Future Market Research Articles

A SWOT Model-Based Analysis of Marketing Strategy Taking Mixue Ice Cream and Tea as an Example

Peoples living needs have become increasingly diversified as a result of the rapid development of the economy. In recent years, the freshly-made tea beverage industry has seen a surge in popularity, with numerous tea beverage brands entering the market. The tea industry has formed a fruit-flavored tea and milk tea beverage model based on high-quality tea, gradually replacing the dominant position of tea powder preparation in the past In order to obtain a place in the fierce market competition, the majority of tea brands must fully understand the market changes and their own development status, and formulate long-term development strategies. This paper takes Mixue Ice Cream and Tea (hereinafter referred to as MIXUE) as the research object and uses the SWOT model to analyze its strengths, weaknesses, opportunities and threats. Besides, we analyze and study its own status quo in the construction of internal control. We also formulate future marketing and development strategies for MIXUE, aiming to improve its brand competitiveness and help it to achieve substantial development.

Certainty of uncertainty for asset pricing

Uncertainty is known to be crucial in asset pricing, yet evidence from a comprehensive analysis of various uncertainty measures remains sparse. By machine learning, we construct a novel economic uncertainty index derived from a heterogeneous range of uncertainty measures and investigate its predictability of stock returns. Our composite uncertainty index exhibits robust in- and out-of-sample predictability of stock market returns over the one- to 12-month horizon. The predictive power stems from the volatility-orthogonal components of individual uncertainty measures and becomes more pronounced during high uncertainty and high sentiment periods. The predictability of our economic uncertainty index aligns with theoretical frameworks linking uncertainty to future investment, cash flows, and market expectations.

Digital Post-Purchase Blues to Strategic Insights: A Bibliometrics-Based Systematic Review of Customer Purchase Regret

Deciphering the trends and evolutionary paths of the research on customers’ post-purchase regret in-side the business milieu is crucial for marketing research and application. The current study in-tends to present a succinct bibliometric review based on synthesising the research on customers’ regret. We followed a systematic, sequential and objective process to retrieve a literature corpus comprising 204 peer-reviewed articles from the Scopus database. The literature corpus was cleaned first and then ana-lysed using statistics-based techniques such as descriptive analysis, co-citation network analysis and keyword co-occurrence analysis. The analyses revealed (1) performance, revealing the most produc-tive constituents and trends of the domain and (2) science of the domain, resulting in five thematic clus-ters representing the domain’s intellectual structure: purchase decision and sales, decision-making and regret, post-purchase regret, and anticipated regret. This study provides crucial insights and pre-sents an agenda for research for future marketing scholars by comparing the recent advancements in the literature on consumerism with those in the wider marketing management literature.

Multi-strategy modified sparrow search algorithm for hyperparameter optimization in arbitrage prediction models.

Deep learning models struggle to effectively capture data features and make accurate predictions because of the strong non-linear characteristics of arbitrage data. Therefore, to fully exploit the model performance, researchers have focused on network structure and hyperparameter selection using various swarm intelligence algorithms for optimization. Sparrow Search Algorithm (SSA), a classic heuristic method that simulates the sparrows' foraging and anti-predatory behavior, has demonstrated excellent performance in various optimization problems. Hence, in this study, the Multi-Strategy Modified Sparrow Search Algorithm (MSMSSA) is applied to the Long Short-Term Memory (LSTM) network to construct an arbitrage spread prediction model (MSMSSA-LSTM). In the modified algorithm, the good point set theory, the proportion-adaptive strategy, and the improved location update method are introduced to further enhance the spatial exploration capability of the sparrow. The proposed model was evaluated using the real spread data of rebar and hot coil futures in the Chinese futures market. The obtained results showed that the mean absolute percentage error, root mean square error, and mean absolute error of the proposed model had decreased by a maximum of 58.5%, 65.2%, and 67.6% compared to several classical models. The model has high accuracy in predicting arbitrage spreads, which can provide some reference for investors.

