Using Market Sentiment Analysis and Genetic Algorithm-Based Least Squares Support Vector Regression to Predict Gold Prices

Yuan, Fong-Ching; Lee, Chao-Hui; Chiu, Chaochang

doi:10.2991/ijcis.d.200214.002

Cited by 27 publications

(12 citation statements)

References 51 publications

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“…On the other hand, interest grew in hybrids of genetic algorithms and modern methods widely known as machine learning (Weng et al 2018;Weng et al 2020;Parida et al 2019). Moreover, some researchers have recently proposed using genetic algorithms together with very efficient data collecting tools, such as web crawling (Yuan et al 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Some other authors like, for instance, Alameer et al (2019b) and Weng et al (2018) used them to estimate the parameters of models such as the unmodified adaptive neuro-fuzzy inference system (ANFIS) or regularization of the extreme learning machine (ELM). They can also be used to optimize parameters in the case of such models as the LSSVR model from Yuan et al (2020), specifying the parameters of the ENN by Shahwan and Odening (2007), or determining the number of electrons in ANN hidden layers (Chuentawat and Loetyingyot 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, Amin-Naseri and Gharacheh ( 2007) reported the superiority of data-mining methods over genetic algorithms and k-means clustering hybrids. Yuan et al (2020) proposed a genetic algorithm-based model containing an element of web scraping through the introduction of investment sentiment measures. This method seems particularly promising, as it combines novel tools that enable implementing sources of information that never before have been available in such a scale with a genetic algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…Weng et al (2020) employed the Akaike informative criterion (AIC) and compared their model with benchmark models such as the ARIMA, backpropagation, or support vector machine and concluded its superiority in terms of the root-mean-square error. Yuan et al (2020) employed a genetic algorithm to optimize the parameters of the least-square support vector regression (LSSVR) model. It is particularly important that they included the investor sentiment measure in their study.…”

Section: Metalsmentioning

confidence: 99%

“…The Yuan et al (2020) model is characterized by the lowest mean absolute percentage error (Yuan et al 2020). However, it seems that it is not always necessary to include genetic algorithms in order to yield proper forecasts.…”

Section: Metalsmentioning

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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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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