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Article
Publication date: 27 March 2020

Luyao Wang, Jianying Feng, Xiaojie Sui, Xiaoquan Chu and Weisong Mu

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

Abstract

Purpose

The purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.

Design/methodology/approach

This paper reviews the main research methods and their application of forecasting of agricultural product prices, summarizes the application examples of common forecasting methods, and prospects the future research directions.

Findings

1) It is the trend to use hybrid models to predict agricultural products prices in the future research; 2) the application of the prediction model based on price influencing factors should be further expanded in the future research; 3) the performance of the model should be evaluated based on DS rather than just error-based metrics in the future research; 4) seasonal adjustment models can be applied to the difficult seasonal forecasting tasks in the agriculture product prices in the future research; 5) hybrid optimization algorithm can be used to improve the prediction performance of the model in the future research.

Originality/value

The methods from this paper can provide reference for researchers, and the research trends proposed at the end of this paper can provide solutions or new research directions for relevant researchers.

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Article
Publication date: 21 October 2019

Xiaoquan Chu, Yue Li, Dong Tian, Jianying Feng and Weisong Mu

The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the…

Abstract

Purpose

The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price prediction issue of China’s table grape.

Design/methodology/approach

The approaches follows the framework of “decomposition and ensemble,” using ensemble empirical mode decomposition (EEMD) to optimize the conventional price forecasting methods, and, integrating the multiple linear regression and support vector machine to build a hybrid model which could be applied in solving price series predicting problems.

Findings

The proposed EEMD-ADD optimized hybrid model is validated to be considered satisfactory in a case of China’ grape price forecasting in terms of its statistical measures and prediction performance.

Practical implications

This study would resolve the difficulties in grape price forecasting and provides an adaptive strategy for other agricultural economic predicting problems as well.

Originality/value

The paper fills the vacancy of concerning researches, proposes an optimized hybrid model integrating both classical econometric and artificial intelligence models to forecast price using time series method.

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Article
Publication date: 22 December 2020

Abdallah Alalawin, Laith Mubarak Arabiyat, Wafa Alalaween, Ahmad Qamar and Adnan Mukattash

These days vehicles' spare parts (SPs) are a very big market, and there is a very high demand for these parts. Forecasting vehicles' SPs price and demand are difficult…

Abstract

Purpose

These days vehicles' spare parts (SPs) are a very big market, and there is a very high demand for these parts. Forecasting vehicles' SPs price and demand are difficult because of the lack of data and the pricing of the SPs is not following the normal value chain methods like normal products.

Design/methodology/approach

A proposed model using multiple linear regression was developed as a guide to forecasting demand and price for vehicles' SPs. A case study of selected hybrid vehicle is held to validate the results of the research. This research is an original study depending on quantitative and qualitative methods; some factors are generated from realistic data or are calculated using numerical equations and the analytic hierarchy process (AHP) method; online questionnaire and expert interview survey.

Findings

The price and demand for SPs have a linear relationship with some independent variables is the hypothesis that is tested. Even though the proposed models are generally recommended for predicting demand and price, in this research the linear relationship models are not significant enough to calculate the expected price and demand.

Originality/value

This research should concern both academics and practitioners since it provides new intuitions on the distinctions between scientific and industrial world regarding SPs for vehicles as it is the first study that investigates price and demand of vehicles' SPs.

Details

Journal of Quality in Maintenance Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1355-2511

Keywords

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Article
Publication date: 20 November 2009

Sanjeev Kumar Aggarwal, L.M. Saini and Ashwani Kumar

Several research papers related to electricity price forecasting have been reported in the leading journals in last 20 years. The purpose of this paper is to present a…

Abstract

Purpose

Several research papers related to electricity price forecasting have been reported in the leading journals in last 20 years. The purpose of this paper is to present a comprehensive survey and comparison of these techniques.

Design/methodology/approach

The present article provides an overview of the statistical short‐term price forecasting (STPF) models. The basic theory of these models, their further classification and their suitability to STPF has been discussed. Quantitative evaluation of the performance of these models in the framework of accuracy achieved and computation time taken has been performed. Some important observations of the literature survey and key issues regarding STPF methodologies are analyzed.

Findings

It has been observed that price forecasting accuracy of the reported models in day‐ahead markets is better as compared to that in real time markets. From a comparative analysis perspective, there is no hard evidence of out‐performance of one model over all other models on a consistent basis for a very long period. In some of the studies, linear models like dynamic regression and transfer function have shown superior performance as compared to non‐linear models like artificial neural networks (ANNs). On the other hand, recent variations in ANNs by employing wavelet transformation, fuzzy logic and genetic algorithm have shown considerable improvement in forecasting accuracy. However more complex models need further comparative analysis.

Originality/value

This paper is intended to supplement the recent survey papers, in which the researchers have restricted the scope to a bibliographical survey. Whereas, in this work, after providing detailed classification and chronological evolution of the STPF techniques, a comparative summary of various priceforecasting techniques, across different electricity markets, is presented.