Short-term extreme value prediction for the structural responses of the IEA 15 MW offshore wind turbine under extreme environmental conditions

As offshore wind turbines enter deep seas, large-scale models ranging from 15 MW to 20 MW are becoming the mainstream choice for the future market. The dynamic responses of 15 MW wind turbines under extreme environmental conditions should be considered in the design process. With increasing structure sizes, the mooring systems and tower base structures play essential roles in maintaining position and supporting wind turbines under harsh environmental conditions. Accurate estimation of the extreme mooring loads and dynamic responses at the tower base is crucial, particularly for the ultimate limit state (ULS) analysis. The dynamic responses of the offshore wind turbine are calculated by Open Fast and validated with other numerical tools. For the safety of deep-sea structures, it is necessary to consider the extreme environmental conditions with a 100-year return period, which is beyond the current standards. An environmental contour-based approach is proposed to select the short-term extreme conditions, enabling approximate prediction of the extreme values of the dynamic responses during the preliminary design stage. The Gumbel method and the ACER method are used to predict the short-term extreme dynamic responses. Comparative evaluations against the relevant standards for floating wind turbines are conducted to assess structural safety.

Deep reinforcement learning for pairs trading: Evidence from China black series futures

Pair trading is one of the main methods of statistical arbitrage, mainly by taking advantage of the temporary price anomalies between related financial products with long-term equilibrium relationships to obtain arbitrage opportunities. In this paper, based on the co-integration method for the selection of allotment, the deep reinforcement learning method is integrated. The deep reinforcement learning method establishes an autonomous learning model and develops paired trading rules under different cycles. At the same time, the deep neural network model is used to implement the learning and training algorithm. This paper uses the data of Shanghai Commodity Exchange and Dalian Commodity Exchange on black futures, and the period is from January 2, 2014 to December 31, 2021. Three different periods were set for five models, namely simple threshold method (ST), simple threshold method based on pairwise cointegration (CA-ST), simple threshold method based on tripartite Cointegration (CA-ST-ALL), deep reinforcement learning method (DRL), and Deep reinforcement learning method based on pairwise cointegration (CA-DRL), and economic indicators were compared. The final results show that, combining the results of the three time periods, the deep reinforcement learning method based on pairwise cointegration performs better regarding return and risk control, validating the feasibility of deep reinforcement learning in China's future market.

Peer-to-peer multi-period energy market with flexible scheduling on a scalable and cost-effective blockchain

Peer-to-Peer (P2P) electricity trading is an emerging means for allowing communities to share local energy production and demand response through direct trading. It is projected to play an important role in the future energy market by increasing the incentive to own and operate distributed energy resources (DERs) and participate in demand response (DR). A common approach to implementing P2P trading is with blockchain, however, existing solutions feature limitations. Typically, concerns involve either scalability and high transaction costs or projects implementing a permissioned blockchain that does not provide the trust and decentralization desired. In addition, the majority of market designs are simplified and do not capture the distinct complexity of electricity markets. They do not consider the inter-temporal constraints of appliances and DERs that need to run for multiple periods. Complex market designs that address these issues have not been implemented in blockchain based systems due to the computational limitations of such systems. To improve these two aspects, this paper proposes a market formulation that supports products running multiple periods with flexible scheduling, modeled as a mixed integer linear programming problem that optimizes social welfare. The trading platform is then implemented as a side chain of Polkadot, a network of blockchains designed to be secure, scalable, and economically feasible to foster an ecosystem of application-specific sidechains. This design is demonstrated to improve social welfare and achieve scalability by benchmarking the number of transactions that can be included per second, based upon typical assumptions of the market size. The result is a decentralized and performant system that allows for complex market designs to be implemented in a blockchain environment. This approach simplifies the process for participants, allowing them to bid DER or DR devices, directly and easily, even when this requires constraints across periods.

Volatility Spillover Benefits of the Hang Seng Index and Shanghai Composite Index: A Vector Autoregressive (VAR) Model and Granger Causality Test Analysis

This study aims to explore the volatility spillover benefits between two major stock markets in Hong Kong and Shanghai, namely the Hang Seng Index and the Shanghai Composite Index. Empirical analysis is conducted using a Vector Autoregressive (VAR) model and Granger causality test based on daily return data from 2013 to 2023. The research finds a significant volatility interaction between the Hang Seng Index and the Shanghai Composite Index, which is more pronounced during periods of global economic instability. Impulse response analysis reveals that positive shocks in Hang Seng Index lead to increased volatility in the short term for the Shanghai Composite Index but gradually weaken to a weak negative effect in the long term. Conversely, positive shocks in the Shanghai Composite Index have a short-term inhibitory effect on the Hang Seng Index but transform into a weak promoting effect in the long term. These findings have important implications for investors and policymakers in risk management, forecasting future stock market trends, and capital allocation.

