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In order to more accurately predict the dynamics of the e-commerce market and increase the comprehensive value of the circular e-commerce industry, proposes to use Grey system theory to analyze the circular economy of the e-commerce market.

Construct a Grey system theory model, analyze the big data of e-commerce and circular economy of the e-commerce market and predict the development potential of China's e-commerce market.

The results show that the Grey system theory model can play an important role in the data analysis of circular economy of the e-commerce market.

Use Grey model to analyze e-commerce data, discover e-commerce market rules and problems and then optimize e-commerce market.

The purpose of this paper is to evaluate the possible advantages of applying prediction markets to supply network management. Are the same encouraging results possible as in the election application of prediction markets?

This is a paper focused on the requirements and the possible results of the application based on the literature for supply network management and prediction markets. It discusses the potential of prediction markets to improve information management in supply networks.

The paper finds that prediction markets are a new instrument to collect the diverse information among the supply chain members, and to publish this information to the other members.

Prediction markets are able to improve the information basis for decision making in supply chains.

This paper shows the application of prediction markets in a supply network management case and the possibilities and limitations of prediction markets to collect, and publish information within the supply chain.

The purpose of this paper is to propose an approach for predicting the movements of Chinese medicinal material price indexes using news based on text mining.

A research framework and three major methods, namely, domain dictionary construction, market convergence time calculation and dimensionality reduction integrating semantic analysis, are proposed for the approach. The proposed approach is applied in practice for predicting the price index movements of the top ten Chinese medicinal materials that receive the greatest media attention.

A set of experiments performed herein show that a predictive relationship exists between the news and the commodity market and that each of the three major methods improves the forecasting performance.

Because the field of Chinese medicinal materials lacks a corpus that can be used for sentiment analysis, the accuracy of a trained automatic sentiment classifier is lower than obtained by a manual method, which can cause the calculated convergence result to be inaccurate, thus affecting the final prediction model. The manual method of having people label news decreases the proposed method’s aspects of being intelligent and automatic.

Using the method proposed herein to predict the trends in Chinese medicinal materials prices helps farmers arrange a reasonable planting plan to pursue their best interests.

The method proposed herein to predict the trends in the prices of Chinese medicinal materials is conducive to the government arranging planned drug availabilities in order to avoid disasters in which herbs are looted.

The produced prediction result is meaningful in supporting farmers and investors to make better decisions in growing and trading Chinese medicinal material, which leads to financial returns on investments and the avoidance of severe losses.

The 2008 U.S. presidential election was of great interest nationally and internationally. Interest in the 2008 election was sufficient to drive a $2.8 million options market by a U.K.-based company INTRADE. The options in this market are priced as European style fixed return options (FRO). In 2008, the Security and Exchanges Commission approved, and both the American Stock Exchange and the Chicago Board Options Exchange began to trade FROs. Little research is available on trading in FROS because these markets are very new. This chapter uses the INTRADE options market data to construct exponential smoothing forecasts, which are then compared under a hypothetical trading strategy. The trading returns indicate that this market is relatively efficient at least in the short term but that because of the all or nothing payout structure of a FRO, there may exist small arbitrage opportunities.

The aim of this paper is to establish the differences between the food and beverages (F&B) and technology-based industries with regards to the relation between previously identified success factors and innovation project performance.

These differences are established on the basis of logistic regression analysis, using 38 innovation projects (18 F&B and 20 technology-based).

Newness of the innovation project to the company, communication capabilities and market potential have a more negative impact on innovation project performance in the F&B than the tech-based industry. Especially functional upstream capabilities increase the likelihood of success in F&B, when compared to tech-based innovation projects.

While functional upstream capabilities are important for success of F&B innovation projects, there is still room for improvement in order to deal effectively with newness of the innovation project to the company. Internalization of resources from the network and a balanced radical/incremental innovation project portfolio contribute to additional enhancement of functional capabilities of the F&B companies, improving their capacity to deal with newness. Through a larger focus on co-innovation with retail, F&B companies can improve their intra- and inter-firm communication capabilities to attain more consumer-oriented integration of R&D and marketing activities, improving the market potential of their innovations.

This paper demonstrates that the previously identified critical success factors for innovation projects differ in impact and importance for F&B innovation project performance when compared to innovation projects in the technology-based industry.

The purpose of this paper is to examine the effects of land banking strategy and market analysis towards the performance of Malaysian housing developers.

Through in-depth interviews, participants shared their opinions on success factors of housing development firms with a focus on land banking and market study. Content analysis was performed on the data, identifying the connection between both strategies and their superior performance.

The study presents interesting findings in that it lends support to the existing literature as such land banking and market analysis do affect the business competitiveness of housing developers. Albeit subjective in nature, the comments received from respondents are revelatory and have implications for the level of performance perceived by the organisations, as well as the experience of housing entrepreneurs in assembling the land bank and gauging the housing market.

Though not a substitute for quantitative problem solving, this piece of work serves as a corroborative evidence to improve the satisfaction of homebuyers, industry players and policymakers. The paper ends by recommending that the study be repeated in Malaysia, this time with the involvement of other stakeholders, to enrich the findings.

To the best of authors’ knowledge, this is the first research performed in the Malaysian context in which the strategies of private housing developers comprising land banking and market analysis were explored in relation to business success. Hence, the present study not only contributes to the existing property literature, but also makes an important contribution to the business performance and firm competitiveness in the lens of Malaysian entrepreneurs.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.

The rapid development of e-commerce has brought not only great convenience to people but a great challenge to online stores. Phenomenon such as out of stock and slow sales has been common in recent years. These issues can be managed only when the occurrence of the sales volume is predicted in advance, and sufficient warnings can be executed in time. Thus, keeping in mind the importance of the sales prediction system, the purpose of this paper is to propose an effective sales prediction model and make digital marketing strategies with the machine learning model.

Based on the consumer purchasing behavior decision theory, we discuss the factors affecting product sales, including external factors, consumer perception, consumer potential purchase behavior and consumer traffic. Then we propose a sales prediction model, M-GNA-XGBOOST, using the time-series prediction that ensures the effective prediction of sales about each product in a short time on online stores based on the sales data in the previous term or month or year. The proposed M-GNA-XGBOOST model serves as an adaptive prediction model, for which the instant factors and the sales data of the previous period are the input, and the optimal computation is based on the proposed methodology. The adaptive prediction using the proposed model is developed based on the LSTM (Long Short-Term Memory), GAN (Generative Adversarial Networks) and XGBOOST (eXtreme Gradient Boosting). The model inherits the advantages among the algorithms with better accuracy and forecasts the sales of each product in the store with instant data characteristics for the first time.

The analysis using Jingdong dataset proves the effectiveness of the proposed prediction method. The effectiveness of the proposed method is enhanced and the accuracy that instant data as input is found to be better compared with the model that lagged data as input. The root means squared error and mean absolute error of the proposed model are found to be around 11.9 and 8.23. According to the sales prediction of each product, the resource can be arranged in advance, and the marketing strategy of product positioning, product display optimization, inventory management and product promotion is designed for online stores.

The paper proposes and implements a new model, M-GNA-XGBOOST, to predict sales of each product for online stores. Our work provides reference and enlightenment for the establishment of accurate sales-based digital marketing strategies for online stores.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.