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

Drachal, Krzysztof; Pawłowski, M.

doi:10.3390/economies9010006

Cited by 38 publications

(15 citation statements)

References 111 publications

(195 reference statements)

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“…However, most of the works focus on the prediction of exchange rates or stock price trends (e.g., [35][36][37][38][39][40]). One can refer to [41] for a recent review of the applications of genetic algorithms to forecasting prices of commodities. For a review of the applications of evolutionary algorithms for financial forecasting, one can refer to [42].…”

Section: Methodsmentioning

confidence: 99%

A Genetic Programming Approach for Economic Forecasting with Survey Expectations

2022

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We apply a soft computing method to generate country-specific economic sentiment indicators that provide estimates of year-on-year GDP growth rates for 19 European economies. First, genetic programming is used to evolve business and consumer economic expectations to derive sentiment indicators for each country. To assess the performance of the proposed indicators, we first design a nowcasting experiment in which we recursively generate estimates of GDP at the end of each quarter, using the latest business and consumer survey data available. Second, we design a forecasting exercise in which we iteratively re-compute the sentiment indicators in each out-of-sample period. When evaluating the accuracy of the predictions obtained for different forecast horizons, we find that the evolved sentiment indicators outperform the time-series models used as a benchmark. These results show the potential of the proposed approach for prediction purposes.

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Section: Methodsmentioning

confidence: 99%

A Genetic Programming Approach for Economic Forecasting with Survey Expectations

2022

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“…Despite being proposed almost 50 years ago, the genetic algorithm, due to its performance, is still an exciting tool in various fields. It can be seen that this algorithm remains a handy tool in finance and economics, and particularly in forecasting the prices of various commodities [47].…”

Section: Methodology and Curve Fittingmentioning

confidence: 99%

Development of Cost Correlations for the Economic Assessment of Power Plant Equipment

et al. 2021

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A comprehensive cost correlation analysis was conducted based on available cost correlations, and new equipment cost correlation models were proposed based on QUE$TOR modeling. Cost correlations for various types of equipment such as pumps, compressors, heat exchangers, air coolers, and pressure vessels were generated on the basis of extracted cost data. The models were derived on the basis of robust multivariable regression with the aim of minimizing the residuals by using the genetic algorithm. The proposed compressor models for both centrifugal and reciprocating types showed that the Turton cost estimation for carbon steel compressor and Matche’s and Mhhe’s data were compatible with the generated model. According to the results, the cost trend in the Turton correlation for carbon steel had a somewhat lower estimation than these correlations. Further, the cost trend of the Turton correlation for carbon steel pressure vessels was close to the presented model trend for both bullet and sphere types. The Turton cost trend for U-tube shell-and-tube heat exchangers with carbon steel shell and stainless steel tube was close to the proposed heat exchanger model. Furthermore, the Turton cost trend for the flat-plate heat exchanger using carbon steel was similar to the proposed model with a slight difference.

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“…The research method used quantitative methods using ARIMA (Autoregressive Integrated Moving Average), Artificial Neural Network, Genetic Algorithm, and The Genetic Algorithm (GA) is an optimization algorithm based on selection an individual who survives in the evolutionary process or Darwin's natural selection (Drachal and Pawłowski, 2021). This method utilized because of its unique characteristic to find optimize result of nonlinear problem.…”

Section: Methodsmentioning

confidence: 99%

Model Effect of Copper Price on Freeport McMoran Stock Price

Riyanto¹,

Achsani

Santoso

2021

jabm

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The copper price in copper mining companies is an essential aspect in terms of profit, revenue, production targets, and hedging. This research aims to determine an alternative of copper price modeling and its causality relationship to Freeport McMoRan (FCX) stock price. The methods utilized in this research were Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), Genetic Algorithms (GA) and Granger Causality Test. Based on this research result, all modeling methods equally show excellent performance for modeling copper price. Another finding from this research is that the copper price positively affects the FCX stock price. Therefore, it can be concluded that the copper commodity price influences the value of a copper mining company. The results of this research can be utilized as a reference for company analysts as a part to estimate profit probability, estimate revenue, estimate production targets, and hedging strategies.

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

Cited by 38 publications

References 111 publications

A Genetic Programming Approach for Economic Forecasting with Survey Expectations

A Genetic Programming Approach for Economic Forecasting with Survey Expectations

Development of Cost Correlations for the Economic Assessment of Power Plant Equipment

Model Effect of Copper Price on Freeport McMoran Stock Price

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