Charitable crowdfunding donation-intention estimation depending on emotional project images using fNIRS-based functional connectivity.

Charitable fundraising increasingly relies on online crowdfunding platforms. Project images of charitable crowdfunding use emotional appeals to promote helping behavior. Negative emotions are commonly used to motivate helping behavior because the image of a happy child may not motivate donors to donate as willingly. However, some research has found that happy images can be more beneficial. These contradictory results suggest that the emotional valence of project imagery and how fundraisers frame project images effectively remain debatable. Thus, we compared and analyzed brain activation differences in the prefrontal cortex governing human emotions depending on donation decisions using functional near-infrared spectroscopy, a neuroimaging device. We advance existing theory on charitable behavior by demonstrating that little correlation exists in donation intentions and brain activity between negative and positive project images, which is consistent with survey results on donation intentions by victim image. We also discovered quantitative brain hemodynamic signal variations between donors and nondonors, which can predict and detect donor mental brain functioning using functional connectivity, that is, the statistical dependence between the time series of electrophysiological activity and oxygenated hemodynamic levels in the prefrontal cortex. These findings are critical in developing future marketing strategies for online charitable crowdfunding platforms, especially project images.

Do agricultural swaps co-move with equity markets? Evidence from the COVID-19 crisis

Using proprietary data reported by swap dealers to the Commodity Futures Trading Commission, we first present new evidence on the size and composition of 13 over-the-counter agricultural swaps markets. We then utilize our novel dataset to show the existence of linkages with the equity markets. We use the spike in the Chicago Board Options Exchange Volatility Index in early 2020 to show that swaps trader positions were significantly impacted by the financial market volatility created by the COVID-19 pandemic. Following similar methods as Cheng et al. (2015), we find index swaps traders reduce their net long positions in response to tightening financial conditions, while commercial swaps traders absorb some of this risk by decreasing their net short positions. This internal swap market netting occurs in three of the four largest agricultural markets: corn, soft red winter wheat, and sugar. Concurrently, we observe a limited swap dealer hedging response in the futures market, especially when compared to other financial traders, consistent with swap market netting. Our results confirm that equity market shocks can affect financial traders in both commodity swaps and futures markets.

Current Research Status on The Production, Storage, And Application of Hydrogen Energy

With the development of society, the demand for energy has significantly increased, and the hydrogen energy industry is an important engine for optimizing energy structure and promoting the transformation of traditional energy. Hydrogen energy is a clean and renewable energy source with abundant sources. The unit mass of hydrogen contains a large amount of energy and can be applied in a wide range of fields. Therefore, analyzing and comparing the hydrogen energy industry chain will contribute to the healthy development of the hydrogen energy industry. This article first investigates the initial stage of development, current situation, and policies of hydrogen energy in China, Japan, and the United States, in order to gain a deeper understanding of the research status and planning of hydrogen energy in different countries. Secondly, the current mature hydrogen production methods (reforming hydrogen, electrolysis hydrogen) and hydrogen storage methods (high-pressure hydrogen storage, solid-state material hydrogen storage, organic liquid hydrogen storage materials) Hydrogen application are analyzed, and their advantages are proposed. Finally, based on research analysis, the future hydrogen energy market is introduced. This research result also provides certain reference value for enterprises that need to transform and enter the hydrogen energy field.

Beyond wages: The transformative role of strikes in accelerating transportation electrification

Transportation electrification is a critical step towards energy transition and low-carbon society, with wide-ranging social and economic impacts. This study examines the significant role of the 2023 United Auto Workers strike in the context of transportation electrification, with a focus on its implications for the automotive industry and labor market dynamics. Through an analysis of the strike’s immediate effects on production and long-term implications for labor practices and competitive strategies, this study explores how labor actions can influence the pace and direction of the transportation decarbonization efforts. We argue that the strike may accelerate automation and the adoption of non-unionized labor, potentially reshaping the future labor market and manufacturing landscape. To support a just and equitable transition, we call for incorporating the safeguarding of labor social benefits into the innovation of public policies and investment portfolios that incentivize the electric vehicle markets.