Details

International Journal of Energy Sector Management, vol. 3 no. 4
Type: Research Article
ISSN: 1750-6220

Keywords

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Article
Publication date: 8 April 2020

Jianping Chen, Nadine Tournois and Qiming Fu

Cross-border e-commerce in China has been booming in recent years. This paper aims to study pricing in Chinese cross-border e-commerce companies and focuses on the baby…

Abstract

Purpose

Cross-border e-commerce in China has been booming in recent years. This paper aims to study pricing in Chinese cross-border e-commerce companies and focuses on the baby food market, which is simply examined as a case study to highlight broader implications. In this intensely competitive sector, the biggest challenge faced by such companies is ensuring that they are in a position to be able set prices in the short-term to maximize their competitive advantage and profitability. The study of pricing will help management to make correct operational decisions.

Design/methodology/approach

This study utilizes transaction data, which were obtained from the Taobao e-commerce platform. Taobao is the largest e-commerce retail platform in the world. We analyzed factors, including business models, homogeneity, reputation ratings and sales volumes, which may affect pricing.

Findings

This study found that consumers in the baby food sector of Chinese cross-border e-commerce are not price-sensitive. Consumers are reputation-rating-sensitive. The reputation ratings of sellers affect the price dispersion in e-commerce markets. The Core Price Dispersion Rate Model not only considers the prices but also takes sales volumes into account in the calculations. Finally, based on Gaussian processes, a model was developed for price forecasting in the area of cross-border e-commerce. The experimental results show that the proposed method is highly valuable for price forecasting.

Originality/value

This study provides a novel understanding of the baby food sector in the Chinese cross-border e-commerce market by examining the business model, price dispersion, reputation rating and correlation between the reputation of sellers, prices and sales volume. Furthermore, a model for price forecasting is proposed.

Details

Journal of Business & Industrial Marketing, vol. 35 no. 10
Type: Research Article
ISSN: 0885-8624

Keywords

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Article
Publication date: 21 July 2020

Pierre Rostan and Alexandra Rostan

The purpose of the paper is to forecast economic indicators of the Saudi economy in the context of low oil prices which have taken a toll on the Saudi oil-dependent…

Abstract

Purpose

The purpose of the paper is to forecast economic indicators of the Saudi economy in the context of low oil prices which have taken a toll on the Saudi oil-dependent economy between 2014 and 2017. Trades and investments have plummeted, leading to significant budget deficits. In response, the government unveiled a plan called Saudi Vision 2030 in 2016 which has triggered structural economic reforms leading to an unprecedented strategy of transition from an oil-driven economy to a modern market economy.

Design/methodology/approach

This paper forecasts with spectral analysis economic indicators of the Saudi economy up to 2030 to provide a clearer picture of the future economy assuming that the effects of recent reforms have not yet been traced by most of the economic indicators.

Findings

2018–2030 forecasts are all bearish except West Texas Intermediate (WTI) oil price expected to average $64.40 during the period 2019–2030. Two additional exceptions are the Saudi population that should grow to 40 million in 2030 and the swelling gross domestic product (GDP) generated by the non-oil sector resulting from bold actions of the Saudi government who is willing to become less dependent on revenues generated by the oil sector.

Research limitations/implications

Government policymakers, economists and investors would have with spectral forecasts better insight and understanding of the Saudi economy dynamics at the early stage of major economic reforms implemented in the country. In 2020, the COVID-19 pandemic has brutally hurt the Saudi economy following a collapse in the global demand for oil and an oversupplied industry. The impact on the Saudi economy will depend on the optimal response brought by its government.

Social implications

Saudi Vision 2030 plan has already triggered a deep transformation of the Saudi society that is reviewed in this paper.

Originality/value

The forecast of Saudi economic indicators is a timely topic considering the challenges facing the economy and reforms being undertaken. Applying an original forecasting technique to economic indicators adds to the originality of the paper.

Details

International Journal of Emerging Markets, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-8809

Keywords

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Article
Publication date: 29 November 2019

Tim Baker, Aysajan Eziz and Robert J. Harrington

This paper aims to (1) organize the open literature on hotel revenue management systems, (2) compare practitioner systems in terms of functionality and (3) integrate…

Abstract

Purpose

This paper aims to (1) organize the open literature on hotel revenue management systems, (2) compare practitioner systems in terms of functionality and (3) integrate (1)-(2) into research stream recommendations for the open literature with an empirical focus.

Design/methodology/approach

The authors use Nickerson’s taxonomy development method from the field of information systems to build the taxonomy.

Findings

New forecasting areas include developing a metric for the degree of strategic fit of a hotel’s pricing strategy and using it in conjunction with quantifications of online reviews for predictions. New price optimization avenues include determining whether a lack of congruence between customer perceptions of fairness and trust and pricing history has a detrimental effect on overall hotel performance and determining which combinations of flexible products, decision-maker risk aversion, nonparametric forecasting and reference effect optimization features work best in which situations.

