages of genetic algorithms and their hybrids are presented, and further conclusions concerning their possible improvements and other future applications are discussed. This article fills a significant literature gap, focusing on particular financial and economic applications. In particular, it combines three important—yet not often jointly discussed—topics: genetic algorithms, their hybrids with other tools, and commodity price forecasting issues.

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Section: Metalsmentioning

confidence: 63%

Section: Discussionmentioning

confidence: 99%

Section: Metalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Drachal

Pawłowski

2021

Economies

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“…Based on the research that has been done in predicting gold prices using ANN, it can provide output with good accuracy, so that it can be taken into consideration as well as important indicators of the economic sectors of various companies [4]. From the results of other prediction processes, ANN can produce maximum results in making predictions with a fairly low percentage of error values [5], [6]. The same research states that ANN is faster and has a high degree of accuracy in making [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Implementation multiple linear regresion in neural network predict gold price

Yanto

Sanjaya

Yulasmi

et al. 2021

IJEECS

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<p>The movement of gold prices in the previous period was crucial for investors. However, fluctuations in gold price movements always occur. The problem in this study is how to apply multiple linear regression (MRL) in predicting artificial neural networks (ANN) of gold prices. MRL is mathematical calculation technique used to measure the correlation between variables. The results of the MRL analysis ensure that the network pattern that is formed can provide precise and accurate prediction results. In addition, this study aims to develop a predictive pattern model that already exists. The results of the correlation test obtained by MRL provide a correlation of 62% so that the test results are said to have a significant effect on gold price movements. Then the prediction results generated using an ANN has a mean squared error (MSE) value of 0.004264%. The benefits obtained in this study provide an overview of the gold price prediction pattern model by conducting learning and approaches in testing the accuracy of the use of predictor variables.</p>

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Stock price forecasting based on the relationship among Asian stock markets using deep learning

Kumar

Singh

Jain³

2023

Concurrency and Computation

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SummaryThe stock price fluctuation of one country can be influenced by the movement of the stock price of other countries implying that there exists some relationship among various stock markets. This study examines the interrelationship among Asian stock markets and forecasts the stock market on the basis of the relationship that exists among Asian stock markets. The interrelationship is tested by using the Granger causality (GC) test and Pearson's correlation (PC) matrix. Further, a deep learning model namely a long short term memory (LSTM) neural network is utilized to forecast the stock price of one country by using the price of other countries that have a correlation and causal relationship with the target stock market. PC matrix shows that there exists a strong correlation among Asian stock markets. Results from the GC show that there exists a unidirectional relationship between Sensex and NIKKEI 225 to SSE composite index, Sensex to NIKKEI 225, and Sensex and TSEC weighted index to KOSPI composite index and a bi‐directional relationship among Sensex, TSEC weighted index and Hang Seng index. Experimental results show that GC and LSTM‐based model namely GC‐LSTM shows better forecasting performance in comparison to PC and LSTM‐based model termed as PC‐LSTM.

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Deep belief network for gold price forecasting

Cited by 55 publications

References 65 publications

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Implementation multiple linear regresion in neural network predict gold price

Stock price forecasting based on the relationship among Asian stock markets using deep learning

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