Development of green pesticides from traditional Chinese medicines based on artificial intelligence and compatibility of traditional Chinese medicines

The food security of China as a big agricultural country is attracting increasing attention. With the progress in the traditional Chinese medicine industry, Chinese medicinal materials and their preparations have been gradually developed as agents for disease prevention and with antimicrobial and insecticidal functions in agriculture. Promoting pesticide innovation by interdisciplinary integration has become the trend in pesticide research globally. Considering the increasingly important roles of green pesticides from traditional Chinese medicines and artificial intelligence in pest target prediction, this paper proposed an innovative green control strategy in line with the concepts of ecological sustainable development and food security protection. CiteSpace was used for visual analysis of the publications. The results showed that artificial intelligence had been extensively applied in the pesticide field in recent years. This paper explores the application and development of biopesticides for the first time, with focus on the plant-derived pesticides. The thought of traditional Chinese medicine compatibility can be employed to creat a new promosing field: pesticides from traditional Chinese medicine. Moreover, artificial intelligence can be employed to build the formulation system of pesticides from traditional Chinese medicines and the target prediction system of diseases and pests. This study provides new ideas for the future development and market application of biopesticides, aiming to provide more healthy and safe agricultural products for human beings, promote the innovation and development of green pesticides in China, and protect the sustainable development of the environment and ecosystem. This may be the research hotspot and competition point for the green development of the pesticide industry chain in the future.

Research and Analysis of College Students' Investment and Financing under the Background of Internet

In recent years, the prosperity of Internet finance has led to the rapid development of investment and financial management industry. People's financial management is not only traditional bank savings, but also gradually transition to Internet financial management. With the increase of national income, the disposable funds obtained by college students will increase. As a generation with high viscosity in the Internet era, college students will be an important force in the future Internet financial management market. College students with strong learning ability and willingness to accept new things of higher advantage, can use the network platform for learning, consumption, financial management, and some students interested in finance and investment also dabbled in financial field. However, the lack of experience of college students and the lack of necessary risk prevention awareness of financial investment leads to bad online loans and financial fraud. Therefore, to finance investment into the students' future career planning curriculum has certain necessity. This paper mainly analyzes the situation of college students' Internet financial management, pointing out the characteristics of college students' Internet financial management, problems and future trends of college students' financial management. The purpose is to cultivate college students' financial habits, financial awareness and ability, establish a correct outlook on life and wealth, guide them, avoid falling into the trap of financial fraud, and lay a solid foundation for future life financial planning.

Strategic Advancements for Competitive Edge in the Food Industry

This study explores the strategic management and enhancement of competitiveness within the food industry. Amidst a backdrop of fluctuating market conditions and a need for modernization, this research identifies a significant gap in the effective utilization of foreign investments and the adaptation of industry practices to contemporary standards. The primary aim of this research is to propose methodologies for fostering investment, modernizing operations, and expanding production capacities to stabilize and grow the market presence of food industry enterprises. Employing a methodological approach that combines theoretical frameworks with empirical data analysis, the findings suggest that increased competition and new production capabilities can significantly improve market supply and price stability. The implications of this research underscore the necessity for strategic interventions that not only respond to current industry demands but also anticipate future market conditions, thereby ensuring sustainable development and competitive advantage in the food industry. Highlights: Foreign Investment: Essential for technology infusion and practice improvement. Operational Modernization: Adapts enterprises to contemporary market demands. Production Expansion: Increases supply and stabilizes prices through new capacities. Keywords: Food Industry, Management Strategy, Market Stability, Production Expansion, Competitive Mechanism

Journal of Futures Markets: Volume 44, Number 6, June 2024

Journal of Futures Markets: Volume 44, Number 6, June 2024

Boric acid-induced preferential deposition of (002) plane for highly stable zinc anode