Originality/value

This is the first study to combine vendor activities outside the technical realms of forecasting and price optimization with an emphasis on the choice modeling technical framework. This study points to several promising studies using qualitative methods, action research and design science.

Details

International Journal of Contemporary Hospitality Management, vol. 32 no. 1
Type: Research Article
ISSN: 0959-6119

Keywords

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Article
Publication date: 5 November 2019

Yi Sun, Quan Jin, Qing Cheng and Kun Guo

The purpose of this paper is to propose a new tool for stock investment risk management through studying stocks with what kind of characteristics can be predicted by…

Abstract

Purpose

The purpose of this paper is to propose a new tool for stock investment risk management through studying stocks with what kind of characteristics can be predicted by individual investor behavior.

Design/methodology/approach

Based on comment data of individual stock from the Snowball, a thermal optimal path method is employed to analyze the lead–lag relationship between investor attention (IA) and the stock price. And machine learning algorithms, including SVM and BP neural network, are used to predict the prices of certain kind of stock.

Findings

It turns out that the lead–lag relationships between IA and the stock price change dynamically. Forecasting based on investor behavior is more accurate only when the IA of the stock is stably leading its price change most of the time.

Research limitations/implications

One limitation of this paper is that it studies China’s stock market only; however, different conclusions could be drawn for other financial markets or mature stock markets.

Practical implications

As for the implications, the new tool could improve the prediction accuracy of the model, thus have practical significance for stock selection and dynamic portfolio management.

Originality/value

This paper is one of the first few research works that introduce individual investor data into portfolio risk management. The new tool put forward in this study can capture the dynamic interplay between IA and stock price change, which help investors identify and control the risk of their portfolios.

Details

Industrial Management & Data Systems, vol. 120 no. 2
Type: Research Article
ISSN: 0263-5577

Keywords

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Article
Publication date: 21 December 2017

Marc Gürtler and Thomas Paulsen

Empirical publications on the time series modeling and forecasting of electricity prices vary widely regarding the conditions, and the findings make it difficult to…

Abstract

Purpose

Empirical publications on the time series modeling and forecasting of electricity prices vary widely regarding the conditions, and the findings make it difficult to generalize results. Against this background, it is surprising that there is a lack of statistics-based literature reviews on the forecasting performance when comparing different models. The purpose of the present study is to fill this gap.

Design/methodology/approach

The authors conduct a comprehensive literature analysis from 2000 to 2015, covering 86 empirical studies on the time series modeling and forecasting of electricity spot prices. Various statistics are presented to characterize the empirical literature on electricity spot price modeling, and the forecasting performance of several model types and modifications is analyzed. The key issue of this study is to offer a comparison between different model types and modeling conditions regarding their forecasting performance, which is referred to as a quasi-meta-analysis, i.e. the analysis of analyses to achieve more general findings independent of the circumstances of single studies.

Findings

The authors find evidence that generalized autoregressive conditional heteroscedasticity models outperform their autoregressive–moving-average counterparts and that the consideration of explanatory variables improves forecasts.

Originality/value

To the best knowledge of the authors, this paper is the first to apply the methodology of meta-analyses in a literature review of the empirical forecasting literature on electricity spot markets.

Details

International Journal of Energy Sector Management, vol. 12 no. 1
Type: Research Article
ISSN: 1750-6220

Keywords

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Article
Publication date: 12 June 2020

Ming-Huan Shou, Zheng-Xin Wang, Dan-Dan Li and Yi-Tong Zhou

Since the issuance in 2009, the digital currency has enjoyed an increasing popularity and has become one of the most important options for global investors. The purpose of…

Abstract

Purpose

Since the issuance in 2009, the digital currency has enjoyed an increasing popularity and has become one of the most important options for global investors. The purpose of this paper is to propose a hybrid model ( KDJ–Markov chain) which integrates the advantages of the stochastic index (KDJ) and grey Markov chain methods and provide a useful decision support tool for investors participating in the digital currency market.

Design/methodology/approach

Taking Litecoin's closing price prediction as an example, the closing prices from May 2 to June 20, 2017, are used as the training set, while those from June 21 to August 9, 2017, are used as the test set. In addition, an adaptive KDJ–Markov chain is proposed to enhance the adaptability for dynamic transaction information. And the paper verifies the effectiveness of the KDJ–Markov chain method and adaptive KDJ–Markov chain method.

Findings

The results show that the proposed methods can provide a reliable foundation for market analysis and investment decisions. Under the circumstances the accuracy of the training set and the accuracy of the test set are 76% and 78%, respectively.

Practical implications

This study not only solves the problems that KDJ method cannot accurately predict the next day's state and the grey Markov chain method cannot divide the states very well, but it also provides two useful decision support tools for investors to make more scientific and reasonable decisions for digital currency where there are no existing methods to analyze the fluctuation.

Originality/value

A new approach to analyze the fluctuation of digital currency, in which there are no existing methods, is proposed based on the stochastic index (KDJ) and grey Markov chain methods. And both of these two models have high accuracy.

Details

Grey Systems: Theory and Application, vol. 11 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

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