Aqueous zinc-ion batteries (ZIBs) hold significant promise in the future energy storage market. However, the uncontrolled growth of zinc dendrites and the occurrence of side reactions severely constrain the practical deployment of ZIBs. To address these challenges, this study suggests incorporating H3BO3 (HBO) as an electrolyte additive into the ZnSO4 electrolyte, with the aim of inducing preferential growth of the (002) plane. HBO molecules selectively adsorb onto the (100) and (101) planes of zinc, promoting the deposition of Zn2+ ions into the (002) plane and resulting in the formation of a uniformly deposited layer while concurrently inhibiting side reactions. The results demonstrate that ZnǁZn symmetric batteries, with the HBO additive, exhibit stable cycling at high current density, achieving a cycling life of 1100 h at 10 and 10 mAh cm−2 as well as 250 h at 50% depth of discharge. Furthermore, the ZnǁVO2 coin cell demonstrates stable cycling for 1700 cycles at 1 A g−1 and 7000 cycles at 5 A g−1. This study presents a promising case for the commercialization of advanced ZIBs.

A Comparative study of the Envisaged and Definite Stock Prices of BSE SMEs Using RNN during the COVID-19 Pandemic

The stock market is unstable, but the use of machine learning algorithms allows to predict its future dynamics before spending. The most popular area of scientific research right nowadays is machine learning, which involves enabling computers to perform tasks that often require human intelligence. The purpose of this paper is to construct a model using a network of Long-Short Term Memory model (LSTM) to forecast future stock market values. The paper presents the advantages and disadvantages of machine learning for assessing and forecasting the stock market. A review of literature on the application of machine learning models in key areas of finance using methodological model assessment and data manipulation is also available. This paper focuses on the losses of the SME sector due to COVID-19 by doing a comparative study using secondary data collection between the predicted closed stock prices and actual stock prices of the BSE SME IPO index for the period from 1 January 2018 to 30 April 2021.The LSTM network of Recurrent Neural Networks (RNNs) most effective deep learning model, is used to predict stock prices. The study provides insight and direction on where lockdown has a massive impact on the stock prices of BSE SME IPOs. The authors developed a model for predicting the future value of stock in the market, the application of which gave some positive results, demonstrating the need for machine learning and how it can change the world of finance. The novelty of the study is that in India, machine learning and deep learning methods in the field of finance are used much less often than in other countries.

Redefining Education in Sports Sciences: A Theoretical Study for Integrating Competency-Based Learning for Sustainable Employment in Spain

In the Spanish context, Sports Sciences education is evolving to emphasize competency-based learning, crucial for adapting to the dynamic global landscape and labor market. This opinion article highlights the shift towards integrating generic and specific competencies, essential for automation and artificial intelligence, aligning with Sustainable Development Goal (SDG) 8’s focus on sustainable economic growth and employment. Despite the recognized importance of these competencies for economic sustainability and job readiness, the literature on this framework, particularly within the context of physical activity and Sports Sciences in Spain, remains underexplored. This paper is structured to first address the current state of the problem, followed by a conceptualization of competencies, including types of competencies. It then analyzes professional competencies within the realm of Physical Activity and Sports Sciences in Spain, moving towards the implementation and evaluation of these competencies in the classroom setting. By bridging the gap between educational outcomes and market demands, this work calls for ongoing research and pedagogical innovation to equip future professionals with the necessary skills for success. This approach not only prepares students for the future labor market but also contributes to the broader economic and sustainable development goals envisioned by SDG 8.

Imagine and Imitate: Cost-Effective Bidding under Partially Observable Price Landscapes

Real-time bidding has become a major means for online advertisement exchange. The goal of a real-time bidding strategy is to maximize the benefits for stakeholders, e.g., click-through rates or conversion rates. However, in practise, the optimal bidding strategy for real-time bidding is constrained by at least three aspects: cost-effectiveness, the dynamic nature of market prices, and the issue of missing bidding values. To address these challenges, we propose Imagine and Imitate Bidding (IIBidder), which includes Strategy Imitation and Imagination modules, to generate cost-effective bidding strategies under partially observable price landscapes. Experimental results on the iPinYou and YOYI datasets demonstrate that IIBidder reduces investment costs, optimizes bidding strategies, and improves future market price predictions.